Systems and methods for assessing immune responses to infection - Patents.com

Abstract

Systems and methods for characterizing immune responses to infection using cellular analysis, such as a hematology cellular analyzer. In some examples, the immune response can be characterized as normal or abnormal based on one or more blood cell population parameters. In some examples, the abnormal characterization can be used to identify patients with sepsis or patients at high risk of developing sepsis. In some embodiments, the present disclosure relates to a system for evaluating variations in cell population parameters. The system can include a flow cell through which a flow of liquid containing a plurality of cells passes.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application is related to and claims the benefit of Provisional Patent Application No. 62 / 873,806, filed on July 12, 2019, in the U.S. Patent and Trademark Office, entitled “Systems and Methods for Evaluating Immune Response to Infection.” [Background technology]

[0002] background Sepsis is a life-threatening organ dysfunction resulting from dysregulation of the host response to infection. Sepsis is a global healthcare crisis, affecting more than 30 million people worldwide each year. The incidence of sepsis is increasing at an annual rate of 1.5%, making it a major global healthcare concern. Sepsis has a high mortality rate, killing more individuals than prostate cancer, breast cancer, and HIV / AIDS combined.

[0003] In addition to the human toll, sepsis is costly to health care organizations: sepsis-related costs, which can include lengthy hospital stays, ICU admissions, readmissions, and extensive testing and monitoring of patients, exceed $24 billion.

[0004] Sepsis is a syndrome defined by a set of signs and symptoms. Sepsis is associated with infection, but there is no single cause of sepsis and it can result from bacterial, viral, or fungal infection. Of course, not all infections result in sepsis, and the etiology of sepsis is not well characterized at this time. Furthermore, there are no known biomarkers specific for sepsis. Clinicians can rely on non-specific indicators such as fever, white blood cell count (WBC), and altered mental status (AMS) to identify patients who may have sepsis. These tests are non-specific in that these indicators are present in a variety of conditions other than sepsis, including some cases of non-septic infection, trauma, burns, cancer, etc. Diagnostic tests are available, including tests for procalcitonin (PCT) and C-reactive protein (CRP), but are desirably neither specific nor sensitive for sepsis. That is, the available diagnostic tests have an undesirable but high percentage of both positive tests in patients who are not septic and negative tests in patients who are septic or have developed sepsis.

[0005] There remains a need for diagnostic tests that can help clinicians distinguish sepsis from other conditions, including the common cold, trauma, cancer, and non-septic inflammation. Summary of the Invention [Means for solving the problem]

[0006] A brief summary In some embodiments, the present disclosure relates to a system for evaluating variations in cell population parameters. The system may include a flow cell through which a flow of liquid containing a plurality of cells passes. The system may include one or more sensors for detecting light scattering, light transmission, electrical impedance, RF conductivity, or a combination thereof as the cells pass through the flow cell. The system may include a processor for calculating cell population parameters based on a plurality of measurements of individual cells of the same or related species. The processor may characterize an immune response to infection based at least in part on one or more of the cell population parameters.

[0007] According to a first aspect, some embodiments may provide a method for characterizing an inflammatory response to an infection. In some embodiments, such a method may include flowing a body fluid sample through a flow cell, irradiating a plurality of cells in the body fluid sample in the flow cell, measuring light scattering and direct current impedance from individual cells of the plurality of cells, identifying individual cells in the plurality of cells based at least in part on the light scattering measurement or the direct current impedance measurement, analyzing one or more cell population parameters, and characterizing an inflammatory response to an infection based at least in part on the one or more cell population parameters. In some such embodiments, the one or more cell population parameters can be selected from the group consisting of MO_DC_SD, NE_DC_SD, NE_DC_MEAN, NNRBC_UMALS_SD, MO_ALL_SD, NE_NO, LY_PC, NNRBC_MALS_SD, WNOP, MO_DC_MEAN, WDOP, NNRBC_UMALS_MEAN, BA_PC, NNRBC_MALS_MEAN, BA_PC, NNRBC_MALS_MEAN, EGC_LMALS_MEAN, NNRBC_DC_SD, NNRBC_LMALS_SD, NNRBC_ALL_MEAN, and combinations thereof. In some embodiments, the analysis of the cell population parameters is based at least in part on measurements of light scatter or direct current impedance.

