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WO2018223005A1 - Predictive factors for venous thromboembolism - Google Patents

Predictive factors for venous thromboembolism Download PDF

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Publication number
WO2018223005A1
WO2018223005A1 PCT/US2018/035617 US2018035617W WO2018223005A1 WO 2018223005 A1 WO2018223005 A1 WO 2018223005A1 US 2018035617 W US2018035617 W US 2018035617W WO 2018223005 A1 WO2018223005 A1 WO 2018223005A1
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subject
risk profile
risk
vte
value
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French (fr)
Inventor
Eric A. ELSTER
John Oh
Beverly J. GAUCHER
Seth A. SCHOBEL-MCHUGH
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Henry M Jackson Foundation for Advancedment of Military Medicine Inc
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Henry M Jackson Foundation for Advancedment of Military Medicine Inc
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K31/00—Medicinal preparations containing organic active ingredients
    • A61K31/70—Carbohydrates; Sugars; Derivatives thereof
    • A61K31/715—Polysaccharides, i.e. having more than five saccharide radicals attached to each other by glycosidic linkages; Derivatives thereof, e.g. ethers, esters
    • A61K31/726—Glycosaminoglycans, i.e. mucopolysaccharides
    • A61K31/727—Heparin; Heparan
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K31/00—Medicinal preparations containing organic active ingredients
    • A61K31/33—Heterocyclic compounds
    • A61K31/335—Heterocyclic compounds having oxygen as the only ring hetero atom, e.g. fungichromin
    • A61K31/365—Lactones
    • A61K31/366—Lactones having six-membered rings, e.g. delta-lactones
    • A61K31/37—Coumarins, e.g. psoralen
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K31/00—Medicinal preparations containing organic active ingredients
    • A61K31/33—Heterocyclic compounds
    • A61K31/395—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins
    • A61K31/435—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having six-membered rings with one nitrogen as the only ring hetero atom
    • A61K31/4353—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having six-membered rings with one nitrogen as the only ring hetero atom ortho- or peri-condensed with heterocyclic ring systems
    • A61K31/437—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having six-membered rings with one nitrogen as the only ring hetero atom ortho- or peri-condensed with heterocyclic ring systems the heterocyclic ring system containing a five-membered ring having nitrogen as a ring hetero atom, e.g. indolizine, beta-carboline
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K31/00—Medicinal preparations containing organic active ingredients
    • A61K31/33—Heterocyclic compounds
    • A61K31/395—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins
    • A61K31/435—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having six-membered rings with one nitrogen as the only ring hetero atom
    • A61K31/44—Non condensed pyridines; Hydrogenated derivatives thereof
    • A61K31/4427—Non condensed pyridines; Hydrogenated derivatives thereof containing further heterocyclic ring systems
    • A61K31/4439—Non condensed pyridines; Hydrogenated derivatives thereof containing further heterocyclic ring systems containing a five-membered ring with nitrogen as a ring hetero atom, e.g. omeprazole
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K31/00—Medicinal preparations containing organic active ingredients
    • A61K31/33—Heterocyclic compounds
    • A61K31/395—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins
    • A61K31/435—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having six-membered rings with one nitrogen as the only ring hetero atom
    • A61K31/44—Non condensed pyridines; Hydrogenated derivatives thereof
    • A61K31/4427—Non condensed pyridines; Hydrogenated derivatives thereof containing further heterocyclic ring systems
    • A61K31/444—Non condensed pyridines; Hydrogenated derivatives thereof containing further heterocyclic ring systems containing a six-membered ring with nitrogen as a ring heteroatom, e.g. amrinone
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K31/00—Medicinal preparations containing organic active ingredients
    • A61K31/33—Heterocyclic compounds
    • A61K31/395—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins
    • A61K31/535—Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having six-membered rings with at least one nitrogen and one oxygen as the ring hetero atoms, e.g. 1,2-oxazines
    • A61K31/5375—1,4-Oxazines, e.g. morpholine
    • A61K31/5377—1,4-Oxazines, e.g. morpholine not condensed and containing further heterocyclic rings, e.g. timolol
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K38/00—Medicinal preparations containing peptides
    • A61K38/16—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof
    • A61K38/43—Enzymes; Proenzymes; Derivatives thereof
    • A61K38/46—Hydrolases (3)
    • A61K38/48—Hydrolases (3) acting on peptide bonds (3.4)
    • A61K38/482—Serine endopeptidases (3.4.21)
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Y—ENZYMES
    • C12Y304/00—Hydrolases acting on peptide bonds, i.e. peptidases (3.4)
    • C12Y304/21—Serine endopeptidases (3.4.21)
    • C12Y304/21068—Tissue plasminogen activator (3.4.21.68), i.e. tPA
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/475—Assays involving growth factors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/475—Assays involving growth factors
    • G01N2333/50—Fibroblast growth factors [FGF]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/52—Assays involving cytokines
    • G01N2333/521—Chemokines
    • G01N2333/522—Alpha-chemokines, e.g. NAP-2, ENA-78, GRO-alpha/MGSA/NAP-3, GRO-beta/MIP-2alpha, GRO-gamma/MIP-2beta, IP-10, GCP-2, MIG, PBSF, PF-4 or KC
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/52—Assays involving cytokines
    • G01N2333/54—Interleukins [IL]
    • G01N2333/545—IL-1
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/22—Haematology
    • G01N2800/226—Thrombotic disorders, i.e. thrombo-embolism irrespective of location/organ involved, e.g. renal vein thrombosis, venous thrombosis

Definitions

  • the present invention relates to methods of determining if a su bject has an increased risk of developing venous thromboembolism (VTE) prior to the onset of any detectable symptoms thereof.
  • the methods comprise analyzing at least one sample from the subject to determine a value of the subject's risk profile and comparing the value of the subject's risk profile with the value of a normal risk profile. A change in the value of the subject's risk profile, over or under normal values is indicative that the subject has an increased risk of having or developing symptoms associated with VTE prior to the onset of any detectable symptoms thereof.
  • VTE thromboembolism
  • the present invention relates to methods of determining if a su bject has an increased risk of developing venous thromboembolism (VTE) prior to the onset of any detectable symptoms thereof.
  • the methods comprise analyzing at least one sample from the subject to determine a value of the subject's risk profile and comparing the value of the subject's risk profile with the value of a normal risk profile. A change in the value of the subject's risk profile, over or under normal values is indicative that the subject has an increased risk of having or developing symptoms associated with VTE prior to the onset of any detectable symptoms thereof.
  • the methods of the invention include a method of determining the susceptibility of a human subject to venus thromboembolism, the method comprising: (a) obtaining a biological sample from the subject, (b) measuring the levels in the biological sample of one or more polypeptides selected from the group consisting of Basic Fibroblast Growth Factor (FG FBasic), lnterleukin- ⁇ (I L-1B), monokine induced by gamma interferon (M IG), vascular endothelial growth factor (VEGF); and (c) determining a risk profile value from the polypeptide levels, wherein the the risk profile value correlates with the susceptibility of the human subject to venus thromboembolism.
  • FG FBasic Basic Fibroblast Growth Factor
  • I L-1B lnterleukin- ⁇
  • M IG monokine induced by gamma interferon
  • VEGF vascular endothelial growth factor
  • the method further comprises determining at least one clinical value selected from the group consisting of age, injury severity score skin (ISS skin), wound volume, and wound surface area; wherein the risk profile value is additiona lly determined from the at least one clinical value.
  • at least one clinical value selected from the group consisting of age, injury severity score skin (ISS skin), wound volume, and wound surface area; wherein the risk profile value is additiona lly determined from the at least one clinical value.
  • At least two polypeptides are selected. In other embodiments, at least two clinical values are selected.
  • the human subject has a traumatic wound.
  • the biological sample is blood, serum, or plasma.
