WO2025240490A1

WO2025240490A1 - Detection of bacteremia using hematology parameters

Info

Publication number: WO2025240490A1
Application number: PCT/US2025/029162
Authority: WO
Inventors: Melissa NAIMAN; Anja Kathrina JAEHNE
Original assignee: Beckman Coulter Inc
Current assignee: Beckman Coulter Inc
Priority date: 2024-05-13
Filing date: 2025-05-13
Publication date: 2025-11-20
Anticipated expiration: 2026-11-13

Abstract

Hematological parameters, such as monocyte distribution width (MOW) and neutrophil to lymphocyte ratio (NLR) may be used for bacteremia screening. Such screening may be performed using a transducer module which comprises an interrogation zone, an illumination source configured to illuminate the blood sample in the interrogation zone, and at least one light sensor configured to detect illumination from cells from a blood sample in the interrogation zone. With such a transducer module, a screening method may be performed which comprises presenting the blood sample to the transducer module, delivering a hydrodynamically focused stream of the blood sample to the interrogation zone of the transducer module, determining one or more white blood cell population parameters for the blood sample based on measurements from the transducer module, and screening the blood sample for bacteremia using a data processing module.

Description

DETECTION OF BACTEREMIA USING HEMATOLOGY PARAMETERS

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application is based on, and claims the benefit of, provisional patent application 63/646,184, filed in the United States patent office on May 13, 2024, the disclosure of which is hereby incorporated by reference in its entirety.

BACKGROUND

[0002] Bacteremia is the presence of viable bacteria in the bloodstream, and poses a particular threat to people with weak immune systems who may be unable to fight off infections. Additionally, without treatment, bacteremia can progress to sepsis, which can cause organ failure and death. Bacteremia can be established with a blood culture, which can also identify the pathogen that is present in the bloodstream. However, blood cultures can be time consuming (e.g., taking between 1 and 3 days), which means that waiting for blood culture results to be available can delay appropriate treatment. Accordingly, there is a need for improved technology for bacteremia screening.

SUMMARY

[0003] The disclosed technology can be applied to screening blood samples for bacteremia. For example, in one aspect, the disclosed technology may be used to provide an automated method for screening a blood sample obtained from an individual for bacteremia which comprises presenting the blood sample to a transducer module, delivering a hydrodynamically focused stream of the blood sample to an interrogation zone of the transducer module, determining one or more hematology parameters for the blood sample based on measurements from the transducer module, and screening the blood sample for bacteremia using a data processing module. In such a method, the transducer to which the blood sample is presented may comprise, in addition to the interrogation zone, an illumination source configured to illuminate the blood sample in the interrogation zone, and at least one light sensor configured to detect illumination from cells from the blood sample in the interrogation zone. Similarly, the data processing module which may be used to screen the blood sample in this type of method may comprise a processor and a tangible non-transitory computer readable medium programmed with an application that, when executed by the processor causes the processor to perform screening acts. These screening acts may comprise performing one or more comparisons by, for at least one of the one or more hematology parameters, comparing that hematology parameter to a corresponding screening cutoff, and screening the blood sample for bacteremia based on the one or more comparisons. Other types of methods, as well as corresponding systems and computer readable media for bacteremia screening may also be implemented based on this disclosure.

[0004] The words “preferred” and “preferably” refer to embodiments or aspects of the invention that may afford certain benefits, under certain circumstances. However, other embodiments or aspects may also be preferred, under the same or other circumstances. Furthermore, the recitation of one or more preferred embodiments or aspects does not imply that other embodiments or aspects are not useful and is not intended to exclude other embodiments or aspects from the scope of the invention.

[0005] The term “comprises” and variations thereof do not have a limiting meaning where these terms appear in the description and claims. Such terms will be understood to imply the inclusion of a stated step or element or group of steps or elements but not the exclusion of any other step or element or group of steps or elements. [0006] By “consisting of is meant including, and limited to, whatever follows the phrase “consisting of.” Thus, the phrase “consisting of indicates that the listed elements are required or mandatory, and that no other elements may be present. By “consisting essentially of is meant including any elements listed after the phrase, and limited to other elements that do not interfere with or contribute to the activity or action specified in the disclosure for the listed elements. Thus, the phrase “consisting essentially of indicates that the listed elements are required or mandatory, but that other elements are optional and may or may not be present depending upon whether or not they materially affect the activity or action of the listed elements.

[0007] Unless otherwise specified, “a,” “an,” “the,” and “at least one” are used interchangeably and mean one or more than one.

[0008] As used herein, the term “or” is generally employed in its usual sense including “and/or” unless the content clearly dictates otherwise.

[0009] The term “and/or” means one or all of the listed elements or a combination of any two or more of the listed elements.

[0010] Also herein, the recitations of numerical ranges by endpoints include all numbers subsumed within that range (e.g., 1 to 5 includes 1, 1.5, 2, 2.75, 3, 3.80, 4, 5, etc.).

[0011] Herein, “up to” a number (for example, up to 50) includes the number (for example, 50).

[0012] The term “in the range” or “within a range” (and similar statements) includes the endpoints of the stated range. [0013] For any method disclosed herein that includes discrete steps, the steps may be conducted in any feasible order. And, as appropriate, any combination of two or more steps may be conducted simultaneously.

[0014] All headings are for the convenience of the reader and should not be used to limit the meaning of the text that follows the heading, unless so specified.

[0015] Reference throughout this specification to “one embodiment,” “one aspect,” “an embodiment,” “an aspect,” “certain embodiments,” “certain aspects, “some embodiments,” or “some aspects,” etc., means that a particular feature, configuration, composition, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. Thus, the appearances of such phrases in various places throughout this specification are not necessarily referring to the same embodiment of the disclosure. Furthermore, the particular features, configurations, compositions, or characteristics may be combined in any suitable manner in one or more embodiments or aspects.

[0016] Unless otherwise indicated, all numbers expressing quantities of components, molecular weights, and so forth used in the specification and claims are to be understood as being modified in all instances by the term “about.” As used herein in connection with a measured quantity, the term “about” refers to that variation in the measured quantity as would be expected by the skilled artisan making the measurement and exercising a level of care commensurate with the objective of the measurement and the precision of the measuring equipment used. Accordingly, unless otherwise indicated to the contrary, the numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the present invention. At the very least, and not as an attempt to limit the doctrine of equivalents to the scope of the claims, each numerical parameter should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques.