[0008] According to the second aspect, in some embodiments as described with respect to the first aspect, the bodily fluid sample may be whole blood.

[0009] According to the third aspect, in some embodiments as described with respect to either the first or second aspect, the cell population parameters may be analyzed for cells derived from the plurality of cells classified as NNRBCs.

[0010] According to the fourth aspect, in some embodiments as described with respect to any of the first to third aspects, each analyzed cell population parameter may be compared to a corresponding reference range.

[0011] According to the fifth aspect, in some embodiments as described with respect to the fourth aspect, the inflammatory response to an infection may be characterized as abnormal if at least one of the analyzed cell population parameters is outside its corresponding reference range.

[0012] According to the sixth aspect, in some embodiments as described with respect to the fourth aspect, the inflammatory response to an infection may be characterized as abnormal if all of the analyzed cell population parameters are outside their corresponding reference ranges.

[0013] According to the seventh aspect, in some embodiments as described with respect to the fourth aspect, the method may comprise determining whether a measurement volume distribution width (MDW) for the population of monocytes is within an MDW reference range.

[0014] According to the eighth aspect, in some embodiments as described with respect to the seventh aspect, the inflammatory response to an infection may be characterized as abnormal if at least one of the analyzed cell population parameters is outside its corresponding reference range and the distribution width of the monocyte volume is greater than 19 channels.

[0015] According to the ninth aspect, in some embodiments as described with respect to the fourth aspect, the method may include determining whether a white blood cell count (WBC) in the bodily fluid sample is within a normal reference range.

[0016] According to the tenth aspect, in some embodiments as described with respect to the ninth aspect, the inflammatory response to the infection is characterized by at least one of the analyzed cell population parameters being outside its corresponding reference range and a WBC of 4,000 cells / mm 3 Less than or equal to 12,000 cells / mm 3 If it is more than that, it can be characterized as abnormal.

[0017] According to the eleventh aspect, in some embodiments as described with respect to the first aspect, the method may comprise performing a first comparison comparing a volume distribution width (MDW) measured for a population of monocytes in the bodily fluid sample to a MDW reference range. In some such embodiments, the method may comprise performing a second comparison comparing at least one of the analyzed cell population parameters to a corresponding reference range. In some such embodiments, the method may comprise performing a third comparison comparing a white blood cell count (WBC) in the bodily fluid sample to a WBC reference range. In some such embodiments, the inflammatory response to the infection may be characterized based on a combination of the first comparison, the second comparison, and the third comparison.

[0018] According to the twelfth aspect, in some embodiments as described with respect to the eleventh aspect, the inflammatory response to an infection may be characterized as abnormal if at least one of the analyzed cell population parameters is outside its corresponding reference range, the MDW is outside the MDW reference range, and the WBC is outside the WBC reference range.

[0019] According to the thirteenth aspect, in some embodiments as described with respect to the eleventh aspect, local determination rules are applied to characterize the inflammatory response to infection when at least one of the analyzed cell population parameters, MDW and WBC, are not all within or all outside the respective reference ranges.

[0020] According to a fourteenth aspect, some embodiments may provide a system comprising a transducer module for measuring at least light scattering and DC impedance due to cells passing through a flow cell of the method of any of the first to thirteenth aspects. In some such embodiments, a system may be provided comprising a processor configured with instructions stored on a non-transitory computer readable medium for performing the method of any of the first to thirteenth aspects.

[0021] According to the fifteenth aspect, in some embodiments as described with respect to the fourteenth aspect, the transducer module may include a means for measuring RF conductivity. In some such embodiments, the means for measuring RF conductivity may be operable to measure the RF conductivity of cells passing through a flow cell of the method of any of the first to thirteenth aspects and may also be operable to measure the RF conductivity of cells passing through a second flow cell included in the transducer module. [Brief description of the drawings]

[0022] [Figure 1] FIG. 1 is a flow chart of an exemplary cell analysis process according to an embodiment of the present disclosure.