  • the invention includes a method of inhibiting the development of venus thromboembolism in a human subject comprising: (a) determining the susceptibility of the human subject to venus thromboembolism by the method of claim 1, and (b) administering a treatment to the human subject prior to the onset of any venus thromboembolism symptom.
  • the human subject is treated with an anticoagulant.
  • the anticoagulant is selected from the group consisting of heparin, apixaban, dabigatran, rivaroxaban, edoxaban and warfarin.
  • the human subject is treated with thrombolytic therapy.
  • the thrombolytic therapy comprises administration of tissue plasminogen activator (tPA) to the human subject
  • Figure 1 depicts the variables selected via Bayesian Network analysis that are correlated with the development of VTE.
  • Figure 2 depicts the receiver operator characteristic curve and area under curve analysis for a binary classification model used to predict presence of VTE in patients of this study cohort.
  • Linear Discriminant Analysis produced the best performing model with an AUC of 0.881 (0.822-0.940).
  • Figure 3 depicts the decision curve analysis for the model produced herein. This model shows positive net benefit for use of this model including in comparison to treatment of all patients, treatment of no patients, and treatment of patients using a model that utilizes all available variables without variable selection.
  • the present invention relates to methods of determining if a subject has an increased risk of developing venous thromboembolism (VTE) prior to the onset of any detectable symptoms thereof.
  • the methods comprise analyzing at least one sample from the subject to determine a value of the subject's risk profile and comparing the value of the subject's risk profile with the value of a normal risk profile. A change in the value of the subject's risk profile, over or under normal values is indicative that the subject has an increased risk of having or developing symptoms associated with VTE prior to the onset of any detectable symptoms thereof.
  • the present invention also relates to methods of detecting elevated levels of a specific collection of analytes in one or more samples obtained from a subject.
  • the collection of analytes comprises serum levels of basic fibroblast growth factor (FGF-basic), vascular endothelial growth factor (VEGF), interleukin 1 ⁇ ( ⁇ - ⁇ ) and monokine-induced gamma interferon (MIG).
  • FGF-basic basic fibroblast growth factor
  • VEGF vascular endothelial growth factor
  • ⁇ - ⁇ interleukin 1 ⁇
  • MIG monokine-induced gamma interferon
  • test subject indicates a mammal, in particular a human or non-human primate.
  • the test subject is in need of an assessment of susceptibility of VTE.
  • the test subject may have no symptoms that VTE may occur.
  • VTE venous thromboembolism
  • VTE venous thromboembolism
  • a DVT is a blood clot in a deep vein, usually the leg, but sometimes found in the arm.
  • PE is a blood clot that starts as a DVT and has broken free where it travels to the lungs and can block all or some blood flow.
  • the term means "increased risk” is used to mean that the test subject has an increased chance of developing VTE compared to a normal individual.
  • the increased risk may be relative or absolute and may be expressed qualitatively or quantitatively.
  • an increased risk may be expressed as simply determining the subject's risk profile and placing the patient in an "increased risk” category, based upon previous population studies.
  • a numerical expression of the subject's increased risk may be determined based upon the risk profile.
  • examples of expressions of an increased risk include but are not limited to, odds, probability, odds ratio, p-values, attributable risk, biomarker index score, relative frequency, positive predictive value, negative predictive value, and relative risk.
  • the attributable risk can a lso be used to express an increased risk.
  • the AR describes the proportion of individuals in a population exhibiting VTE to a specific member of the risk profile. AR may also be important in quantifying the role of individual components (specific member) in condition etiology and in terms of the public health impact of the individual risk factor.
  • the public health relevance of the AR measurement lies in estimating the proportion of cases of VTE in a population of wounded subjects that could be prevented if the profile or individual factor were absent.
  • the increased risk of a patient can be determined from p-values that are derived from association studies. Specifically, associations with specific profiles can be performed using regression analysis by regressing the risk profile with the presence or absence of VTE. In addition, the regression may or may not be corrected or adjusted for one or more factors.
  • the factors for which the analyses may be adjusted include, but are not limited to age, sex, weight, ethnicity, type of wound, number of wounds, number of days from injury, number of previous surgical debridements, geographic location, fasting state, state of pregnancy or post-pregnancy, menstrual cycle, general health of the subject, alcohol or drug consumption, caffeine or nicotine intake and circadian rhythms, to name a few.
  • Increased risk can also be determined from p-values that are derived using logistic regression.
  • Binomial (or binary) logistic regression is a form of regression which is used when the dependent is a dichotomy and the independents are of any type.
  • Logistic regression can be used to predict a dependent variable on the basis of continuous or categorical or both (continuous and categorical) independents and to determine the percent of variance in the dependent variable explained by the independents; to rank the relative importance of independents; to assess interaction effects; and to understand the impact of covariate control variables.
  • Logistic regression applies maximum likelihood estimation after transforming the dependent into a "logit" variable (the natural log of the odds of the dependent occurring or not). In this way, logistic regression estimates the probability of a certain event occurring.
  • SAS statistical analysis software
  • a general purpose package (similar to Stata and SPSS) created by Jim Goodnight and N.C. State University colleagues. Ready-to-use procedures handle a wide range of statistical analyses, including but not limited to, analysis of variance, regression, categorical data analysis, multivariate analysis, survival analysis, psychometric analysis, cluster analysis, and nonparametric analysis.
  • R package is free, general purpose package that complies with and runs on a variety of UNIX platforms.
  • select embodiments of the present invention comprise the use of a computer comprising a processor and the computer is configured or programmed to generate one or more risk profiles and/or to determine statistical risk.
  • the methods may also comprise displaying the one or risk profiles on a screen that is communicatively connected to the computer.
  • two different computers can be used: one computer configured or programmed to generate one or more risk profiles and a second computer configured or programmed to determine statistical risk. Each of these separate computers can be communicatively linked to its own display or to the same display.
  • risk profile means the combination of a subject's risk factors analyzed or observed.
  • factor and/or “component” are used to mean the individual constituents that are assessed when generating the profile.
  • the risk profile is a collection of measurements, such as but not limited to a quantity or concentration, for individual factors taken from a test sample of the subject.
  • test samples or sources of components for the risk profile include, but are not limited to, biological fluids, which can be tested by the methods of the present invention described herein, and include but are not limited to whole blood, such as but not limited to peripheral blood, serum, plasma, cerebrospinal fluid, wound effluent, urine, amniotic fluid, lymph fluids, various external secretions of the respiratory, intestinal and genitourinary tracts, tears, saliva, white blood cells, myelomas and the like.
  • the risk profile can include a "biological effector" aspect and/or a non-biological effector aspect.
  • biological effector is used to mean a molecule, such as but not limited to, a protein, peptide, a carbohydrate, a fatty acid, a nucleic acid, a glycoprotein, a proteoglycan, etc. that can be assayed.
  • biological effectors can include, cytokines, growth factors, antibodies, hormones, cell surface receptors, cell surface proteins, carbohydrates, etc.
  • I Ls interleukins
  • I L-1RA I L-1 receptor antagonist
  • I L-2 I L-2 receptor
  • I L-2R I L-3, I L-4, I L-5, I L-6, I L-7, I L-8, I L-10, I L-12, I L-13, I L-15, I L-17
  • growth factors such as tumor necrosis factor alpha (TN Fa), granulocyte colony stimulating factor (G-CSF), granulocyte macrophage colony stimulating factor (GM-CSF), interferon alpha (I N F-a), interferon gamma (I FN-y), epithelial growth factor (EGF), basic endothelial growth factor (bEGF), hepatocyte growth factor (HGF), vascular endothelial growth factor (VEGF), and chemokines such as monocyte chemoattractant protein-1 (CCL2/MCP-1), macrophage inflammatory protein-1 alpha (CCL2/MCP-1), macrophage inflammatory protein-1 alpha (CCL2/MCP-1
  • levels of individual components of the risk profile from test samples are well known to the skilled technician, and the invention is not limited by the means by which the components are assessed.