[0017] Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as possible. All numerical values, however, inherently contain a range necessarily resulting from the standard deviation found in their respective testing measurements.

[0018] The above summary of the present invention is not intended to describe each disclosed embodiment or every implementation of the present invention. The description that follows more particularly exemplifies illustrative aspects. In several places throughout the application, guidance is provided through lists of examples, which examples can be used in various combinations. In each instance, the recited list serves only as a representative group and should not be interpreted as an exclusive list.

BRIEF DESCRIPTION OF THE FIGURES

[0019] Illustrative embodiments of the present invention are described in detail below with reference to the attached drawing figures, and wherein:

[0020] FIG. 1 is a schematic depiction of an example operating environment, in accordance with aspects of the present disclosure. [0021 ] FIG. 2 is schematic depiction of an example analyzer, in accordance with aspects of the present disclosure.

[0022] FIG. 3 is a schematic depiction of an example analyzer process, in accordance with aspects of the present disclosure.

[0023] FIG. 4 is a schematic depiction of an example analysis engine, in accordance with aspects of the present disclosure.

[0024] FIG. 5 depicts a flow chart of an example method for evaluating sepsis or septic shock from a blood sample, in accordance with aspects of the present disclosure.

DETAILED DESCRIPTION

[0025] This disclosure describes methods of and systems for screening for bacteremia in a patient. Illustrative methods and systems described herein can rule out bacteremia in a patient using parameters such as standard deviation of monocyte volume (otherwise known as monocyte distribute width, or MDW) and/or neutrophil to lymphocyte ratio (NLR) in a blood sample from the patient. Such methods and systems can allow for faster screening of patients, since they can be used to screen for bacteremia based on the results of a complete blood count (CBC) a commonly ordered test whose results are reported sooner than the results of a blood culture. In particular, since MDW is directly included in CBC results, and NLR can be readily calculated from information obtained in a CBC (e.g., neutrophil and lymphocyte counts or percentages), illustrative methods and systems described herein may preferably be used for screening patients in an emergency department (ED), thereby allowing providers to focus on other potential causes of a patient’s symptoms once bacteremia has been ruled out. [0026] Operating Environment and Exemplary Analyzers

[0027] Turning to FIG. 1, an example operating environment 500 is depicted in accordance with some aspects described herein. Generally, example operating environment 500 includes systems that can facilitate diagnostic, predictive, and medical intervention action described herein. Operating environment 500 is one example of a suitable environment and system architecture for implementing an embodiment of the disclosure. Some embodiments may be implemented as a system, comprising one or more computers and associated network and equipment, upon which a method or computer software application is executed. Accordingly, aspects of the present disclosure may take the form of an embodiment combining software and hardware aspects that may all generally be referred to herein as a “module” or “system.” Further, the methods of the present disclosure may take the form of a computer application embodied in computer readable media having machine-readable application software embodied thereon. In this regard, a machine-readable storage media may be any tangible medium that can contain or store a software application for use by the computing apparatus.

[0028] Some aspects of example operating environment 500 include at least one analyzer 502. An analyzer is a clinical diagnostic machine capable of measuring one or more anatomical or physiological properties of a sample, including: vitals, metabolic measurements (also referred to as blood chemistry); cell counts; viral protein, viral gene or microbial cell measurements; urine measurements; genomic characterizations; or mass spectrometry and/or immunological measurements. Analyzers as used herein include workcells or modular systems where two or more types of measurements are taken; for example, a workcell comprising blood chemistry and immunoassay. In some aspects, the analyzer may include a hematology analyzer.

[0029] Some aspects of example operating environment 500 include network 504. Network 504 generally facilitates communication between analyzer 502 and any other device communicatively coupled to network 504. As such, network 504 can include access points, routers, switches, or any other network component commonly understood to facilitate communication among devices. By way of example network 504 can include one or more wide area networks, one or more local area networks, one or more public networks, one or more private networks, one or more telecommunications networks, or any combination thereof. In other words, network 504 may include multiple networks, or a network of networks, but is depicted in a simple form so as not to obscure aspects described herein.

[0030] Some aspects of example operating environment 500 include remote device 506. Remote device 506 can take on a variety of forms, such as a personal computer (PC), a smart phone, a smart watch, a laptop computer, a mobile phone, a mobile device, a tablet computer, a wearable computer, a personal digital assistant (PDA), any combination of these delineated devices, or any other device that can communicate directly or indirectly with an analyzer (e.g., analyzer 502) and/or a data store (for example, data store 508). For example, in a particular aspect remote device 506 comprises a work station PC that can execute a local client application. The local client application can communicatively connect with analyzer 502, data store 508, or both. For example, the local client application can be an application that facilitates user interaction with the analyzer 502. The local client application. In another example, the local client application can be an electronic medical record system application that facilitates user interaction with an electronic medical record system maintained by a data store.

[0031] Some aspects of example operating environment 500 includes one or more data stores 508. Data store 508 generally stores, maintains, and communicates data through network 504. Data store 508 may comprise hardware, software, firmware in any combination. For example, data store 508 can include an electronic medical record (EMR) system. The EMR system can store medical information (for example, demographic, physical, biological, and so forth) about a plurality of individuals. In other words, an EMR is a real-time, comprehensive collection of patent data including medical history, physician notes, diagnoses, medication, allergies, immunizations, laboratory test results and vital signs. An EMR system stores and maintains a plurality of EMRs.

[0032] In another example, data store 508 may comprise a laboratory information system (LIS). A LIS is a software system that stores, processes, and manages laboratory analyzer data, and information about an individual, including sample measurements. Laboratory test results derived from an individual’s biological sample, such as WBC (white blood cell count) and MDW, may also be input to the LIS manually, by a laboratory professional, indirectly through laboratory middleware connected to one or more analyzers, or directly from an analyzer. In some aspects, an LIS system can add or modify patent data stored in an EMR system.