[0023] [Diagram 2] FIG. 2 is a schematic diagram of an exemplary cell analysis system according to an embodiment of the present disclosure.

[0024] [Diagram 3] FIG. 3 is a diagram of an example transducer module and associated components according to an embodiment of the present disclosure.

[0025] [Figure 4] FIG. 4 is a simplified block diagram of an exemplary module system according to an embodiment of the present disclosure.

[0026] [Diagram 5] FIG. 5 is an exemplary heatmap highlighting markers that were differentially expressed between septic and non-septic patients according to an embodiment of the present disclosure.

[0027] [Figure 6] FIG. 6 is an AUC-ROC graph for selected biomarkers differentially expressed in sepsis patients according to an embodiment of the present disclosure.

[0028] [Figure 7] FIG. 7 is a flowchart of an exemplary possible algorithm according to an aspect of the disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0029] Detailed Description In traditional efforts to achieve this goal, diagnostic testing for sepsis includes hematological cell analysis. Abnormal white blood cell counts (WBC) are often associated with infection but are not specific for sepsis. More recently, evaluation of other cell populations, such as immature granular cells, has been proposed as a factor to consider in assessing the likelihood that a patient has or will develop sepsis. Other suggestions include examining cell population parameters, such as monocyte volume distribution width or neutrophil volume distribution width, as potential indicators of sepsis. However, the literature is replete with a variety of suggestions that are either vague suggestions to look for changes in specific cell subpopulations (e.g., neutrophils) without guidance as to which characteristics or population parameters may be appropriate, or highly specific suggestions to examine specific cell population parameters without generalizable insights. Thus, prior literature on the use of cell population parameters to evaluate or characterize immune responses to infection, and particularly to identify or predict sepsis, is not helpful in streamlining research designs to identify novel cell population parameters of interest for these purposes.

[0030] A blood sample from a patient can be analyzed manually, for example, by making a smear of blood on a slide and visually inspecting the slide. A manual operator can determine the cell count and identify the cells by type, e.g., red blood cells, platelets, white blood cells, perhaps using visual aids to facilitate counting and / or sorting of the cells on the slide. However, it is desirable to automate the analysis of blood samples from patients. In addition to the convenience of the automation process, an automated or semi-automated cell analysis system can, for example, count a significant number of cells in a blood sample or aggregate information about individual cells and / or cell populations that would be very difficult or impossible for a human to collect in a comparable sample size. These capabilities are important to create enough data points for cell population statistics, such as distribution widths, that are robust based on sample size.

[0031] As used herein, "patient" refers to a person or other animal from whom a bodily fluid sample is taken, and may apply to a research subject, an outpatient (a human or animal seen for a brief visit to a physician, typically for a period of less than a day, for medical evaluation or treatment), an inpatient (a human or animal admitted to a medical facility for a day or longer, including but not limited to, a hospital, hospice care, rehabilitation facility, etc.), or otherwise. In some embodiments, a patient may be under the current care of a clinician, such as a physician, medical assistant, nurse, nursing professional, chiropractor, surgeon, dentist, etc. In some embodiments, a "patient" may donate a sample without receiving any medical evaluation or treatment from the donation.