  • levels of the individual factors in the serum or wound effluent of the risk profile are assessed using mass spectrometry in conjunction with ultra- performance liquid chromatography (U PLC), high-performance liquid chromatography (H PLC), gas chromatography (GC), gas chromatography/mass spectroscopy (GC/MS), and UPLC to name a few.
  • Other methods of assessing levels of some of the individual components include biological methods, such as but not limited to ELISA assays, Western Blot and multiplexed immunoassays etc.
  • Other techniques may include using quantitative arrays, PCR, Northern Blot analysis. To determine levels of components or factors, it is not necessary that an entire component, e.g., a full length protein or an entire RNA transcript, be present or fully sequenced. In other words, determining levels of, for example, a fragment of protein being analyzed may be sufficient to conclude or assess that an individual component of the risk profile being analyzed is increased or decreased. Similarly, if, for example, arrays or blots are used to determine component levels, the presence/absence/strength of a detectable signal may be sufficient to assess levels of components.
  • non-biological effector is a component that is generally considered not to be a specific molecule. Although not a specific molecule, a non-biological effector may nonetheless still be quantifiable, either through routine measurements or through measurements that stratify the data being assessed. For example, number or concentration of red blood cells, white blood cells, platelets, coagulation time, blood oxygen content, etc. would be a non-biological effector component of the risk profile. All of these components are measureable or quantifiable using routine methods and equipment. Other non-biological components include data that may not be readily or routinely quantifiable or that may require a practitioner's judgment or opinion. For example, wound severity may be a component of the risk profile.
  • the quantity or measurement assigned to a non-biological effector could be binary, e.g., "0" if absent or "1" if present.
  • the non-biological effector aspect of the risk profile may involve qualitative components that cannot or should not be quantified.
  • the mechanism of injury is included in the risk profile.
  • the phrase "mechanism of injury” means the manner in which the subject received an injury.
  • the mechanism of injury may be described as a gunshot wound, a vehicle accident, laceration, etc.
  • data regarding injury type is included in the risk profile.
  • data on total wound volume is included in the risk profile.
  • data on surface area of all wounds included in the risk profile is included in the risk profile.
  • ISS score injury severity score
  • ISS score injury severity score
  • data on ISS score is included in the risk profile.
  • CC critical colonization
  • the term critical colonization is a measure of CFU that the subject has in serum and/or tissue for at least one wound when initially examined by the attending physician. For example, if a subject has CFU of lxlO 5 per ml of serum, or if at least one wound has CFU of 1x10 s per mg of tissue, the subject is said to be "positive” for CC. If the total serum CFU or no single would has CFU of at least lxlO 5 the subject is said to be "negative” for CC.
  • rflmpute from the randomForest R package can be used to impute missing data.
  • Up-sampling and predictor rank transformations can be performed on the data set only for variable selection to accommodate class imbalance and non-normality in the data.
  • the constraint-based algorithms /asi./amfa, iamb and gs and the constraint-based local discovery learning algorithms mmpc and si.hiton.pc from the "bnlearn" R package can be used to search the input dataset for nodes of Bayesian networks. The nodes can be chosen as the reduced variable sets.
  • the variables Before running the data through variable selection and binary classification algorithms, the variables may or may not randomly re-ordered.
  • the data can be run through the variable selection and binary classification algorithms more than once, for example, 10, 20, 30, 40, 50 or even more times.
  • each variable set can be pulled from the raw data and run in sundry binary classification algorithms using the train function from the R caret package: linear discriminant analysis ⁇ Ida), classification and regression trees ⁇ cart), k-nearest neighbors ⁇ knn), support vector machine ⁇ svm), logistic regression ⁇ glm), random forest ⁇ rf), generalized linear models ⁇ glmnet) and naive Bayes ⁇ nb).
  • the best variable set and binary classification algorithm combination that first produces the highest kappa and then the highest sensitivity with reasonable specificity can then be chosen.
  • the assessment of the levels of the individual components of the risk profile can be expressed as absolute or relative values and may or may not be expressed in relation to another component, a standard an internal standard or another molecule of compound known to be in the sample. If the levels are assessed as relative to a standard or internal standard, the standard may be added to the test sample prior to, during or after sample processing.
  • a sample may be taken from the subject.
  • the sample may or may not processed prior assaying levels of the components of the risk profile.
  • whole blood may be taken from an individual and the blood sample may be processed, e.g., centrifuged, to isolate plasma or serum from the blood.
  • the sample may or may not be stored, e.g., frozen, prior to processing or analysis.
  • the individua l levels of each of the risk factors are higher or lower than those compared to normal levels.
  • one, two, three, four, five, six, seven or eight of the levels of each of the factor are higher or lower than normal levels.
  • the levels of depletion of the factors or components compared to normal levels can vary.
  • the levels of any one or more of the factors or components is at least 1.05, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 higher than normal levels (where, for sake of clarity, a marker a level of "1" would indicate that the component is at the same level in both the subject and normal samples).
  • the number of "times" the levels of a factor are higher over normal can be a relative or absolute number of times.
  • the levels of the factors or components may be normalized to a standard and these normalized levels can then be compared to one another to determine if a factor or component is lower, higher or about the same.
  • the risk profile comprises at least one, two, three, four, five, six, seven or eight of the factors or components for the prediction of VTE. If one factor or component of the biological effector aspect of the risk profile is used in generating the risk profile for the prediction of VTE, then any one of the listed factors or components can be used to generate the profile. If two factors or components of the biological effector aspect of the risk profile are used in generating the risk profile for the prediction of VTE, any combination of the two listed above can be used. If three factors or components of the biological effector aspect of the risk profile are used in generating the risk profile for the prediction of VTE, any combination of three of the factors or components listed above can be used.
  • any combination of four of the factors or components listed above can be used. If four factors or components of the biological effector aspect of the risk profile are used in generating the risk profile for the prediction of VTE, any combination of four of the factors or components listed above can be used. If five factors or components of the biological effector aspect of the risk profile are used in generating the risk profile for the prediction of VTE, any combination of five of the factors or components listed above can be used. If six factors or components of the biological effector aspect of the risk profile are used in generating the risk profile for the prediction of VTE, any combination of six of the factors or components listed above can be used. If seven factors or components of the biological effector aspect of the risk profile are used in generating the risk profile for the prediction of VTE, any combination of seven of the factors or components listed above can be used. Of course all members or components of the biological effector aspect of the risk profile can be used in generating the risk profile for the prediction of VTE.
  • the subject's risk profile is compared to the profile that is deemed to be a normal risk profile.
  • an individual or group of individuals may be first assessed to ensure they have no signs, symptoms or diagnostic indicators of VTE.
  • the risk profile of the individual or group of individuals can then be determined to establish a "normal risk profile.”
  • a normal risk profile can be ascertained from the same subject when the subject is deemed as healthy with no signs, symptoms or diagnostic indicators of VTE.
  • a risk profile from a "normal subject,” e.g., a "normal risk profile” is a subject with a wound but did not exhibit or display VTE.
  • the normal subject has a chest wound, head wound or as extremity (arm, hand, finger(s), leg, foot, toe(s)) wound but did not exhibit VTE.
  • a "normal" risk profile is assessed in the same subject from whom the sample is taken prior to the onset of any signs, symptoms or diagnostic indicators that they may exhibit VTE. That is, the term "normal” with respect to a risk profile can be used to mean the subject's baseline risk profile prior to the onset of any wounds, signs, symptoms or diagnostic indicators of potential VTE. The risk profile can then be reassessed periodically and compared to the subject's baseline risk profile.
  • the present invention also includes methods of monitoring the progression of VTE in a subject, with the methods comprising determining the subject's risk profile at more than one time point.