[0033] Turning to FIG. 2, a depiction of an example analyzer 600 is provided consistent with aspects described herein. Analyzer 600 depicts components in a system which may be used to take measurements of a sample, for example a blood sample. As will be understood by those skilled in the art, analyzers are available that work on many principles, including electrical impedance, stained fluorescence analysis, cell image analysis, and light scatter analysis. In particular, many commercially available hematology analyzers use a combination of these methodologies. For example, a Beckman Coulter DxH™ 900 Hematology Analyzer uses electrical impedance (also called DC current) to size and count cells and uses Radio Frequency (RF), light loss, and light scatter to evaluate cell morphology and further distinguish sub-populations of cells. Exemplary systems and methods are described, for example, in US Pat. No. 5,125,737. As will be appreciated from the disclosure of US Pat. No. 5,125,737, there is often more than one way of distinguishing cells in a blood sample. For example, cells may be distinguished based on volume (often measured by impedance), or by light scatter, or by combinations of parameters. If distinguished by light scatter, different angles of light scatter may be used, such as low angle light scatter (LALS), axial light loss (ALL), upper median angle light scatter (UMALS), and the like. In some cases, cells that are similar in size and morphology may best be distinguished using combinations of different measures, which may use plots (for example, with one measure on the x-axis and another measure on the y-axis) or formulas, such as ratios or sums. As one example, eosinophils have several light scatter measures similar to neutrophils, and can be difficult to distinguish based on any single measurement. However, by looking at medium angle light scatter (MALS), a combination of UMALS and lower median angle light scatter (LMALS), eosinophils can be clearly distinguished from neutrophils as well as monocytes, lymphocytes, and basophils.

[0034] The DxH systems use high-speed, high-resolution analog-to-digital conversion with

Digital Signal Processing (DSP) circuitry to measure multiple parameters for each cellular event. DSP algorithms analyze the cellular data digitally, providing cellular definition and resolution. Differential accuracy and flagging technology are obtained by combining the additional light scatter measurements with data analysis techniques to further define and separate cell populations. For example, in some cases, MALS may be transformed to a modified rotated MALS (RMALS) parameter through a mathematical transformation to eliminate overlap and create distinct neutrophil, lymphocyte, monocyte and eosinophil populations, enabling optimal analysis and visual confidence in the results. Current internal and visual differential data transformations include: RMALS (rotated MALS), opacity (conductivity minus the size aspect), SOP (stretched opacity), and non-linear AL2 transformations. The DxH systems report “VCS parameters”.

[0035] Other hematology analyzers, such as those manufactured by Abbott, Sysmex, and Mindray, use the cluster of differentiation (CD) system to differentiate white blood cell populations. CD molecules can act in numerous ways, often acting as receptors or ligands; by which a signal cascade is initiated, altering the behavior of the cell. Some CD proteins do not play a role in cell signaling, but have other functions, such as cell adhesion. The CD system nomenclature commonly used to identify cell markers thus allows cells to be defined based on what molecules are present on their surface. There are more than 350 CD molecules identified for humans. For example, monocytes can be identified with CD45+ and CD14+. Using fluorescently labeled antibodies, these cell markers can used to sort cells. Fluorescence activated cell sorting (FACS) provides a method of sorting a heterogeneous mixture of cells into two or more containers, a single cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell. Fluorescence flow cytometry or FACS may also provide information about cell composition of a labeled cell. For example, information about cell density or complexity may be obtained by measuring light scattered by the cell and information about cell size and internal structure may be obtained by measuring the fluorescence signal intensity of the cell. Systems based on fluorescence flow cytometry or FACS report “FACS parameters.”

[0036] There are many thousands of possible combinations of sensor readings and calculated relationships that might correlate to a particular characteristic of a blood sample, and, once subpopulations of cells have been identified, a particular subpopulation of cells may be further characterized by one or more sensor readings (such as, for example, LALS, ALL, UMALS, LMALS, MALS, impedance, etc.), in addition to or in lieu of cytochemical staining, marker affinity, or other cell identification techniques. That is, hematology analyzers can often provide data about a subpopulation of cells that is much richer than simply a count or proportion of those cells compared to other subpopulations of cells within a sample. One example is Monocyte Distribution Width (MDW), a calculation of the standard deviation of cell volumes within the subpopulation of monocytes within a blood sample. MDW may be determined by passing an electric current through a blood sample and measuring the volume of individual cells passing through a measurement module based on measuring the amplitude of the resulting impedance measurement (e.g., in a flow cell 630 of a system such as shown in FIG. 2). This volume may also be measured by a system which transmits light through a blood sample and measures the resulting light scatter to determine cell volume. In some cases, more than one characterization of a subpopulation of cells or relationship between subpopulation of cells may be indicative of the same or related conditions, such as viral infection, sepsis, anemia, leukemia, etc. [0037] As shown in FIG. 2, analyzer 600 includes a transducer module 610 having a light or irradiation source such as a laser 612 emitting a beam 614. The laser 612 can be, for example, a 635 nm, 5 mW, solid-state laser. In some instances, analyzer 600 may include a focus-alignment system 620 that adjusts beam 614 such that a resulting beam 622 is focused and positioned at a cell interrogation zone 632 of a flow cell 630. In some instances, the flow cell 630 receives a sample aliquot from a preparation system 602. Various fluidic mechanisms and techniques can be employed for hydrodynamic focusing of the sample aliquot within flow cell 630.

[0038] In some instances, the aliquot generally flows through the cell interrogation zone 632 such that its constituents pass through the cell interrogation zone 632 one at a time. In some cases, an analyzer 600 may include a cell interrogation zone or other feature of a transducer module or blood analysis instrument such as those described in U.S. Pat. Nos. 5,125,737; 6,228,652; 7,390,662; 8,094,299; 8,189,187; and 9,939,453, the contents of which are incorporated herein by reference for all purposes. For example, a cell interrogation zone 632 may be defined by a square transverse cross section measuring approximately 50x50 microns, and having a length (measured in the direction of flow) of approximately 65 microns. Flow cell 630 may include an electrode assembly having first and second electrodes 634, 636 for performing DC impedance and/or RF conductivity measurements of the cells passing through cell interrogation zone 632. Signals from electrodes 634, 636 can be transmitted to the analysis system 604. The electrode assembly can analyze volume and conductivity characteristics of the cells using low-frequency current and high- frequency current, respectively. For example, low -frequency DC impedance measurements can be used to analyze the volume of each individual cell passing through the cell interrogation zone. High-frequency RF current measurements can be used to determine the conductivity of cells passing through the cell interrogation zone. Because cell walls act as conductors to high frequency current, the high frequency current can be used to detect differences in the insulating properties of the cell components, as the current passes through the cell walls and through each cell interior. High frequency current can be used to characterize nuclear and granular constituents and the chemical composition of the cell interior.

