WO2011036091A1 - Method for the in vitro detection and differentiation of pathophysiological states - Google Patents

Abstract

The present invention relates to the use of defined polynucleotides for forming at least one multigene biomarker for producing a multiplex assay as an aid for the in vitro detection and/or early detection and/or differentiation and/or sequence observation and/or evaluation of pathophysiological states of a patient, wherein the pathophysiological state is selected from the group comprising: SIRS, sepsis, and the severities thereof; sepsis-like states; septic shock; bacteremia, infectious/non-infectious multiple organ failure; early detecting of said states; focus control; control of surgical removal measures of the infection focus; responder/non-responder for a particular therapy; therapy monitoring; differentiating between infectious and non-infectious genesis for systemic reactions of the organism, such as SIRS, sepsis, post-operative complications, chronic and/or acute organ dysfunction, shock reactions, inflammation reactions, and/or trauma.

Description

 description

Method for in vitro detection and differentiation of pathophysiological

 states

The present invention relates to a method for the in vitro detection and / or early detection and / or differentiation and / or course monitoring of pathophysiological conditions according to claim 1, the use of

at least three polynucleotides for the formation of at least one multigene biomarker for the preparation of a multiplex assay as an aid for assessing whether a patient has a pathophysiological condition, and / or for determining the severity and / or early detection and / or for distinguishing and / or monitoring of pathophysiological conditions according to claim 5, a Verwenung according to claim 12; A primer for carrying out the method according to the invention as claimed in claim 16, and a kit for carrying out the method according to claim 17.

In particular, the present invention relates to the use of

Polynucleotides for detecting gene activities of at least one

Multigene biomarker for the preparation of a diagnostic aid in patients with certain pathophysiological conditions, such as sepsis and sepsis-like conditions, with similar features as an in vitro diagnostic multivariate index assay (IVDMIA).

Sepsis ("blood poisoning") is a life-threatening infection that affects the entire organism, is associated with high mortality, is becoming more prevalent and affects people of all ages.Sepisis endangers medical progress in many areas of high-performance medicine and consumes much of it Healthcare resources The mortality rate of severe sepsis has not improved significantly in recent decades, with the last two breakthroughs in innovation since the introduction of blood culture (around 1880) being the introduction of antibiotics over 60 years ago and the onset of intensive care about 50 years ago. In order to achieve similarly decisive treatment progress today, novel diagnostic agents must be made available. Sepsis is caused by infectious agents. As there is currently no specific therapy for sepsis, the success of the treatment largely depends on the successful control of the underlying infection and the quality of the intensive care treatment. Critical to survival is the early administration of an antibiotic, which also successfully fights the causative agent [Kumar et. al., 2006]. However, deficits in sepsis diagnostics delay the initiation of therapy and the choice of a suitable antibiotic. Since the identification of the Sepsiserregers with the current methods of blood culture succeed only in less than 25% of sepsis cases and the findings in the case of

Pathogen detection only after 2-3 days, the initial choice of the

Antibiotic or antifungal (anti-fungal) substances are "calculated", that is to say suspected, and in 20-30% of cases, this choice is not correct.

Other causes for the delay of the therapy lie in the

Misinterpretation of the disease symptoms and laboratory values. improved

Diagnostics that simplify and speed up the diagnosis of sepsis can lead to a significant reduction in sepsis mortality and shortened sepsis mortality

Contribute treatment duration. Medical societies confirm the deficits of previous sepsis diagnostics in surveys among North American and European intensive care physicians [Marshall et. al., 2003]. Also the

Affected initiative "German sepsis help e.V." and the German Sepsis Society complain about the deficits.

As part of the development of market-ready in-vitro diagnostics in the field of molecular diagnostics on 26.7.2007 a draft directive of the Food and Drug Administration (FDA) of the United States of America was published. This guideline provides recommendations, definitions and indications for the development and approval process. Furthermore, specifications are proposed for the new class of "in vitro diagnostic multivariate index assays (IVDMIA)".

Characteristics of these assays are:

 1) The combination of several individual values by means of a

Interpretation step to obtain a single patient-specific output value in the form of an index, score or classification. This value is for diagnostic statements, damage limitation, treatment or prevention of a disease.

 2) The result obtained is derived from the measured values in a way that does not allow any conclusions to be drawn about the actual measured data. Therefore, that can

Result not confirmed or reconstructed by the end user.

 3) This makes it necessary to give the user all the information about

To provide interpretation of the test result.

Infection is associated with the uptake of pathogens, their multiplication in the organism and the associated triggering of pathophysiological and symptomatic reactions. In contrast, there are no disease symptoms of the host organism in a colonization.

As a result of infection, there is a confrontation between the pathogens and the body's defense within the body. Nonspecific defenses are endogenous, germ-damaging substances that are dissolved in the blood (humoral) as well as granulocytes and macrophages, which are limited in their ability to eliminate invaders, foreign bodies and cell debris. The

The mode of action of the specific defense is to mark foreign bodies and pathogens with antibodies circulating in the blood in order to have them subsequently destroyed by T lymphocytes.

 In the propagation of a pathogen can cause multiple disease

Processes are initiated. On the one hand, defense reactions such. As fever, vasodilation and / or encapsulation triggered. It can become one

Damage or destruction of tissues, organs or organ systems, eg. B. Multi-organ failure (MOV) come. Depending on a pathogen the activator can excrete poisons, exotoxins which lead to partially violent reactions of a host response. Another possibility is that yourself

Influenza components, so-called endotoxins, in the case of germ killing as a poison can affect.

 In a limitation of the infection on a range of

Organism is called a local infection, such. In the case of abscesses or wound infections. The symptoms of a local infection are redness,

Swelling, pain and limited function. In contrast, if the pathogens in the whole body z. B. on the bloodstream or the lymph channels spread is a general or generalized or systemic infection. From the beginning of an infection to the triggering of reactions

(Symptoms) is dependent on the individual to observe a different period of time, which is referred to as the incubation period.

 The variegated nature, symptoms, severity and course of infection make specific detection or differential diagnosis of sterile inflammatory disease in clinical routine very difficult and often imprecise. This is a major cause of frequent serious infectious

Complications in many different indications and medical

To see disciplines. There is a great medical need in a variety of medical disciplines with sufficient sensitivity and specificity to recognize such infectious complications, to treat by adequate clinical interventions and to provide a follow-up of individual clinical measures for the treatment of infectious complications. This applies in particular to the transition from local to generalized infections, which lead to life-threatening conditions in a short time.

The distinction between systemic-inflammatory and infectious-related

Disease states play an important role in clinical decisions for the treatment of patients and subsequent follow-up in addition to sepsis in a number of other indications. In this context, the treatment of acutely and chronically ill patients and the peri-operative control can be seen. It is known that in the case of acute pancreatitis, the prognosis of a fatal outcome by infection significantly worsens from 16% to 40%. When developing a complex superinfection, there is a high risk of sepsis with a mortality of up to 90%. Furthermore, the

Course monitoring of intra-abdominal inflammation and / or infection in chronically ill, postoperative and trauma patients of importance. Even today there are difficulties in a clear clinical diagnosis of intra-abdominal infections. Follow-up monitoring of chronically ill patients, such as patients with cirrhosis of the liver or renal insufficiency, is of clinical relevance, since this group of patients may be predestined to take inflammatory and / or infectious outcomes depending on organ compensation. In particular, kidney-deficient patients with peritoneal dialysis are prone to chronic inflammation and infection [Blake 2008]. Of special Interest is in the observation of cirrhotic patients as they can develop spontaneous bacterial peritonitis, which has a high mortality.

[Koulaouzidis et al. 2009]. The diagnosis of secondary peritonitis as part of a postoperative follow-up treatment is of great clinical value and can greatly influence the success of the operation. Postoperative infections are still a major problem in surgical treatment today. One percent of laparotomies performed lead to complications after surgery. It can the

Complication rates vary greatly between surgical procedures.

In particular, interventions on the gastrointestinal tract can lead to a fulminant spread of bacteria into the sterile abdominal cavity due to suture insufficiencies.

Infectious courses also play a role in the follow-up treatment after transplantations, thoracotomies, extremity and joint corrections and neurosurgical interventions.

 It will be appreciated by those skilled in the art that these embodiments are merely illustrative choices and that there are numerous other fields of application for which the identification of an infectious complication is of great importance. The present invention provides a solution to this diagnostic problem.

In particular, the present invention relates to genes and / or fragments thereof and their use for generating multigene biomarkers which are specific to a condition and / or investigation.

The invention further relates to marker primers derived PCR primers and probes for hybridization or duplication methods.

Sepsis is still one of the most difficult clinical pictures in modern intensive care, with not only the therapy but also the diagnosis being a challenge for the clinician. Despite advances in the pathophysiological understanding and supportive treatment of

In intensive care patients, generalized inflammatory conditions such as SIRS and sepsis are very common in ICU patients and occur significantly

Mortality contributing diseases [Marshai et al., 2003; Alberti et al., 2003]. The mortality is about 20% in SIRS, about 40% in sepsis and increases Development of multiple organ dysfunctions up to 70-80% [Brun-Buisson et al., 1995; Le-Gall et al., 1995; Brun-Buisson et al., 2003]. The morbidity and mortality contribution of SIRS and sepsis is of interdisciplinary clinical-medical significance, as it increasingly the treatment successes of the most advanced therapeutic procedures numerous medical specialties (eg traumatology, neurosurgery, heart / lung surgery, visceral surgery, transplantation medicine, hematology /

Oncology, etc.), which are without exception an increase in the

Disease risk for SIRS and sepsis is immanent. This is also reflected in the continuous increase in the frequency of sepsis: Between 1979 and 1987, there was an increase of 139%, from 73.6 to 176 cases per 100,000 hospital patients [MMWR Morb Mortal Wkly Rep 1990]. The reduction in morbidity and mortality of a variety of critically ill patients is therefore linked to a concomitant progress in the prevention, treatment and, in particular, the detection and follow-up of sepsis and severe sepsis.

Over time, the concept of sepsis has undergone a significant change in meaning. An infection or the urgent suspicion of an infection are still an essential part of current sepsis definitions. Special consideration is given to the description infection site-further

Organ dysfunctions in the context of the inflammatory host reaction. in the

In the meantime, international literature has met the criteria of

1992 Consensus Conference of the "American College of Chest Physicians / Society of Critical Care Medicine Consensus Conference (ACCP / SCCM)" from 1992 to the widest definition of the sepsis term enforced [Bone et al., 1992].

According to these criteria, the clinically defined severity levels "systemic inflammatory response syndrome" (SIRS), "sepsis", "severe sepsis" and "septic shock" are differentiated. SIRS defines the systemic response of the inflammatory system to a non-infectious stimulus. For this, at least two of the following clinical criteria must be met: fever> 38 ° C or hypothermia <36 ° C, leukocytosis> 12g / l or leukopenia <4g / l or a left shift in the differential blood count, a heart rate of over 90 / min , a tachypnoea> 20 breaths / min or a PaCO ₂ (partial pressure of carbon dioxide in the arterial blood) <4.3 kPa. This definition has a high sensitivity, but a low specificity. For intensive medical care, it is of little help, since, as a rule, every intensive care patient meets the SIRS criteria, at least for a short time.

Sepsis is defined as those clinical conditions in which the SIRS criteria are fulfilled and the cause of an infection is proven or at least very probable. Infection is defined as a pathological process caused by invasion of pathogens or potentially pathogenic organisms into a normally sterile tissue. If the body fails to limit this infection to the site of origin, the pathogens or their toxins induce inflammation in the organs or tissues of the body remote from the site of infection. Immediate intensive care treatment, the targeted administration of antibiotics and the surgical rehabilitation of the infectious focus are needed to achieve recovery. Severe sepsis is characterized by the additional occurrence of organ dysfunctions. Common organ dysfunctions include changes in the state of consciousness, oliguria, lactic acidosis or sepsis-induced hypotension with a systolic

Blood pressure of less than 90 mmHg or a pressure drop of more than 40 mmHg from baseline. If such hypotension is not remedied by the administration of crystalloids and / or colloids and in addition to a

Catecholamines the patient comes, it is called a septic shock. This is detected in about 20% of all sepsis patients.

There is a consensus among many physicians that the

Consensus criteria according to [Bone et al., 1992] do not correspond to any specific definition of sepsis. For example, a survey conducted by the European Society of Intensive Care Medicine (ESICM) found that 71% of physicians surveyed had insecurity in diagnosing sepsis despite many years of clinical experience [Poeze et al., 2003]. The attempt to enforce a consistent terminology has found variable acceptance in clinical implementation. In particular, progress in understanding the pathophysiology of sepsis has led several experts to seek a corresponding modification of the previous definitions. The definitions of sepsis, severe sepsis, and septic shock have been confirmed and assessed as useful for clinicians and researchers. However, the The diagnostic criteria of sepsis have been significantly extended to meet the clinical aspect of infection control. The International Sepsis Conference 2001 also proposed a new concept (PIRO) for the description of sepsis, which consists of the criteria predisposition, infection, immune response

(Response) and organ dysfunction [Levy et al., 2003]. Despite a new definition of SIRS / sepsis with the acronym PI RO [Opal et al., 2005], most studies still use the 1992 ACCP / SCCM consensus conference [Bone et al., 1992] for their patients to classify.

Several approaches to diagnosing SIRS and sepsis have been developed. These approaches can be divided into 3 groups.

The first group includes score systems such as APACHE, SAPS and SIRS, which can stratify patients based on a variety of physiological indices. While some studies have demonstrated diagnostic potential for the APACHE II score, other studies have shown that APACHE II and SAPS II can not differentiate between sepsis and SIRS [Carrigan et al., 2004].

The second group contains protein markers derived from plasma and serum

be detected. Such are, for example, CA125, S100B, copeptin, glycine N-acyltransferase (GNAT), protachykinin and / or its fragments, aldose 1-epimerase (mutarotase), Chp, carbamoyl phosphate synthetase 1, LASP-1 (Brahms Diagnostika GmbH Germany), IL -1 Ra, MCP-1, MPIF-1, TNF-alpha, TNF-R1, MIG, BLC, HVEM, IL-10, IL-15, MCP-2, M-CSF, MIP-3b, MMP-9, PARC, ST-2; IL-6, SIL-2R, CD141, MMP-9, EGF, ENA-78, EOT, Gro-beta, IL-1b, leptin, MIF, MIP-1 a, OSM, protein C, P-selectin, and HCC4 (Molecular Staging, Inc., USA) or CD14 antigen, lipopolysaccharide binding sites on the proteins Alkaline

Phosphatase and Inter-Alpha-Trypsin Inhibitor (Mochida Pharm Co, Ltd. Japan).

Despite the large amount of patented biomarkers, only a few could prevail in everyday clinical practice. Of these, procalcitonin (PCT, BRAHMS) and C-reactive protein (CRP, Eli Lilly) appear to be the markers that best distinguish between infectious and non-infectious causes of SIRS. Procalcitonin is a 1 16 amino acid peptide that plays a role in

Inflammatory reactions plays. This marker has been increasingly used over time as a new infection marker in intensive care units [Sponholz et al., 2006]. This marker is used as an infection marker and serves to determine the severity of sepsis, with the dynamics of the values is more important than the absolute values themselves, for. To differentiate between infectious and non-infectious complication in cardiac surgery patients [Sponholz et al., 2006]. Despite widespread acceptance of the biomarker PCT, it has been shown in international studies that the achieved sensitivities and specificities of the sepsis marker PCT are still insufficient, especially in the delineation of a systemic bacterial SIRS, ie sepsis, from a non-bacterial SIRS [Ruokonen et al ., 1999;

Suprin et al. 2000; Ruokonen et al., 2002; Tang et al., 2007a]. The meta-analysis by Tang and colleagues [Tang et al., 2007a], which included 18 studies, shows that PCT is poorly suited to discriminate SIRS from sepsis. Moreover, the authors emphasize that PCT has a very poor diagnostic accuracy with an odds ratio (OR) of 7.79. As a rule, call the

Authors that an OR <25 is not meaningful, between 25 and 100 is helpful and in case of more than 100 is highly accurate [Tang et al., 2007a].

C-reactive protein (CRP) is a 224 amino acid protein that plays a role in inflammatory reactions. The measurement of CRP should serve to track the disease process as well as the efficacy of the chosen therapy.

Several reports have described PCT as a more appropriate diagnostic marker than CRP in the intensive care field [Sponholz et al., 2006; Kofoed et al., 2007]. In addition, PCT is considered more suitable as a CRP to differentiate non-infectious versus infectious SIRS as well as bacterial versus viral infection [Simon et al., 2004].

It is obvious to the person skilled in the art that provided with this invention, solution with the aforementioned biomarkers such. B. but not exclusively PCT or CRP can be combined to expand the diagnostic statement. The third group contains biomarkers or profiles identified at the transcriptome level. These molecular parameters should better correlate the molecular inflammatory / immunological host response with the

The severity of sepsis, but also statements on the individual

Provide forecast. Such biomarkers are currently being searched extensively by various scientific groups and commercial organizations, such as changes in blood cytokine concentrations caused by bacterial cell wall components such as lipopolysaccharides [Mathiak et al., 2003], or use of gene expression profiles in a blood sample to determine differences in survivors and non-survivors sepsis patients [Pachot et al., 2006]. Gene expression profiles or classifiers are for the determination of the severity of sepsis [WO 2004/087949], the distinction between a local or systemic infection [unpublished DE 10 2007 036 678.9], the identification of the source of infection [WO 2007/124820] or of

Gene expression signatures are useful for distinguishing between multiple etiologies and pathogen-associated signatures [Ramilo et al., 2007]. However, due to the insufficient specificity and sensitivity of the consensus criteria according to [Bone et al., 1992], the currently available protein markers, as well as due to the time required to detect the cause of the infection by blood culture, there is an urgent need for new methods that take into account the complexity of the disease. Many gene expression studies using either single genes and / or combinations of genes named as classifiers, as well as numerous

Descriptions of statistical methods for deriving a score and / or index [WO03084388; US6960439] belong to the state of the art.

There is a consensus today that complex diseases can only be sensibly described using several parameters.

Increasingly, molecular signatures are finding their way into clinical diagnostics, especially in complex diseases that can not be detected with traditional biomarkers, but also to assess patient risks and to identify responders in the use of

Medicines and therapies. The following list should clarify the current state and the fields of application of gene expression diagnostics. 1) The microarray-based, 70-gene signature called MammaPrint (Aqendia, NL) allows predicting the risk of recurrence and metastasis in women with breast cancer. It is being investigated whether the risk of developing distant metastases in the next few years could be considered high or low and that they would benefit from chemotherapy. The approval of this test by the FDA has led to the development of guidelines for a new class of diagnostic tests known as IVDMIA (in vitro diagnostic multivariate index assay). The MammaPrint signature is measured and calculated on a microarray in the manufacturer's laboratories.

2) On formalin-fixed tissue samples, the Oncotype DX multigene assav (Genomic Health, USA) is used to assess the likelihood of breast cancer recurrence in patients and to evaluate the response of patients to chemotherapy. 21 genes are summarized as "Recurrence-Score." The measurement takes place in the rooms of the company, it is also the TaqMan- PCR technology used.

3) The AlloMap gene expression test from XDx (USA) is used to monitor any rejection reactions in patients with heart transplantation, which show approximately 30% of patients within one year. So far, were to

Diagnosis several biopsies necessary. The test is based on 1 1 quantitative PCR assays (additional 9 controls and references) using the TaqMan

Technology (Hoffman-La Roche) in the manufacturer's premises. The

Sample material is blood. Already two months after the transplantation, the results are reliable and predict the absence of rejection reactions for the next 80 days.

A common feature of these tests is that the addressed diagnostic question allows examination times until the result of several days is available. For diagnostic tests in the indication of sepsis, however, the information must be available within a single working day. When using gene expression markers for the determination of a pathophysiological state, the amounts of the corresponding mRNA present in a sample, the gene expression levels, are always determined quantitatively. The information determined by these gene expression levels is the respective over or under-expression of these mRNAs, which is determined experimentally with reference to a control state or with reference to control genes. The detection of over or underexpression can be seen analogously to the determination of the concentration of a protein biomarker.

Several applications of gene expression profiles are known in the art.

Pachot and colleagues demonstrate the utility of expression signatures for the follow-up assessment of patients with septic shock. Here are molecular differences that help restore a functioning

Immune system in the survivors reflect. Within the first day after the diagnosis of septic shock, marker genes with innate immune functions (100%) and specificity (88%) show whether the immune paralysis is reversible, thus enabling the patient to survive. However, in the study, the patient population was too small (38) to create a robust profile and validation of this dataset by an independent dataset has not yet been done. The prior art contains numerous studies for the identification of gene expression markers [Tang et al., 2007b] or gene expression profiles for the detection of systemic infection

[Johnson et al., 2007].

Tang and colleagues [Tang et al., 2007b] searched in a particular

Blood cell population, the neutrophils, after a signature, which is a

Differentiation of patients with SIRS and sepsis allows. 50 markers from this cell population are sufficient to reflect the immune response to a systemic infection and to provide new insights into the pathophysiology and the signaling pathways involved. The classification of patients with and without sepsis succeeds with high

Security (PPV 88% or 91% in training and test data). However, the applicability for clinical diagnosis is limited by the fact that in the blood

Signature of signals from other types of blood cells can be superimposed. Regarding the applicability of the preparation of this blood cell population is associated with significantly increased effort. However, the informative value of the results published in this study is limited for practical applications because the

Patient selection was very heterogeneous. Patients were included in the study who had very different comorbidities, such as B to 1 1% to 16% had tumors or very different

therapeutic measures (e.g., 27% to 64% vasopressor therapy), which greatly affected gene expression profiles.

Johnson and colleagues [Johnson et al., 2007] describe in a collective of trauma patients that the severity of sepsis can be measured by molecular changes up to 48 hours before clinical diagnosis. The trauma patients were examined over several days. Part of the patients developed sepsis. Non-infectious SIRS patients were compared with pre-septic patients. The identified signature of 459 transcripts is composed of markers of the immune response and inflammatory markers.

Sample material was whole blood, the analyzes were carried out on a microarray. It is unclear whether this signature can be extended to other groups of septic or pre-septic patients. A classification and the diagnostic utility of this signature has not been shown.

Furthermore, other signatures are described in the prior art, for example the host's response to an infection.