  • some embodiments of the methods of the present invention will comprise determining the subject's risk profile at two, three, four, five, six, seven, eight, nine, 10 or even more time points over a period of time, such as a week or more, two weeks or more, three weeks or more, four weeks or more, a month or more, two months or more, three months or more, four months or more, five months or more, six months or more, seven months or more, eight months or more, nine months or more, ten months or more, 11 months or more, a year or more or even two years.
  • the methods of monitoring a subject's risk of developing VTE would also include embodiments in which the subject's risk profile is assessed before and/or during and/or after treatment of VTE.
  • the present invention also includes methods of monitoring the efficacy of treatment of VTE by assessing the subject's risk profile over the course of the treatment and after the treatment.
  • the methods of monitoring the efficacy of treatment of VTE comprise determining the subject's risk profile at at least one, two, three, four, five, six, seven, eight, nine or 10 or more different time points prior to the receipt of treatment for VTE and subsequently determining the subject's risk profile at at least one, two, three, four, five, six, seven, eight, nine or 10 or more different time points after beginning of treatment for VTE, and determining the changes, if any, in the risk profile of the subject.
  • the treatment may be any treatment designed to cure, remove or diminish the likelihood of developing VTE.
  • a normal risk profile is assessed in a sample from a different subject or patient (from the subject being analyzed) and this different subject does not have or is not suspected of developing VTE.
  • the normal risk profile is assessed in a population of healthy individuals, the constituents of which display no signs, symptoms or diagnostic indicators that they may have or will develop VTE.
  • the subject's risk profile can be compared to a normal risk profile generated from a single normal sample or a risk profile generated from more than one normal sample.
  • measurements of the individual components e.g., concentration, ratio, log ratios etc.
  • concentration, ratio, log ratios etc. of the normal risk profile can fall within a range of values, and values that do not fall within this "normal range” are said to be outside the normal range.
  • These measurements may or may not be converted to a value, number, factor or score as compared to measurements in the "normal range.” For example, a measurement for a specific factor or component that is below the normal range, may be assigned a value or -1, -2, -3, etc., depending on the scoring system devised.
  • the measurements of the individual components themselves are used in the risk profile, and these levels can be used to provide a "binary" value to each component, e.g., “elevated” or “not elevated.”
  • Each of the binary values can be converted to a number, e.g., "1" or "0,” respectively.
  • the "risk profile value" can be a single value, number, factor or score given as an overall collective value to the individual components of the profile. For example, if each component is assigned a value, such as above, the component value may simply be the overall score of each individual or categorical value.
  • the risk profile value could be a useful single number or score, the actual value or magnitude of which could be an indication of the actual risk of developing VTE, e.g., the "more positive" the value, the greater the risk of developing VTE.
  • the "risk profile value” can be a series of values, numbers, factors or scores given to the individual components of the overall profile.
  • the "risk profile value” may be a combination of values, numbers, factors or scores given to individual components of the profile as well as values, numbers, factors or scores collectively given to a group of components, such as a biological effector portion.
  • the risk profile value may comprise or consist of individual values, number or scores for specific component as well as values, numbers or scores for a group of components.
  • individual values from the risk profile and/or the mechanism of injury can be used to develop a single score, such as a "combined risk index,” which may utilize weighted scores from the individual component values reduced to a diagnostic number value.
  • the combined risk index may also be generated using non-weighted scores from the individual component values.
  • the threshold value would be or could be set by the combined risk index from one or more normal subjects.
  • the value of the risk profile can be the collection of data from the individual measurements and need not be converted to a scoring system, such that the "risk profile value" is a collection of the individual measurements of the individual components of the profile.
  • a subject is diagnosed of having an increased risk of suffering from VTE if the subject's eight, seven, six, five, four, three, two or even one of the components or factors herein are at abnormal levels.
  • the attending health care provider may subsequently prescribe or institute a treatment program.
  • the present invention also provides for methods of treating individuals for VTE.
  • the attending healthcare worker may begin treatment, based on the subject's risk profile, before there are perceivable, noticeable or measurable signs of VTE in the individual.
  • the invention provides methods of treating VTE in a subject in need thereof.
  • the treatment methods include obtaining a subject's risk profile as defined herein and prescribing a treatment regimen to the subject if the risk profile indicates that the subject is at risk of developing VTE.
  • the methods of treatment also include methods of monitoring the effectiveness of a treatment for VTE.
  • a treatment regimen has been established, with or without the use of the methods of the present invention to assist in a diagnosis of a risk of developing VTE
  • the methods of monitoring a subject's risk profile over time can be used to assess the effectiveness of treatments for VTE.
  • the subject's risk profile can be assessed over time, including before, during and after treatments for VTE.
  • the risk profile can be monitored, with, for example, the normalization or decline in the values of the profile over time being indicative that the treatment may be showing efficacy of treatment.
  • kits that can be used in the methods of the present invention.
  • the present invention provides kits for assessing the increased risk of developing VTE, with the kits comprising one or more sets of antibodies that are immobilized onto a solid substrate and specifically bind to at least one of the factors or components listed herein.
  • the kits comprise at least two, three, four, five, six or seven sets of antibodies immobilized onto a solid substrate, with each set corresponding to a factor.
  • the antibodies that are immobilized onto the substrate may or may not be labeled.
  • the antibodies may be labeled, e.g., bound to a labeled protein, in such a manner that binding of the specific protein may displace the label and the presence of the marker in the sample is marked by the absence of a signal.
  • the antibodies that are immobilized onto the substrate may be directly or indirectly immobilized onto the surface.
  • Methods for immobilizing proteins, including antibodies are well-known in the art, and such methods may be used to immobilize a target protein, e.g., I L- ⁇ , or another antibody onto the surface of the substrate to which the antibody directed to the specific factor can then be specifically bound. In this manner, the antibody directed to the specific biomarker is immobilized onto the surface of the substrate for the purposes of the present invention.
  • kits of the present invention may or may not include containers for collecting samples from the subject and one or more reagents, e.g., purified target biomarker for preparing a calibration curve.
  • the kits may or may not include additional reagents such as wash buffers, labeling reagents and reagents that are used to detect the presence (or absence) of the label.
  • Clinical and biomarker data was collected from 73 injured soldiers with severe combat extremity wounds. Clinical data included detailed information on wound burden as well as the occurrence of VTE. Thirty-two inflammatory biomarkers were assayed using the Luminex platform. The data were processed to address class imbalance and non-normal distributions. A Bayesian network analysis was then utilized to identify the variables associated with the outcome of VTE. The variable sets were run in binary classification algorithms. The best variable set and binary classification algorithm combination that first produced the highest Kappa and then the highest sensitivity and reasonable specificity was chosen. The resultant models were examined using accuracy, no information rate, positive predictive value and negative predictive value. Model performance was further assessed using receiver operator characteristic curves (ROC), area under curve (AUC), and decision curve analysis (DCA).
  • ROC receiver operator characteristic curves
  • AUC area under curve
  • DCA decision curve analysis
  • VTE The overall incidence of VTE was 12% (9/73).
  • Variables selected into this model were the serum cytokines and chemokines FGF-basic, VEGF, ⁇ ⁇ , and MIG as well as the clinical variables of injury severity score skin, total wound volume, age, and surface area of all wounds.
  • the LDA algorithm run with the selected variables produced a Kappa of 0.52, an Accuracy of 0.9, a sensitivity of 0.6, a specificity of 0.94, a positive predictive value of 0.57, a negative predictive value of 0.94, and an AUC of 0.88 with AUC 95% confidence interval of 0.82-0.94.
  • the decision curve of the reduced model outperformed the decision curve of the full model.

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Abstract

The present invention relates to methods of determining if a subject has an increased risk of developing venous thromboembolism (VTE) prior to the onset of any detectable symptoms thereof. The methods comprise analyzing at least one sample from the subject to determine a value of the subject's risk profile and comparing the value of the subject's risk profile with the value of a normal risk profile. A change in the value of the subject's risk profile, over or under normal values is indicative that the subject has an increased risk of having or developing symptoms associated with VTE prior to the onset of any detectable symptoms thereof.