[0039] The light source in FIG. 2 has been described as a laser, however, the light source may alternatively or additionally include a xenon lamp, an LED lamp, an incandescent lamp, or any other suitable source of light, including combinations of the same or different kinds of lamps (e.g., multiple LED lamps or at least one LED lamp and at least one xenon lamp). As shown in FIG. 2, for example, incoming beam 622 irradiates the cells passing through cell interrogation zone 632, resulting in light propagation within an angular range a (e.g. scatter, transmission) emanating from the zone 632. Exemplary systems are equipped with sensor assemblies that can detect light within one, two, three, four, five, or more angular ranges within the angular range a, including light associated with an extinction or axial light loss measure. As shown, light propagation 640 can be detected by a light detection assembly 650, optionally having a light scatter detector unit 650A and a light scatter and/or transmission detector unit 650B. In some instances, light scatter detector unit 650A includes a photoactive region or sensor zone for detecting and measuring upper median angle light scatter (UMALS), for example, light that is scattered or otherwise propagated at angles relative to a light beam axis within a range from 20 to 42 degrees. In some instances, UMALS corresponds to light propagated within an angular range from between 20 to 43 degrees, relative to the incoming beam axis, which irradiates cells flowing through the interrogation zone. Light scatter detector unit 650A may also include a photoactive region or sensor zone for detecting and measuring lower median angle light scatter (LMALS), for example, light that is scattered or otherwise propagated at angles relative to a light beam axis within a range from 10 to 20 degrees. In some instances, LMALS corresponds to light propagated within an angular range from between 9 to 19 degrees, relative to the incoming beam axis which irradiates cells flowing through the interrogation zone.

[0040] A combination of UMALS and LMALS is defined as median angle light scatter (MALS), which may be light scatter or propagation at angles between 9 degrees and 43 degrees relative to the incoming beam axis which irradiates cells flowing through the interrogation zone. One of skill in the art will understand that these angles (and the other angles described herein) may vary somewhat based on the configuration of the interrogation, sensing and analysis systems.

[0041] As shown in FIG. 2, the light scatter detector unit 650A may include an opening 651 that allows low angle light scatter or propagation 640 to pass beyond light scatter detector unit 650A and thereby reach and be detected by light scatter and transmission detector unit 650B. According to some embodiments, light scatter and transmission detector unit 650B may include a photoactive region or sensor zone for detecting and measuring lower angle light scatter (LALS), for example, light that is scattered or propagated at angles relative to an irradiating light beam axis of less than 5.1 degrees. In some instances, LALS corresponds to light propagated at an angle of less than 9 degrees, relative to the incoming beam axis, which irradiates cells flowing through the interrogation zone. In some instances, LALS corresponds to light propagated at an angle of less than 10 degrees, relative to the incoming beam axis, which irradiates cells flowing through the interrogation zone. In some instances, LALS corresponds to light propagated at an angle of 1.9 degrees±0.5 degrees, relative to the incoming beam axis, which irradiates cells flowing through the interrogation zone. In some instances, LALS corresponds to light propagated at an angle of 3.0 degrees±0.5 degrees, relative to the incoming beam axis, which irradiates cells flowing through the interrogation zone. In some instances, LALS corresponds to light propagated at an angle of 3.7 degrees±0.5 degrees, relative to the incoming beam axis, which irradiates cells flowing through the interrogation zone. In some instances, LALS corresponds to light propagated at an angle of 5.1 degrees±0.5 degrees, relative to the incoming beam axis, which irradiates cells flowing through the interrogation zone. In some instances, LALS corresponds to light propagated at an angle of 7.0 degrees±0.5 degrees, relative to the incoming beam axis, which irradiates cells flowing through the interrogation zone. In each instance, LALS may correspond to light propagated an angle of 1.0 degrees or more. That is, LALs may correspond to light propagated at angles between 1.0 degrees and 1.9 degrees; between 1.0 degrees and 3.0 degrees; between 1.0 degrees and 3.7 degrees; between 1.0 degrees and 5.1 degrees, between 1.0 degrees and 7.0 degrees, between 1.0 degrees and 9.0 degrees; or between 1.0 degrees and 10.0 degrees.

[0042] According to some embodiments, light scatter and transmission detector unit 650B may include a photoactive region or sensor zone for detecting and measuring light transmitted axially through the cells, or propagated from the irradiated cells, at an angle of 0 degrees relative to the incoming light beam axis. In some cases, the photoactive region or sensor zone may detect and measure light propagated axially from cells at angles of less than 1 degree relative to the incoming light beam axis. In some cases, the photoactive region or sensor zone may detect and measure light propagated axially from cells at angles of less than 0.5 degrees relative to the incoming light beam axis less. Such axially transmitted or propagated light measurements correspond to axial light loss (ALL or AL2). As noted in previously incorporated U.S. Pat. No. 7,390,662, when light interacts with a particle, some of the incident light changes direction through the scattering process (i.e., light scatter) and part of the light is absorbed by the particles. Both of these processes remove energy from the incident beam. When viewed along the incident axis of the beam, the light loss can be referred to as forward extinction or axial light loss. Additional aspects of axial light loss measurement techniques are described in U.S. Pat. No. 7,390,662 at column 5, line 58 to column 6, line 4.

[0043] As such, the analyzer 600 provides means for obtaining light propagation measurements, including light scatter and/or light transmission, for light emanating from the irradiated cells of the biological sample at any of a variety of angles or within any of a variety of angular ranges, including ALL and multiple distinct light scatter or propagation angles. For example, light detection assembly 650, including appropriate circuitry and/or processing units, provides a means for detecting and measuring UMALS, LMALS, LALS, MALS, and ALL.

[0044] Wires or other transmission or connectivity mechanisms can transmit signals from the electrode assembly (e.g. electrodes 634, 636), light scatter detector unit 650A, and/or light scatter and transmission detector unit 650B to the analysis system 604 for processing. For example, measured DC impedance, RF conductivity, light transmission, and/or light scatter parameters can be provided or transmitted to the analysis system 604 (also referred to as a data processing module) for data processing. In some instances, analysis system 604 may include computer processing features and/or one or more modules or components, which can evaluate the measured parameters, identify and enumerate biological sample constituents, and correlate a subset of data characterizing elements of the biological sample with one or more features or parameters of interest. Some aspects of analysis system 604 include an analysis engine such as described in relation to FIG. 4.