The specificity of the host response to different pathogens has been investigated in several experimental systems so far. However, no study described gene expression profiles and / or signatures by whole blood sepsis testing. The goal of Feezor and colleagues [Feezor et al., 2003] was to agree on differences between infections with gram-negative and gram-positive pathogens

identify. Blood samples from three different donors were stimulated ex vivo with E. coli LPS and heat-inactivated S. aureus. Gene expression studies were performed using microarray technology. The group found both genes that were up-regulated after S.aivreivs stimulation and downregulated after LPS stimulation, and genes that were more strongly expressed after LPS treatment than after addition of heat-inactivated S. aureus cords , At the same time, many genes were up-regulated to the same extent by gram-positive and gram-negative stimulation. This concerns, for example, the cytokines TNF-α, IL-1β and IL-6. Unfortunately, the differentially expressed genes were not published by name, so that only an indirect comparison of other results is possible. In addition to the

Gene expression was reported by Feezor et al. also examined the plasma concentrations of some cytokines. The gene expression data did not necessarily correlate with the plasma concentrations. In gene expression, the amount of mRNA is measured. However, this is subject to posttranscriptional regulation prior to protein synthesis, from which the observed differences may result.

The most interesting publication on this topic was published by a research group led by Ramilo [Ramilo et al., 2007]. Gene expression assays were also performed on human blood cells, revealing differences in the molecular host response to various pathogens. For this purpose, pediatric patients with acute infections, e.g. acute respiratory diseases, urinary tract infections, bacteraemias, local

Abscesses, bone and joint infections as well as meningitis examined.

Microarray experiments were performed on RNA samples isolated from peripheral blood mononuclear cells of ten patients each with E. coli and S. aureus infection, respectively. The identification of the pathogens was done by blood culture. Based on the training dataset, 30 genes were identified, through which

Using the causative pathogens could be diagnosed with high accuracy. Despite the numerous published studies and the individual signatures described therein, which state the state of the art, none of them permits a diagnostic statement about sepsis and / or sepsis-like conditions. None of these publications offers the reliability, accuracy and robustness of the invention disclosed herein. These studies are focused on getting out

but not, as in the present invention, the multigene biomarker optimal for a specific clinical problem [Simon et al., 2005].

Thus, it is an object of the present invention to provide a test system with which a fast and reliable statement about a

pathophysiological condition, such as sepsis and generalized infection, is possible.

The solution of this object is procedurally by the features of claim 1.

With regard to use, the object is characterized by the features of

Claims 5 and 12 solved.

The solution of the stated object is also achieved by a primer according to claim 16.

A kit according to claim 17 solves the problem as well.

In general terms, the present invention relates to a system comprising the following elements:

• Set of gene activity markers

• Reference genes as an internal control of gene activity marker signals in whole blood

• Detection mainly via real-time PCR or others

Amplification method or hybridization method • Using an algorithm to calculate the individual results of the

 Gene activity marker to a common numerical value, index or score to convert

• Representation of this numerical value on a corresponding scale

• Calibration, i. Division of the scale according to the intended

 Application by previous validation experiments.

The system provides a solution to the problem of finding

Disease states such. For example, the distinction between infectious and non-infectious adult organ failure but also for other relevant applications and issues in this context.

In particular, the present invention relates to a method for in vitro detection and / or early detection and / or differentiation and / or course observation and / or assessment of pathophysiological conditions selected from the group consisting of: SIRS, sepsis and their degrees of severity; sepsis-like states; septic shock; Bacteremia, infectious / non-infectious

Ivlultiorganversagen; Early detection of these conditions; Focus control; Control of surgical remedial measures of the infection focus; Responder / non-responder for a given therapy; Therapy monitoring; Differentiation between infectious and non-infectious genesis in systemic reactions of the

Organism, e.g. SIRS, sepsis, postoperative complications, chronic and / or acute organ dysfunction, shock reaction, inflammatory response and / or trauma; the method comprising the steps of: a) isolating sample nucleic acids from one of a patient

originating sample; b) determining gene activities using a plurality of at least three polynucleotides selected from the group consisting of M2, M3, M4, M6, M7, M8, M9, M10, M12, M13, M15, M16 and M17; and / or their Isoforms and / or their gene loci and / or their transcripts and / or fragments at least 5 nucleotides in length thereof, for the formation of at least one multigene biomarker characteristic of the detection and / or differentiation and / or course of pathophysiological conditions of a patient; wherein the polynucleotides are defined according to the following table:

Determination of gene activities of at least one internal reference gene to which the gene activities determined under b) relate, in particular

normalized; d) forming a value from the individual particular gene activities of the multigene biomarker indicating the pathophysiological status.

A preferred method is characterized in that the reference gene is selected from polynucleotides of the group consisting of R1, R2 and R3 and / or their isoforms and / or their gene loci and / or their transcripts and / or fragments having a length of at least 5 nucleotides thereof , wherein the reference genes are defined according to the following table:

A further preferred method is characterized in that as

Polynucleotide sequences Genloci, sense and / or antisense strands of pre-mRNA and / or mRNA, small RNA, especially scRNA, snoRNA, micro RNA, siRNA, dsRNA, ncRNA or transposable elements for the detection of

Gene expression profiles are used.

Another preferred embodiment lies in a method characterized in that in step b) the gene activity of 4, 5, 6, 7, 8, 9, 10, 1 1, or 12 polynucleotides, or of all 13 polynucleotides is determined wherein the polynucleotides are selected from the group consisting of: M2, M3, M4, M6, M7, M8, M9, M10, M12, M13, M15, M16 and M17 and / or their isoforms and / or their gene loci and / or their transcripts and / or fragments of at least 5 nucleotides in length thereof, the polynucleotides being defined according to the following table: Transcript variant / ds' accession SEQ

 polynucleotide

 regulatory sequences Number ID

 M2_l NM_001031700 1

 M2 M2_2 NM_016613 2

 M2_3 NM_001128424 3

 M4_l NM_203330 4

 M4_2 NM_000611 5

 M4_3 NM_203329 6

 M4_4 NM_203331 7

 M4

 M4_5 NM_001127223 8

 M4_6 NM_001127225 9

 M4_7 NM_001127226 10

 M4_8 NM_001127227 11

 M6_l NM_001831 12

 M6

 M6_2 NM_203339 13

 M7_l NM_031311 14

 M7

 M7_2 NM_019029 15

 M9 M9 NM_006682 16

 MIO MIO NM_033554 17

 M15_l NM_003580 18

 M15

 M15_2 NM_001144772 19

 M3_A NM_001123041 20

 M3

 M3_B NM_001123396 21

 M8 NM_025209 22

 M8

 MS_cis AI807985 23

 M12 NM_002185 24

 M12

 M12_cw DB155561 25

 M13 M13 NM_001080394 26

 M16 M16 NM_003268 27

 M17 M17 NM_182491 28

It has been found that the use of 7 polynucleotides is often optimal.

In a further embodiment, the invention relates to the use of at least three polynucleotides selected from the group consisting of: M2, M3, M4, M6, M7, M8, M9, M10, M12, M13, M15, M16 and M17 and / or their isoforms and / or their gene loci and / or their transcripts and / or

Fragments at least 5 nucleotides in length thereof, for the production of at least one multigene biomarker for the production of a multiplex assay as an aid for in vitro detection and / or early detection and / or differentiation and / or course monitoring and / or assessment of pathophysiological conditions of a patient, wherein the pathophysiological condition is selected from the group consisting of: SIRS, sepsis and their degrees of severity;

sepsis-like states; septic shock; Bacteremia, infectious / non-infectious multi-organ failure; Early detection of these conditions; Focus control; Control of surgical remedial measures of the infection focus;

Responder / non-responder for a given therapy; Therapy monitoring;

Distinction between infectious and non-infectious genesis in systemic reactions of the organism, e.g. SIRS, sepsis, postoperative complications, chronic and / or acute organ dysfunction, shock reaction,

Inflammatory reaction and / or trauma; wherein the polynucleotides are defined according to the following table:

Transcript variant / ds' accession SEQ

 polynucleotide

 regulatory sequences Number ID

 M2_l NM_001031700 1

 M2 M2_2 NM_016613 2

 M2_3 NM_001128424 3

 M4_l NM_203330 4

 M4_2 NM_000611 5

 M4_3 NM_203329 6

 M4_4 NM_203331 7

 M4

 M4_5 NM_001127223 8

 M4_6 NM_001127225 9

 M4_7 NM_001127226 10

 M4_8 NM_001127227 11

 M6_l NM_001831 12

 M6

 M6_2 NM_203339 13

 M7_l NM_031311 14

 M7

 M7_2 NM_019029 15

 M9 M9 NM_006682 16

 MIO MIO NM_033554 17

 M15_l NM_003580 18

 M15

 M15_2 NM_001144772 19

 M3_A NM_001123041 20

 M3

 M3_B NM_001123396 21

 M8 NM_025209 22

 M8

 MS_cis AI807985 23

 M12 NM_002185 24

 M12

 M12_cw DB155561 25

 M13 M13 NM_001080394 26

 M16 M16 NM_003268 27

M17 M17 NM_182491 28 A preferred embodiment of the present invention is a use in which the multigene biomarker is a combination of a plurality of polynucleotide, in particular gene sequences, by means of whose gene activities by means of a

Interpretation function is performed a classification and / or an index is formed.

In the practice of the Applicant, it has been found that such a use is particularly suitable, which is characterized in that the gene activities by enzymatic methods, in particular amplification method, preferably polymerase chain reaction (PCR), preferably real-time PCR, in particular probe-based methods such Taq Man, Scorpions, Molecular Beacons; and / or by means of hybridization methods, in particular those on microarrays; and / or direct mRNA detection, in particular sequencing or mass spectrometry; and / or isothermal amplification.

A further preferred embodiment of the present invention is a use, which is characterized in that from the individual specific gene activities an index is formed, which is a measure of the severity and / or the course of the pathophysiological state after appropriate calibration, preferably the index displayed on an easily interpretable scale.

It is further preferred that the obtained gene activity data for the production of software for the description of at least one pathophysiological condition and / or an investigation question and / or as aids for diagnostic purposes and / or patient data management systems, in particular for use for Patientenstratifikation and inclusion criterion for clinical trials.

In addition, a use is preferred in which for generating the gene activity data such specific gene loci, sense and / or antisense strands of pre-mRNA and / or mRNA, small RNA, especially scRNA, snoRNA, microRNA, siRNA, dsRNA, ncRNA or transposable Elements, genes and / or

Gene fragments of at least 5 nucleotides in length, which have a sequence homology of at least about 10%, in particular about 20%, preferably about 50%, particularly preferably about 80% to the polynucleotide sequences M2, M3, M4, M6, M7, M8, M9, M10, M12, M13, M15, M16 and

M17aufweisen. Another preferred embodiment of the present invention is a use characterized in that 4, 5, 6, 7, 8, 9, 10, 1 1 or 12 polynucleotides, or all 13 polynucleotides are used, wherein the polynucleotides are selected from A group consisting of: M2, M3, M4, M6, M7, M8, M9, M10, M12, M13, M15, M16 and M17 and / or their isoforms and / or their gene loci and / or their transcripts and / or fragments a length of at least 5 nucleotides thereof, the polynucleotides being defined according to the following table:

und/oder wobei eine Anzahl von 7 Polynucleotiden bevorzugt ist. Grundsätzlich kann die Erfindung gemäß einer alternativen Ausführungsform auch ausgeführt werden unter Verwendung mindestens eines Polynukleotids, ausgewählt aus der Gruppe, bestehend aus: M2, M3, M4, M6, M7, M8, M9, M10, M12, M13, M15, M16 und M17 und/oder deren Isoformen und/oder deren Genloci und/oder deren Transkripten und/oder Fragmenten mit einer Länge von mindestens 5

and / or wherein a number of 7 polynucleotides is preferred. In principle, according to an alternative embodiment, the invention can also be carried out using at least one polynucleotide selected from the group consisting of: M2, M3, M4, M6, M7, M8, M9, M10, M12, M13, M15, M16 and M17 and / or their isoforms and / or their gene loci and / or their transcripts and / or fragments with a length of at least 5

Nucleotides thereof, wherein the polynucleotides are defined according to the following table:

zur Herstellung eines Assays zur Beurteilung, ob bei einem Patienten ein

for making an assay to assess whether a patient has a

pathophysiological condition is present, and / or to determine the severity and / or the course of a pathophysiological condition. Here, the pathophysiological condition is selected from the group consisting of: SIRS, sepsis and their severity; sepsis-like

states; septic shock; Bacteremia, infectious / non-infectious

Multi-organ failure; Early detection of these conditions; Focus control; Control of surgical remedial measures of the infection focus; Responder / non-responder for a given therapy; Therapy monitoring; Differentiation between infectious and non-infectious genesis in systemic reactions of the

Organism, e.g. SIRS, sepsis, postoperative complications, chronic and / or acute organ dysfunction, shock reaction, inflammatory response and / or trauma.

Preferably, the sample nucleic acid is RNA, in particular total RNA or mRNA, or DNA, in particular cDNA.

For a further refined diagnostic indication, it may be advantageous to assess the pathophysiological status, in addition to at least one of the polynucleotides selected from the group consisting of: M2, M3, M4, M6, M7, M8, M9, M10, M12, M13, M15, M16 and M17 and / or their isoforms and / or their gene loci and / or their transcripts and / or fragments having a length of at least 5 nucleotides thereof, the polynucleotides being defined according to the following table:

Transcript variant / ds' accession SEQ

 polynucleotide

 regulatory sequences Number ID

 M2_l NM_001031700 1

 M2 M2_2 NM_016613 2

 M2_3 NM_001128424 3

 M4_l NM_203330 4

 M4_2 NM_000611 5

 M4_3 NM_203329 6

 M4_4 NM_203331 7

 M4

 M4_5 NM_001127223 8

 M4_6 NM_001127225 9

 M4_7 NM_001127226 10

 M4_8 NM_001127227 11

 M6_l NM_001831 12

 M6

 M6_2 NM_203339 13

 M7_l NM_031311 14

 M7

 M7_2 NM_019029 15

 M9 M9 NM_006682 16

 MIO MIO NM_033554 17

M15 M15_l NM_003580 18 M15_2 NM_001144772 19

 M3_A NM_001123041 20

 M3

 M3_B NM_001123396 21

 M8 NM_025209 22

 M8

 MS_cis AI807985 23

 M12 NM_002185 24

 M12

 M12_cw DB155561 25

 M13 M13 NM_001080394 26

 M16 M16 NM_003268 27

 M17 M17 NM_182491 28 to use at least one further marker which is selected from the group consisting of: procalcitonin (PCT), C-reactive protein (CRP);

white blood cell count; cytokines; Interleukins as well as other clinical laboratory parameters and genetic, transcriptomic and proteomic markers known to date in the art which are well known to the person skilled in the art.

To carry out the present invention, it is necessary to use suitable primer pairs (forward and reverse). Such particularly suitable primers are those listed in the following table:

Primer for quantitative

 Markers and reference genes SEQ ID

 PCR / resulting amplicon

 M2-fw 38

M2 M2-rev 39

 M2 amplicon 40

M4-fw 41

M4 M4-rev 42

 M4 amplicon 43

M6-fw 44

M6 M6-rev 45

 M6 amplicon 46

M7-fw 47

M7 M7-rev 48

 M7 amplicon 49

M9 fw 50

M9 M9-rev 51

 M9 amplicon 52

M10-fw 53

MIO MIO-rev 54

 MIO Amplicon 55

M15-fw 56

M15 M15-rev 57

 Ml 5- amplicon 58

M3-fw 59

M3 M3-rev 60th

 M3 amplicon 61

M8-fw 62

M8 M8-rev 63

 M8 Amplicon 64

M12-fw 65

M12 M12-rev 66

 Ml 2- amplicon 67

M13-fw 68

M13 M13-rev 69

 M1-amplicon 70

M16-fw 71

M16 M16-rev 72

 Ml 6- amplicon 73

M17-fw 74

M17 M17-rev 75

M17 Amplicon 76 Rl-fw 77

Rl Rl-rev 78

 Rl Amplicon 79

 R2-fw 80

 R2 R2-rev 81

 R2 Amplicon 82

 R3-fw 83

 R3 R3-rev 84

 R3 amplicon 85

It must be emphasized, however, that the said primers are merely exemplary.

 The above-mentioned amplicons can be used, for example, as probes for

Hybridization methods are used.

The invention further relates to a kit for carrying out the method according to the invention, comprising at least one IVIigenigen biomarker comprising a plurality of polynucleotide sequences which are selected from the group consisting of: M2, M3, M4, M6, M7, M8, M9, M10, M12, M13, M15, M16 and M17 and / or their isoforms and / or their gene loci and / or their transcripts and / or fragments having a length of at least 5 nucleotides thereof, the polynucleotides being defined according to the following table:

Marker and transcript variant / ds' accession SEQ

Reference genes regulatory sequences Number ID

 M2_l NM_001031700 1

 M2 M2_2 NM_016613 2

 M2_3 NM_001128424 3

 M4_l NM_203330 4

 M4_2 NM_000611 5

 M4_3 NM_203329 6

 M4_4 NM_203331 7

 M4

 M4_5 NM_001127223 8

 M4_6 NM_001127225 9

 M4_7 NM_001127226 10

 M4_8 NM_001127227 11

 M6_l NM_001831 12

 M6

 M6_2 NM_203339 13

 M7_l NM_031311 14

 M7

 M7_2 NM_019029 15

 M9 M9 NM_006682 16

MIO MIO NM_033554 17 M15_l NM_003580 18

 M15

 M15_2 NM_001144772 19

 M3_A NM_001123041 20

 M3

 M3_B NM_001123396 21

 M8 NM_025209 22

 M8

 MS_cis AI807985 23

 M12 NM_002185 24

 M12

 MYl cis DB155561 25

 M13 M13 NM_001080394 26

M16 M16 NM_003268 27

M17 M17 NM_182491 28

wherein the multigene biomarker is specific for a

pathophysiological condition of a patient and includes such conditions selected from the group consisting of: SIRS, sepsis and theirs

Severity; sepsis-like states; septic shock; Bacteremia, infectious / non-infectious multi-organ failure; Early detection of these conditions; Focus control; Control of Surgical Rehabilitation Measures

Infection focus; Responder / non-responder for a given therapy;

Therapy monitoring; Distinction between infectious and non-infectious genesis in systemic reactions of the organism, e.g. SIRS, sepsis, postoperative complications, chronic and / or acute organ dysfunction, shock reaction, inflammatory response and / or trauma.

A preferred kit is characterized in that the polynucleotide sequences also include gene loci, sense and / or antisense strands of pre-mRNA and / or mRNA, small RNA, in particular scRNA, snoRNA, microRNA, siRNA, dsRNA, ncRNA or transposable elements.

A further preferred kit is characterized in that it contains at least one reference gene which is selected from the group consisting of: R 1, R 2 and R 3 and / or their isoforms and / or their gene loci and / or their

Transcripts and / or fragments at least 5 nucleotides in length thereof, the reference genes being defined according to the following table:

Transcript variant / cis- Accession Number SEQ TD

 Reference genes regulatory

 sequences

 Rl R1_A NM_001228 29

R1_B NM_033355 30 R1_C NM_033356 31

 R1_E NM_033358 32

 R1_F NM_001080124 33

 R1_G NM_001080125 34

 R2_l NM_002209 35

 R2

 R2_2 NM_001114380 36

 R3 R3 NM_003082 37

A likewise preferred use is characterized in that an index (score) is formed from the individual specific gene activities, which, after appropriate calibration, is a measure of the severity and / or the course of the pathophysiological state, in particular sepsis or the sepsis-like state.

It is further preferred to display the index on an easily interpretable scale.

In the Applicant's practice, it has been found that here a dimensionless scale of -5 to +5 or to increase the differences, a corresponding multiple, e.g. from -50 to +50 is particularly suitable for classifying pathophysiological conditions. This score is called "SIQ score".

In the context of optimized computerized hospital management as well as further research in the field of sepsis, it has proven to be advantageous to use the obtained gene activity data for the production of software for the description of at least one pathophysiological condition and / or an investigation question and / or as Aids for diagnostic purposes and / or for patient data management systems.

The index is preferably determined by statistical techniques such as supervised machine and static learning classification techniques such as (diagonal, linear, quadratic) discriminant analysis, super vector machines, generalized partial least squares, k-nearest neighbors, random forests, k-nearest neighbor determined. For a linear discriminant analysis, for example, the following formula can be used:

Preferably, the multigene biomarker is a combination of several

Polynucleotide, in particular gene sequences, based on their gene activities by means of an interpretation function performed a classification and / or an index or score is formed.

For the purposes of the present invention, it has also proved to be advantageous that the gene activities by means of enzymatic methods,

in particular amplification method, preferably polymerase chain reaction (PCR), preferably real-time PCR; and / or by means of hybridization methods, in particular those on microarrays.

In the detection of gene activities occurring differential

Expression signals of those contained in the multigene biomarker

Polynucleotide sequences can advantageously and uniquely

pathophysiological status, a course and / or therapy monitoring.

Typically, an index (score, SIQ score) is formed from the individual determined gene activities, which, after appropriate calibration, is a measure of the severity and / or the course of the pathophysiological state, in particular sepsis or the sepsis-like state.

This score can be a quick diagnostic for the attending physician

Give aids in the hand.

The present invention, as part of an integrated system (In Vitro Diagnostic Multivariate Index Assay, IVDMIA), allows for a potential infectious

Complication in patients with SIRS or possible sepsis. This system includes the choice of patients and their determination

Gene expression signals in an interpretable index, which the physician can use as an aid to diagnosis. The Applicant has developed several methods that are different

Sequence pools used to determine states and / or to distinguish or answer defined investigation questions. Examples are in the following

To find patents: distinction between SIRS, sepsis and

sepsis-like states [WO 2004/087949; WO 2005/0831 15], development of criteria for predicting the course of the disease in sepsis [WO 05/106020],

Distinction between non-infectious and infectious causes of a

Multiple organ failure [WO 2006/042581], in vitro classification of

Gene expression profiles of patients with infectious / non-infectious

Multi-organ failure [WO 2006/100203], determination of the local causes of a fever of unclear origin [WO 2007/144105], polynucleotides for the detection of gene activities for the distinction between local and systemic infection [DE 10 2007 036 678.9].

The invention relates to polynucleotide sequences, to a method and furthermore to kits for generating multigene biomarkers which have features of an "in vitro diagnostic multivariate index assay" (IVDMIA) in one and / or several modules.

With respect to the nucleotide sequences used in the present application, the following should be noted:

 RefSeq is a public database that includes nucleotide and protein sequences with their characteristics and bibliographic information.

The RefSeq database was developed by the National Center for Biotechnology

Information (NCBI), a division created by National Library of Medicine, which belongs to the US National Institute of Health, is maintained and maintained on an ongoing basis

updated (1).