Description

PREDICTIVE FACTORS FOR VENOUS THROMBOEMBOLISM
Statement Regarding Federally Sponsored Research or Development
[0001] This invention was made with government support under HT9404-13-1 and H U0001-15-2- 0001 awarded by The Department of Defense. The government has certain rights in the invention.
Background of the Invention
Field of the Invention
[0002] The present invention relates to methods of determining if a su bject has an increased risk of developing venous thromboembolism (VTE) prior to the onset of any detectable symptoms thereof. The methods comprise analyzing at least one sample from the subject to determine a value of the subject's risk profile and comparing the value of the subject's risk profile with the value of a normal risk profile. A change in the value of the subject's risk profile, over or under normal values is indicative that the subject has an increased risk of having or developing symptoms associated with VTE prior to the onset of any detectable symptoms thereof.
Background of the Invention
[0003] Inflammatory cytokines have been implicated in the pathogenesis of venous
thromboembolism (VTE) in non-trauma patients through various mechanisms. Previous literature demonstrated univariate associations with inflammatory cytokines and VTE.
Summary of the Invention
[0004] The present invention relates to methods of determining if a su bject has an increased risk of developing venous thromboembolism (VTE) prior to the onset of any detectable symptoms thereof. The methods comprise analyzing at least one sample from the subject to determine a value of the subject's risk profile and comparing the value of the subject's risk profile with the value of a normal risk profile. A change in the value of the subject's risk profile, over or under normal values is indicative that the subject has an increased risk of having or developing symptoms associated with VTE prior to the onset of any detectable symptoms thereof.
[0005] The methods of the invention include a method of determining the susceptibility of a human subject to venus thromboembolism, the method comprising: (a) obtaining a biological sample from the subject, (b) measuring the levels in the biological sample of one or more polypeptides selected from the group consisting of Basic Fibroblast Growth Factor (FG FBasic), lnterleukin-ΐβ (I L-1B), monokine induced by gamma interferon (M IG), vascular endothelial growth factor (VEGF); and (c) determining a risk profile value from the polypeptide levels, wherein the the risk profile value correlates with the susceptibility of the human subject to venus thromboembolism.
[0006] In some embodiments, the method further comprises determining at least one clinical value selected from the group consisting of age, injury severity score skin (ISS skin), wound volume, and wound surface area; wherein the risk profile value is additiona lly determined from the at least one clinical value.
[0007] In some embodiments at least two polypeptides are selected. In other embodiments, at least two clinical values are selected. In some embodiments, the human subject has a traumatic wound. In some embodiments, the biological sample is blood, serum, or plasma.
[0008] The invention includes a method of inhibiting the development of venus thromboembolism in a human subject comprising: (a) determining the susceptibility of the human subject to venus thromboembolism by the method of claim 1, and (b) administering a treatment to the human subject prior to the onset of any venus thromboembolism symptom.
[0009] In some embodiments, the human subject is treated with an anticoagulant. In some embodiments, the anticoagulant is selected from the group consisting of heparin, apixaban, dabigatran, rivaroxaban, edoxaban and warfarin. In some embodiments, the human subject is treated with thrombolytic therapy. In some embodiments, the thrombolytic therapy comprises administration of tissue plasminogen activator (tPA) to the human subject
Brief Description of the Drawings
[0010] Figure 1 depicts the variables selected via Bayesian Network analysis that are correlated with the development of VTE.
[0011] Figure 2 depicts the receiver operator characteristic curve and area under curve analysis for a binary classification model used to predict presence of VTE in patients of this study cohort. Linear Discriminant Analysis produced the best performing model with an AUC of 0.881 (0.822-0.940).
[0012] Figure 3 depicts the decision curve analysis for the model produced herein. This model shows positive net benefit for use of this model including in comparison to treatment of all patients, treatment of no patients, and treatment of patients using a model that utilizes all available variables without variable selection. Detailed Description of the Invention
[0013] The present invention relates to methods of determining if a subject has an increased risk of developing venous thromboembolism (VTE) prior to the onset of any detectable symptoms thereof. The methods comprise analyzing at least one sample from the subject to determine a value of the subject's risk profile and comparing the value of the subject's risk profile with the value of a normal risk profile. A change in the value of the subject's risk profile, over or under normal values is indicative that the subject has an increased risk of having or developing symptoms associated with VTE prior to the onset of any detectable symptoms thereof.
[0014] The present invention also relates to methods of detecting elevated levels of a specific collection of analytes in one or more samples obtained from a subject. In one embodiment, the collection of analytes comprises serum levels of basic fibroblast growth factor (FGF-basic), vascular endothelial growth factor (VEGF), interleukin 1β (Ι- β) and monokine-induced gamma interferon (MIG).
[0015] As used herein, the term "subject" or "test subject" indicates a mammal, in particular a human or non-human primate. The test subject is in need of an assessment of susceptibility of VTE. For example, the test subject may have no symptoms that VTE may occur.
[0016] The term venous thromboembolism, or VTE, is used herein to mean a blood clot that begins in a vein. This clot can be found in two forms a deep vein thrombosis (DVT), or a pulmonary embolism (PE). A DVT is a blood clot in a deep vein, usually the leg, but sometimes found in the arm. A PE is a blood clot that starts as a DVT and has broken free where it travels to the lungs and can block all or some blood flow.
[0017] As used herein, the term means "increased risk" is used to mean that the test subject has an increased chance of developing VTE compared to a normal individual. The increased risk may be relative or absolute and may be expressed qualitatively or quantitatively. For example, an increased risk may be expressed as simply determining the subject's risk profile and placing the patient in an "increased risk" category, based upon previous population studies. Alternatively, a numerical expression of the subject's increased risk may be determined based upon the risk profile. As used herein, examples of expressions of an increased risk include but are not limited to, odds, probability, odds ratio, p-values, attributable risk, biomarker index score, relative frequency, positive predictive value, negative predictive value, and relative risk. [0018] For example, the correlation between a subject's risk profile and the likelihood of developing VTE may be measured by an odds ratio (OR) and by the relative risk (RR). If P(R+) is the probability of developing VTE for individuals with the risk profile (R) and P( R") is the probability of developing VTE for individuals without the risk profile, then the relative risk is the ratio of the two probabilities: RR=P(R+)/P(R-).
[0019] In case-control studies, however, direct measures of the relative risk often cannot be obtained because of sampling design. The odds ratio allows for an approximation of the relative risk for low-incidence diseases and can be calculated: OR=(F+/( l-F+))/(F"/(l-F")), where F+ is the frequency of a risk profile in cases studies and F" is the frequency of risk profile in controls. F+ and F" can be calculated using the risk profile frequencies of the study.
[0020] The attributable risk (AR) can a lso be used to express an increased risk. The AR describes the proportion of individuals in a population exhibiting VTE to a specific member of the risk profile. AR may also be important in quantifying the role of individual components (specific member) in condition etiology and in terms of the public health impact of the individual risk factor. The public health relevance of the AR measurement lies in estimating the proportion of cases of VTE in a population of wounded subjects that could be prevented if the profile or individual factor were absent. AR may be determined as follows: AR=PE(RR-1)/(PE(RR-1)+1), where AR is the risk attributable to a profile or individual factor of the profile, and PE is the frequency of exposure to a profile or individual component of the profile within the population at large. RR is the relative risk, which can be approximated with the odds ratio when the profile or individual factor of the profile under study has a relatively low incidence in the general population.
[0021] In one embodiment, the increased risk of a patient can be determined from p-values that are derived from association studies. Specifically, associations with specific profiles can be performed using regression analysis by regressing the risk profile with the presence or absence of VTE. In addition, the regression may or may not be corrected or adjusted for one or more factors. The factors for which the analyses may be adjusted include, but are not limited to age, sex, weight, ethnicity, type of wound, number of wounds, number of days from injury, number of previous surgical debridements, geographic location, fasting state, state of pregnancy or post-pregnancy, menstrual cycle, general health of the subject, alcohol or drug consumption, caffeine or nicotine intake and circadian rhythms, to name a few.