[0045] Additionally, or alternatively, as depicted in FIG. 2, analyzer 600 may generate or output a report 606 presenting measurements made or parameters calculated for the sample. The measurements made or parameters calculated for a sample can include UMALS, LMALS, LALS, MALS, ALL, WBC, MDW, monocyte %, absolute lymphocyte count (ALC), lymphocyte %, eosinophil %, absolute neutrophil count (ANC), neutrophil %, and/or absolute counts, average volumes, percents, and/or standard deviation of volumes for white blood cells, monocytes, neutrophils, lymphocytes, eosinophils, basophils, ratios of any two of the foregoing measurements or parameters, or any combination thereof. As further described herein, it may be particularly advantageous in some aspects for the parameters to include MDW, WBC, absolute lymphocyte count (ALC), absolute neutrophil count (ANC), and for the absolute lymphocyte count (ALC) and absolute neutrophil count (ANC) to be used to calculate a neutrophil-to-lymphocyte ratio (NLR).

[0046] In some instances, excess biological sample from transducer module 610 can be directed to an external (or alternatively internal) waste system 608. In some instances, the analyzer

600 may include one or more features of a transducer module or blood analysis instrument such as those described in previously incorporated U.S. Pat. Nos. 5,125,737; 6,228,652; 8,094,299; 8,189,187; and 9,939,453. FIG. 3 schematically depicts an exemplary analyzer process 700, for example, which can optionally utilize the analyzer 600 of FIG. 2. In this embodiment, at step 702, an individual’s blood sample may be delivered to the analyzer, at which point the analyzer may prepare the sample for analysis. Once the sample preparation is concluded at step 704, the sample may pass through one or more measurement modules at step 706. The measurement modules of the step 706 can include a conductivity module, a light scatter module, an RF module, or any combination thereof. Other modules may be used instead of or in addition to a conductivity module or a light scatter module. For example, a hematology analyzer may use sensors to detect dyes or fluorescent markers, imaging, immunoassay markers, size sorting, or other approaches to identify cells or other sample components. Sample measurements may then be evaluated by a data processing module in step 708. In some aspects, once the sample measurements are complete, the measurements may be displayed by a reporting module in step 710. Additionally, or alternatively, once the sample measurements are complete the measurements may be communicated to an analysis engine for further processing, such as the example analysis engine 800 of FIG. 4.

[0047] FIG. 4 depicts an example analysis engine 800, in accordance with aspects described herein. Aspects of analysis engine 800 can be incorporated into a processing feature and/or module or component of an analyzer (such as analysis system 604 depicted in FIG. 2), an application executed by a remote device (e.g., remote device 506 depicted in FIG. 1), or can operate as an independent component of an operating environment (e g., operating environment 500 depicted in FIG. 1).

[0048] Generally, the analysis engine 800 evaluates a set of measurements or parameters, identifies and enumerates biological sample constituents, and correlates a subset of data characterizing elements of the biological sample with one or more features or parameters of interest. As such, analysis engine 800 includes a receiver module 804, an analyzer module 806, and a communicator module 808.

[0049] A receiver, such as receiver 802, generally collects measurements made or parameters calculated based on analysis of an individual’s sample. The data (for example, measurements made or parameters calculated) can be received directly from a subsystem of an analyzer or from a data store in some aspects. Receiver 802 can use any data collection technique known in the art.

[0050] Data analyzer 806 includes modules that include logical expressions for the evaluation of measurements and parameters received by the analysis engine 800. The logical expressions can include linear or parallel processes that evaluate measurements made by or parameters calculated by a hematology analyzer, such as analyzer 502 described in relation to FIG 1 or analyzer 600 described in relation to FIG. 2. The data analyzer 806 includes at least one of an acuity analyzer 810a, a decision rules analyzer 810b, and a risk analyzer 810c.

[0051] Acuity analyzer 810a comprises a library of rules, models, and logic expressions, in any combination that facilitate the determination of a probability and/or risk of one or more outcomes based on one or more parameters or characteristics of a blood sample. A potential outcome can be associated with a recommendation, treatment, or intervention in some aspects.

[0052] Decision rules analyzer 810b comprises a library of decision rules. A decision rule is a logic expression that compares an individual parameter or characteristic of a blood sample with a threshold value (e.g., a cutoff). The decision rules analyzer 810b assembles one or more decision rules from the library to build a logical expression that the analysis engine can evaluate. In one aspect, the analyzer 810b can utilize a linear combination or two or more parameters. In combination, the decision rules can be used to determine a probability that an individual associated with a blood sample currently has or does not have a condition, such as, for example, an infection, including viral infection.

[0053] Risk analyzer 810c can include rules, models, logic expressions, in any combination that are configured to forecast medical conditions.

[0054] In some aspects, the data analysis engine 800 can incorporate the operations of one or more analyzer modules to generate an output. For example, the decision rules maintained by a decision rules analyzer 810b can be used to determine if an individual currently has a condition, such as an infection. Additionally, in some aspects, the acuity analyzer 810a may first identify an individual is at risk of needing critical care and/or at risk of in-hospital mortality, e.g., within 48 hours of obtaining the blood sample, then one or more of the decision rules analyzer 810b and/or the risk analyzer 810c can be utilized for further determinations. Communicator 808 generally communicates the results of the analysis engine 800 to at least one predetermined target. In some aspects, the predetermined target can include a remote device that is executing a local client of a laboratory information system or a local client of an electronic medical record system (e.g., remote device 506 described in relation to FIG. 1). In such aspects, the results can include presentation of a visual display or audio signal that provides a recommendation of care, recommendation to authorize discharge, a recommendation of diagnosis, or an alert that the individual corresponding to the analyzed sample may have or be at risk of developing a severe condition (e.g., sepsis, which can result from untreated bacteremia). [0055] In some aspects, the predetermined target can include a data store maintaining a laboratory information system or an electronic medical record system (for example, data store 508 described in relation to FIG. 1). In such aspects, the communicated results can include entering orders for the individual associated with the analyzed sample’ s medical records. For example, the orders can include transferring the individual to a critical care unit, increasing monitoring of the individual by medical personal or devices, or specific testing or standard of care protocols.