 NCBI builds RefSeq from the sequence data of the GenBank archive database (2), a large public database of sequences posted and shared between GenBank in the United States, the EMBL Data Library in the United Kingdom, and the Japan DNA Database The RefSeq collection is unique when it comes to delivering

error-corrected, non-redundant, explicitly linked nucleotide and

Protein databases goes. The entries are not redundant with the aim of the represent different biological molecules that are characteristic of organism, strain or haplotype.

If certain entries occur multiple times in the collection, there can be several reasons:

 it encodes alternative spliced transcripts for the same protein product (so-called transcript variants),

 there are several genomic regions within a species or between species encoding the same protein product,

 -if RefSeqs are created that represent alternative haplotypes and some mRNA and protein sequences are identical in all haplotypes.

RefSeq database provides the critical foundation for sequence integration, genetic and functional information and is internationally recognized as the standard for genome annotation. BLAST Sequence Search provides RefSeq information in multiple NCBI resources, including Entrez Nucleotide, Entrez Protein, Entrez Gene, Map Viewer, for FTP Download; or by networking with PubMed (Pruitt et al., 2007; The NCBI Handbook, 2002). RefSeq statements can be identified by the unique Accession format, which includes the underscore (_).

 Workgroups use different methods and protocols and compile the RefSeq collection for different organisms. RefSeq documents are created by several different methods (The NCBI handbook 2002):

 1 . scientific cooperation

 2. Computer-assisted genome annotation procedures

 3. Error correction by the NCBI staff

 4. Excerpts from GenBank

 Each entry has a comment that shows the status of the respective error correction as well as the assignment of the cooperating working group. As a result, the RefSeq specification either contains the essentially unchanged, initially valid copy of the original GenBank entries or corrected as well as additional ones

Information added by co-operation partners or experts (The NCBI handbook 2002). If a molecule in GenBank is represented by multiple sequences, a decision is made by the NCBI staff for the "best" sequence, which is then presented as RefSeq.

The main objective is to avoid known mutations, sequencing errors, cloning artifacts and erroneous annotations. RefSeq sequences that are affected by such types of errors are corrected. Sequences are validated by checking that the genomic sequence corresponding to the annotated mRNA actually matches the mRNA sequence indication and whether coding regions

actually translate into the corresponding protein sequence. Another important task is to expand the collection by adding previously unknown underrepresented genes and / or alternative splice products, as well as provide additional annotation of sequence properties representing mature peptide products and their functional domains and / or rare biological phenomena such as e.g. non-AUG initiation sites of transcription or selenoproteins, highlight (The NCBI handbook 2002).

The quality check is done on a regular basis to be questionable

Find and check sequences. These quality tests control the errors and conflicts in nomenclature, sequence similarities and genomic

Localization, potential cloning errors (such as chimeras) and aligning the data with other NCBI resources, including homolog gene, map viewer and GenBank related sequences (The NCBI handbook 2002).

In the present high-quality human and mouse genomic sequences, the main focus was on testing cDNA-based RefSeqs relative to the genome. The CCDS collaboration (The NCBI handbook 2002) has also helped to focus attention on areas where mRNA and protein levels are mismatched.

 The quality assurance processes involve the registration of database attributes to document that

 - the category of the quality test has been updated,

 - no problems with RefSeq transcript and protein were found and therefore the reported error should be ignored,

- due to genome assembly a problem has arisen at this position Problems of genome assembly can be:

 There are gaps in the composition of the individual sequences, in some cases the established sequence contains a known mutation or rare polymorphism and is therefore not an ideal representative sequence (Pruitt et al., 2007).

The decision to use the marker population named in the present application based on its RefSeq identity for purposes of the present invention was made based on the properties of the RefSeq database described above. The properties of this database, concerning the generation, quality, maintenance and updates of the biological sequences, as well as the presence of functional information at the nucleic acid level, equally for alternative splice variants, were the decisive factors.

As already explained, the biological mechanism of alternative splicing provides space for extensions of the scope of protection well known to those skilled in the art. Thus, it is conceivable that completely new primary structures can be identified with new transcript variants, or that sequence changes of the known transcript variants result. On the other hand, the genomic regions are claimed, which for all these known and unknown variants of the coding transcripts, including their cis-regulatory sequences as fully functional genomic functional units and thus fall within the scope of the present invention, or at least to those skilled easily findable equivalents to the in the claims, description and sequences mentioned in the Sequence Listing.

definitions:

For the purposes of the present invention, the following definitions are used: Condition: the clinically defined severity levels "systemic inflammatory response syndrome" (SIRS), "sepsis", "severe sepsis" and "septic shock" as defined in [Bone et al., 1992] and the PIRO concept [Levy et al., 2003].

Multiple organ failure: Multi-organ failure is the failure of two or more vital organ systems occurring simultaneously or in rapid succession. Multi-organ dysfunction syndrome (MODS) precedes MOV as initial organ failure [Zeni et al., 1997]. We speak today of

Multi-organ failure if two or more organs show functional disorders simultaneously or consecutively, whereby chronic persistent organ failure is ruled out. The prognosis of MOV is closely related to the number of involved organ systems. Mortality is 22% in the first 24 hours of an organ failure and 41% after 7 days. When three organ systems fail, mortality increases to 80% on the first day and to 100% on the first day [Knaus et al., 1985].

An important pathomechanism for the development of MODS and MOV is the development of a systemic inflammatory syndrome (SIRS, Bone et al., 1992). MODS and MOV can be both infectiologic and non-infectiological.

Fever of unclear origin: Fever of unknow origin (FUO) is clinically defined as a fever in which the temperature is higher than 38.8 ° C for more than 3 weeks, with no evidence of a 1-week follow-up there is a clear diagnosis of the cause. Depending on the origin, four classes of FUO have been described: FUO classic, nosocomial, immunodeficient or HIV-related origin [Roth and Basello, 2003]. FUO was also called "a more well-known disease with one

unusual appearance as a rare disorder "[Amin and Kauffman, 2003].

Only 10% of patients with postoperative fever report infection [Pile et al., 2006]. In most cases, the temperature of the Patients return to normal temperature within four days of surgery. Nevertheless, some patients develop infection on or after the fifth postoperative day, with pneumonia being present in 12% of cases. Likewise, Pile and colleagues report that fever occurring two days after the procedure is most likely an infection, such as urinary and / or internal abdominal (peritonitis) infection, pneumonia, or one an infection caused by an intravenous catheter.

Examination Question: A clinically relevant question that is important for the treatment of a patient, for example: prediction of disease progression, therapy monitoring, focus of infection, chances of survival, predisposition, etc.

A systemic infection is an infection in which the pathogens have spread through the bloodstream throughout the organism.

SIRS: Systemic Inflammatory Response Syndrome, according to Bone [Bone et al., 1992] and Levy [Levy et al., 2003] a generalized, inflammatory,

non-infectious condition of a patient.

Sepsis: According to Bone [Bone et al., 1992] and Levy [Levy et al., 2003] a generalized, inflammatory infectious condition of a patient.

Biological fluid: Biological fluids within the meaning of the invention are understood to be all body fluids of mammals, including humans.

Gene: A gene is a section of the deoxyribonucleic acid (DNA) that contains the basic information needed to produce a biologically active ribonucleic acid (RNA) as well as regulatory elements that activate or inactivate this production. For the purposes of the invention, genes are also understood as meaning all derived DNA sequences, partial sequences and synthetic analogs (for example peptido-nucleic acids (PNA)). The determination of gene expression on RNA Level-related description of the invention thus expressly none

Restriction but only an exemplary application.

Genlocus: Genlocus is the position of a gene in the genome. If the genome consists of several chromosomes, the position is within the

Chromosome meant on which the gene is located. Different manifestations or variants of this gene are referred to as alleles, all located at the same site on the chromosome, namely the gene locus. Thus, the term "gene locus" includes, on the one hand, the pure genetic information for a specific gene product and, on the other hand, all regulatory DNA segments as well as any additional DNA sequences that are in any functional relationship with the gene at the gene locus in the immediate vicinity (1 Kb) but outside the 5 'and / or 3' end of a gene locus The specification of the gene locus is made by the accession number and / or RefSeq ID of the RNA main product, which is from this locus descended.

Gene activity: Genetic activity is the measure of the ability of a gene to be transcribed and / or to form translation products.

Gene Expression: The process of forming a gene product and / or expression of a genotype into a phenotype.

Multigenbiomarker: Combination of several gene sequences whose gene activities form a combined overall result (for example a classification and / or an index) by means of an interpretation function. This result is specific to a condition and / or an investigation question.

Hybridization Conditions: Physical and chemical parameters well known to those skilled in the art that may affect the establishment of a thermodynamic equilibrium of free and bound molecules. In the interest of optimal hybridization conditions, the duration of contact of the probe and sample molecules, cation concentration in the hybridization buffer, Temperature, volume and concentrations and ratios of the hybridizing molecules are coordinated.

Amplification conditions: Constant or cyclic reaction conditions that allow the amplification of the starting material in the form of nucleic acids. In the reaction mixture are the individual components (deoxyribonucleotides) for the resulting nucleic acids, as well as short oligonucleotides, which can attach to complementary regions in the starting material, as well as a nucleic acid synthesis enzyme, called polymerase. The cation concentrations, pH, volume and the duration and temperature of the individual reaction steps which are well known to the person skilled in the art are of importance for the course of the amplification.

Primer: A primer in the present invention is an oligonucleotide which can be used as a starting point for nucleic acid-replicating enzymes, such as, for example, B. the DNA polymerase is used. Primers may consist of both DNA and RNA (primer 3, see, e.g., http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_www.cgi of MIT).

Probe: In the present application, a probe is a

Nucleic acid fragment (DNA or RNA) having a molecular tag (for example fluorescent markers, in particular Scorpion ^®, molecular beacons, Minor Groove Binding- probes, TaqMan ^® probes, isotopic label, etc.) can be provided and for sequence-specific detection of target DNA - And / or target RNA molecules is used.

PCR: is the abbreviation for the term "polymerase chain reaction." The polymerase chain reaction is a method to amplify DNA in vitro outside a living organism using a DNA-dependent DNA polymerase used according to the present invention to short parts - up to about 3,000

Base pairs - to amplify a DNA strand of interest. It may be a gene or even a part of a gene or non-coding DNA sequences. It is well known to the skilled person that a A number of PCR methods are known in the art, all of which are encompassed by the term "PCR." This applies in particular to "real-time PCR" (see also the explanations below).

PCR Primer: Typically, a PCR requires two primers to detect the starting point of DNA synthesis on the two single strands of DNA

which limits the area to be duplicated from both sides. Such primers are well known to those skilled in the art, for example, from the website "Primer3", see, e.g., http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_www.cgi of MIT.

Transcript: For the purposes of the present application, a transcript is understood to mean any RNA product which is produced on the basis of a DNA template.

Small RNA: Small RNAs in general. Representatives of this group are in particular, but not exclusively:

 a) small cytoplasmic RNA (scRNA), which is one of several small RNA molecules in the cytoplasm of a eukaryote.

 b) snRNA (small nuclear RNA), one of the many small RNA forms found only in the nucleus. Some of the snRNAs play a role in splicing or in other RNA-processing reactions.

 c) small non-protein codinq RNAs, which include the so-called small nucleolar RNAs (snoRNAs), microRNAs (miRNAs), short interfering RNAs (siRNAs) and small double-stranded RNAs (dsRNAs), which increase gene expression on many levels, including chromatin architecture, RNA editing, RNA stability, translation, and possibly also transcription and splicing. In general, these RNAs are multiply processed from the introns and exons of longer primary transcripts, including protein-coding transcripts. Although only 1, 2% of the human genome encodes proteins, a large part is nevertheless transcribed. In fact, about 98% of them are in mammals and humans

found transcripts of non-protein-coding RNAs (ncRNA) from introns of protein-encoding genes and the exons and introns of non-protein coding Genes, including many, which are anti-sense to protein encoding genes or overlap with them. Small nucleolar RNAs (snoRNAs) regulate the sequence-specific modification of nucleotides in target RNAs. Here are two types of modifications, namely 2'-0-ribosemethylation and

Pseudouridylation regulated by two large snoRNA families called box C / D snoRNAs on the one hand and box H / ACA snoRNAs on the other. Such snoRNAs have a length of about 60 to 300 nucleotides.

miRNAs (microRNAs) and siRNAs (short interfering RNAs) are even smaller RNAs with generally 21 to 25 nucleotides. miRNAs are derived from endogenous short hairpin precursor structures and usually use other loci with similar - but not identical - sequences as the target of translational repression. siRNAs arise from longer double-stranded RNAs or long hairpins, often of exogenous origin. They usually target homologous sequences at the same locus or elsewhere in the genome, where they participate in so-called gene silencing, a phenomenon also called RNAi. However, the boundaries between miRNAs and siRNAs are fluid.

 d) In addition, the term "small RNA" may also include so-called transposable elements (TEs) and in particular retroelements, which are also understood for the purposes of the present invention by the term "small RNA".

RefSeq ID: This name refers to entries in the NCBI

Database (www.ncbi.nlm.nih.gov). This database provides non-redundant

Reference standards for genomic information. This genomic information includes, among others, chromosomes, mRNAs, RNAs, and proteins. Each RefSeq ID represents a single, naturally occurring molecule of an organism. The biological sequences representing a RefSeq are derived from GenBank entries (also NCBI), but are a compilation of

Information elements. These information elements come from primary research at the DNA, RNA and protein levels.

Accession number: An accession number represents the entry number of a polynucleotide in the NCBI gene bank known to the person skilled in the art. In this Both RefSeq IDs and less well characterized and redundant sequences are managed as entries and made available to the public (www.ncbi.nlm.nih.gov/genbank/index.html).

Local Infection: The infection is limited to the entry portal of the pathogen (e.g., wound infection)

 Generalized Infection: Pathogens invade the vascular system and affect the entire organism. Generalized infections can lead to sepsis.

 Colonization: The presence of microorganisms does not cause any disease symptoms in the organism.

 Severe Infection: Infectious Stove with the risk of increasing spread with the symptoms of fever above 39 ° C and / or bacteremia.

Bacteremia: A condition in which there are transient and short-term presence of bacteria in the blood, without this being associated with the appearance of bacterial-related clinical symptoms.

Alternative splicing: a process in which the exons of the primary gene transcript (pre-mRNA) are recombined after excision of the introns in different combinations.

BLAST: Basic Local Alignment Search Tool (after Altschul et al., J Mol Biol 215: 403-410; 1990). Sequence comparison algorithm, optimized for speed, is used for searching in sequence databases for optimal local adaptation to the query sequence. cDNA: Complementary DNA. DNA sequence, product of reverse transcription of mRNA.

Coding Sequence: Protein-encoding portion of a gene or mRNA as distinct from introns (non-coding sequences) and 5- or 3 'untranslated portions. Coding sequences of the cDNA or mature mRNA include the region between start (AUG or ATG) and stop codon. EST: Expressed Sequence Tag. Short ssDNA sections of the cDNA (usually -300-500 bp), usually produced in large quantities. Represent the genes that are expressed in certain tissues and / or during certain stages of development. Partial coding or non-coding labels of expression for cDNA libraries. Useful for sizing complete genes and in mapping.

Exon: Coding, the mRNA corresponding sequence region of typical eukaryotic genes. Exons may comprise the coding sequences, the 5 'untranslated region or the 3' untranslated region. Exons encode specific portions of the complete protein and are usually separated by long sections (introns), sometimes referred to as "junk DNA", whose function is not well known but likely to encode short, untranslated RNAs (snRNA) or regulatory information.

GenBank nucleotide sequence database with sequences from more than 100,000 organisms. Entries annotated with properties of the coding regions also include the translation products. GenBank is part of the international cooperation of the sequence databases, which also includes EMBL and DDBJ.

Intron: Non-coding sequence region of a typical eukaryotic gene is excised from the primary transcript during RNA splicing and is thus no longer present in the mature, functional mRNA, rRNA or tRNA. mRNA: messenger RNA or sometimes just "message". RNA containing the sequences necessary for protein coding. The term mRNA, in contrast to the (unspliced) primary transcript, is only used for the mature transcript with polyA tail (excluding the introns removed via the splicing). Has 5'-untranslated, amino acid-encoding, 3'-untranslated regions and (almost always) a poly (A) tail. Typically represents about 2% of total cellular RNA.

Poly (A) tail: ss adenosine extension (~ 50-200 monomers) attached to the 3 'end of the mRNA during splicing. The polyA tail probably increases the stability of the mRNA (possibly protection against nucleases). Not all mRNAs have the construct, such as the histone mRNA. RefSeq NCBI database of the reference sequences. Error-corrected, non-redundant sequence collection of genomic DNA contigs, mRNA and protein sequences or of known genes and complete chromosomes.

SNPs: Single Nucleotide Polymorphisms. Genetic differences between alleles of the same gene based on single nucleotide aberrations. Emerge at specific individual positions within a gene.

Transcript variants: Alternative splicing products. The exons of the primary gene transcript (pre-mRNA) were reconnected in different ways and are subsequently translated.

3 'untranslated region: Transcribed 3' terminal mRNA region without protein coding information (region between stop codon and polyA tail). May affect translation efficiency or stability of mRNA.

5'-untranslated region: Transcribed 5'-terminal mRNA region without protein-coding information (region between initial 7-methylguanosine and the base immediately before the ATG start codon). May affect translation efficiency or stability of mRNA.

Polynucleotide isoforms: polynucleotides having the same function, however

different sequence.

Abbreviations

AUC (area under curve) area under the curve

 CRP C-reactive protein

 CV (cross validation) cross validation

 DLDA diagonal linear discriminant analysis

(diagonal linear discriminant analysis) Classification method

 GPLS (generalized partial least squares) generalized partial smallest

 Squares (classification method)

IQR (inter quartile rank) distance between the 75% and 25%

 percentile

 kNN (k nearest neighbors) k-nearest neighbors

 (Classification methods)

LDA (linear discriminant analysis) linear discriminant analysis (Classification methods)

NPV (negative predictive value) negative predicative value (proportion of correctly negative tests)

 OR odd ratio

 PCT procalcitonin

 PPV (positive predictive value) positive predicative value (proportion of correctly positive tests)

 RF (random forests) classification method

 ROC (receiver operator characteristics) Illustration for the representation of

 classification results

 Sensitivity proportion of correct tests in the

 Group with predefined

 Disease (infectious SIRS or sepsis)

 Specificity Proportion of correct tests in the

 Group without predefined

 Disease (non-infectious SIRS)

SVM (support vector machines) classification method

For a quick diagnosis, it has been found in practice that real-time or real-time amplification techniques are the preferred methods. Therefore, the bases which are well known to those skilled in the art are briefly summarized below with regard to their meaning for the present invention.

Other methods known to those skilled in the art, e.g. sequencing,

Microarray based methods, NASBA etc. are also possible.

With the help of the polymerase chain reaction (PCR), it is possible to rapidly amplify specific sequence regions from the lowest initial amounts of nucleic acids in vitro, and thus to make them accessible for analysis or further processing. A double-stranded DNA molecule is through

Heat action melted (denatured). The single strands serve as template for the enzymatically catalyzed polymerization of

Deoxyribonucleotides, which again arise double-stranded DNA molecules. The oligodeoxyribonucleotides designated as primers define the sequence segment to be copied by hybridizing at locations of complementary sequence with the target DNA and serve as a starter for the polymerization. The process of exponential product formation is limited by several factors. In the course of Therefore, the PCR ultimately returns net product formation to zero and the total amount of PCR product reaches a plateau.

Suitable PCR primers are, for example, primers having the sequences according to Table 3. However, it is known to the person skilled in the art that a large number of further primers can be used to carry out the present invention.

Since its introduction into the molecular biology range of methods an almost unmanageable variety of technical variants has been developed. Today, PCR is one of the most important methods in molecular biology and molecular medicine. Today it is used in a very broad thematic spectrum, eg. As in the detection of viruses or germs, in the

Sequencing, the proof of kinship, the creation of

Transcription profiles and the quantification of nucleic acids [Valasek and Repa, 2005; Klein, 2002]. In addition, any sequence sections of the nucleic acid inventory of an organism can be cloned in a simple manner with the aid of the PCR. The variety of developed PCR variants allows u. a. a targeted or random change in the DNA sequence and even the synthesis of larger, in this form previously nonexistent sequence sequences.

With this classical method, DNA can be detected with high sensitivity and RNA can also be qualitatively detected by reverse transcription (RT) [Wong et al., 2005;

Bustin 2002]. A further development of this method is the real-time PCR, which was first introduced in 1991 and in addition to qualitative statements also a

Quantification allows.

Real-time PCR, also called quantitative PCR (qPCR), is a method for detecting and quantifying nucleic acids in real time [Nolan et al., 2006]. In molecular biology, it has been one of the established ones for several years

Standard techniques.

In contrast to PCR, detection already takes place during the

Amplification takes place. Based on fluorescently labeled probes, the fluorophores, amplification can be monitored in real time. Take in every reaction cycle the fluorescent PCR products and thus the intensity of the light-induced fluorescence emission. Since the increase in fluorescence and the amount of newly synthesized PCR products over a wide range are proportional to each other, the data obtained from the data obtained

Templates are determined. Gel electrophoretic separation of the amplificates is no longer necessary. The results are directly available, resulting in significant time savings. Since the reactions take place in closed vessels and no further pipetting steps are required after the start of the PCR, the risk of contamination is reduced to a minimum. The fluorophores used are either nucleic acid-binding fluorescent dyes such as SYBRGreen or sequence-specific fluorescent probes such as Taq-Man probes, LightCycler probes and molecular beacons [Kubista et al., 2006]. SYBRGreen is a

Dye whose fluorescence increases strongly as the molecule binds to double-stranded DNA. This cost effective solution is especially parallel

Execution of several reactions with different primer pairs suitable. Disadvantages lie in the low specificity, since SYBRGreen binds sequences nonspecifically to each double-stranded DNA, and in that no multiplex measurements can be performed. With the aid of a melting curve analysis, however, it is possible to differentiate between the target product and nonspecific DNA after PCR: Depending on the nucleotide length and composition, each DNA double strand breaks down into its two single strands at a characteristic temperature, the melting temperature. Since the double-stranded DNA of specific PCR products has a higher melting point than nonspecifically resulting primer dimers, a distinction based on the

Fluorescence decrease with increase in temperature possible.