[0022] Increased risk can also be determined from p-values that are derived using logistic regression. Binomial (or binary) logistic regression is a form of regression which is used when the dependent is a dichotomy and the independents are of any type. Logistic regression can be used to predict a dependent variable on the basis of continuous or categorical or both (continuous and categorical) independents and to determine the percent of variance in the dependent variable explained by the independents; to rank the relative importance of independents; to assess interaction effects; and to understand the impact of covariate control variables. Logistic regression applies maximum likelihood estimation after transforming the dependent into a "logit" variable (the natural log of the odds of the dependent occurring or not). In this way, logistic regression estimates the probability of a certain event occurring. These analyses may be conducted with virtually any statistics program, such as not limited to SAS, R package available through CRAN repository.
[0023] SAS ("statistical analysis software") is a general purpose package (similar to Stata and SPSS) created by Jim Goodnight and N.C. State University colleagues. Ready-to-use procedures handle a wide range of statistical analyses, including but not limited to, analysis of variance, regression, categorical data analysis, multivariate analysis, survival analysis, psychometric analysis, cluster analysis, and nonparametric analysis. R package is free, general purpose package that complies with and runs on a variety of UNIX platforms.
[0024] Accordingly, select embodiments of the present invention comprise the use of a computer comprising a processor and the computer is configured or programmed to generate one or more risk profiles and/or to determine statistical risk. The methods may also comprise displaying the one or risk profiles on a screen that is communicatively connected to the computer. In another embodiment, two different computers can be used: one computer configured or programmed to generate one or more risk profiles and a second computer configured or programmed to determine statistical risk. Each of these separate computers can be communicatively linked to its own display or to the same display.
[0025] As used herein, the phrase "risk profile" means the combination of a subject's risk factors analyzed or observed. The terms "factor" and/or "component" are used to mean the individual constituents that are assessed when generating the profile. The risk profile is a collection of measurements, such as but not limited to a quantity or concentration, for individual factors taken from a test sample of the subject. Examples of test samples or sources of components for the risk profile include, but are not limited to, biological fluids, which can be tested by the methods of the present invention described herein, and include but are not limited to whole blood, such as but not limited to peripheral blood, serum, plasma, cerebrospinal fluid, wound effluent, urine, amniotic fluid, lymph fluids, various external secretions of the respiratory, intestinal and genitourinary tracts, tears, saliva, white blood cells, myelomas and the like. [0026] The risk profile can include a "biological effector" aspect and/or a non-biological effector aspect. As used herein, the term "biological effector" is used to mean a molecule, such as but not limited to, a protein, peptide, a carbohydrate, a fatty acid, a nucleic acid, a glycoprotein, a proteoglycan, etc. that can be assayed. Specific examples of biological effectors can include, cytokines, growth factors, antibodies, hormones, cell surface receptors, cell surface proteins, carbohydrates, etc. More specific examples of biological effectors include interleukins (I Ls) such as I L-la, IL-Ιβ, I L-1 receptor antagonist (I L-1RA), I L-2, I L-2 receptor (I L-2R), I L-3, I L-4, I L-5, I L-6, I L-7, I L-8, I L-10, I L-12, I L-13, I L-15, I L-17, as well as growth factors such as tumor necrosis factor alpha (TN Fa), granulocyte colony stimulating factor (G-CSF), granulocyte macrophage colony stimulating factor (GM-CSF), interferon alpha (I N F-a), interferon gamma (I FN-y), epithelial growth factor (EGF), basic endothelial growth factor (bEGF), hepatocyte growth factor (HGF), vascular endothelial growth factor (VEGF), and chemokines such as monocyte chemoattractant protein-1 (CCL2/MCP-1), macrophage inflammatory protein-1 alpha (CCL3/M I P-la), macrophage inflammatory protein-1 beta (Ο- /Μ Ι Ρ-Ιβ), CCL5/RANTES, CCLll/eotaxin, monokine induced by gamma interferon (CXCL9/MIG) and interferon gamma-induced protein-10 (CXCL10/I P10).
[0027] Techniques to assay levels of individual components of the risk profile from test samples are well known to the skilled technician, and the invention is not limited by the means by which the components are assessed. In one embodiment, levels of the individual factors in the serum or wound effluent of the risk profile are assessed using mass spectrometry in conjunction with ultra- performance liquid chromatography (U PLC), high-performance liquid chromatography (H PLC), gas chromatography (GC), gas chromatography/mass spectroscopy (GC/MS), and UPLC to name a few. Other methods of assessing levels of some of the individual components include biological methods, such as but not limited to ELISA assays, Western Blot and multiplexed immunoassays etc. Other techniques may include using quantitative arrays, PCR, Northern Blot analysis. To determine levels of components or factors, it is not necessary that an entire component, e.g., a full length protein or an entire RNA transcript, be present or fully sequenced. In other words, determining levels of, for example, a fragment of protein being analyzed may be sufficient to conclude or assess that an individual component of the risk profile being analyzed is increased or decreased. Similarly, if, for example, arrays or blots are used to determine component levels, the presence/absence/strength of a detectable signal may be sufficient to assess levels of components.
[0028] As used herein, the term non-biological effector is a component that is generally considered not to be a specific molecule. Although not a specific molecule, a non-biological effector may nonetheless still be quantifiable, either through routine measurements or through measurements that stratify the data being assessed. For example, number or concentration of red blood cells, white blood cells, platelets, coagulation time, blood oxygen content, etc. would be a non-biological effector component of the risk profile. All of these components are measureable or quantifiable using routine methods and equipment. Other non-biological components include data that may not be readily or routinely quantifiable or that may require a practitioner's judgment or opinion. For example, wound severity may be a component of the risk profile. While there may be published guidance on classifying wound severity, stratifying wound severity and, for example, assigning a numerical value to the severity, still involves observation and, to a certain extent, judgment or opinion. In some instances the quantity or measurement assigned to a non-biological effector could be binary, e.g., "0" if absent or "1" if present. In other instances, the non-biological effector aspect of the risk profile may involve qualitative components that cannot or should not be quantified.
[0029] In one embodiment, the mechanism of injury is included in the risk profile. As used herein, the phrase "mechanism of injury" means the manner in which the subject received an injury. For example, the mechanism of injury may be described as a gunshot wound, a vehicle accident, laceration, etc. In another embodiment, data regarding injury type is included in the risk profile. In another embodiment, data on total wound volume is included in the risk profile. In another embodiment, data on surface area of all wounds included in the risk profile.
[0030] Other examples of individual components of the risk profile include but are not limited to ISS score of head, ISS score of chest (thorax) and critical colonization. "ISS score" (injury severity score) is well-known in the art and is used routinely in clinics to assess severity of wounds or injuries. In another embodiment, data on ISS score is included in the risk profile.
[0031] As used herein, the term critical colonization (or "CC") is a measure of CFU that the subject has in serum and/or tissue for at least one wound when initially examined by the attending physician. For example, if a subject has CFU of lxlO5 per ml of serum, or if at least one wound has CFU of 1x10s per mg of tissue, the subject is said to be "positive" for CC. If the total serum CFU or no single would has CFU of at least lxlO5 the subject is said to be "negative" for CC.
[0032] To determine which of the biological effector or non-biological effector components may be critical in the subjects' risk profiles, routine statistical methods can be employed. For example, rflmpute from the randomForest R package can be used to impute missing data. Up-sampling and predictor rank transformations can be performed on the data set only for variable selection to accommodate class imbalance and non-normality in the data. [0033] For variable selection, the constraint-based algorithms /asi./amfa, iamb and gs and the constraint-based local discovery learning algorithms mmpc and si.hiton.pc from the "bnlearn" R package can be used to search the input dataset for nodes of Bayesian networks. The nodes can be chosen as the reduced variable sets. Before running the data through variable selection and binary classification algorithms, the variables may or may not randomly re-ordered. The data can be run through the variable selection and binary classification algorithms more than once, for example, 10, 20, 30, 40, 50 or even more times.