[0056] As described in more detail above, data analysis engine 800 includes at least one analyzer that processes the measurements or parameters provided to the analysis engine. The processing can include rules, models, logic expressions, in any combination that are configured to screen, detect and/or forecast medical conditions. For example, some aspects of a decision rules analyzer (for example, decision rules analyzer 810b described in relation to FIG. 4) can include programs that characterize the information received from an analyzer to determine if an individual is not likely to have bacteremia. In particular, an exemplary method 900 for screening for bacteremia is depicted in FIG. 5 in accordance with some aspects described herein. Method 900 may generally be described as a “decision rules” approach, where individual parameters or characteristics of a blood sample are considered against cutoff values for each parameter or characteristic.

[0057] Hematological Screening for Bacteremia

[0058] As described above, an analyzer may count and differentiate the various cells included in a blood sample. Some aspects of a method 900 which may use this capability in screening for bacteremia include presenting a blood sample to a transducer module in block 901. This may be done by, for example, loading the blood sample into the analyzer which includes the transducer module, so that the sample (or an aliquot thereof) can be conveyed to the transducer module for measurement. After the sample is presented in block 901, a hydrodynamically focused stream of the sample may be delivered to the transducer module’s interrogation zone in block 902. The transducer module may then perform various measurements on the sample (e.g., measure light reflected, scattered or emitted from cells in the sample, measure changes in conductivity as cells in the sample pass through an aperture, etc.), and those measurements may be used to determine one or more hematology parameters for the blood sample (e.g., measurements which may be included in a CBC) in block 903. Once the hematology parameters are determined in block 903, the blood sample may be screened for bacteremia using a data processing module in block 904.

[0059] As shown in FIG. 5, screening a blood sample for bacteremia may include performing a screening step of comparing a hematology parameter with a corresponding cutoff at least one time in block 905. For example, if the hematology parameters determined in block 903 include MOW, then the comparison of block 905 may be performed by comparing the MDW value for the sample with a MDW screening cutoff (e.g., 20). After the comparison of block 905 had been made, there may be a determination in block 906 of whether further comparisons should be performed. In implementations where the determination of block 906 takes place, it may be performed in a variety of manners. For example, in some cases, as soon as the comparison of block 905 indicates that one of the hematology parameters is less than its corresponding cutoff, the determination of block 906 may automatically be that no further comparisons should take place. Alternatively, in other implementations, a data processing module may be configured to perform comparisons for multiple parameters, and to continue to perform the comparisons until all of the parameters had been considered. For example, in some cases, a data processing module may be configured with a first rule specifying that a MDW value should be compared with a cutoff of 20, and a second rule specifying that a NLR value should be compared with a cutoff of 3. In such as case, the determination of block 906 may be that further comparisons should be made until both the first and second rules had been evaluated (i.e., both the NLR and MDW values had been compared with their corresponding cutoffs).

[0060] In any case, once the comparison(s) had been completed, those comparison(s) may be used to screen the blood sample for bacteremia in block 907. As with the determination of block 906, the screening of block 907 may be performed in a variety of manners. For example, in some cases, if any of the comparison(s) indicated that the patient from whom the blood sample was obtained did not have bacteremia (e.g., to continue the example described above, if either MDW or NLR was below its corresponding cutoff), then the screening of block 907 may be indicating that the patient form whom the sample was obtained did not have bacteremia. However, in other cases, the screening of block 907 may be implemented so that the screening of block 907 would indicate that the patient from whom the sample was obtained did not have bacteremia only if each of the parameters considered in block 905 indicated that the patient did not have bacteremia (e.g., to continue the previous example, indicating the patient did not have bacteremia only if both MDW and NLR were below their corresponding cutoffs). Other approaches, such as calculating a bacteremia score by assigning weights to the various parameters and combining the differences between parameters and their cutoffs using the weights, then comparing the bacteremia score with an overall cutoff value, are also possible, and could be implemented by those of skill in the art based on this disclosure. Accordingly, the above examples of how the screening of block 907, like the examples of how to perform the other acts of FIG. 5, should be understood as being illustrative, and should not be treated as limiting.

[0061] After performing a process such as shown in FIG. 5, an analyzer may generate a screening message. In some instances, a screening message may include a flag, message, or other signal on a test report to indicate that the patient did not have bacteremia to a clinician or researcher. In some aspects, the screening message may include an audio or visual message communicated to a remote device that indicates that the individual associated with the sample does not have bacteremia. The indication may be provided on a screen, such as a display for a hematology analyzer, Laboratory Information System (LIS) or Electronic Medical Record (EMR), or may be provided in a print-out, fax, e-mail or other digital or hard copy report of the hematology test results.

[0062] As further described here, it might be advantageous for the analyzer to further include other clinical data. Such clinical data could be incorporated through the use of the same analyzer or an additional analyzer. Incorporation of other clinical data may further include the use of an algorithm. Exemplary additional clinical data may include patient demographics, medical history, presenting complaint and vital signs, etc. In some aspects, the clinical data may preferably be clinical data available to the clinician and/or the analyzer prior to CBC results.

[0063] In various aspects, the systems and methods disclosed herein can identify individuals for discharge. For instance, in certain aspects, MDW values (alone or in combination with NLR) may be compared to one or more predetermined criteria to identify an individual as a candidate for discharge. In various aspects, parameter values may be obtained on multiple blood samples over the course of care, or observation. In such aspects, identifying an individual for discharge can aid in freeing up hospital resources, and/or allocate hospital resources more efficiently.

[0064] In practice, it has been found that both MDW and NLR can be used effectively for early recognition of bacteremia. For example, in an observational cohort study of emergency department patients 18 years or older for whom 9,400 blood cultures and 5,174 differential CBCs were ordered MDW and NLR were found to be suitable for early (i.e., at the time of CBC results) recognition of bacteremia, as shown by the performance data set forth below in table 1.

Table 1: illustrative performance data.