In contrast, the detection with fluorescence-based probes is highly specific, but also very costly. In the TaqMan principle, the PCR approach contains, in addition to the PCR primers, a sequence-specific TaqMan hybridization probe which has a quencher and a reporter dye. The probe is complementary to a sequence lying between the primers. In free solution, the fluorescence is suppressed by the proximity of the quencher. Following the FRET (fluorescence resonance energy transfer) principle, the quencher swallows the fluorescence emission of the excited fluorophore. However, when this probe hybridizes to the target sequence, it is hydrolyzed by the Taq polymerase during PCR, the reporter dye is spatially removed from the quencher and emits detectable fluorescence upon excitation. With the LightCycler principle, the PCR approach contains two fluorescence-labeled probes (donor and acceptor fluorescent dye) in addition to the PCR primers. A fluorescence signal which can be measured outwardly only arises in the case of directly adjacent hybridization of the two probes with the specific target sequence. In a subsequent melting curve analysis, even the presence and the nature of individual

Point mutations are detected within the hybridization regions of the probes. Another example is the Molecular Beacons. These oligonucleotides contain mutually complementary sequences at the 5 ^' and 3 ^' ends, which hybridize in an unbound state and form a hairpin structure.

Reporter fluorophore and quencher, located at both ends, are in close proximity. Only when the probe binds to the template, the two

Dyes spatially separated, so that fluorescence can be measured after excitation again. Scorpion and Sunrise primers form two further modifications for

sequence-specific probes [Whitcombe et al. 1999].

The quantitative determination of a template can be done by absolute or relative quantification. In absolute quantification finds the

Measurement by external standards, e.g. Plasmid DNA in different

Dilutions, instead. The relative quantification, in contrast, uses so-called

Housekeeping or reference genes as reference [Huggett et al., 2005]. These

Reference genes are constantly expressed and thus offer the possibility to standardize different expression analyzes. The selection of

Housekeeping genes must be done individually for each experiment. For the

Present invention preferably housekeeping genes are used with the sequences shown in Table 2.

The generated experiment data are evaluated using the device's own software. For the plot, the measured fluorescence intensity is plotted against the number of cycles. The resulting curve is divided into three areas. In the first phase, ie at the beginning of the reaction, the background noise predominates, a signal of the PCR product is not yet detectable. The second phase corresponds to the exponential growth phase. In this

Segment doubled the DNA template approximately in each reaction step. Critical to the evaluation is the cycle at which detectable fluorescence occurs and the exponential phase of amplification begins. This Threshold Cycle (CT) value or Crossing Point provides the basis for calculating the starting amount of existing target DNA. For example, the software determines the crossing points of the different reference dilutions for an absolute quantification and quantifies the values based on the calculated standard curve

Amount of template. In the last phase, the reaction finally reaches a plateau.

Quantitative PCR is an important tool for gene expression studies in clinical research. With the ability to accurately quantify mRNA, the search for new drugs can be used to analyze the effects of certain factors on cells, to observe the differentiation of precursor cells into different cell types, or to respond to gene expression in host cells

Track infections. By comparing wild-type and cancer cells at the RNA level, it is possible to identify genes in the cell culture that produce a

have a decisive influence on carcinogenesis. In routine laboratory diagnostics, real-time PCR is used primarily for the qualitative and quantitative detection of viruses and bacteria. In clinical routine, especially in the field of intensive care, the doctor needs a quick and clear finding. On the basis of real-time PCR, tests can be carried out that deliver the result on the same day. This is an enormous advance in the clinical diagnosis of sepsis.

In addition to the technical variants of the PCR method described here, so-called isothermal amplification methods such as NASBA or SDA or other technical variants for the detection

preceding amplification of the target sequence.

A preferred method for selecting multigene biomarker sequences comprises the following steps: a. Patient selection is based on the extreme group procedure; b. Generation of at least one multigene biomarker; c. Determination of final multigene biomarkers.

A preferred method of the assay similar to the "in vitro diagnostic multivariate index assay" comprises the following steps: a) isolation of sample nucleic acids from a patient-derived sample b detection of gene activities using sequences from at least one condition and / or C) detecting gene activities for at least one internal reference gene to normalize the gene activities detected in b);

d. Use of an interpretation function for those normalized in c)

 Gene activities to derive a state and / or investigation question specific index.

A preferred embodiment of the present invention is also in a use in which the gene activities are determined by means of a hybridization method, in particular on at least one microarray. The advantage of a microarray lies in the higher information density of the biochip compared to the amplification method. So it is e.g. It is easily possible to provide several 100 probes on a microarray in order to examine several questions simultaneously in a single examination.

The gene activity data obtained by means of the invention can also be used advantageously for electronic further processing, e.g. B. be used for recording in the electronic medical record. A further embodiment of the invention consists of the use of recombinantly or synthetically produced, specific nucleic acid sequences, partial sequences, individually or in part, as multigene biomarkers in sepsis assays and / or for evaluating the effect and toxicity in drug screening and / or for the preparation of therapeutics and of substances and mixtures intended as therapeutic agents for the prevention and treatment of SIRS and sepsis.

For the inventive method (array technique and / or

Amplification method), the sample is selected from: tissue,

Body fluids, in particular blood, serum, plasma, urine, saliva or cells or cell components; or a mixture of them.

It is preferred that samples, especially cell samples, have a lytic

Be subjected to treatment to release their cell contents.

It is clear to the person skilled in the art that the individual features of the invention set out in the claims can be combined with one another without restriction.

classification methods

The theory of learning plays a key role in the field of statistics, data analysis and artificial intelligence with numerous applications in

Engineering. Classification procedures are mainly used in 2 different tasks, in delineating previously unknown classes (unlearned learning, class discovery) and in assigning certain data / samples / patients to a ready-defined class

(Class prediction, class prediction) [Golub et al., 1999].

Class prediction uses data / samples / patients assigned to existing or defined classes / groups (so-called training data set) to develop an analytical method (classification algorithm) that reflects the differences between the groups. Independent samples (so-called test data set) are used to determine the separation quality of the To evaluate the classification rule. The procedure can be divided into the following steps:

1 . Define an ideal data / samples / patient set to be characteristic

 To get profiles of the groups to be detected.

 2. Each group is then divided so that 2 equal subsets, one

 Training record and a test record, arise.

 3. Profiles for the training record ideally contain data that has a

 reflect maximum difference between the groups.

 4. The difference between the groups is quantified using appropriate distance measures and evaluated using an algorithm. This algorithm should lead to a classification rule with the highest specificity and

 Sensitivity places the data in the right class. Typical representatives of such algorithms in the field of supervised learning are the

 Discriminant Analysis (DA), Random Forests (RF), Generalized Partial Least Squares (GPLS), Support Vector Machines (SVM) or k-Nearest Neighbors (kNN).

 5. Finally, the quality of the classification rule is checked on the test data record. definitions:

Discriminant Analysis (DA): In linear discriminant analysis, we obtain a linear discriminant function in quadratic discriminant analysis (QDA). The discriminant function is determined by the

Covariance matrix and the group mean values. In the case of the square

Discriminant analysis is also assumed to vary the covariance matrix between groups [Hastie et al., 2001].

Random Forests (RF): The Random Forests classification is based on the combination of decision trees, [Breiman, 2001]. The process of the algorithm is something like this: 1 . Drag and drop to select a training data set (out-of-bag data).

 2. Randomly select variables at each node of the decision tree.

 Use these variables to calculate the best distribution of the training data set among the classes.

 3. Once all the decision trees have been generated, group the class maps of each decision tree into a class map.

Generalized Partial Least Squares (GPLS): The Generalized Partial Least Squares method is a very flexible generalization of the multiple regression model. Because of its great flexibility, this method can also be used in many situations where the classic model fails.

Support Vector Machine (SVM): The Support Vector Machine classifier is a generalized linear classifier. The input data is mapped into a higher dimensional space and in this space an optimal separating (hyper) plane is constructed. These barriers, linear in higher-dimensional space, transform into nonlinear barriers in the space of input data, [Vapnik, 1999]. k-nearest neighbors (k-nearest neighbors, kNN): In the k-nearest neighbor method, the class affiliation of an observation (a

Patients) based on the nearest k neighbors. The neighborhood is usually determined with the help of the Euclidean distance and the class membership then on the basis of a

Majority vote [Hastie et al., 2001].

The following describes a general concept, such as the

methods according to the invention are carried out. It is well known to those skilled in the art that minor adjustments of the statistical methods may be required if other patient groups and / or other issues to be examined. For the generation of the clasification rule different statistical methods (discriminant analysis and / or Random Forests etc.) as well as strategies are used (single and multiple cross-validation, random bootstrapping samples etc.)

Based on gene expression data, a method for the determination of a multigene biomarker should be developed, which reflects an infectious complication such as sepsis. The biomarker and associated index value, also known as "score", form the basis of a so-called "in vitro diagnostic multivariate index assay" [IVDMIA, FDA Guidelines, 2003] for improving the diagnosis of systemic infections. The resulting from the process

In particular, the classification rule should allow differentiation of SIRS and sepsis patients with improved sensitivity and specificity compared to the established biomarker procalcitonin, but is not limited to this question.

To develop such a multigene biomarker, the following steps are necessary: I.Step: Traininqsdatensatz. In order to uncover the relationship between gene expression of certain genes and the disease under study, populations (cohorts) are defined that are their presence or absence.

To represent nonexistence most clearly. When diagnosing an infectious complication, usually sepsis patients (infectious) and patients with a so-called sterile SIRS (non-infectious) are included in the study. According to this definition, a plan for the collection or selection of the associated RNA samples is determined. From the selected samples, the gene expression profiles are measured on a suitable platform, preprocessed and subjected to quality control. Systematic measurement errors are corrected and outliers eliminated.

2nd step: Genvorauswahl. In the generation of a formal classifier on the basis of microarray data, gene pre-selection is a key step, since only a small proportion of the genes measured contributes to the

Group distinction makes. Most classification methods also require a gene selection. By a precise gene selection that can Classification procedures as simple as possible designed and overfitting the training data (overfitting) are avoided. For preselection of

Classification genes will be suitable filter options such as the threshold of

statistical inference, the minimum accepted distance between the groups, the minimum signal intensity u. a. established. Only genes that meet such conditions are considered for classification.

3rd step: Classification procedure. Different classification methods are regarding their ability to separate with respect to the differentiated

pathophysiological conditions tested. For this purpose methods of cross-validation are used. A classification method with the smallest

Classification error is selected, with the smallest necessary number of genes being co-determined. As a reasonable rule has been found that the number of genes should always be smaller than the number of samples in the

Training data set to avoid over-fitting. Finally, the resulting classification rule is defined.

Patient Selection The patient selection is used when setting up the

Training data set significant. In a preliminary study within the scope of the present invention, a sensitivity of approximately 75% in the training data and approximately 65% in the test data set was achieved for the time being. However, this relatively low classification quality could not be explained by the poor optimization of the classifier but by the insufficiently precise selection of sepsis patients. Correspondingly, sepsis patients after peritonitis were classified much more correctly than sepsis patients after a "VAP" (ventilator-associated pneumonia) .In fact, the infectious complication after peritonitis is inherent, whereas in VAP a true infection of one Difficult to distinguish colonization [Mayhall, 2001].

To evaluate the quality of patient selection, the principle of so-called extreme groups can be useful. Thereafter, in a study, only those patient groups are taken into account that reflect the examined effect as clearly as possible. The selected samples represent an idealized case in which many effects occurring in practice (eg the frequency of the disease) not considered. Liu [Liu et al., 2005] suggested forming extreme groups for the training data set of a microarray-based classifier. Using cancer survivor survival as an example, it has been shown that the use of extreme groups (patients who died in a short time vs. patients who survived for a long time) led to a better pre-selection of classification genes and a higher classification quality, even if the training data set consisted of fewer profiles (patients) than in the usual case when all patients (also with median survival times) were included.

In what follows, how far the patient selection can influence the generation of a multigene biomarker to diagnose the infectious complication. In a study by the applicant, patients who developed sepsis following a major surgery were examined. Samples from the first day of sepsis diagnosis were compared to the sample from the first post-operative day. However, the significantly differentially expressed genes reflect a mixing effect; the infectious complication is obscured by effects such as

Recovery after surgical stress or post-operative treatment. In the previously mentioned pilot study, patients with a clinical (not always microbiologically confirmed) sepsis diagnosis were included in the training population

which resulted in mixing of the two groups studied (septic and controls) and worsened the sensitivity. By doing

Example of U.S. Patent Application No. 20060246495 also used clinical sepsis diagnosis for the selection of the sepsis group.

In addition, the severity of the disease between the group of

Sepsis patients and the control group of SIRS patients not included. This may be the reason for the low classification quality and its dependence on

Be classification algorithm. In the study by Johnson [Johnson et al., 2007] patients were divided into two groups after trauma, with one infectious complication and no infection. The advantage of this study was that patients in the two groups differed little in comorbidity and pretreatment. However, the preselection is not representative for all sepsis patients and the generalization of the sepsis relevant here

Gene expression pattern on patients with other background (on others

Risk groups) is not self-evident. In general, it has to be It can be assumed that different classifiers have to be generated in studies with different risk groups. In the study by Tang [Tang et al., 2007a], the principle of extreme groups was applied indirectly, in which only patients with a microbiologically confirmed sepsis diagnosis in the

Training data record were considered. However, the sample collection schedule resulted in a small control group (one third of the samples: 14 out of 44). Accordingly, a specificity of 77% was achieved in training and only 60% in an independent test dataset (under more real conditions). The description of the patient groups in the SIRS-Lab study and the study by Tang [Tang et al., 2007a] shows another influencing factor. It shows that the sepsis groups that are heterogeneous with regard to the infection focus are not balanced, but that groups with different infection foci are represented differently. In fact, in the majority of intensive care unit (ITS) cases, the lungs (about 45-50%) or the abdomen (about 25%) are the focus of infection in sepsis diagnosis.

Accordingly, these groups of patients are in the investigations

Overrepresented, many other Foci are then only occasionally. Similarly, in the control groups in particular postoperative and trauma patients are represented and other risk groups are represented only by individual patients. The presented analysis shows that in all studies the selected groups of patients do not clearly represent the infectious complication, causing the

Classification weaknesses can be explained. On the other hand, from the

In summary, it is hardly possible to consider all influencing factors in the selection of patient groups in the infectious complication. For this reason, the following way to patient selection for the

Training record proposed.

General information on material and methods of the present invention:

patient selection

The selection of representative samples was at the center of the procedure described. In the training data set, patients with a microbiological confirmed or excluded infection diagnosis were each selected from two of the best represented sepsis or control subgroups locked in. Thus, the principle of extreme groups not only for the

Main effect (infectious vs. non-infectious) but also for the control of the main factors (stratification of populations) applied. For the time being, the advantage of this selection is that it generates a classifier for the most common risk or disease groups. In addition, it is expected that a classifier that reflects systemic infection for a few but very different subgroups can be applied to other groups of patients. When selecting the training data, the procedure was as follows. In the

Applicant's patient database has included 400 ITS patients who were suspected to be at risk for sepsis over a two-and-a-half-year timeframe and detailed clinical data throughout their stay

documented. The RNA samples were collected for approximately 7-14 sepsis-relevant days. Approaching the PIRO concept [Levy et al., 2003], patients were retrospectively stratified according to the following criteria: (i) which indication led to the transfer to the intensive care unit (postoperative complication, trauma or polytrauma, acute suspected sepsis), ( ii) was diagnosed with an infectious complication, what was the infection focus, (iii) how was the response of the organism (number of SIRS criteria present, shock treatment, PCT, CRP levels), (iv) how severe was the disease (SOFA, M ODS score). The database research revealed that infectious complications (sepsis) in particular included patients after pneumonia (40%) and after peritonitis (23%) in the study. Another focus occurred in isolation. These data correspond to the epidemiological studies of the German Sepsis Society, which classifies the collection as representative. The patient data of these groups were independent of 2 physicians [according to ACCP / SCCM, 1992; Levy et al., 2003; Calandra and Cohen, 2005] and final patient selection. 29 patients with a microbiologically confirmed diagnosis were selected and the first septic day was determined. The summary of the severity criteria revealed that patients were diagnosed with severe sepsis or septic shock that day. They achieved an average SOFA value of 10, the sum of acute organ dysfunction was about 3. As a control group, 29 risk patients were included after a bypass operation. It was the first day with a similar severity as in the sepsis groups determined but selected without signs of infection. A list of important clinical and Laboratory parameters for the selected patients are exemplified but not limited to Table 1.

 Table 1: Summary of clinical parameters for patients

 Training data set. The values correspond to the number or, marked with an asterisk, the median (interquartile distance) of the values.

Generate the classifier and establish the SIQ score

On the way to the classifier development following steps were

 carried out:

1 . Step: Quality Control: From the confirmed by the expert knowledge

 Preselection from a patient collective were the associated

Subjecting gene expression data to various similarity analyzes to exclude untypical hybridization results [Buneß et al., 2005], generating the final training data matrix. 2nd step: normalization or preprocessing of the data: For normalization, the mean of the 3 selected housekeeper genes (R1, R2 and R3) was calculated for each sample. From this value, the Ct value of each individual marker was subtracted. Each delta Ct value thus obtained reflects the relative abundance of the target transcript relative to the calibrator, where a positive delta Ct value means an abundance higher than the mean of the references and negative delta Ct value means an abundance less than the mean of the references.

3rd step: Rankinq: In order to arrange the gene markers according to their separation quality, the linear discriminant analysis (LDA) [Hastie et al., 2001] was used together with the method of forward selection, the separability being evaluated by the F value [Hocking, RR, 1976). This analysis step was for 1000

Bootstrap samples repeated. The marker ranks determined in each repetition were averaged over the 1000 runs and the marker candidates ranked in ascending order of the middle rank. This arrangement means that the least median rank marker was the one that most often made the most contribution to separation quality, and the highest mean rank marker did little to separate in most repeats.

Step 4: Classification: For the markers that gave the best results in the ranking analysis, a discriminant function was determined based on the LDA. The associated weights are set forth in Table 9.

Step 5: Internal validation: To assess the quality of the classification for growing number of markers, the simple cross-validation was used.

Step 6: Establishment of the SIQ score: Based on the discriminant function, a sepsis-related diagnostic parameter, a so-called SIQ score (SIQ), was introduced as follows. For a new independent sample, one generally obtains a dimension-free value of the discriminant function as a classification result. A positive value classifies the sample as infectious and a negative value as non-infectious. Absolutely higher values are obtained for typical representatives of the respective group, hard-to-classify samples reach values close to zero. The spreading area The discriminant values generally correspond to the variability of the data matrix. Discriminant values of about -5 to 5 were achieved in the classification. To emphasize the differences more clearly, the SIQ score (SIQ) was introduced as the 10-fold value of the discriminant function with the weights in Table 9.

Accordingly, the SIQ values of the test data varied from about -50 to 50.

The present invention will be explained in more detail below by way of examples and with reference to the sequence listing, which is also part of this description, without this being a limitation of the invention.

Results

In the next step, applicant's patient database gene expression data that was not used in the training data set became one

Subjected to classification. This independent test dataset consisted of 13 samples from 65 individuals (see Tables 4 and 5). Samples from 38 sepsis patients representing a broad range of clinical phenotypes at risk of generalized infection were examined. In addition, samples were analyzed on the SIRS course of 22 postoperative patients and 5 healthy volunteers.

 For this independent test dataset, the best classification efficiency of 81.4% was achieved with 7 following markers: M6, M15, M9, M7, M2, M10, M4. The ROC curve for classification of test data is shown in FIG. As a comparison, the ROC curve for the classification of test data by means of PCT or CRP is shown in FIG. It can be seen from FIG. 4 that for both parameters the area under the curve, which reflects the quality of the classification, is below 70% and thus diagnostically of little relevance.

FIG. 2 (patient 81-12) shows the course of the SIQ score for a patient who has developed sepsis postoperatively. From Fig. 2 it can be seen that SIQ score exceeds the diagnostic-relevant threshold already 2 days before the clinical manifestation of sepsis. The course of further sepsis-relevant clinical parameters (PCT, CRP, SOFA, body temperature,

Shock treatment) is shown as a comparison with. In this comparison will be It can be seen that SIQ score is the only parameter that prematurely reflects the infectious complication. This demonstrates that the described

Invention for the early detection of infectious complications such as sepsis and / or generalized infection.

 FIG. 3 (Patient 7084) shows the course of the SIQ score for a patient who developed postoperative sepsis, fell into septic shock, but has recovered from an acute phase through relevant treatment. From Figure 3, it can be seen that SIQ score increases above the diagnostic threshold one day before the clinical manifestation of sepsis and remains above the threshold in the acute phase. After the acute phase, the SIQ score falls below this threshold. This demonstrates that the invention described for the

Follow-up and / or therapy control of e.g. Antibiotic therapy and / or adjunctive clinical measures and / or surgical remedial measures can be applied.

 Further advantages and features of the present invention will become apparent from the description of Ausführungsbeipielen and with reference to the drawing.

It shows:

1 shows an ROC curve for the classification of test data by means of SIQ scores;

Fig. 2 is an illustration of an exemplary course of an inventive

Scores (SIQ score) and the sepsis-relevant clinical parameters PCT and CRP (FIG. 2A) as well as SOFA score, body temperature and

 Catecholamine dosage (Figure 2B) for a first patient;

Fig. 3 is an illustration of an exemplary course of an inventive

Scores (SIQ score) and the sepsis-relevant clinical parameters PCT and CRP (FIG. 3A) as well as SOFA score, body temperature and

 Catecholamine dosage (Figure 3B) for a second patient; and

4 shows an ROC curve for the classification of test data by means of PCT or CRP. The present invention will be explained in more detail below by way of examples and with reference to the sequence listing, which is also part of this description, without this being a limitation of the invention.

Fig. 1 shows an ROC curve for the classification of the test data by means of the SIQ score. In Fig. 1, the relationship between true positives (sensitivity) and false positives (1 specificity) is marked, dashed gray for the threshold of zero and dashed black for the best achieved classification of 81, 4%.

2 shows a profile of the SIQ score of an exemplary patient and the sepsis-relevant clinical parameters PCT, CRP, SOFA, body temperature and the dose of catecholamines (norepinephrine), which reflects the shock treatment. In Part A of the figure, the scale of each parameter was adjusted so that the black horizontal center line marks the diagnostically relevant threshold. Sepsis was diagnosed on the 6th day, the SIQ score rises above the -4.9 threshold on the 4th day.

3 shows a course of the SIQ score of another patient and the sepsis-relevant clinical parameters PCT, CRP, SOFA, body temperature and the dosage of catecholamines (norepinephrine), which reflects the shock treatment. In Part A of the figure, the scale of each parameter was adjusted so that the black horizontal center line marks the diagnostically relevant threshold. The sepsis was diagnosed on the 4th ITS day, the SIQ score rises above the threshold of -4.9 already on the 3rd day. After the acute phase, which ends with catecholamine discontinuation (shock treatment) on the 8th day, the SIQ score falls below the -4.9 threshold.