[0032] The cell analysis system can use a variety of techniques to identify, count, and / or characterize cells. For example, the cell analysis system can use electrical impedance to determine the volume and amount of cells passing through an interrogation zone in a flow cell. As another example, the cell analysis system can use imaging techniques, either in a stationary system or in a flow cell, to capture an optical representation of the cells and analyze the optical representation (whether human understandable or subject to conversion into a human understandable image) to determine the size and amount of cells in the interrogation zone. As yet another example, the cell analysis system can use flow cytometry, which illuminates cells passing through a flow cell and measures the transmission and / or scattering of light passing through the cells. Light scattering can inherently distinguish different cells of different types, sizes, or characteristics, or the cells can be prepared with markers, such as fluorescent markers, to facilitate identification, quantification, and / or characterization of the cells based on the characteristics of the cells marked or unmarked by the marker. The cell analysis system can use a combination of these techniques and / or other techniques to count, identify, and / or characterize the cells. For example, a cell analysis system may use a combination of electrical impedance and light scattering to analyze cells in a blood sample. When a combination of techniques is used, the techniques may utilize hardware configured in a serial progression (e.g., the same sample or aliquots of a sample are passed through multiple separate interrogation zones), or in a parallel progression (e.g., different aliquots of a sample are passed through multiple separate interrogation zones essentially simultaneously), or two or more techniques may be utilized essentially simultaneously (e.g., a flow cell may be equipped to measure both electrical impedance and light scattering from the same sample or aliquots of a sample in the same flow cell essentially simultaneously). In this regard, essentially simultaneously means that the processing is performed at overlapping time intervals on the same sample or different aliquots of the same sample.It is not essential to the practice of the invention that the different techniques be coordinated to occur at exactly the same time, or at time intervals of equal duration.

[0033] The sample for analysis can be any biological fluid that contains cells. The biological fluid can be, for example, blood. The sample can be whole blood, for example, blood that has not been treated or modified except for the possible addition of an anticoagulant to prevent the blood from clotting, which complicates the flow of blood through a flow cell for analysis. The sample can be treated, for example, by dilution, concentration, separation into components (e.g., plasma, serum, and cells); pretreatment with a sphering agent (e.g., with a cytometric marker, lysis to destroy / remove certain cell types, stains to modify the appearance of one or more cells, etc.), or can be useful to prepare the sample for analysis. The blood can be human blood or non-human animal blood. In some circumstances, the sample can be from a non-blood bodily fluid, such as urine, joint fluid, saliva, bile, cerebrospinal fluid, amniotic fluid, semen, mucus, sputum, lymphatic fluid, aqueous humor, tears, vaginal secretions, pleural fluid, pericardial fluid, peritoneal fluid, etc. Similar to blood, when sampling non-blood body fluids, non-blood body fluids can be treated, for example concentrated, or cells concentrated by centrifugation to achieve desired cell concentration, or to concentrate or modify a certain subpopulation of cells for analysis.The possible advantage of evaluating whole blood can be that there are more cells available for analysis in a relatively small sample.The possible advantage of analyzing non-blood body fluids and / or processed blood can be the pre-separation of specific cells of interest and / or a reduction in the number of cells, due to the differences in cell types and numbers that usually occur in different body fluids.A smaller number of cells can be useful, for example, to characterize individual cells.

[0034] In some embodiments, cells passing through the flow cell are analyzed using light scattering. As shown in FIG. 1, a method 100 for assessing morphology of a cell population may include flowing 110 a sample through a flow cell. Cells in the sample flowing through the flow cell may be illuminated 120 with visible light. The cell analysis system may include one or more sensors that allow the analyzer to measure 130 light transmission and / or scattering as the cells are illuminated in the flow cell. The cell analysis system may include a processor or means for communicating with a remote processor that collects 140 light transmission and / or scattering for a plurality of cells in the sample as the cells flow through the flow cell. The processor may use an algorithm to identify 150 cells based at least in part on the light transmission and / or scattering. The processor or a separate processor may analyze 160 the light transmission and / or scattering data for a particular cell or a population of a particular cell type (e.g., monocytes, neutrophils, red blood cells). Analysis can include, for example, calculating parameters such as extremes, ranges, standard deviations, distribution widths, etc. for specific measurements such as cell volume or light scatter, and / or for specific cell types such as monocytes, neutrophils, or NNRBCs. For example, the cell analysis system may calculate light scatter, or the standard deviation of a specific angle of light scatter, such as upper median angle light scatter (UMALS), for cells identified as NNRBCs. In some embodiments, measurements 130 can include alternative measurements of cell size and / or granularity, such as image analysis, electrical impedance, radio frequency (RF) response, flow cytometry with or without markers, alone or in combination or supplemental combination, with or without measurements of light transmission and / or light scatter.