[0065] To further illustrate potential implementations and applications of the disclosed technology, below there is provided a non-exhaustive listing of non-limiting exemplary aspects. Any one or more of the features of these aspects may be combined with any one or more features of another example, embodiment, or aspect described herein. [0066] Illustrative Examples

[0067] Example 1

[0068] An automated method for screening a blood sample obtained from an individual for bacteremia, the method comprising: (a) presenting the blood sample to a transducer module, wherein the transducer module comprises: (i) an interrogation zone; and (ii) an illumination source configured to illuminate the blood sample in the interrogation zone; and (iii) at least one light sensor configured to detect illumination from cells from the blood sample in the interrogation zone; (b) delivering a hydrodynamically focused stream of the blood sample to the interrogation zone of the transducer module; (c) determining one or more hematology parameters for the blood sample based on measurements from the transducer module; and (d) screening, using a data processing module, the blood sample for bacteremia; wherein the data processing module comprises a processor and a tangible non-transitory computer readable medium, and the computer readable medium is programmed with a computer application that, when executed by the processor, causes the processor to perform screening acts comprising: performing one or more comparisons by, for at least one of the one or more hematology parameters, comparing that hematology parameter to a corresponding screening cutoff; and screening the blood sample for bacteremia based on the one or more comparisons

[0069] Example 2

[0070] An automated method for screening a blood sample obtained from an individual for bacteremia, the method comprising: (a) presenting the blood sample to a transducer module; (b) determining one or more hematology parameters for the blood sample based on measurements from the transducer module; and (c) screening, using a data processing module, the blood sample for bacteremia; wherein the data processing module comprises a processor and a tangible non-transitory computer readable medium, and the computer readable medium is programmed with a computer application that, when executed by the processor, causes the processor to perform screening acts comprising: performing one or more comparisons by, for at least one of the one or more hematology parameters, comparing that hematology parameter to a corresponding screening cutoff; and screening the blood sample for bacteremia based on the one or more comparisons.

[0071] Example 3

[0072] The automated method of any of examples 1-2, wherein the at least one hematology parameter comprises, for one or more white blood cell subtypes from: (a) monocytes; (b) neutrophils; (c) lymphocytes; (d) eosinophils; and (e) basophils; a population parameter from: (i) a count for that white blood cell subtype; (ii) an average volume for cells of that white blood cell subtype; (iii) a standard deviation of volume of cells of that white blood cell subtype; and (iv) a ratio of the count for that white blood cell subtype to a count for a different white blood cell subtype.

[0073] Example 4

[0074] The automated method of example 3, wherein the at least one hematology parameter comprises standard deviation of monocyte volume.

[0075] Example 5 [0076] The automated method of example 4, wherein the corresponding screening cutoff for standard deviation of monocyte volume is 20.

[0077] Example 6

[0078] The automated method of any of examples 3-5, wherein the at least one hematology parameter comprises neutrophil to lymphocyte ratio.

[0079] Example 7

[0080] The automated method of example 6, wherein the corresponding screening cutoff for neutrophil to lymphocyte ratio is 3.

[0081] Example 8

[0082] The automated method of any of examples 1-7, wherein screening the blood sample for bacteremia based on the one or more comparisons comprises indicating that the individual from whom the blood sample was obtained does not have bacteremia when any of the at least one of the one or more hematology parameters is below its corresponding screening cutoff.

[0083] Example 9

[0084] The automated method of any of examples 1-7, wherein screening the blood sample for bacteremia based on the one or more comparisons comprises indicating that the individual from whom the blood sample was obtained does not have bacteremia when each of the at least one of the one or more hematology parameters is below its corresponding screening cutoff. [0085] Example 10

[0086] A computer readable medium having stored thereon instructions to configure an analyzer which comprises a processor to prepare the method of any of examples 1-9.

[0087] Example 11

[0088] An automated system for screening a blood sample associated with an individual for bacteremia, the system comprising: (a) a transducer module configured to capture measurements of the blood sample, the transducer module comprising: (i) an interrogation zone; (ii) an illumination source configured to illuminate the blood sample in the interrogation zone; and (iii) at least one light sensor configured to detect illumination from cells from the blood sample in the interrogation zone; and (b) a data processing module in connectivity with the transducer module, the data processing module comprising a processor and a tangible non-transitory computer readable medium, wherein the computer readable medium is programmed with a computer application that, when executed by the processor, causes the processor to perform screening acts comprising: (i) determining one or more hematology parameters for the blood sample based on measurements from the transducer module; (ii) performing one or more comparisons by, for at least one of the one or more hematology parameters, comparing that hematology parameter to a corresponding screening cutoff; and (iii) screening the blood sample for bacteremia based on the one or more comparisons.

[0089] Example 12 [0090] An automated system for screening a blood sample associated with an individual for bacteremia, the system comprising: (a) a transducer module configured to capture measurements of the blood sample; and (b) a data processing module in connectivity with the transducer module, the data processing module comprising a processor and a tangible non-transitory computer readable medium, wherein the computer readable medium is programmed with a computer application that, when executed by the processor, causes the processor to perform screening acts comprising: (i) determining one or more hematology parameters for the blood sample based on measurements from the transducer module; (ii) performing one or more comparisons by, for at least one of the one or more hematology parameters, comparing that hematology parameter to a corresponding screening cutoff; and (iii) screening the blood sample for bacteremia based on the one or more comparisons.

[0091] Example 13

[0092] The system of any of examples 11-12, wherein the at least one hematology parameter comprises, for one or more white blood cell subtypes from:

(a) monocytes;

(b) neutrophils;

(c) lymphocytes;

(d) eosinophils; and

(e) basophils; a population parameter from:

(i) a count for that white blood cell subtype;

(ii) an average volume for cells of that white blood cell subtype;

(iii) a standard deviation of volume of cells of that white blood cell subtype; and

(iv) a ratio of the count for that white blood cell subtype to a count for a different white blood cell subtype. [0093] Example 14

[0094] The system of example 13, wherein the at least one hematology parameter comprises standard deviation of monocyte volume.

[0095] Example 15

[0096] The system of example 14, wherein the corresponding screening cutoff for standard deviation of monocyte volume is 20.

[0097] Example 16

[0098] The system of any of examples 13-15, wherein the at least one hematology parameter comprises neutrophil to lymphocyte ratio.

[0099] Example 17

[0100] The system of example 16, wherein the corresponding screening cutoff for neutrophil to lymphocyte ratio is 3.

[0101] Example 18

[0102] The system of any of examples 11-17, wherein screening the blood sample for bacteremia based on the one or more comparisons comprises indicating that the individual associated with the blood sample does not have bacteremia when any of the at least one of the one or more hematology parameters is below its corresponding screening cutoff.