4 shows ROC curves for the classification of the test data by means of the parameter PCT or CRP. In FIG. 4, the ROC curve is shown in PCT in black and the ROC curve in CRG is shown in CRP. The area under the curve that indicates the quality the classification is 56.8% for PCT and 66.9% for CRP.

Table 2 below gives the one-to-one correspondence of

marker polynucleotides according to the invention to their transcript variants / cis

regulatory sequences (isoforms), the gene database accession number and the SEQ ID of the sequence listing. Transkriptvariante / ds'-

Markers and

 Regulatory Accession Number SEQ ID Reference Genes

 sequences

 M2_l NM_001031700 1

M2 M2_2 NM_016613 2

 M2_3 NM_001128424 3

M4_l NM_203330 4

M4_2 NM_000611 5

M4_3 NM_203329 6

M4_4 NM_203331 7

M4

 M4_5 NM_001127223 8

M4_6 NM_001127225 9

M4_7 NM_001127226 10

M4_8 NM_001127227 11

M6_l NM_001831 12

M6

 M6_2 NM_203339 13

M7_l NM_031311 14

M7

 M7_2 NM_019029 15

M9 M9 NM_006682 16

MIO MIO NM_033554 17

 M15_l NM_003580 18

M15

 M15_2 NM_001144772 19

M3_A NM_001123041 20

M3

 M3_B NM_001123396 21

M8 NM_025209 22

M8

 MS_cis AI807985 23

M12 NM_002185 24

M12

 MYl cis DB155561 25

M13 M13 NM_001080394 26

M16 M16 NM_003268 27

M17 M17 NM_182491 28

 R1_A NM_001228 29

R1_B NM_033355 30

R1_C NM_033356 31

rl

 R1_E NM_033358 32

R1_F NM_001080124 33

R1_G NM_001080125 34

R2_l NM_002209 35

R2

 R2_2 NM_001114380 36

R3 R3 NM_003082 37

Table 2

Table 3 gives, for each of the marker polynucleotides of the invention, the primers (forward and reverse) for quantitative PCR and the resulting Amplicon and their one-to-one assignment to the respective SEQ ID of the sequence listing again.

Table 3

 Primer for quantitative

 Markers and reference genes SEQ ID

 PCR / resulting amplicon

 M2-fw 38

M2 M2-rev 39

 M2 amplicon 40

M4-fw 41

M4 M4-rev 42

 M4 amplicon 43

M6-fw 44

M6 M6-rev 45

 M6 amplicon 46

M7-fw 47

M7 M7-rev 48

 M7 amplicon 49

M9 fw 50

M9 M9-rev 51

 M9 amplicon 52

M10-fw 53

MIO M10-rev 54

 MIO Amplicon 55

M15-fw 56

M15 M15-rev 57

 Ml 5- amplicon 58

M3-fw 59

M3 M3-rev 60th

 M3 amplicon 61

M8-fw 62

M8 M8-rev 63

 M8 Amplicon 64

M12-fw 65

M12 M12-rev 66

 Ml 2- amplicon 67

M13-fw 68

M13 M13-rev 69

 M1-amplicon 70

M16-fw 71

M16 M16-rev 72

 Ml 6- amplicon 73

M17-fw 74

M17 M17-rev 75

M17 Amplicon 76 Rl-fw 77

Rl Rl-rev 78

 Rl Amplicon 79

 R2-fw 80

 R2 R2-rev 81

 R2 Amplicon 82

 R3-fw 83

 R3 R3-rev 84

 R3 amplicon 85

Biological plausibility of identified biomarkers

The described biomarkers are functionally associated with high significance immunological and inflammatory signaling pathways. A knowledge-based biomarker population analysis was performed using the Ingenuity Pathways Analysis software (Ingenuity Systems, USA, www ngenuity.com) to clarify the functional context of the identified markers. Based on all publicly available database knowledge, the markers are categorized into functional networks and categories. The major categories of the present marker population are the complement system, Toll-like receptor signal transduction, communication between cells of the innate and adaptive immune defense, TREM-1 signal transduction, ceramide signal transduction. Accordingly, the markers are of high significance for immunological and inflammatory processes, which underlines the relevance for the clinical picture of sepsis. Thus, an important prerequisite for biomarkers, the presence of biological plausibility, could be demonstrated.

Theragnostic potential of biomarkers using the example of coagulation:

Analysis of the biological plausibility of the biomarkers revealed a functional role for M6 and M9 in the context of coagulation and fibrinolysis. Both processes are among the most deregulated physiological functions in septic patients. A treatment option for patients with severe sepsis and organ failure is treatment with activated protein C or thrombomodulin. M6 is overexpressed in septic patients and at the same time negative Subjected to transcriptional control by activated protein C. M9 is suppressed in septic patients and may not fulfill the attributed activity of prothrombin cleavage to provide thrombin. Thrombin, in turn, is an important factor for the activation of protein C after association with thrombomodulin. Because of these close functional relationships, it seems possible to search in appropriate clinical trials for patterns that are characteristic of the successful use of the abovementioned therapeutic possibilities. This theragnostic approach could allow the responder to be identified and nonresponders to avoid the potentially serious side effects. The identified markers for such application thus also include the potential for decision making regarding specific therapies of the septic patient.

The following are the clinically relevant data of the examined

 Patient collective shown as Table 4:

Table 4

Organversagen

organ failure

 (Metabolically)

severe sepsis,

 postoperative cardiovascular,

 5010 67 female 0 postoperative- acute peritonitis no 31 gastrointestinal,

 postoperative metabolic

 severe sepsis,

 intervention

 Coronary vessels,

 postoperativ-

5018 71 male 28 Left heart failure

 cardiovascular, yes 4 postoperative respiratory,

 postoperatively-rel

 5019 female neurosurgical herniated disc yes

 5020 male neurosurgical herniated disc yes

 5023 female neurosurgical herniated disc yes

 6005 48 female 17 severe sepsis acute cholecystitis no 28

 Peritonitis, not closer

 6008 62 female 14 gastrointestinal

 means yes 13

Sepsis: Escherichia

 6024 64 male 12 severe sepsis yes 6 coli [E. coli]

 Perforation of the

 severe sepsis,

 6035 63 male 29 intestine no 20 gastrointestil

 (Nontraumatic)

 Not closer

 6036 33 male 9 Polytrauma called multiple yes 20

 injury

 intervention

 Coronary vessels,

 6056 76 female 23 Aortic valve stenosis

 Intervention yes 36

heart valves

 intervention

 6061 68 female 25 coronary arteries, aortic valve stenosis no 29

 thorax

 Rückenmark¬

6063 59 male 17 spine compression, not yes 9 specified

 Other closer

 Marked

 6064 78 male 16 DHI arrhythmias

 Diseases of the pancreas

 chronic

 severe sepsis,

 6070 66 male 14 renal insufficiency, 19

 Thorax yes

 not further described

 Ileus, not closer

 6075 73 female 22 gastrointestinal

 indicates 47 serious

 Sepsis,

 respiratory

 insufficiency

 Acute respiratory

 (Asthma),

 Insufficiency,

 6104 40 female 17 respiratory

 elsewhere not yes 28

insufficiency

 classified

 (Aspiration),

 respiratory

 insufficiency

(Infection) Perforation of the

 6120 54 male 18 severe sepsis no 19

 esophagus

 Abnormal findings at

 severe sepsis,

 6124 69 male 15 of the imaging 13

 Thorax yes

 Diagnosis of the lung

 severe sepsis, respiratory distress syndrome

 6126 39 male 23

 Polytrauma adult [ARDS] yes 126

 Emphysema, not

 6141 70 male 21 thorax no 38 specified

 intervention

 7023 75 male 21 no 61

 Coronary artery

 Sepsis, not closer

 7040 70 male 21 27 means yes

 subarachnoid hemorrhage

 Intracranial, from the A.

 7052 67 female 22 33

 Bleeding communicans anterior yes

 outgoing

 Malignant neoplasm:

 7077 63 male 17 Mouth of the floor, not so 38 nearer

 intervention

 7079 77 male 26

 Coronary vessels yes 33

 Diseases of the mitral

intervention

 7084 69 male 17 and tricuspid valve,

 Coronary vessels yes 24 combined

 Atherosclerosis the

 Extremitäterterien:

 7096 85 male 18

 Pelvic-leg type, with yes 8 gangrene

 Sepsis, not closer

 7105 75 female 20 severe sepsis 10 indicates yes

 atherosclerotic

 intervention

 7112 75 female 27 Heart disease: three- 67

 Coronary artery

 vascular disease

 intervention

 7119 64 female 14 Unstable Angi pectoris

 Coronary vessels yes 6

 Malignant neoplasm

 7120 84 female 21 severe sepsis on the rectosigmoid, no 13

 crossing

 Ulcer duodeni:

 Chronically or not

 714 79 female 26 Gastrointestil no 7 specified, with

 perforation

 intervention

 749 75 female 16 heart valves, aortic valve stenosis yes 27

 thorax

 atherosclerotic

 Disease heart disease: Without

 8009 60 male 9 51

 Coronary arteries hemodymic yes

 effective stenoses

 atherosclerotic

 Disease heart disease: Without

 801 1 64 male 4

 Coronary arteries hemodymic yes 2 effective stenoses

 intervention

 atherosclerotic

 Coronary vessels,

 8026 68 female 12 heart disease: Ein¬

Intervention yes 6

 vascular disease

heart valves atherosclerotic

 Disease heart disease: Without

 8034 77 female 12 2

 Coronary arteries hemodymic yes

 effective stenoses

 intervention

 8039 55 female 16 Aortic valve stenosis

 Heart valves yes 7

 atherosclerotic

 Disease heart disease: Without

 8044 70 male 9

 Coronary arteries hemodymic yes 2 effective stenoses

 atherosclerotic

 Disease heart disease: Without

 8052 71 male 11 2

 Coronary arteries hemodymic yes

 effective stenoses

 Intervention mitral valve

8056 70 female 13

 Heart valves failure yes 5

 other

 intervention

 8058 63 female 21 aortic valves heart valves yes 5 diseases

 atherosclerotic

 Disease heart disease: Without

 8073 82 male 15

 Coronary arteries hemodymic yes 2 effective stenoses

 atherosclerotic

 intervention

 8086 78 male 13 heart disease: Ein¬

Coronary vessels yes 6

 vascular disease

 intervention

 Coronary vessels,

 8096 61 male 11 Mitral valve stenosis no 12

 intervention

 heart valves

 atherosclerotic

 Disease heart disease: Without

 8101 63 male 12 no 8

 Coronary arteries hemodymic

 effective stenoses

 atherosclerotic

 intervention

 8102 70 male 17 heart disease: Ein¬

Coronary vessels yes 6

 vascular disease

 intervention

 8103 54 male 6 Aortic valve stenosis

 Heart valves yes 2

 atherosclerotic

 Disease heart disease: Without

 8108 66 male 11

 Coronary arteries hemodymic yes 2 effective stenoses

 atherosclerotic

 Disease heart disease: Without

 811 1 65 male 16

 Coronary arteries hemodymic yes 14 effective stenoses

 atherosclerotic

 Disease heart disease: Without

 8112 76 male 13 no 10

 Coronary arteries hemodymic

 effective stenoses

 atherosclerotic

 intervention

 8116 80 Female 18 Heart disease: A5

 Coronary vessels yes

 vascular disease

 intervention

 8122 67 male 17 Unstable Angi pectoris

 Coronary vessels yes 5

 Sepsis, not closer

920 74 male 23 severe sepsis 4 indicates yes P _itt aen

Tabe _I STTag-lle 5: general description of the patients from the test dataset, were recorded general clinical parameters, by which the ITS treatment is justified.

SOFASCORE-

S _h dcwe ^r ee ^r

E _k ^k _r ^r anung

 A ODFnz.

 superficial gentamicin, severe surgical definitely, flucloxacillin,

1013 1 3.9 8 1 238 ^Cooling euozyenza-00 S 3 0.04

 Sepsis wound infection, definitely clindamycin,

 Spinal abscesses Ceftriaxone

G ^r oup

AS ^I RSK ^it nz ^r - ..

 sept. meropenem,

1015 10 5.12 12 2 11100 S 3 1.3 Pneumonia definitely

 Shock Linezolid

Nd ^li d ⁱ o ^r a ^r enoss-

2042 2 10.8 97.6 9 SIRS 2 18600 c 3 0.26

severe probably oxacillin, Tiem,

5008 6 10 200 11 2 17500 s 2 0.2 Pneumonia

 Sepsis h Klion, Diflucan

W ^hhi a ^r scenl ^ihki CDCS ^t ce

 ampicillin,

Ciprofloxacin, probably

 Unknown: severe pneumonia,

 5009 3 2 250 8 1 7900 s 2 0.05 h,

 Fortum, sepsis gastroenteritis probikellic

 Colimycin, h

unbeknown A ⁱ b ^iict noant: V-fend, herpesin

cefuroxime,

Tracheobronchitis, Metronidazole, Unlikely Tiem infection, sept.

 5010 2 0 164 8 3 11600 s 2 0.06 Gastrintesti Itraktes, me, definitely, vancomycin,

 shock

 Intraabdominal definitely Sulperazon, infection Amikacin,

 Flucozole sept. probably amoxicillin,

5018 2 280 3 17000 s 2 0.3 Endocarditis

 Shock h gentamicin

5019 1 0 SIRS c 0.3

5020 1 SIRS c 0.3 5023 1 SIRS C 0.3

serious

 6005 2 290.1 8 2 6900 S 3 0.3 Cholecystitis definitely

 sepsis

sept. Intraabdominal cefuroxime,

6008 2 6 111 7 3 16800 S 3 0.56 definitely

 Shock infection metronidazole

Meningitis / definitely,

 heavy meropenem,

6024 2 2.46 49.8 10 3 10400 S 2 0.03 Ventriculitis, prob

 Sepsis ceftriaxone

 Catheter sepsis h sept. Intraabdominal cefuroxime,

6035 3 4.72 103 13 3 16100 S 4 0.11 definitely

 Shock infection metronidazole

sept. wahrscheinlic

 6036 10 0.65 8 1 19200 S 2 0.16 Pneumonia Ciprofloxacin

 Shock h

sept. probably ciprofloxacin,

6056 14 2.32 94.2 10 3 19300 S 4 0.31 Pneumonia

 Shock h Imipenem probably

 sept. Pneumonia, catheters

 6061 10 0 2 78.8 8 2 19900 S 4 0.21 h, flucozole,

 .5

 Shock r probably imipenem h

 wahrscheinlic

 deep surgical h,

 sept. Wound infection, probably ceftriaxone,

6063 2 4.07 236 13 2 14200 S 4 0.17

 Shock pneumonia, h, clindamycin

 Tracheobronchitis probably

 H

 wahrscheinlic

 deep surgical

 Wound infection, h,

 sept. wahrscheinlic

 6064 8 0.54 218 7 3 17400 S 4 0.28 Intraabdominal levofloxacin

 shock

 Infection, pancreatitis,

 wahrscheinlic

 s

 H

 wahrscheinlic

 Piperacillin / severe pneumonia,

 6070 3 2.31 404 7 2 10600 S 1 0.093 h, tazobactam,

 Sepsis tracheobronchitis likely

 Vancomycin h sept. Intraabdominal likely piperacillin /

6075 3 37.6 269 9 3 37900 S 3 0.43

Shock infection h Tazobactam probably imipenem,

6104 7 32.9 325 6 Sepsis 1 11800 S 3 Pneumonia

 h Vancomycin

sept. Intraabdominal likely

 6120 3 36.8 478 12 3 11600 S 2 0.22 Cefuroxime

 Shock infection h

sept.

 6124 5 0.3 159 9 3 9100 S 2 0.22 Pneumonia definitely

 Shock superficial

 sept.

 6126 10 86.2 80.4 10 4 13700 S 4 0.22 Surgical definitely

 shock

 Wound infection sept. Piperacillin /

6141 5 1.18 247 7 3 13800 S 4 0.17 Pneumonia definitely

 Shock tazobactam probably

 sept. Pneumonia, h, piperacillin /

7023 9 0.3 124 10 2 14200 S 4 0.13

 Shock bacteremia unlikely tazobactam me

 definitely,

 deep surgical

 wahrscheinlic

 sept. Wound infection, meropenem,

7040 2 13.5 304 11 2 28400 S 3 0.36

 Shock pneumonia, h, vancomycin probably

 hematogenous

 H

12 0.45 229 9 SIRS 2 12400 C 2 0.082 levofloxacin

7052 13 0.37 230 10 SIRS 2 14200 C 2 0.1 Levofloxacin

14 0.47 234 8 none 2 11500 C 1 0.082 Levofloxacin

Amoxicillin /

7077 9 0.65 335 10 SIRS 2 13700 C 3 0.27 clavulanic acid,

 gentamicin

Amoxycillin / sept. clavulanic acid,

10 0.74 415 9 2 16400 S 3 0.25 Soft tissue infection definitely

 Shock gentamicin,

imipenem 12300 gentamicin,

11 0.66 378 10 Sepsis 2 S 4 0.2 Soft tissue infection definitely

 0 imipenem

sept. levofloxacin,

7079 12 5.62 233 11 3 37000 S 3 0.92 Mediastinitis definitely

 Shock vancomycin

serious

 2 6.11 135 7 1 13100 C 3 0.09 Cefazolin

 SIRS

3 7.95 355 6 SIRS 1 14400 C 3 0.09 Cefazolin

7084

 Cefazolin, sept. wahrscheinlic

 4 6.41 379 8 1 10900 S 3 0.22 Pneumonia Piperacillin /

 Shock h

 Tazobactam sept. Probably piperacillin /

5 11.4 449 10 2 10100 S 3 0.37 Pneumonia

 Shock h Tazobactam

7096 heavy

 2 1.24 134 7 2 12400 C 2 0.17

 SIRS

serious

 3 1.27 200 8 1 12700 C 3 0.062

 SIRS

4 0.62 164 6 SIRS 0 9600 C 2

serious

 5 0.5 120 8 1 15700 C 3

 SIRS

heavy piperacillin /

6 0.9 151 8 1 14800 C 3

 SIRS Tazobactam

Probably piperacillin /

7 0.96 177 7 Sepsis 0 15400 S 2 Pneumonia

h Tazobactam Probably piperacillin /

8 1.1 215 7 Sepsis 0 20800 S 2 Pneumonia

 h Tazobactam

sept. Myocarditis probably

 7105 1 3.29 311 12 3 14700 S 3 0.25 Imipenem

 Shock / pericarditis h

Cefepim, severe probably

 7112 23 0.9 56.7 9 2 13200 S 4 Pneumonia Flucozole,

 Sepsis h

 Levofloxacin heavy piperacillin /

3 1.31 288 7 2 19200 C 2 0.16

 SIRS Tazobactam

7119 4 0.61 295 5 none 1 11900 C 1 0.01

5 228 4 SIRS 0 9000 C 2

sept. Intraabdominal likely cefuroxime,

7120 2 153 6 2 13000 S 2 0.73

 Shock infection h metronidazole

sept. Intraabdominal metronidazole,

714 1 0.89 111 9 3 17000 S 4 0.87 definitely

 Shock infection Cefuroxime

Probably piperacillin /

749 8 2.88 173 8 Sepsis 0 16000 S 3 Tracheobronchitis

 h Tazobactam

8009 2 7.25 34.8 8 SIRS 2 10100 C 4 0.17

Piperacillin /

3 5.38 206 6 SIRS 1 5800 C 4 0.22

 tazobactam

Piperacillin /

4 4.4 256 8 SIRS 2 16600 C 4 0.23

tazobactam meropenem,

5 7.67 288 10 SIRS 4 18900 C 4 0.16 Piperacillin /

 tazobactam

6 6.56 136 10 SIRS 2 15800 C 4 0.2 Meropenem

7 3.97 162 9 SIRS 4 23700 C 4 0.11 Meropenem

8 2.47 207 11 SIRS 4 26200 C 4 0.39 Meropenem

Pneumonia,

 sept. definitely,

 11 9.84 207 11 4 28300 S 3 0.02 Intraabdominal meropenem

 Shock definitely

 infection

8011 2 0.3 82.9 5 SIRS 0 11400 C 2

2 3.28 83.8 4 SIRS 2 8900 C 3 Cefazolin

3 3.01 205 7 SIRS 1 9200 C 3 0.052 Cefazolin

8026 4 1.55 70 5 SIRS 0 10800 C 3 Cefazolin

5 0.77 39.6 4 SIRS 2 6900 C 3 Cefazolin

6 0.35 23.6 3 SIRS 0 7200 C 2 Cefazolin

8034 2 42.5 1 SIRS 0 9000 C 2 2 1.39 53.8 6 SIRS 3 22500 C 4 0.12 Cefazolin

3 0.84 178 7 SIRS 2 19900 C 4 Cefazolin

cefazolin,

8039 4 0.86 197 8 SIRS 2 20800 C 4 piperacillin /

 tazobactam

Piperacillin /

5 0.63 131 10 SIRS 2 17.4 C 3 Tazobactam,

 erythromycin

Piperacillin /

6 0.47 78.4 9 SIRS 2 14400 C 2

 tazobactam

8044 2 0.43 48.3 1 SIRS 0 10700 C 3 Cefazolin

8052 2 84.7 0 SIRS 0 8700 C 2

8056 3 0.82 128 5 SIRS 1 22000 C 2 Cefazolin

cefazolin,

8058 3 22.7 72.7 11 SIRS 5 6300 C 2 piperacillin /

 tazobactam

8073 2 0.5 67.5 4 SIRS 1 6800 C 2

8086 2 0.35 37.7 5 SIRS 3 12400 C 3 0.042

8096 2 21.9 117 10 SIRS 3 15300 C 3 0.32 Cefazolin 3 14.5 294 12 SIRS 5 13500 C 4 1.3 Cefazolin

4 9.38 291 14 SIRS 5 13900 C 3 0.78 Cefazolin

cefazolin,

3 1.27 92.9 8 SIRS 3 17900 C 4 0.17 Piperacillin /

 tazobactam

Piperacillin /

4 1.23 213 11 SIRS 5 15000 C 4 0.23

 tazobactam

8101 piperacillin /

5 1.42 195 11 SIRS 3 22600 C 4 0.46

 tazobactam

Imipenem, sept. vancomycin,

6 3.64 233 14 5 39500 S 4 0.94

 Shock piperacillin /

 Tazobactam sept. imipenem,

7 6.59 184 17 5 33600 S 4 1

 Shock vancomycin

8102 2 1.83 35.6 5 SIRS 3 13100 C 4 0.016

8103 2 0.52 55.8 1 SIRS 0 6900 C 2 Cefazolin

8108 2 0.3 73.7 3 SIRS 0 7300 C 2

8111 2 6.82 67 7 SIRS 3 19700 C 4 Cefazolin

wahrscheinlic

4 3.82 182 SIRS 3 13800 C 2 0.4 Pneumonia Ciprofloxacin h 5 2.24 133 8 SIRS 3 14300 C 4 0.12 Ciprofloxacin

6 0.82 67 5 SIRS 2 8700 C 3 Ciprofloxacin

heavy probable

 7 0.35 128 3 1 10400 S 2 Tracheobronchitis ciprofloxacin

 Sepsis h

Ciprofloxacin, severe probably

 8 0.3 98.2 5 3 19800 S 4 0.089 Tracheobronchitis Piperacillin /

 Sepsis h

 tazobactam

2 2.55 SIRS 3 14100 C 4 0.066 Cefazolin

cefazolin,

3 1.49 168 9 SIRS 3 17400 C 4 0.11 Piperacillin /

 tazobactam

Piperacillin /

4 1.06 175 10 SIRS 4 13000 C 4 0.2

 tazobactam

8112

 Piperacillin /

5 0.88 151 14 SIRS 2 12500 C 4 0.077

 tazobactam

severe probable piperacillin /

6 0.64 128 12 3 13300 S 4 0.09 Pneumonia

 Sepsis h Tazobactam

sept. wahrscheinlic

 7 0.44 113 12 3 9000 S 3 0.11 Pneumonia

 Shock h

8116 2 81.1 10 SIRS 5 15100 C 3

8122 4 0.3 171 4 SIRS 3 11200 C 2 severe deep surgical

 2 1 .26 124 10 5 10600 S 2 0.031 definitely meropenem

 Sepsis wound infection

Table 5 shows sepsis-relevant clinical parameters from the patient history sheets from the test dataset, and recorded clinical parameters that document the course of the disease.