[0034] For binary classification and model selection, each variable set can be pulled from the raw data and run in sundry binary classification algorithms using the train function from the R caret package: linear discriminant analysis {Ida), classification and regression trees {cart), k-nearest neighbors {knn), support vector machine {svm), logistic regression {glm), random forest {rf), generalized linear models {glmnet) and naive Bayes {nb). The best variable set and binary classification algorithm combination that first produces the highest kappa and then the highest sensitivity with reasonable specificity can then be chosen.
[0035] The resultant models are then examined using accuracy, no information rate, positive predictive value and negative predictive value. Model performance can be further assessed using the plot. roc command to compute the Receiver Operator Characteristic Curves (ROC) and area under curve (AUC). The dca R command from the Memorial Sloan Kettering Cancer Center website, www.mskcc.org, can be used to compute the Decision Curve Analysis (DCA).
[0036] Finally, for univariate analysis, a Wilcoxon rank-sum test can be used used to identify which biomarkers from specific patient groups are were associated with a specific indication.
[0037] The assessment of the levels of the individual components of the risk profile can be expressed as absolute or relative values and may or may not be expressed in relation to another component, a standard an internal standard or another molecule of compound known to be in the sample. If the levels are assessed as relative to a standard or internal standard, the standard may be added to the test sample prior to, during or after sample processing.
[0038] To assess levels of the individual components of the risk profile, a sample may be taken from the subject. The sample may or may not processed prior assaying levels of the components of the risk profile. For example, whole blood may be taken from an individual and the blood sample may be processed, e.g., centrifuged, to isolate plasma or serum from the blood. The sample may or may not be stored, e.g., frozen, prior to processing or analysis. [0039] In one embodiment, the individua l levels of each of the risk factors are higher or lower than those compared to normal levels. In another embodiment, one, two, three, four, five, six, seven or eight of the levels of each of the factor are higher or lower than normal levels.
[0040] The levels of depletion of the factors or components compared to normal levels can vary. In one embodiment, the levels of any one or more of the factors or components is at least 1.05, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 higher than normal levels (where, for sake of clarity, a marker a level of "1" would indicate that the component is at the same level in both the subject and normal samples). For the purposes of the present invention, the number of "times" the levels of a factor are higher over normal can be a relative or absolute number of times. In the alternative, the levels of the factors or components may be normalized to a standard and these normalized levels can then be compared to one another to determine if a factor or component is lower, higher or about the same.
[0041] For the purposes of the present invention the risk profile comprises at least one, two, three, four, five, six, seven or eight of the factors or components for the prediction of VTE. If one factor or component of the biological effector aspect of the risk profile is used in generating the risk profile for the prediction of VTE, then any one of the listed factors or components can be used to generate the profile. If two factors or components of the biological effector aspect of the risk profile are used in generating the risk profile for the prediction of VTE, any combination of the two listed above can be used. If three factors or components of the biological effector aspect of the risk profile are used in generating the risk profile for the prediction of VTE, any combination of three of the factors or components listed above can be used. If four factors or components of the biological effector aspect of the risk profile are used in generating the risk profile for the prediction of VTE, any combination of four of the factors or components listed above can be used. If five factors or components of the biological effector aspect of the risk profile are used in generating the risk profile for the prediction of VTE, any combination of five of the factors or components listed above can be used. If six factors or components of the biological effector aspect of the risk profile are used in generating the risk profile for the prediction of VTE, any combination of six of the factors or components listed above can be used. If seven factors or components of the biological effector aspect of the risk profile are used in generating the risk profile for the prediction of VTE, any combination of seven of the factors or components listed above can be used. Of course all members or components of the biological effector aspect of the risk profile can be used in generating the risk profile for the prediction of VTE.
[0042] The subject's risk profile is compared to the profile that is deemed to be a normal risk profile. To establish the risk profile of a normal individual, an individual or group of individuals may be first assessed to ensure they have no signs, symptoms or diagnostic indicators of VTE. Once established, the risk profile of the individual or group of individuals can then be determined to establish a "normal risk profile." In one embodiment, a normal risk profile can be ascertained from the same subject when the subject is deemed as healthy with no signs, symptoms or diagnostic indicators of VTE. In one embodiment, a risk profile from a "normal subject," e.g., a "normal risk profile," is a subject with a wound but did not exhibit or display VTE. In one specific embodiment, the normal subject has a chest wound, head wound or as extremity (arm, hand, finger(s), leg, foot, toe(s)) wound but did not exhibit VTE.
[0043] In one embodiment, a "normal" risk profile is assessed in the same subject from whom the sample is taken prior to the onset of any signs, symptoms or diagnostic indicators that they may exhibit VTE. That is, the term "normal" with respect to a risk profile can be used to mean the subject's baseline risk profile prior to the onset of any wounds, signs, symptoms or diagnostic indicators of potential VTE. The risk profile can then be reassessed periodically and compared to the subject's baseline risk profile. Thus, the present invention also includes methods of monitoring the progression of VTE in a subject, with the methods comprising determining the subject's risk profile at more than one time point. For example, some embodiments of the methods of the present invention will comprise determining the subject's risk profile at two, three, four, five, six, seven, eight, nine, 10 or even more time points over a period of time, such as a week or more, two weeks or more, three weeks or more, four weeks or more, a month or more, two months or more, three months or more, four months or more, five months or more, six months or more, seven months or more, eight months or more, nine months or more, ten months or more, 11 months or more, a year or more or even two years. The methods of monitoring a subject's risk of developing VTE would also include embodiments in which the subject's risk profile is assessed before and/or during and/or after treatment of VTE. In other words, the present invention also includes methods of monitoring the efficacy of treatment of VTE by assessing the subject's risk profile over the course of the treatment and after the treatment. In specific embodiments, the methods of monitoring the efficacy of treatment of VTE comprise determining the subject's risk profile at at least one, two, three, four, five, six, seven, eight, nine or 10 or more different time points prior to the receipt of treatment for VTE and subsequently determining the subject's risk profile at at least one, two, three, four, five, six, seven, eight, nine or 10 or more different time points after beginning of treatment for VTE, and determining the changes, if any, in the risk profile of the subject. The treatment may be any treatment designed to cure, remove or diminish the likelihood of developing VTE. [0044] In another embodiment, a normal risk profile is assessed in a sample from a different subject or patient (from the subject being analyzed) and this different subject does not have or is not suspected of developing VTE. In still another embodiment, the normal risk profile is assessed in a population of healthy individuals, the constituents of which display no signs, symptoms or diagnostic indicators that they may have or will develop VTE. Thus, the subject's risk profile can be compared to a normal risk profile generated from a single normal sample or a risk profile generated from more than one normal sample.
[0045] Of course, measurements of the individual components, e.g., concentration, ratio, log ratios etc., of the normal risk profile can fall within a range of values, and values that do not fall within this "normal range" are said to be outside the normal range. These measurements may or may not be converted to a value, number, factor or score as compared to measurements in the "normal range." For example, a measurement for a specific factor or component that is below the normal range, may be assigned a value or -1, -2, -3, etc., depending on the scoring system devised.
[0046] In another embodiment, the measurements of the individual components themselves are used in the risk profile, and these levels can be used to provide a "binary" value to each component, e.g., "elevated" or "not elevated." Each of the binary values can be converted to a number, e.g., "1" or "0," respectively.