[0103] Example 19 [0104] The system of any of examples 1 1-17, wherein screening the blood sample for bacteremia based on the one or more comparisons comprises indicating that the individual associated with the blood sample does not have bacteremia when each of the at least one of the one or more hematology parameters is below its corresponding screening cutoff.

[0105] The complete disclosure of all patents, patent applications, and publications, and electronically available material cited herein are incorporated by reference. In the event that any inconsistency exists between the disclosure of the present application and the disclosure(s) of any document incorporated herein by reference, the disclosure of the present application shall govern. The foregoing detailed description and examples have been given for clarity of understanding only.

[0106] No unnecessary limitations are to be understood therefrom. The invention is not limited to the exact details shown and described, for variations obvious to one skilled in the art will be included within the invention defined by the claims.

Claims

What is claimed is:

1. An automated method for screening a blood sample obtained from an individual for bacteremia, the method comprising:

(a) presenting the blood sample to a transducer module, wherein the transducer module comprises:

(i) an interrogation zone; and

(ii) an illumination source configured to illuminate the blood sample in the interrogation zone; and

(iii) at least one light sensor configured to detect illumination from cells from the blood sample in the interrogation zone;

(b) delivering a hydrodynamically focused stream of the blood sample to the interrogation zone of the transducer module;

(c) determining one or more hematology parameters for the blood sample based on measurements from the transducer module; and

(d) screening, using a data processing module, the blood sample for bacteremia; wherein the data processing module comprises a processor and a tangible non-transitory computer readable medium, and the computer readable medium is programmed with a computer application that, when executed by the processor, causes the processor to perform screening acts comprising: performing one or more comparisons by, for at least one of the one or more hematology parameters, comparing that hematology parameter to a corresponding screening cutoff; and screening the blood sample for bacteremia based on the one or more comparisons.

2. An automated method for screening a blood sample obtained from an individual for bacteremia, the method comprising:

(a) presenting the blood sample to a transducer module;

(b) determining one or more hematology parameters for the blood sample based on measurements from the transducer module; and

(c) screening, using a data processing module, the blood sample for bacteremia; wherein the data processing module comprises a processor and a tangible non-transitory computer readable medium, and the computer readable medium is programmed with a computer application that, when executed by the processor, causes the processor to perform screening acts comprising: performing one or more comparisons by, for at least one of the one or more hematology parameters, comparing that hematology parameter to a corresponding screening cutoff; and screening the blood sample for bacteremia based on the one or more comparisons.

3. The automated method of any of claims 1 -2, wherein the at least one hematology parameter comprises, for one or more white blood cell subtypes from:

(a) monocytes;

(b) neutrophils;

(c) lymphocytes;

(d) eosinophils; and

(e) basophils; a population parameter from:

(i) a count for that white blood cell subtype;

(ii) an average volume for cells of that white blood cell subtype;

(iv) a ratio of the count for that white blood cell subtype to a count for a different white blood cell subtype.

4. The automated method of claim 3, wherein the at least one hematology parameter comprises standard deviation of monocyte volume.

5. The automated method of claim 4, wherein the corresponding screening cutoff for standard deviation of monocyte volume is 20.

6. The automated method of any of claims 3-5, wherein the at least one hematology parameter comprises neutrophil to lymphocyte ratio.

7. The automated method of claim 6, wherein the corresponding screening cutoff for neutrophil to lymphocyte ratio is 3.

8. The automated method of any of claims 1-7, wherein screening the blood sample for bacteremia based on the one or more comparisons comprises indicating that the individual from whom the blood sample was obtained does not have bacteremia when any of the at least one of the one or more hematology parameters is below its corresponding screening cutoff.

9. The automated method of any of claims 1-7, wherein screening the blood sample for bacteremia based on the one or more comparisons comprises indicating that the individual from whom the blood sample was obtained does not have bacteremia when each of the at least one of the one or more hematology parameters is below its corresponding screening cutoff.

10. An automated system for screening a blood sample associated with an individual for bacteremia, the system comprising:

(a) a transducer module configured to capture measurements of the blood sample, the transducer module comprising:

(i) an interrogation zone;

(iii) at least one light sensor configured to detect illumination from cells from the blood sample in the interrogation zone; and

(b) a data processing module in connectivity with the transducer module, the data processing module comprising a processor and a tangible non-transitory computer readable medium, wherein the computer readable medium is programmed with a computer application that, when executed by the processor, causes the processor to perform screening acts comprising: (i) determining one or more hematology parameters for the blood sample based on measurements from the transducer module;

(ii) performing one or more comparisons by, for at least one of the one or more hematology parameters, comparing that hematology parameter to a corresponding screening cutoff; and

(iii) screening the blood sample for bacteremia based on the one or more comparisons.

11. An automated system for screening a blood sample associated with an individual for bacteremia, the system comprising:

(a) a transducer module configured to capture measurements of the blood sample; and

(b) a data processing module in connectivity with the transducer module, the data processing module comprising a processor and a tangible non-transitory computer readable medium, wherein the computer readable medium is programmed with a computer application that, when executed by the processor, causes the processor to perform screening acts comprising:

(i) determining one or more hematology parameters for the blood sample based on measurements from the transducer module;

12. The system of any of claims 10-11, wherein the at least one hematology parameter comprises, for one or more white blood cell subtypes from:

(a) monocytes;

(b) neutrophils;

(c) lymphocytes;

(d) eosinophils; and (e) basophils; a population parameter from:

(i) a count for that white blood cell subtype;

(ii) an average volume for cells of that white blood cell subtype;

13. The system of claim 12, wherein the at least one hematology parameter comprises standard deviation of monocyte volume.

14. The system of claim 13, wherein the corresponding screening cutoff for standard deviation of monocyte volume is 20.

15. The system of any of claims 12-14, wherein the at least one hematology parameter comprises neutrophil to lymphocyte ratio.

16. The system of claim 15, wherein the corresponding screening cutoff for neutrophil to lymphocyte ratio is 3.

17. The system of any of claims 10-16, wherein screening the blood sample for bacteremia based on the one or more comparisons comprises indicating that the individual associated with the blood sample does not have bacteremia when any of the at least one of the one or more hematology parameters is below its corresponding screening cutoff.

18. The system of any of claims 10-16, wherein screening the blood sample for bacteremia based on the one or more comparisons comprises indicating that the individual associated with the blood sample does not have bacteremia when each of the at least one of the one or more hematology parameters is below its corresponding screening cutoff.