[0035] FIG. 2 illustrates a schematic of a cellular analysis system 200. As shown herein, the system 200 includes a preparation system 210, a transducer module 220, and an analysis system 230. While the system 200 is generally described with three core system blocks (210, 220, and 230), one skilled in the art will readily appreciate that the system 200 may include other system components such as a central control processor, a display system, a fluidic system, a temperature control system, a user safety control system, and the like. In operation, a whole blood sample (WBS) 240 may be presented to the system 200 for analysis. In some examples, the WBS 240 is aspirated into the system 200. Exemplary aspiration techniques are known to those skilled in the art. After aspiration, the WBS 240 may be delivered to the preparation system 210. The preparation system 210 may perform operations involved in obtaining the WBS 240 and preparing the WBS 240 for further measurement and analysis. For example, the preparation system 210 may separate the WBS 240 into one or more predefined aliquots for presentation to the transducer module 220. In some embodiments, the preparation system 210 may not alter the composition of the WBS 240. Alternatively, the preparation system 210 may include mixing chambers such that appropriate reagents may be added to one or more of the aliquots. For example, if an aliquot is to be examined for differentiation of a white blood cell subset population, a lysis reagent (e.g., ERYTHROLYSE, red blood cell lysis buffer) may be added to the aliquot to disrupt and remove the RBCs. The preparation system 210 may also include a temperature control component to control the temperature of the reagents and / or the mixing chamber. Proper temperature control may improve the consistency of operation of the preparation system 210 and may facilitate pretreatment of cells in the sample, for example with fluorescent markers, stains, or lysis.

[0036] In some examples, one or more predefined aliquots can be transferred from preparation system 210 to transducer module 220. As described in more detail below, transducer module 220 can perform measurements of light transmission and / or light scattering of cells from individually passing WBS 240. The measured light propagation (e.g., light transmission, light scattering) parameters can be provided or transmitted to analysis system 230 for data processing. In some examples, analysis system 230 can include computer processing capabilities and / or one or more modules or components, such as those described herein for the system shown in FIG. 4 and further described below, that can evaluate the measured parameters, identify and enumerate at least one of the blood cell constituents, and calculate cell population parameters for one or more cell populations within the aliquots. As shown herein, the cellular analysis system 200 can generate or output a report 250 containing measured and / or calculated parameters for one or more cell populations within the aliquot, such as the volume distribution width of monocytes, the volume distribution width of neutrophils, the count or percentage of immature granular cells, and / or the standard deviation of the UMALS measurements for NNRBC. In some examples, excess biological sample from the transducer module 220 can be directed to an external (or internal) waste system 260. An exemplary cellular analysis system is the Beckman Coulter DxH hematology analyzer, which measures DC impedance to determine cell volume, conductivity, and light scatter for cytoplasmic granularity and nuclear structure.

[0037] There are currently no hematological assays that can definitively diagnose sepsis, as there are no known biomarkers specific for sepsis (e.g., in the sense that identifying malaria parasites in blood cells definitively indicates malaria infection). However, identifying, enumerating, and / or characterizing one or more cell populations in a patient sample, potentially in combination with clinical signs and symptoms and other testing or characterization studies, can provide information that can reliably increase or decrease clinical suspicion of sepsis or the onset of sepsis. In particular, because sepsis is a syndrome defined based on clinical symptoms and changes in cell populations can be observed before the clinical symptoms of sepsis, cell population data can help identify patients at high risk of developing sepsis, allowing for preventative treatment. This is advantageous because preventative treatment often involves the administration of antibiotics, antivirals, and / or antifungals, which pose challenges. For example, overuse of antibiotics in patients who are not septic or have not developed sepsis can contribute to the development of antimicrobial resistance. In addition, some drugs may have side effects or induce adverse events that may be dangerous to patients who are seriously ill or whose clinical condition is compromised.Therefore, tests that can help clinicians develop informed clinical treatment plans are valuable even if they are not definitive diagnostics.In addition, characterizing and / or enumerating the cell populations that change in patients during or prior to the onset of sepsis may be useful for non-diagnostic purposes, such as investigating the etiology or progression of sepsis, or observing cellular responses to infection.