EXEMPLARY EMBODIMENTS

Example 1: Preparation of a Classifier for the Identification of SIRS and Sepsis Patients with High Sensitivity / Specificity

Patient groups

 The first step in the analysis (training) included samples from patients in intensive care units (ITS). For the sepsis group, patients with a microbiologically confirmed infection focus were selected, taking the sample into account from the first day of sepsis. In the control group, patients were selected after a severe cardiac surgery (cardiopulmonary bypass, CPB), who developed a sterile SIRS postoperatively. The control group was fitted to the sepsis group in terms of number of patients, age, gender distribution and disease severity so that the main difference between the groups was the presence of an infectious complication. Each patient in the control group was represented with one sample each. The training dataset consisted of 29 sepsis and 29 control cases. The most important clinical parameters were summarized in Table 6.

 In the second step (validation), a test dataset consisting of 13 samples of 65 persons was examined (see Tables 4 and 5). Samples from other sepsis patients representing a broad range of clinical phenotypes at risk of generalized infection were examined. In addition, samples were selected on the course of SIRS sepsis. The analysis included at most samples from the first 2 days of sepsis diagnosis.

Other SIRS patient samples from the training group, technical repeats, samples from further postoperative cases and 5 healthy ones served as controls Controls. This selection of controls also ensures the applicability of the method in a broad phenotype.

Measurement of gene expression

 Total RNA was isolated from the patient's blood and transcribed into cDNA. This was used as a template in the assay. The marker candidates for classification were summarized in Tables 2 and 3. At the end of the table 3 so-called reference genes (also housekeeping genes) were inserted (R1, R2 and R3). They allow a relative quantification of gene expression, which is an indication of the abundance of the target transcript relative to a calibrator. For each organism and tissue, such reference genes are specific and must be carefully selected for the particular application, here the distinction of an infectious from a non-infectious cause of a systemic inflammatory response from human whole blood samples. Based on the gene expression profiles from the whole blood of the sepsis and control patients, the most stable genes with the lowest variability were selected and used in the quantitative PCR for normalization.

Experimental execution of blood collection and RNA isolation:

 The whole blood of the patients was taken in intensive care by the patients in PAXgene tubes (PreAnalytix, Hombrechtikon, CH) and stored according to the manufacturer's instructions until work-up. RNA was isolated with PAXGene Blood RNA kits and stored at -80 ° C until analysis, according to the manufacturer's instructions (Qiagen, Hilden, FRG).

Reverse transcription:

From each patient sample, 0.5 μg of the total RNA to complementary DNA (cDNA) with the reverse transcriptase Superscript II (Invitrogen Germany, Karlsruhe, Germany) in a 20 μΙ batch (contains 1 μΙ 10 mM dNTP mix of Fermentas and 1 μΙ 0.5 μ9 / μΙ oligo (dT) primer). The RNA was subsequently removed from the batch by alkaline hydrolysis. The reaction mixtures were not purified, but made up to 50 μΙ with water.

Real-time PCR

 The Platinum SYBR Green qPCR SuperMix UDG kit from Invitrogen (Invitrogen Germany, Karlsruhe, Germany) was used. The patient cDNA was diluted 1:25 with water, of which 1 μΙ was used in the PCR. The samples were each pipetted into 3 replicates.

PCR assay per well (10 μΙ) 2 μΙ template cDNA 1: 100

 1 μΙ forward primer, 10 mM

 1 μΙ reverse primer, 10 mM

 1 μΙ fluorescein reference dye

 5 μΙ Platinum SYBR Green qPCR SuperMix UDG

A mastermix without template was prepared, this was aliquoted into 9 μΙ aliquots in the PCR plate, to each of the patient cDNAs were pipetted.

The ensuing PCR program consisted of the following steps ::

 95 ° C 2 min (activation of polymerase)

 95 ° C 10 sec (denaturation)

 58 ° C 15 sec (annealing)

 72 ° C 20 sec (extension)

10 sec (Creation of the Sc x increase of the beginning

 after each step by 1 ° C)

The iQ 5 Multicolor Real-Time PCR Detection System from BIORAD was used with the associated evaluation software. The so-called Ct values (number of cycles) were automatically entered as measurement results by the program

Range of linear increase of the curves calculated. The measured values were saved in string format. Data analysis:

 The data analysis was performed under the free software R Project Version R 2.8.0 (R.app GUI 1 .26 (5256), S.Urbanek & SMIacus, © R Foundation for Statistical Computing, 2008), available at www.r- project.org is available.

Data Vorverarbeitunq:

 The data matrices of the measured Ct values that were included in the analysis were presented in Tables 6 and 7 for the training and test dataset respectively. For normalization, the mean of the 3 selected housekeeper genes (R1, R2 and R3) was calculated for each sample. From this value, the Ct value of each individual marker was subtracted. Each delta Ct value thus obtained reflects the relative abundance of the target transcript relative to the calibrator, where a positive delta Ct value means an abundance higher than the mean of the references and negative delta Ct value means an abundance less than the mean of the references.

Table 6: Ct values of the training data set per marker (mean of the triple determination) and the group affiliation (last column)

M2 M3 M4 M5 M6 M8 M9 M10 M12 M13 M15 M16 M17 R1 R2 R3 group

2038J01 27.9 28.2 26.9 24.7 26.1 27.2 25.6 24.0 27.5 32.3 27.6 25.0 29.9 26.4 25.3 31.4 no sepsis

8001J01 26.1 28.2 25.9 22.9 24.8 26.7 24.0 24.1 25.2 31.5 25.6 24.1 27.2 25.2 23.1 30.0 no sepsis

8002_001 27.0 27.5 25.6 22.4 25.6 26.6 24.8 22.8 24.7 30.3 26.6 23.8 27.7 26.0 23.7 31.3 no sepsis

8009 _. 001 28.3 28.4 28.1 26.3 27.9 27.4 25.7 25.1 25.9 32.2 28.2 27.3 29.4 27.6 27.2 32.5 no sepsis

8010 _. 001 27.2 28.2 26.4 24.8 26.6 27.2 25.5 24.5 26.2 32.1 27.4 25.2 28.8 27.0 25.3 31.6 no sepsis

8011_001 25.5 26.8 26.1 23.8 25.3 27.3 24.4 24.4 25.4 31.1 27.0 25.2 28.1 26.0 25.0 30.1 no sepsis

8012 _. 001 28.3 29.6 28.1 25.2 27.3 28.3 26.1 25.9 26.7 34.2 28.2 26.2 29.1 27.7 26.3 32.5 no sepsis

8025 _. 002 26.8 28.8 25.9 24.8 25.4 26.7 25.8 24.6 24.9 32.4 27.1 24.9 29.0 26.3 25.3 29.9 no sepsis

8030 _. 001 26.7 29.1 26.9 24.7 27.3 27.5 25.9 23.6 25.3 32.1 28.0 25.7 28.7 26.6 25.7 31.7 no sepsis

8032 _. 003 26.9 27.9 28.3 26.7 26.6 27.4 26.2 24.4 25.9 32.5 28.7 25.6 28.4 26.3 26.3 32.6 no sepsis

8034 .001 25.9 27.2 26.0 24.4 25.9 27.0 25.6 24.9 26.3 31.9 26.9 24.9 28.6 25.3 25.2 32.1 no sepsis

8044 _. 001 26.3 27.2 25.4 24.4 25.2 26.3 24.5 24.7 25.8 30.7 25.8 24.7 26.8 25.7 25.2 30.5 no sepsis

8051 _. 002 26.8 28.1 26.6 24.4 25.1 26.8 25.1 24.3 25.4 31.9 26.9 25.1 28.3 25.9 24.8 31.9 no sepsis

8052 _. 001 27.4 28.7 27.8 24.8 25.9 26.6 25.2 24.7 26.4 31.7 27.7 25.9 28.9 26.5 25.9 33.2 no sepsis

8056 _. 003 27.2 27.9 26.6 24.2 27.2 27.3 24.4 25.6 25.7 25.3 31.9 26.0 28.6 29.6 33.6 25.8 no sepsis

8058 _. 001 27.5 29.1 27.5 26.0 27.3 28.8 26.3 24.9 27.0 32.2 27.7 25.2 29.5 27.2 26.2 32.2 no sepsis

8068 _. 001 27.8 28.8 26.5 24.9 26.8 27.5 25.7 24.9 26.4 33.8 26.9 25.5 28.9 26.8 25.8 32.1 no sepsis

8073.001 26.8 27.9 27.3 24.8 26.0 27.4 25.0 23.3 25.6 33.2 26.9 25.2 29.1 26.3 24.9 31.5 no sepsis

8076 _. 002 27.5 29.4 27.5 26.3 26.4 27.4 26.7 25.8 26.2 32.1 28.3 25.9 29.1 27.3 27.0 32.0 no sepsis 8084 .003 28.7 29.4 27.1 25.9 26.3 27.4 25.6 24.2 25.7 33.5 28.1 26.7 28.4 26.3 26.1 32.4 no sepsis

8094.001 25.9 26.6 25.8 23.8 25.5 26.2 24.2 23.6 25.6 29.9 26.3 24.0 28.2 25.5 24.1 31.4 no sepsis

8096 _. 001 27.5 28.5 25.7 24.1 26.8 26.9 25.8 24.3 25.8 33.1 26.7 25.5 28.2 26.4 25.4 31.5 no sepsis

8103.001 25.8 27.4 26.2 25.2 24.6 25.7 24.3 22.6 24.9 31.6 26.7 24.9 28.4 25.4 24.9 30.5 no sepsis

8108 _. 001 26.1 27.6 26.0 25.0 25.9 27.0 24.9 24.3 26.2 32.3 27.2 24.9 29.0 26.3 25.4 32.3 no sepsis

8111 _. 002 26.2 26.9 26.6 24.9 25.0 26.4 24.4 23.7 25.1 31.8 27.0 24.4 27.9 25.3 24.6 30.3 no sepsis

8112 _. 002 27.4 28.3 25.6 24.3 25.8 25.7 25.3 24.7 25.0 32.2 26.3 24.0 28.9 26.0 24.5 31.4 no sepsis

8116 _. 003 29.4 29.0 27.9 25.8 28.2 30.0 25.7 26.7 27.4 27.8 33.1 27.5 30.8 32.0 37.3 26.9 no sepsis

8122 _. 001 28.1 29.4 27.1 24.0 26.5 27.1 24.9 24.9 26.6 33.4 27.2 25.7 28.2 26.4 25.3 32.0 no sepsis

814 _. 001 26.5 27.3 26.0 25.8 25.9 24.5 24.4 23.4 23.1 31.0 25.8 25.7 27.5 25.2 24.5 30.0 no sepsis

1014 _. 002 27.8 29.1 25.6 24.5 28.4 26.2 26.8 25.4 24.8 32.0 27.7 25.0 29.0 26.3 25.9 31.4 Sepsis

1020 _. 001 29.2 28.6 23.9 22.8 28.7 28.2 26.0 26.3 27.6 31.7 28.0 23.8 28.5 26.2 23.7 30.1 Sepsis

1021 _. 001 27.0 26.3 26.3 23.6 28.6 25.9 24.8 26.1 25.8 31.0 30.8 26.0 28.8 27.8 31.2 23.7 Sepsis

6008 _. 001 27.7 28.6 25.3 23.0 26.5 27.3 25.3 23.6 25.1 32.0 27.2 24.6 29.2 26.2 24.5 30.9 Sepsis

6009 _. 001 28.9 29.8 25.0 23.6 28.6 27.3 26.6 27.3 25.2 31.9 27.4 24.1 28.9 25.5 24.6 30.7 Sepsis

6025 _. 001 28.9 27.6 24.7 23.3 27.5 26.7 26.4 26.7 23.5 26.7 26.3 24.5 26.5 28.3 26.2 28.7 Sepsis

6032 _. 001 28.5 30.4 27.0 23.7 27.9 29.0 26.6 25.2 25.3 34.6 28.0 25.3 29.0 27.4 25.2 32.1 Sepsis

6035 _. 001 27.0 28.2 24.5 24.1 25.7 26.4 26.0 25.5 26.6 32.1 26.9 24.2 28.0 25.8 24.8 31.0 Sepsis

6040 _. 001 27.4 28.1 23.6 21.7 28.4 26.1 27.1 23.8 24.4 29.6 26.0 25.3 26.5 25.5 25.3 30.1 Sepsis

6046 _. 001 28.6 30.0 26.1 24.4 28.7 27.5 27.7 25.6 25.4 32.5 28.5 25.8 28.5 26.6 26.2 31.6 Sepsis

6048 _. 001 29.6 30.7 27.0 26.7 29.7 29.4 28.2 26.9 26.9 34.5 28.9 26.3 29.8 26.8 27.4 32.7 Sepsis

6062 _. 001 29.6 31.7 25.9 23.9 29.6 27.8 27.4 26.7 27.4 33.6 28.6 24.3 28.6 26.5 25.6 31.7 Sepsis

6065 _. 001 28.1 28.6 26.2 24.4 28.9 30.0 26.5 26.2 26.5 34.8 27.7 24.9 29.4 26.4 25.7 32.5 Sepsis

6070 _. 001 28.4 29.9 26.8 25.2 27.6 28.0 26.8 26.9 26.0 34.9 28.8 25.9 29.9 27.4 26.4 32.3 Sepsis

6073 _. 001 29.7 31.6 26.3 24.9 29.7 29.3 28.9 26.9 25.7 33.7 29.9 25.9 29.9 27.4 27.6 32.4 Sepsis

6075 _. 001 31.6 33.2 24.0 23.4 32.5 27.2 28.5 26.9 27.3 34.0 27.3 24.3 27.2 26.1 26.5 32.8 Sepsis

6078 _. 001 27.3 30.0 24.7 24.6 28.0 27.8 26.4 25.2 26.4 31.6 28.0 24.8 28.0 26.7 26.2 32.6 Sepsis

6081 _. 001 30.4 30.7 27.2 24.2 30.1 29.3 29.1 27.8 27.5 33.6 30.4 26.9 29.6 29.4 28.7 33.9 Sepsis

6082 _. 001 30.5 32.5 27.8 26.1 29.6 30.4 28.0 28.0 28.5 33.0 29.8 27.8 30.6 28.1 26.4 31.5 Sepsis

6084 _. 001 28.2 27.4 25.2 24.7 27.3 28.4 26.0 26.8 25.9 27.6 32.1 25.5 29.2 29.0 32.0 24.8 Sepsis

6085 _. 001 29.1 30.0 27.2 24.4 28.1 28.5 27.1 26.0 26.0 33.5 29.1 25.2 29.3 27.5 26.1 32.0 Sepsis

6098 _. 001 28.2 29.1 26.8 24.9 25.7 28.5 26.4 25.0 25.8 32.9 27.7 24.8 29.6 26.5 24.8 32.2 Sepsis

6104 _. 001 28.4 27.7 26.7 23.9 26.8 27.9 26.3 24.1 25.8 25.2 31.3 25.7 28.4 29.8 32.4 25.0 Sepsis

6110 _. 001 28.7 31.0 26.6 24.2 27.9 27.3 28.0 26.8 26.8 33.7 28.9 26.4 28.9 27.8 26.6 33.6 Sepsis

6115 _. 001 30.1 31.2 28.8 24.9 27.9 29.3 26.7 26.1 27.5 34.1 29.2 27.3 31.1 28.4 26.6 32.4 Sepsis

6125 _. 001 28.3 29.2 26.5 23.4 26.9 27.8 25.5 24.7 26.7 32.4 28.2 25.3 29.2 27.0 25.4 31.3 Sepsis

829 _. 001 28.7 31.3 25.5 24.8 28.5 27.1 26.1 26.5 25.5 31.3 27.5 24.6 27.6 24.9 24.2 30.5 Sepsis

942 _. 001 30.0 31.9 27.7 25.2 27.7 27.7 25.8 25.4 25.9 33.8 28.7 25.8 28.8 26.8 24.6 31.7 Sepsis

987 _. 001 26.7 28.2 25.8 22.5 25.5 26.0 24.9 24.6 26.0 31.9 26.7 24.2 28.6 26.2 24.2 31.7 Sepsis Table 7: Ct values of the test data set per marker (mean of triplicate, missing values were noted with NA and excluded from analysis) and group affiliation (last column). The first 5 samples belong to healthy volunteers, the others to the patients described in Table 4. The associated experiment ID consists of the case number and the sample ID (introduced with 2 zeros), an additional 1 marks a repetition

Experiment ID M2 M3 M4 M5 M6 M8 M9 M10 M12 M13 M15 M16 M17 R1 R2 R3 group

12A 27.84 28.1 27.5 26.6 26.2 24.2 24.7 22.7 23.9 31.4 27.3 27.8 28.8 25.7 24.2 30.2 no sepsis

2A 26.72 28.2 27.3 27.7 26.7 24.5 24.4 22.5 23.6 32.6 26.9 27.9 29.4 26.5 24.9 31.1 no sepsis

2C 28.18 28.4 27.8 26.0 27.0 26.2 24.3 25.1 24.9 31.1 27.2 28.2 29.4 26.3 24.8 31.6 no sepsis

7A 27.31 27.6 27.4 26.1 25.3 24.9 24.0 22.9 22.8 30.2 26.4 27.5 28.7 25.2 24.1 30.8 no sepsis

7C 27.94 27.6 27.3 25.4 25.4 25.5 24.0 23.0 23.9 30.8 26.1 27.2 28.7 25.0 23.8 30.2 no sepsis

8011_001_ _1 23.06 23.8 23.5 21.3 22.3 26.9 21.3 18.7 22.8 26.6 23.8 22.5 25.2 22.6 21.4 28.0 no sepsis

8034_001_ _1 25.82 26.6 25.5 23.8 25.7 26.7 24.3 24.7 25.7 31.5 25.7 24.4 28.4 25.2 24.6 30.8 no sepsis

8044_001_ _1 24.31 24.4 23.5 22.5 23.9 25.1 22.0 22.9 23.8 28.6 23.2 22.4 25.5 23.1 23.0 29.5 no sepsis

8052_001_ _1 26.64 27.2 27.1 23.6 25.3 26.7 24.3 24.0 24.9 32.2 26.3 25.2 28.0 26.0 25.0 31.6 no sepsis

8073_001_ _1 25.93 27.1 26.9 25.3 25.5 27.4 25.3 23.8 24.7 30.5 27.1 25.6 28.6 27.1 25.8 31.4 no sepsis

8103_001_ _1 23.68 23.9 22.7 21.4 21.7 23.2 22.1 19.9 21.7 27.9 21.3 21.6 26.0 23.3 18.9 27.3 no sepsis

8108_001_ _1 25.79 25.5 24.0 21.0 24.4 26.5 23.4 22.5 24.1 30.1 25.6 23.2 26.1 24.4 23.4 31.3 no sepsis

5019.001 27.41 28.0 27.2 25.5 26.3 25.5 24.6 24.4 24.5 32.4 27.2 26.0 28.8 26.5 25.5 32.0 no sepsis

5020.001 23.28 25.3 25.4 24.3 24.0 24.7 23.1 22.3 23.1 27.4 25.1 23.9 27.0 24.7 23.5 27.8 no sepsis

5023 _. 001 25.14 25.5 25.4 25.1 24.6 25.0 22.9 22.8 23.3 31.6 26.0 23.3 27.5 24.5 24.0 30.2 no sepsis

8026 _. 001 27.06 27.8 27.5 24.4 26.3 28.6 24.5 24.1 26.3 31.8 27.1 26.0 27.9 26.8 25.7 32.0 no sepsis

8056 _. 002 26.30 28.4 25.9 25.2 26.3 27.7 25.5 23.9 26.1 31.4 27.3 24.5 27.7 25.6 26.0 31.8 no sepsis

8058 _. 002 27.73 29.7 27.7 24.9 26.2 28.0 24.7 23.5 25.3 32.7 27.5 24.7 29.3 26.4 24.4 30.7 no sepsis

8086 _. 001 25.46 25.9 25.5 24.1 24.4 26.2 23.1 22.9 23.7 30.7 25.1 24.1 27.6 24.9 24.0 30.2 no sepsis

8122 _. 003 27.51 28.3 26.0 22.3 25.3 26.0 23.6 23.8 24.6 31.5 25.4 25.4 27.2 25.0 24.2 30.0 no sepsis

2042 _. 001 28.20 31.6 28.5 24.3 28.3 30.3 26.8 25.4 27.8 34.1 29.1 25.8 29.9 29.1 26.4 33.4 no sepsis