[0047] In one embodiment, the "risk profile value" can be a single value, number, factor or score given as an overall collective value to the individual components of the profile. For example, if each component is assigned a value, such as above, the component value may simply be the overall score of each individual or categorical value. For example, if five components of the risk profile for predicting VTE are used and three of those components are assigned values of "+2" and two are assigned values of the risk profile in this example would be +8, with a normal value being, for example, "0." In this manner, the risk profile value could be a useful single number or score, the actual value or magnitude of which could be an indication of the actual risk of developing VTE, e.g., the "more positive" the value, the greater the risk of developing VTE.
[0048] In another embodiment the "risk profile value" can be a series of values, numbers, factors or scores given to the individual components of the overall profile. In another embodiment, the "risk profile value" may be a combination of values, numbers, factors or scores given to individual components of the profile as well as values, numbers, factors or scores collectively given to a group of components, such as a biological effector portion. In another example, the risk profile value may comprise or consist of individual values, number or scores for specific component as well as values, numbers or scores for a group of components.
[0049] In another embodiment individual values from the risk profile and/or the mechanism of injury can be used to develop a single score, such as a "combined risk index," which may utilize weighted scores from the individual component values reduced to a diagnostic number value. The combined risk index may also be generated using non-weighted scores from the individual component values. When the "combined risk index" exceeds a specific threshold level, determined by a range of values developed similarly from control (normal) subjects, the individual has a high risk, or higher than normal risk, of developing VTE, whereas maintaining a normal range value of the "combined risk index" would indicate a low or minimal risk of developing VTE. In this embodiment, the threshold value would be or could be set by the combined risk index from one or more normal subjects.
[0050] In another embodiment, the value of the risk profile can be the collection of data from the individual measurements and need not be converted to a scoring system, such that the "risk profile value" is a collection of the individual measurements of the individual components of the profile.
[0051] In specific embodiments, a subject is diagnosed of having an increased risk of suffering from VTE if the subject's eight, seven, six, five, four, three, two or even one of the components or factors herein are at abnormal levels.
[0052] If it is determined that a subject has an increased risk of developing VTE, the attending health care provider may subsequently prescribe or institute a treatment program. In this manner, the present invention also provides for methods of treating individuals for VTE. The attending healthcare worker may begin treatment, based on the subject's risk profile, before there are perceivable, noticeable or measurable signs of VTE in the individual.
[0053] Accordingly, the invention provides methods of treating VTE in a subject in need thereof. The treatment methods include obtaining a subject's risk profile as defined herein and prescribing a treatment regimen to the subject if the risk profile indicates that the subject is at risk of developing VTE.
[0054] The methods of treatment also include methods of monitoring the effectiveness of a treatment for VTE. Once a treatment regimen has been established, with or without the use of the methods of the present invention to assist in a diagnosis of a risk of developing VTE, the methods of monitoring a subject's risk profile over time can be used to assess the effectiveness of treatments for VTE. Specifically, the subject's risk profile can be assessed over time, including before, during and after treatments for VTE. The risk profile can be monitored, with, for example, the normalization or decline in the values of the profile over time being indicative that the treatment may be showing efficacy of treatment.
[0055] The present invention also provides kits that can be used in the methods of the present invention. Specifically, the present invention provides kits for assessing the increased risk of developing VTE, with the kits comprising one or more sets of antibodies that are immobilized onto a solid substrate and specifically bind to at least one of the factors or components listed herein. In specific embodiments, the kits comprise at least two, three, four, five, six or seven sets of antibodies immobilized onto a solid substrate, with each set corresponding to a factor.
[0056] The antibodies that are immobilized onto the substrate may or may not be labeled. For example, the antibodies may be labeled, e.g., bound to a labeled protein, in such a manner that binding of the specific protein may displace the label and the presence of the marker in the sample is marked by the absence of a signal. In addition, the antibodies that are immobilized onto the substrate may be directly or indirectly immobilized onto the surface. Methods for immobilizing proteins, including antibodies, are well-known in the art, and such methods may be used to immobilize a target protein, e.g., I L-Ιβ, or another antibody onto the surface of the substrate to which the antibody directed to the specific factor can then be specifically bound. In this manner, the antibody directed to the specific biomarker is immobilized onto the surface of the substrate for the purposes of the present invention.
[0057] The kits of the present invention may or may not include containers for collecting samples from the subject and one or more reagents, e.g., purified target biomarker for preparing a calibration curve. The kits may or may not include additional reagents such as wash buffers, labeling reagents and reagents that are used to detect the presence (or absence) of the label.
[0058] All patents and publications cited herein are incorporated by reference to the same extent as if each individual publication was specifically and individually indicated as having been incorporated by reference in its entirety.
Examples
[0059] Clinical and biomarker data was collected from 73 injured soldiers with severe combat extremity wounds. Clinical data included detailed information on wound burden as well as the occurrence of VTE. Thirty-two inflammatory biomarkers were assayed using the Luminex platform. The data were processed to address class imbalance and non-normal distributions. A Bayesian network analysis was then utilized to identify the variables associated with the outcome of VTE. The variable sets were run in binary classification algorithms. The best variable set and binary classification algorithm combination that first produced the highest Kappa and then the highest sensitivity and reasonable specificity was chosen. The resultant models were examined using accuracy, no information rate, positive predictive value and negative predictive value. Model performance was further assessed using receiver operator characteristic curves (ROC), area under curve (AUC), and decision curve analysis (DCA).
[0060] The overall incidence of VTE was 12% (9/73). The variables selected by the algorithm run in the linear discriminant analysis (LDA) binary classification algorithm outperformed all other sets of variables with all other binary classification algorithms. Variables selected into this model were the serum cytokines and chemokines FGF-basic, VEGF, Ιί β, and MIG as well as the clinical variables of injury severity score skin, total wound volume, age, and surface area of all wounds. The LDA algorithm run with the selected variables produced a Kappa of 0.52, an Accuracy of 0.9, a sensitivity of 0.6, a specificity of 0.94, a positive predictive value of 0.57, a negative predictive value of 0.94, and an AUC of 0.88 with AUC 95% confidence interval of 0.82-0.94. The decision curve of the reduced model outperformed the decision curve of the full model.

Claims

A method of determining the susceptibility of a human subject to venus thromboembolism, the method comprising:
(a) obtaining a biological sample from the subject,
(b) measuring the levels in the biological sample of one or more polypeptides selected from the group consisting of Basic Fibroblast Growth Factor (FGFBasic), lnterleukin-ΐβ (IL-1B), monokine induced by gamma interferon (MIG), vascular endothelial growth factor (VEGF); and
(c) determining a risk profile value from the polypeptide levels,
wherein the the risk profile value correlates with the susceptibility of the human subject to venus thromboembolism.
The method of claim 1 wherein the polypeptides are selected from the group consisting of IL-1B, MIG, and VEGF.
The method of any of the preceding claims further comprising determining at least one clinical value selected from the group consisting of age, injury severity score skin (ISS skin), wound volume, and wound surface area; wherein the risk profile value is additionally determined from the at least one clinical value.
The method of claim 3 wherein the clinical values are selected from the group consisting of age, wound volume, and wound surface area.
The method of any of the preceding claims wherein at least two polypeptides are selected. The method of any of the preceding claims wherein at least two clinical values are selected. The method of any of the preceding claims wherein the human subject has a traumatic wound.
The method of any of the preceding claims wherein the biological sample is blood, serum, or plasma.
A method of inhibiting the development of venus thromboembolism in a human subject comprising:
(a) determining the susceptibility of the human subject to venus thromboembolism by the method of claim 1, and
(b) administering a treatment to the human subject prior to the onset of any venus thromboembolism symptom.
The method of claim 9 wherein the human subject is treated with an anticoagulant.
The method of claim 10 wherein the anticoagulant is selected from the group consisting of heparin, apixaban, dabigatran, rivaroxaban, edoxaban and warfarin. The method of claim 9 wherein the human subject is treated with thrombolytic therapy. The method of claim 12, wherein the thrombolytic therapy comprises administration of tissue plasminogen activator (tPA).
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