[0038] In some embodiments, analysis of patient samples allows clinicians to initiate and / or modify treatment regimens. Treatment regimens may include administration of one or more drugs or therapeutic agents to an individual to address the patient's condition. Any of a variety of therapies can be used to treat an individual identified as having an abnormal immune response to infection or having one or more abnormal cell population parameters as discussed herein. Exemplary therapies include administration of fluids, vasopressors, antibiotics, antifungals, antivirals, vitamins (including thiamine), minerals, steroids (including corticosteroids), and combinations thereof. In some instances, patients may receive more or less rigorous monitoring, including hospitalization for specialized observation, based on analysis of patient samples.

[0039] 3 illustrates the transducer module and more detailed associated components in greater detail. As shown herein, the system 300 includes a transducer module 310 having a light source or illumination source, such as a laser 312 that emits a beam 314. The laser 312 can be, for example, a 635 nm, 5 mW, solid-state laser. In some examples, the system 300 can include a focus alignment system 320 that adjusts the beam 314 so that the resulting beam 322 is focused and positioned in a cell interrogation zone 332 of a flow cell 330. In some examples, the flow cell 330 receives a sample aliquot from the preparation system 302. Various fluidic mechanisms and techniques can be utilized for hydrodynamic focusing of the sample aliquot within the flow cell 330.

[0040] In some examples, the aliquot flows through the cell interrogation zone 332 such that a component of it passes through the cell interrogation zone 332 generally one at a time. In some cases, the system 300 may include a transducer module or cell interrogation zone or other features of a hematology analyzer 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, the cell interrogation zone 332 may be defined by a square cross-section measuring approximately 50×50 microns and having a length (measured in the direction of flow) of approximately 65 microns. The flow cell 330 may include an electrode assembly having first and second electrodes 334, 336 for performing DC impedance and / or RF conductivity measurements of cells passing through the cell interrogation zone 332. The signals from the electrodes 334, 336 can be transmitted to the analysis system 304. The electrode assemblies can be analyzed to characterize the volume and conductivity of cells using low frequency and high frequency currents, 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. Since the cell walls act as conductors for the high frequency current, the high frequency current can be used to detect differences in the insulation of the cellular components as the current passes through the cell walls and each cell interior. The high frequency current can be used to characterize the nuclear and granular components as well as the chemical composition of the cell interior.

[0041] While the light source in FIG. 3 is described as a laser, the light source may alternatively or additionally include a xenon lamp, an LED lamp, an incandescent lamp, or any other suitable light source, including combinations of the same or different types of lamps (e.g., multiple LED lamps or at least one LED lamp and at least one xenon lamp). As shown in FIG. 3, for example, incident beam 322 illuminates cells passing through cell interrogation zone 332, resulting in light propagation (e.g., scattering, transmission) within an angular range α emanating from zone 332. Exemplary systems include a sensor assembly capable of detecting light within one, two, three, four, five, or more angles within angular range α, including light relevant to measuring absorbance or on-axis light loss. As shown, light propagation 340 may be detected by a light detection assembly 350, optionally having a light scatter detector unit 350A and a light scatter and / or transmission detector unit 350B. In some examples, the light scatter detector unit 350A includes a photoactive region or sensor zone for detecting and measuring upper median angle light scatter (UMALS), e.g., light scattered or propagating at an angle relative to the axis of a light beam in a range of about 20 to about 42°. In some examples, UMALS corresponds to light propagating within an angle range of about 20 to about 43° relative to the axis of an incident beam illuminating cells flowing through the interrogation zone. The light scatter detector unit 350A also includes a photoactive region or sensor zone for detecting and measuring lower median angle light scatter (LMALS), e.g., light scattered or propagating at an angle relative to the axis of a light beam in a range of about 10 to about 20°. In some examples, LMALS corresponds to light propagating within an angle range of about 9 to about 19° relative to the axis of an incident beam illuminating cells flowing through the interrogation zone.