8102 _. 001 27.30 29.7 27.8 24.9 26.9 28.9 24.8 23.4 26.7 33.0 28.1 26.2 30.1 26.9 25.5 31.7 no sepsis

8111 _. 001 25.79 27.0 26.7 23.7 25.0 27.0 23.9 24.8 26.0 32.0 26.5 24.5 29.1 26.0 24.4 31.4 no sepsis

8112 _. 001 24.47 24.8 23.5 21.9 24.7 26.1 22.6 22.8 22.9 30.1 23.3 22.8 28.3 22.0 21.7 29.4 no sepsis

8116 _. 001 26.60 30.1 27.8 24.6 26.7 28.7 25.8 24.5 26.6 32.8 28.1 25.9 29.8 27.0 25.9 32.1 no sepsis

8039 _. 001 25.09 27.1 25.8 23.2 24.6 25.2 22.6 23.1 23.8 31.2 25.6 26.1 27.8 25.3 23.8 29.9 no sepsis

8039 _. 002 25.19 26.7 24.9 23.1 24.1 28.7 22.9 26.3 22.9 30.1 25.1 25.6 27.8 24.7 23.5 30.3 no sepsis

8039 _. 003 26.72 27.7 26.1 24.1 25.1 25.5 24.0 24.3 25.1 32.1 27.2 25.6 28.6 26.2 25.1 30.8 no sepsis

8039 _. 004 27.61 30.2 26.3 24.3 26.5 30.6 24.4 27.6 25.4 33.2 27.3 26.5 28.7 26.3 25.2 31.3 no sepsis

8039 _. 005 26.85 25.9 25.7 25.7 24.9 27.2 25.2 25.3 25.8 30.7 26.1 25.8 27.5 25.2 24.1 30.7 no sepsis

7052 _. 001 28.40 29.9 26.8 24.2 27.7 26.6 25.7 24.3 25.0 34.3 28.0 26.5 28.6 26.6 25.8 31.2 no sepsis

7052 _. 002 29.62 31.3 27.1 24.0 29.0 27.2 26.4 25.3 26.2 33.2 29.0 26.2 29.8 26.9 25.9 31.7 no sepsis

7052 _. 003 29.62 31.0 28.1 24.2 29.0 27.4 26.7 26.0 27.2 33.4 29.0 27.8 29.5 28.3 26.8 32.7 no sepsis

7119 _. 001 26.68 27.4 26.5 24.2 25.2 26.1 24.5 23.3 24.4 31.2 26.2 24.5 28.4 25.9 23.8 31.5 no sepsis

7119 _. 002 27.97 29.1 28.6 25.1 27.2 26.6 26.0 24.7 26.0 32.6 27.7 26.3 29.1 26.9 25.9 32.1 no sepsis

7119 _. 003 28.56 29.1 28.3 24.8 26.8 26.7 25.6 24.7 25.3 31.8 27.6 27.1 29.4 26.6 25.7 30.7 no sepsis

8026 _. 001_ _1 28.10 28.8 33.3 25.0 26.8 28.2 25.6 24.4 26.2 32.4 27.5 26.6 28.4 27.0 26.3 33.4 no sepsis

8026 _. 002 29.11 29.6 32.7 25.1 27.5 28.6 26.0 25.2 26.1 32.9 27.8 26.1 29.0 27.1 25.7 32.5 no sepsis

8026 _. 003 32.38 33.0 35.0 26.2 29.9 30.5 27.4 26.4 27.7 34.9 29.3 28.5 30.0 31.6 28.1 34.4 no sepsis 8026 _. 004 29.79 30.5 32.2 25.4 28.7 28.9 27.7 26.8 27.0 NA 29.1 28.1 29.8 28.5 26.9 32.5 no sepsis

8026 _. 005 28.06 28.6 32.0 24.5 26.1 27.0 25.3 24.0 24.9 32.5 27.4 26.7 24.9 25.7 24.8 31.5 no sepsis

7077 _. 001 27.22 28.8 27.3 24.1 27.8 27.1 26.5 25.6 25.8 32.6 28.1 25.9 29.8 27.1 25.6 31.5 no sepsis

7077 _. 002 25.50 27.6 25.8 23.0 26.2 25.5 25.3 24.5 25.3 31.5 27.4 25.2 28.4 25.1 24.8 30.4 Sepsis

7077 _. 003 26.50 28.4 27.5 24.3 27.8 27.1 26.0 25.4 26.1 32.1 27.4 26.6 29.4 26.6 25.2 30.7 Sepsis

7084 _. 001 26.16 28.2 27.0 23.6 26.1 27.4 25.3 24.2 27.0 33.3 26.8 24.7 29.9 26.0 25.5 32.0 no sepsis

7084 _. 002 26.19 27.7 26.2 22.6 26.6 27.0 25.8 24.1 25.1 32.2 25.8 23.5 29.1 25.7 24.0 31.2 no sepsis

7084 .003 27.42 30.0 28.2 23.9 28.6 28.5 27.0 25.6 26.2 32.9 26.9 24.6 30.3 26.5 25.1 31.5 Sepsis

7084.004 26.61 29.9 25.8 23.9 27.9 28.2 27.2 25.4 25.5 33.0 26.3 23.6 29.7 26.8 25.7 32.2 Sepsis

7096.001 25.59 27.0 26.9 23.2 25.3 26.0 23.7 23.3 24.6 31.4 26.7 25.4 28.7 25.8 23.8 29.9 no sepsis

7096 _. 002 26.40 28.4 26.9 23.6 24.8 29.7 24.5 23.7 24.6 31.0 27.6 26.2 28.2 25.8 24.4 30.9 no sepsis

7096 _. 003 27.50 29.5 27.6 23.8 26.0 26.4 24.6 23.9 25.1 32.5 27.8 26.7 28.9 26.5 25.0 31.2 no sepsis

7096 _. 004 25.80 27.9 25.8 23.6 25.8 29.8 24.2 23.9 25.1 30.7 26.5 24.5 28.5 25.2 24.5 30.6 no sepsis

7096 _. 005 26.01 27.7 26.3 23.2 25.2 26.1 24.1 23.8 24.0 31.7 26.3 24.5 29.1 25.6 23.6 30.3 no sepsis

7096 _. 006 27.14 28.5 26.9 24.2 26.8 31.2 25.4 24.7 25.0 32.4 27.8 25.5 29.6 26.5 25.2 31.1 Sepsis

7096 _. 007 25.97 27.5 24.9 22.7 25.3 25.6 24.3 23.9 24.6 31.3 26.5 23.8 28.4 25.0 23.8 30.2 Sepsis

8009 _. 001 _{. ,} 1 27.01 27.5 31.7 25.0 26.5 26.9 24.0 23.8 24.3 32.4 27.0 26.1 28.0 26.0 25.6 31.6 no sepsis

8009 _. 002 25.06 26.4 24.9 22.9 24.7 NA 22.9 22.1 23.9 31.4 26.2 23.7 26.9 24.6 23.5 31.0 no sepsis

8009 _. 003 24.75 24.6 21.8 20.7 24.6 NA 23.1 22.5 22.1 29.7 23.5 20.6 24.3 22.7 21.2 28.7 no sepsis

8009 _. 004 27.57 28.9 29.6 23.6 26.8 27.2 25.7 23.8 23.7 33.1 26.8 23.4 27.8 26.0 24.4 31.8 no sepsis

8009 _. 005 28.02 28.1 30.2 24.6 27.5 27.4 25.5 24.7 25.0 33.7 27.0 24.0 27.8 25.9 24.5 31.1 no sepsis

8009 _. 006 27.17 28.1 28.9 24.6 26.5 26.3 25.6 24.0 24.9 32.2 26.5 23.3 27.5 25.3 23.9 30.7 no sepsis

8009 _. 007 26.97 28.5 28.9 23.8 27.6 25.8 25.8 23.9 24.7 33.1 26.5 23.4 28.2 25.1 23.9 30.3 no sepsis

8009 _. 010 29.35 29.7 30.3 25.8 28.6 28.1 26.6 25.7 26.8 34.4 27.8 24.9 29.1 26.6 25.4 32.8 Sepsis

8096 _. 001 _{. ,} 1 28.75 30.6 26.1 25.0 28.7 27.2 27.5 26.2 25.7 35.8 28.2 25.4 29.2 27.0 25.8 31.6 no sepsis

8096 _. 002 27.69 28.8 25.8 24.2 27.3 26.6 25.6 24.4 24.0 32.7 27.7 24.8 28.3 26.2 26.0 31.6 no sepsis

8096 _. 003 28.48 31.4 27.0 23.1 28.0 26.1 26.5 26.2 24.5 33.2 28.8 26.6 28.8 26.4 25.6 31.2 no sepsis

8112 _. 001 _{. ,} 1 27.42 28.7 27.0 25.3 27.3 30.1 26.5 27.2 26.7 32.6 27.5 25.9 30.0 26.4 25.6 32.8 no sepsis

8112 _. 002 _{. ,} 1 27.36 28.9 26.8 25.4 26.6 31.7 26.3 26.7 26.5 32.7 27.4 25.2 29.4 26.5 25.2 32.3 no sepsis

8112 _. 003 27.18 28.8 27.3 24.2 26.5 29.1 26.2 26.3 24.7 32.3 27.4 25.1 29.6 26.2 24.8 31.9 no sepsis

8112 _. 004 27.83 29.8 27.7 25.9 26.9 32.0 27.0 26.7 25.5 33.1 28.0 25.9 29.0 26.6 24.9 31.4 no sepsis

8112 _. 005 28.19 30.0 27.4 24.8 26.9 30.3 26.4 26.7 25.8 34.1 27.9 26.2 29.3 26.4 24.7 31.2 Sepsis

8112 _. 006 28.64 30.2 27.7 26.0 27.6 32.2 26.9 27.1 26.4 34.2 28.0 26.9 30.3 27.1 25.8 32.7 Sepsis

8101 _. 001 24.47 25.9 24.7 21.9 24.3 24.5 23.0 22.5 25.1 30.6 25.1 23.3 26.5 24.2 23.3 30.6 no sepsis

8101 _. 002 24.87 27.0 25.3 22.8 25.2 24.8 23.9 23.2 24.0 30.8 25.6 23.7 26.7 24.5 23.5 17.6 no sepsis

8101 _. 003 24.48 25.7 23.7 22.2 24.2 29.5 23.7 23.5 24.8 31.1 25.0 22.3 26.4 24.0 23.2 31.0 no sepsis

8101 _. 004 24.88 26.9 24.1 22.3 24.6 24.9 24.2 23.7 23.6 30.2 24.9 22.2 25.7 24.1 22.6 29.2 Sepsis

8101 _. 005 23.84 26.4 22.4 22.6 24.9 24.2 23.9 22.8 24.9 28.9 24.6 22.4 25.0 24.1 22.9 28.7 Sepsis

8111 _. 003 25.77 26.7 24.8 23.1 24.9 24.9 24.2 24.0 26.1 31.0 25.3 23.3 27.3 24.4 23.1 30.1 no sepsis

8111 _. 004 25.15 26.7 26.0 22.5 24.1 24.4 23.6 24.2 25.5 30.1 25.6 23.5 27.3 25.6 24.4 30.0 no sepsis

8111 _. 005 25.32 26.6 26.8 22.7 24.7 25.1 23.6 23.2 24.2 30.4 25.8 24.4 27.5 25.1 24.3 31.4 no sepsis

8111 _. 006 26.53 26.9 25.3 23.2 25.9 25.3 24.7 25.7 26.2 31.4 25.7 24.1 28.3 24.6 24.8 30.6 Sepsis

8111 _. 007 25.88 26.8 26.5 23.6 25.8 26.5 25.6 26.0 27.1 29.4 26.4 23.6 28.4 26.0 24.1 28.5 Sepsis

6008 _. 002 25.89 26.1 22.4 20.5 26.0 25.6 24.5 22.5 22.8 28.4 23.4 21.9 28.2 23.0 22.6 28.1 Sepsis

6063 _. 001 26.45 26.8 25.0 23.0 25.6 25.7 24.3 23.3 25.0 30.9 25.9 23.5 28.3 25.2 23.6 30.0 Sepsis

6141 _. 001 27.23 29.9 25.6 23.9 28.0 27.9 26.4 26.0 26.6 31.8 27.2 24.1 27.9 25.2 25.0 31.6 Sepsis 1013.001 28.86 30.9 26.9 23.6 28.4 27.1 26.0 25.6 26.3 33.3 28.1 25.0 25.5 26.6 24.6 31.6 Sepsis

6024.002 26.64 28.5 25.3 21.9 26.1 24.8 24.7 23.9 23.7 32.2 26.0 24.7 28.3 25.6 25.1 31.2 Sepsis

7040 _. 001 28.55 31.1 26.1 23.7 28.4 26.3 26.8 25.7 26.1 33.9 27.6 24.7 28.5 26.1 25.0 31.5 Sepsis

7079 _. 002 27.32 30.4 26.3 23.6 28.6 26.6 27.1 26.5 25.8 30.7 27.2 25.3 28.6 26.4 25.0 29.8 Sepsis

920 _. 001 29.50 31.4 29.1 26.0 28.2 27.5 26.6 25.1 25.8 32.7 28.6 27.0 29.1 27.4 25.5 30.7 Sepsis

6036 _. 001 28.82 30.0 26.8 24.6 28.7 26.5 27.1 25.3 24.0 34.7 27.7 26.7 29.5 26.8 25.9 31.3 Sepsis

6056 _. 001 26.28 27.9 25.4 24.6 27.6 26.0 25.5 25.3 27.2 31.0 26.5 25.5 27.5 25.7 24.8 30.1 Sepsis

6061 _. 001 28.63 29.0 27.0 25.2 28.2 26.5 26.3 25.6 26.2 32.6 26.7 25.5 30.0 26.4 25.0 30.8 Sepsis

7023 _. 001 26.83 29.2 26.5 24.3 26.5 25.5 25.7 24.5 25.5 34.7 26.3 25.9 28.6 27.4 25.7 31.7 Sepsis

7112 _. 001 28.60 29.5 31.3 25.9 27.9 27.9 25.4 26.3 25.3 33.8 27.0 24.7 30.1 26.1 24.9 31.6 Sepsis

6064 _. 001 28.63 30.0 30.0 22.8 29.6 26.2 26.2 26.2 26.1 33.8 27.1 24.3 28.9 25.6 25.1 30.6 Sepsis

6120 _. 001 27.58 30.7 25.7 23.4 29.0 27.8 27.2 27.3 27.2 33.7 28.1 24.6 28.6 26.4 25.3 31.4 Sepsis

7105 _. 001 27.19 28.6 32.3 22.7 26.6 26.4 25.1 25.1 24.6 31.7 27.7 24.4 29.1 26.0 25.2 30.8 Sepsis

7120 _. 001 29.38 29.8 29.6 24.4 29.3 28.2 26.9 26.3 26.2 33.5 27.9 24.7 28.2 26.6 25.7 32.2 Sepsis

749 _. 001 27.59 28.8 26.4 23.5 26.5 25.6 24.0 25.1 25.3 31.2 26.3 25.1 28.6 25.1 24.1 30.5 Sepsis

5008 _. 001 26.87 27.9 31.2 22.0 26.5 26.1 24.5 24.0 25.5 32.3 27.2 24.8 28.3 25.6 24.3 30.8 Sepsis

5009 _. 001 25.52 26.2 29.7 22.3 25.9 25.5 23.2 24.2 26.3 30.8 26.0 24.2 27.4 25.0 23.4 29.0 Sepsis

5010 _. 001 27.96 29.1 29.5 22.8 28.8 27.4 28.5 26.3 27.0 32.6 27.9 25.6 27.8 26.1 26.7 31.9 Sepsis

5018 _. 001 28.47 30.4 30.0 23.7 28.4 27.6 26.8 26.6 26.7 34.0 27.3 25.3 29.2 26.2 25.9 31.9 Sepsis

1015 _. 001 29.77 30.5 26.7 24.8 28.8 28.3 26.7 26.1 26.8 34.5 28.3 25.0 29.3 26.9 25.5 30.5 Sepsis

6005 _. 001 29.27 30.0 26.9 23.5 28.3 26.4 26.8 24.9 21.6 33.4 27.7 24.9 28.8 26.2 25.6 32.1 Sepsis

6035 _. 002 28.80 28.0 26.3 25.0 28.0 27.4 26.9 26.2 25.7 32.8 27.1 25.6 29.1 26.6 25.3 31.6 Sepsis

6070 _. 002 29.09 31.0 27.7 24.7 28.2 28.9 26.9 25.8 27.4 34.1 29.8 26.5 29.8 28.1 26.4 33.0 Sepsis

6075 _. 002 29.35 30.6 25.6 25.8 28.9 28.7 26.8 26.4 27.2 32.6 28.0 24.6 28.5 26.3 25.4 31.6 Sepsis

6104 _. 002 30.63 32.3 28.8 25.8 28.5 28.5 27.2 27.2 26.8 34.4 28.9 27.2 29.0 27.5 26.3 32.1 Sepsis

6124 _. 001 30.50 31.3 26.7 24.8 29.1 26.7 28.1 25.0 26.1 32.7 28.3 26.0 28.5 27.0 26.4 31.9 Sepsis

6126 _. 001 30.72 28.6 27.6 24.5 27.8 26.2 26.0 24.7 24.5 31.4 26.8 25.7 26.9 25.9 24.7 30.4 Sepsis

714 _. 001 32.80 31.9 27.6 27.5 29.9 30.3 29.8 28.0 25.2 NA 29.5 26.8 31.6 28.9 28.0 33.5 Sepsis

Classification:

 The aim of the classification was to determine the marker set, which allows the best separation between samples from patients with and without sepsis.

 In order to arrange the gene markers according to their separation quality, the linear discriminant analysis (LDA) [Hastie et al., 2001] was used together with the method of forward selection, the separability being evaluated by the F value [Hocking, RR, 1976 ].

 The calculation was performed using the function Ida from the R

Library MASS. For p markers, the weights were h ^w p) the

Discriminant function f _L D, represented by the formula f _LD (x ₁ , ..., x _p ) = _d w _i x _i - w ₀

 i = l

is defined, calculated from the training data. Each training sample was retrospectively classified using the delta Ct values of the sample in the previous formula for x. The weights of the discriminant function were calculated so that a positive value of the function means the assignment to the group with an infectious complication and a negative value of the function means the assignment to the group without an infectious complication.

 The classification procedure was repeated for an increasing number of markers, successively including the marker candidates in the discriminant analysis which made the highest contribution to the separation quality (forward selection). This analysis step was repeated for 1000 bootstrap samples obtained by random dragging from the training data set. The marker ranks determined in each repetition were averaged over the 1000 runs. The marker candidates were placed in ascending order after the middle rank. This arrangement means that the least median rank marker was the one that most often made the most contribution to separation quality, and the highest mean rank marker did little to separate in most repeats.

 The quality of the determined marker ranking was examined, in which the separability of the training groups for marker sets with ascending marker number was evaluated. For this, the linear discriminant analysis with a simple

Cross validation used. For p markers, the weights ( ^w o> - - -> ^w _p ) of the discriminant function f _{LD were} calculated from the reduced training data by sequentially omitting a sample. This sample was classified retrospectively using the delta Ct values of the sample in the preceding formula for x.

Finally, in order to verify that the separation quality does not depend primarily on the classification method but on the marker selection, the simple cross-validation was repeated in the same way for the quadratic discriminant analysis (QDA) [Hastie et al., 2001]. The calculation was carried out using the function qda from the R library MASS. The classification results of the training dataset have been validated for the matrix of the test dataset. From the training data set, the discriminant function was determined for each marker set with ascending marker number and used to classify the test samples. The quality of the classification was evaluated by the receiver operating characteristic (ROC) curve, which maps the rate of true positivity (sensitivity) against the rate of false positives (1 specificity) for an ascending sequence of classification thresholds [Fawcett T. , 2006]. For the evaluation, the area under each curve (AUC) was calculated from each ROC curve and the highest achievable classification efficiency (proportion of correctly classified samples) was determined.

Results

 The results of the classification analysis described above were summarized in Table 8. The ranking procedure resulted in the following array of marker candidates: M6, M15, M9, M7, M2, M10, M4, M12, M17, M3, M8, M13, M16. The cross-validation rate of LDA and ODA increased significantly to 94.8% for the first 3 markers. The best separation of the training groups at 96.6% was achieved with LDA for the first 6 markers, in which 56 out of 58 samples were correctly classified (ODA gives a maximum with 3 markers following with 7 markers). For more than 7 markers no improvement of the classification was achieved.

 For the independent test dataset, the largest area under the ROC curve of more than 85% was achieved for the first 6 and 7 markers, the best classification at 81, 4% yielded the first 7 markers.

Table 8: Results of classification optimization. Bolded is the maximum value of the respective column, which reflects the best result for the marker combination.

Training data (n = 58) test data (n = 1 13)

Marker Mean Rank Cross Validation Rate (%) Area Under the ROC Classification Ranking (1000 Bootstraps) Curve, AUC (%) Efficiency (%)

 LDA QDA

 M6 3.3 70.7 74.1 60.1 60.2

M15 4.1 79.3 81 .0 68.1 68.1

M9 4.4 94.8 94.8 84.0 78.8 M7 5.7 93.1 87.9 84.3 77.9

M2 5.7 93.1 89.7 83.8 75.2

M10 7.0 96.6 87.9 85.2 78.8

M4 7.1 91 .4 93.1 85.4 81.4

M12 7.2 89.7 89.7 83.9 78.8

M17 8.7 91 .4 89.7 82.5 77.9

M3 8.9 91 .4 87.9 81 .9 78.8

M8 9.4 89.7 84.5 80.3 75.2

M13 9.4 87.9 81 .0 79.2 73.5

M16 10.3 87.9 77.6 78.4 73.5

Since in the classification analysis the best results were achieved predominantly with the first 7 markers from the above table, for further classifications the 7-marker LDA was used whose discriminant function f _{LD was determined} from the training data set. The associated weights w ₀ , ..., w ₇ ) were set forth in Table 9.

 Based on this function, a sepsis-related diagnostic parameter, a so-called SIQ score (SIQ), was introduced as follows. For a new independent sample, the classification result obtained is a dimension-free value of the discriminant function. A positive value classifies the sample as infectious and a negative value as non-infectious. Absolutely higher values are obtained for typical representatives of the respective group, hard-to-classify samples reach values close to zero. The range of the discriminant values corresponds i.a. the variability of the delta Ct data matrix. Discriminant values of about -5 to 5 were achieved in the classification. To emphasize the differences more clearly, the SIQ score (SIQ) was introduced as the 10-fold value of the discriminant function with the weights in Table 9. Accordingly, the SIQ values of the test data varied from about -50 to 50.

Table 9: Weights of the discriminant function obtained from the training dataset. w0 w1 w2 w3 w4 w5 w6 w7

 (M6) (M15) (M9) (M7) (M2) (M10) (M4)

0.160 0.733 -0.722 -1 .006 0.188 -0.387 -0.268 0.161 In the linear discriminant analysis, the discriminant function is generally determined such that the separation threshold between the two training groups is zero. Using the ROC curve, an independent test data record can be used to determine at which threshold value the best separation of the associated test groups is achieved. In Fig. 1, the ROC curve for classification of the test data is represented by the SIQ score and the relationship between true positives (sensitivity) and false positives (1 specificity) is marked, dashed gray for the threshold of zero and dashed black for the best achieved classification of 81, 4%. This classification result was achieved for the threshold of SIQ = - 4.9. From Fig. 1 it was seen that by shifting the threshold from 0 to -4.9, a sensitivity gain of about 63% to over 80% at cost of specificity of about 81% to about 80% is achieved. This result reflects the discrepancy between the preselected patient of the training dataset and the heterogeneous collective of the test dataset. The classification quality achieved with the updated classification thresholds of SIQ = -4.9 is shown in Table 10.

 This demonstrates that the described invention is useful for distinguishing between infectious and non-infectious genesis in systemic reactions of the organism, such as e.g. SIRS, sepsis, postoperative complications, chronic and / or acute organ dysfunction, shock response, inflammatory response and / or trauma, can be used in differential diagnosis.

Table 10: Quality of classification for the test dataset using the SIQ score at a threshold

Beispiel 2: Frühe Sepsis-Erkennung

Example 2: Early sepsis detection

In the test data set of the 1. In the embodiment no. 81 12 4 samples were examined before and 2 samples after the development of sepsis (compare Tables 4 and 7). In the classification analysis, a SIQ score above the value of -4.9 was achieved 2 days before the clinical manifestation of sepsis. The course of the in 1. Embodiment introduced SIQ score and the course of other sepsis-relevant clinical parameters (PCT, CRP, SOFA, body temperature, shock treatment) is shown in Fig. 2. From Fig. 2 it can be seen that SIQ score is the only parameter that prematurely reflects the infectious complication. This demonstrates that the described invention can be used for the early detection of infectious complications such as sepsis and / or generalized infection.

Example 3: Observation of the course of therapy

In the test data set of the 1. In the case of Example No. 7084, 2 samples were examined before and 2 samples after development of sepsis (see Tables 4 and 7). For the third exemplary embodiment, 5 subsequent samples of this patient were prepared in the same way as described in FIG. Example was described, measured and evaluated (see Table 1 1). In Fig. 3, the determined values of the in 1. Embodiment introduced SIQ score along with the sepsis-relevant clinical parameters PCT, CRP, SOFA, body temperature and the dosage of catecholamines (norepinephrine), which reflects the shock treatment shown. From Figure 3, it can be seen that SIQ score rises above the threshold of -4.9 one day before the clinical manifestation of sepsis and remains above the threshold in the acute phase. Following clinical manifestation of sepsis, appropriate antibiotic therapy and shock treatment were initiated (see Table 4 and Figure 3). The acute phase ended on cessation of catecholamine (shock treatment) on the 8th day. The improved condition of the patient is also reflected in the drop in the SOFA score from the 7th day. After Acute phase also decreases the SIQ score and falls below the threshold of -4.9 on the 8th day. The parameters PCT and CRP also decrease, but remain above the associated diagnostic decision value. This demonstrates that the described invention can be used for the follow-up and / or therapy control of eg antibiotic therapy and / or adjunctive clinical measures and / or surgical remedial measures.

Table 1 1: Ct values of the training data set per marker (average of the triple determination) and the group affiliation (last column)

Experiment ID M2 M3 M4 M5 M6 M8 M9 M10 M12 M13 M15 M16 M17 R1 R2 R3

 7084 .001 26.2 28.2 27.0 23.6 26.1 27.4 25.3 24.2 27.0 33.3 26.8 24.7 29.9 26.0 25.5 32.0 no sepsis

7084.002 26.2 27.7 26.2 22.6 26.6 27.0 25.8 24.1 25.1 32.2 25.8 23.5 29.1 25.7 24.0 31.2 no sepsis

7084.003 27.4 30.0 28.2 23.9 28.6 28.5 27.0 25.6 26.2 32.9 26.9 24.6 30.3 26.5 25.1 31.5 Sepsis

7084 _. 004 26.6 29.9 25.8 23.9 27.9 28.2 27.2 25.4 25.5 33.0 26.3 23.6 29.7 26.8 25.7 32.2 Sepsis

7084 _. 005 24.8 28.3 25.1 22.8 27.2 27.6 26.4 24.6 24.1 29.5 26.1 22.4 28.4 25.7 24.1 28.7 Sepsis

7084 _. 006 26.6 28.9 26.0 23.5 26.7 30.9 26.5 24.8 25.3 32.1 26.6 23.0 29.5 25.9 24.7 31.8 Sepsis

7084 _. 007 25.4 27.5 25.2 23.7 26.7 28.0 25.0 23.8 22.1 31.5 25.7 21.9 28.8 25.0 23.3 31.7 Sepsis

7084 _. 008 24.8 26.7 24.4 23.0 26.4 30.5 25.4 24.3 24.0 31.1 25.2 21.5 28.9 24.6 23.5 31.5 Sepsis

7084 _. 009 25.9 28.0 25.8 23.7 27.4 28.3 25.4 24.3 25.9 32.3 26.8 23.6 29.3 26.3 24.6 31.7 Sepsis

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Claims

Claims Method for in vitro detection and / or early detection and / or Differentiation and / or follow-up and / or assessment of pathophysiological conditions selected from the group consisting of: SIRS, sepsis and their severity; sepsis-like states; septic shock; Bacteremia, infectious / non-infectious multi-organ failure; Early detection of these conditions; Focus control; Control of surgical remedial measures of the infection focus; Responder / non-responder for a given therapy; Therapy monitoring; Distinction between infectious and non-infectious genesis in systemic reactions of the organism, e.g. SIRS, sepsis, postoperative complications, chronic and / or acute Organ dysfunction, shock reaction, inflammatory response and / or trauma; the method comprising the steps of: a) Isolation of sample nucleic acids from one of a patient  originating sample; b) determining gene activities using a plurality of at least three polynucleotides selected from the group consisting of M2, M3, M4, M6, M7, M8, M9, M10, M12, M13, M15, M16 and M17; and / or their isoforms and / or their gene loci and / or their transcripts and / or fragments having a length of at least 5 nucleotides thereof, for the formation of at least one for the detection and / or differentiation and / or the course of pathophysiological conditions of a patient characteristic multigene biomarker; wherein the polynucleotides are defined according to the following table: Transcript variant / ds Accession SEQ  polynucleotide  regulatory sequences Number ID  M2_l NM_001031700 1  M2 M2_2 NM_016613 2  M2_3 NM_001128424 3  M4_l NM_203330 4  M4_2 NM_000611 5  M4_3 NM_203329 6  M4_4 NM_203331 7  M4  M4_5 NM_001127223 8  M4_6 NM_001127225 9  M4_7 NM_001127226 10  M4_8 NM_001127227 11  M6_l NM_001831 12  M6  M6_2 NM_203339 13  M7_l NM_031311 14  M7  M7_2 NM_019029 15  M9 M9 NM_006682 16  MIO MIO NM_033554 17  M15_l NM_003580 18  M15  M15_2 NM_001144772 19  M3_A NM_001123041 20  M3  M3_B NM_001123396 21  M8 NM_025209 22  M8  M8_ds AI807985 23  M12 NM_002185 24  M12  M12_cw DB155561 25  M13 M13 NM_001080394 26  M16 M16 NM_003268 27  M17 M17 NM_182491 28 Determination of gene activities of at least one internal reference gene to which the gene activities determined under b) are obtained, in particular normalized; Forming a value from the individual particular gene activities of the multigene biomarker indicating the pathophysiological status. A method according to claim 1, characterized in that the Reference gene is selected from polynucleotides of the group consisting of R1, R2 and R3 and / or their isoforms and / or their gene loci and / or their transcripts and / or fragments having a length of at least 5 nucleotides thereof, wherein the reference genes are defined according to the following table :

The method of claim 1 or 2, characterized in that as polynucleotide gene loci, sense and / or antisense strands of pre-mRNA and / or mRNA, small RNA, in particular scRNA, snoRNA, micro RNA, siRNA, dsRNA, ncRNA or transposable elements Capture the Gene expression profiles are used. Method according to one of claims 1 to 3, characterized in that in step b) the gene activity of 4, 5, 6, 7, 8, 9, 10, 1 1, or 12 Polynucleotides, or is determined from all 13 polynucleotides, wherein the polynucleotides are selected from the group consisting of: M2, M3, M4, M6, M7, M8, M9, M10, M12, M13, M15, M16 and M17 and / or their isoforms and / or their gene loci and / or their transcripts and / or fragments having a length of at least 5 nucleotides thereof, the polynucleotides being defined according to the following table: Transcript variant / ds Accession SEQ polynucleotide  regulatory sequences Number ID  M2_l NM_001031700 1  M2 M2_2 NM_016613 2  M2_3 NM_001128424 3  M4_l NM_203330 4  M4_2 NM_000611 5  M4_3 NM_203329 6  M4_4 NM_203331 7  M4  M4_5 NM_001127223 8  M4_6 NM_001127225 9  M4_7 NM_001127226 10  M4_8 NM_001127227 11  M6_l NM_001831 12  M6  M6_2 NM_203339 13  M7_l NM_031311 14  M7  M7_2 NM_019029 15  M9 M9 NM_006682 16  MIO MIO NM_033554 17  M15_l NM_003580 18  M15  M15_2 NM_001144772 19  M3_A NM_001123041 20  M3  M3_B NM_001123396 21  M8 NM_025209 22  M8  M8_ds AI807985 23  M12 NM_002185 24  M12  M12_cw DB155561 25  M13 M13 NM_001080394 26 M16 M16 NM_003268 27 M17 M17 NM_182491 28 and / or wherein a number of 7 polynucleotides is preferred. 5. Use of at least three polynucleotides selected from A group consisting of: M2, M3, M4, M6, M7, M8, M9, M10, M12, M13, M15, M16 and M17 and / or their isoforms and / or their gene loci and / or their transcripts and / or fragments a length of at least 5 nucleotides thereof, for the formation of at least one multigene biomarker for the preparation of a multiplex assay as an aid for in vitro detection and / or early detection and / or differentiation and / or course monitoring and / or assessment of pathophysiological conditions of a patient, wherein the pathophysiological Condition is selected from the group consisting of: SIRS, sepsis and their degrees of severity; sepsis-like states; septic shock; Bacteremia, infectious / non-infectious multi-organ failure; Early detection of this Conditions; Focus control; Control of surgical remedial measures of the infection focus; Responder / non-responder for a given therapy; Therapy monitoring; Distinction between infectious and non-infectious genesis in systemic reactions of the organism, e.g. SIRS, sepsis, postoperative complications, chronic and / or acute organ dysfunction, shock reaction, inflammatory response and / or trauma; the Polynucleotides are defined according to the following table: Transcript variant / ds Accession SEQ  polynucleotide  regulatory sequences Number ID  M2_l NM_001031700 1  M2 M2_2 NM_016613 2  M2_3 NM_001128424 3  M4_l NM_203330 4  M4_2 NM_000611 5  M4_3 NM_203329 6  M4_4 NM_203331 7  M4  M4_5 NM_001127223 8  M4_6 NM_001127225 9  M4_7 NM_001127226 10  M4_8 NM_001127227 11  M6_l NM_001831 12  M6  M6_2 NM_203339 13  M7_l NM_031311 14  M7  M7_2 NM_019029 15  M9 M9 NM_006682 16  MIO MIO NM_033554 17  M15_l NM_003580 18  M15  M15_2 NM_001144772 19  M3_A NM_001123041 20  M3  M3_B NM_001123396 21  M8 NM_025209 22  M8  M8_ds AI807985 23  M12 NM_002185 24  M12  M12_cw DB155561 25  M13 M13 NM_001080394 26  M16 M16 NM_003268 27 M17 M17 NM_182491 28 Use according to Claim 5, characterized in that the multigene biomarker is a combination of a plurality of polynucleotide sequences, in particular gene sequences, by means of which their gene activities are determined by means of a Interpretation function is performed a classification and / or an index is formed. Use according to claim 5 or 6, characterized in that the gene activities by enzymatic methods, in particular Amplification method, preferably polymerase chain reaction (PCR), preferably real-time PCR, in particular probe-based methods such as Taq-Man, Scorpions, Molecular Beacons; and / or by means of hybridization methods, in particular those on microarrays; and / or direct mRNA detection, in particular sequencing or mass spectrometry; and / or isothermal amplification. Use according to one of claims 5 to 7, characterized characterized in that an index is formed from the individual determined gene activities, which after appropriate calibration is a measure of the severity and / or the course of the pathophysiological state, wherein preferably the index is displayed on an easily interpretable scale. Use according to one of claims 5 to 8, characterized characterized in that the gene activity data obtained for the production of software for the description of at least one pathophysiological condition and / or an investigation question and / or as aids for diagnostic purposes and / or for patient data management systems, in particular for Use for patient stratification and as an inclusion criterion for clinical trials. Use according to one of claims 5 to 9, wherein for generating the gene activity data such specific gene loci, sense and / or antisense strands of pre-mRNA and / or mRNA, small RNA, in particular scRNA, snoRNA, micro RNA, siRNA, dsRNA, ncRNA or transposable elements, genes and / or gene fragments with a length of at least 5 nucleotides are used which have a sequence homology of at least about 10%, in particular about 20%, preferably about 50%, most preferably about 80% to the Polynucleotide sequences M2, M3, M4, M6, M7, M8, M9, M10, M12, M13, M15, M16 and M17. 1. Use according to one of claims 5 to 10, characterized  characterized in that 4, 5, 6, 7, 8, 9, 10, 11 or 12 polynucleotides, or all 13 polynucleotides are used, wherein the polynucleotides are selected from the group consisting of: M2, M3, M4, M6, M7, M8, M9, M10, M12, M13, M15, M16 and M17 and / or their isoforms and / or their gene loci and / or their transcripts and / or fragments having a length of at least 5 nucleotides thereof, wherein the polynucleotides according to the following Table are defined: Transcript variant / ds Accession SEQ  polynucleotide  regulatory sequences Number ID  M2_l NM_001031700 1  M2 M2_2 NM_016613 2  M2_3 NM_001128424 3  M4_l NM_203330 4  M4_2 NM_000611 5  M4_3 NM_203329 6  M4_4 NM_203331 7  M4  M4_5 NM_001127223 8  M4_6 NM_001127225 9  M4_7 NM_001127226 10  M4_8 NM_001127227 11  M6_l NM_001831 12  M6  M6_2 NM_203339 13  M7_l NM_031311 14  M7  M7_2 NM_019029 15  M9 M9 NM_006682 16 MIO MIO NM_033554 17  M15_l NM_003580 18  M15  M15_2 NM_001144772 19  M3_A NM_001123041 20  M3  M3_B NM_001123396 21  M8 NM_025209 22  M8  M8_ds AI807985 23  M12 NM_002185 24  M12  M12_cw DB155561 25  M13 M13 NM_001080394 26 M16 M16 NM_003268 27 M17 M17 NM_182491 28 and / or wherein a number of 7 polynucleotides is preferred. 12. Use of at least one polynucleotide selected from  A group consisting of: M2, M3, M4, M6, M7, M8, M9, M10, M12, M13, M15, M16 and M17 and / or their isoforms and / or their gene loci and / or their transcripts and / or fragments a length of at least 5 nucleotides thereof, the polynucleotides being defined according to the following table: Transcript variant / ds Accession SEQ  polynucleotide  regulatory sequences Number ID  M2_l NM_001031700 1  M2 M2_2 NM_016613 2  M2_3 NM_001128424 3  M4_l NM_203330 4  M4_2 NM_000611 5  M4_3 NM_203329 6  M4_4 NM_203331 7  M4  M4_5 NM_001127223 8  M4_6 NM_001127225 9  M4_7 NM_001127226 10  M4_8 NM_001127227 11  M6_l NM_001831 12  M6  M6_2 NM_203339 13  M7_l NM_031311 14  M7  M7_2 NM_019029 15  M9 M9 NM_006682 16  MIO MIO NM_033554 17  M15_l NM_003580 18  M15  M15_2 NM_001144772 19  M3_A NM_001123041 20  M3  M3_B NM_001123396 21  M8 NM_025209 22  M8  M8_ds AI807985 23  M12 NM_002185 24  M12  M12_cw DB155561 25  M13 M13 NM_001080394 26  M16 M16 NM_003268 27 M17 M17 NM_182491 28 for making an assay to assess whether a patient is in a pathophysiological condition and / or to determine the severity and / or course of a pathophysiological condition. 13. Use according to one of claims 5 to 12, characterized  characterized in that the pathophysiological condition is selected from the group consisting of: SI RS, sepsis and their severity; sepsis-like states; septic shock; Bacteremia, infectious / non-infectious multi-organ failure; Early detection of these conditions; Focus control; Control of surgical remedial measures of the infection focus; Responder / non-responder for a given therapy; Therapy monitoring; Distinction between infectious and non-infectious genesis in systemic reactions of the organism, e.g. SI RS, sepsis, postoperative complications, chronic and / or acute organ dysfunction, shock reaction,  Inflammatory reaction and / or trauma. 14. Use according to one of claims 5 to 13, characterized  in that the sample nucleic acid is RNA, in particular total RNA or mRNA, or DNA, in particular cDNA. 15. Use according to claim 13 or 14, characterized in that for the assessment of the pathophysiological state in addition to at least one of the polynucleotides selected from the group consisting of: M2, M3, M4, M6, M7, M8, M9, M1 0, M12, M13, M15, M16 and M17 and / or their isoforms and / or their gene loci and / or their transcripts and / or fragments having a length of at least 5 nucleotides thereof, the polynucleotides being defined according to the following table: Transcript variant / ds Accession SEQ  polynucleotide  regulatory sequences Number ID  M2_l NM_001031700 1  M2 M2_2 NM_016613 2  M2_3 NM_001128424 3  M4 M4_l NM_203330 4  M4_2 NM_000611 5  M4_3 NM_203329 6  M4_4 NM_203331 7 M4_5 NM_001127223 8 M4_6 NM_001127225 9  M4_7 NM_001127226 10  M4_8 NM_001127227 11  M6_l NM_001831 12  M6  M6_2 NM_203339 13  M7_l NM_031311 14  M7  M7_2 NM_019029 15  M9 M9 NM_006682 16  MIO MIO NM_033554 17  M15_l NM_003580 18  M15  M15_2 NM_001144772 19  M3_A NM_001123041 20  M3  M3_B NM_001123396 21  M8 NM_025209 22  M8  M8_ds AI807985 23  M12 NM_002185 24  M12  M12_cw DB155561 25  M13 M13 NM_001080394 26  M16 M16 NM_003268 27  M17 M17 NM_182491 28 at least one further marker is used, which is selected from the group consisting of: clinical laboratory parameters, in particular procalcitonin (PCT), C-reactive protein (CRP); white blood cell count; cytokines; Interleukins and genetic, transcriptomic and proteomic markers. 16. Primer for carrying out the method according to one of claims 1 to 4, wherein the primer is selected according to the following table: Primer for quantitative  Markers and reference genes SEQ ID  PCR / resulting amplicon  M2-fw 38  M2 M2-rev 39  M2 amplicon 40  M4-fw 41  M4 M4-rev 42  M4 amplicon 43  M6-fw 44  M6 M6-rev 45  M6 amplicon 46  M7 M7-fw 47 M7-rev 48 M7 amplicon 49  M9 fw 50  M9 M9-rev 51  M9 amplicon 52  M10-fw 53  MIO M10-rev 54  MIO Amplicon 55  M15-fw 56  M15 M15-rev 57  M15 Amplicon 58  M3-fw 59  M3 M3-rev 60th  M3 amplicon 61  M8-fw 62  M8 M8-rev 63  M8 Amplicon 64  M12-fw 65  M12 M12-rev 66  M12 amplicon 67  M13-fw 68  M13 M13-rev 69  M13 amplicon 70  M16-fw 71  M16 M16-rev 72  M16 amplicon 73  M17-fw 74  M17 M17-rev 75  M17 Amplicon 76  Rl-fw 77  Rl Rl-rev 78  Rl Amplicon 79  R2-fw 80  R2 R2-rev 81  R2 Amplicon 82  R3-fw 83  R3 R3-rev 84  R3 amplicon 85 17. A kit for carrying out the method of claim 1, comprising at least one multigene biomarker comprising a plurality of polynucleotide sequences selected from the group consisting of: M2, M3, M4, M6, M7, M8, M9 , M10, M12, M13, M15, M16 and M17 and / or their isoforms and / or their gene loci and / or their Transcripts and / or fragments of at least 5 nucleotides in length thereof, the polynucleotides being defined according to the following table:

wherein the multigene biomarker is specific for a pathophysiological condition of a patient and includes such conditions selected from the group consisting of: SIRS, sepsis, and severity levels; sepsis-like states; septic shock; Bacteremia, infectious / non-infectious Multi-organ failure; Early detection of these conditions; Focus control; Control of surgical remedial measures of the infection focus; Responder / non-responder for a particular therapy; Therapy monitoring; Differentiation between infectious and non-infectious genesis in systemic reactions of the organism, such as SIRS, sepsis, postoperative complications, chronic and / or acute Organ dysfunction, shock reaction, inflammatory response and / or trauma. 18. Kit according to claim 17, characterized in that the  Polynucleotide sequences also include gene loci, sense and / or antisense strands of pre-mRNA and / or mRNA, small RNA, in particular scRNA, snoRNA, microRNA, siRNA, dsRNA, ncRNA or transposable elements. 19. Kit according to claim 17 or 18, characterized in that it contains at least one reference gene which is selected from the group consisting of: R1, R2 and R3 and / or their isoforms and / or their gene loci and / or their transcripts and / or fragments of length  at least 5 nucleotides thereof, the reference genes being defined according to the following table: Transcript variant / cis-Accession Number SEQ ID  Reference genes regulatory  sequences  R1_A NM_001228 29  R1_B NM_033355 30  R1_C NM_033356 31  rl  R1_E NM_033358 32  R1_F NM_001080124 33  R1_G NM_001080125 34  R2_l NM_002209 35  R2  R2_2 NM_001114380 36  R3 R3 NM_003082 37