[0042] The combination of UMALS and LMALS is defined as median angle light scatter (MALS), which can be light scattering or propagation at angles of about 9° to about 43° relative to the axis of the incident beam illuminating cells flowing through the interrogation zone. One of skill in the art will appreciate that these angles (and other angles described herein) can vary somewhat based on the configuration of the interrogation, sensing, and analysis systems.

[0043] As shown in FIG. 3, light scatter detector unit 350A includes an aperture 351 that allows low angle light scatter or propagation 340 to pass beyond light scatter detector unit 350A and thereby reach and can be detected by light scatter and transmission detector unit 350B. According to some embodiments, light scatter and transmission detector unit 350B includes a photoactive region or sensor zone for detecting and measuring low angle light scatter (LALS), e.g., light scattered or propagated at an angle relative to the axis of the irradiated light beam of less than about 5.1°. In some examples, LALS corresponds to light propagating at an angle less than about 9° relative to the axis of the incident beam irradiating the cells flowing through the interrogation zone. In some examples, LALS corresponds to light propagating at an angle less than about 10° relative to the axis of the incident beam irradiating the cells flowing through the interrogation zone. In some examples, LALS corresponds to light propagating at an angle of about 1.9°±0.5° relative to the axis of the incident beam irradiating the cells flowing through the interrogation zone. In some examples, LALS corresponds to light propagating at an angle of about 3.0°±0.5° relative to the axis of the incident beam illuminating the cells flowing through the interrogation zone. In some examples, LALS corresponds to light propagating at an angle of about 3.7°±0.5° relative to the axis of the incident beam illuminating the cells flowing through the interrogation zone. In some examples, LALS corresponds to light propagating at an angle of about 5.1°±0.5° relative to the axis of the incident beam illuminating the cells flowing through the interrogation zone. In some examples, LALS corresponds to light propagating at an angle of about 7.0°±0.5° relative to the axis of the incident beam illuminating the cells flowing through the interrogation zone. In each example, LALS may correspond to light propagating at an angle of about 1.0° or greater. That is, LALS can correspond to light propagating at an angle of about 1.0° to about 1.9°; about 1.0° to about 3.0°; about 1.0° to about 3.7°; about 1.0° to about 5.1°; about 1.0° to about 7.0°; about 1.0° to about 9.0°; or about 1.0° to about 10.0°.

[0044] According to some embodiments, the light scattering and transmission detector unit 350B may include a photoactive region or sensor zone for detecting and measuring light that transmits axially through the cell or propagates from the illuminated cell at an angle of about 0° relative to the axis of the incident light beam. In some examples, the photoactive region or sensor zone can detect and measure light that propagates axially from the cell at an angle of less than about 1° relative to the axis of the incident light beam. In some cases, the photoactive region or sensor zone can detect and measure light that propagates axially from the cell at an angle of less than about 0.5° relative to the axis of the incident light beam. Such measurements of axially transmitted or propagated light correspond to axial light loss (ALL or AL2). As shown in previously incorporated U.S. Patent No. 7,390,662, when light interacts with a particle, some of the incident light is changed direction through a scattering process (i.e., light scattering) and some of the light is absorbed by the particle. Both of these processes remove energy from the incident beam. When viewed along the incident axis of the beam, light loss can be referred to as forward extinction or axial light loss. Further aspects of the technique for measuring on-axis optical loss are described in US Pat. No. 7,390,662 at column 5, line 58 to column 6, line 4.

[0045] Thus, the cellular analysis system 300 provides a means for obtaining measurements of light propagation, including light scatter and / or light transmission for light emanating from illuminated cells of a biological sample at any of a variety of angles or within any of a range of angles, including ALL and a number of distinct light scatter or propagation angles. For example, the light detection assembly 350, including appropriate circuitry and / or processing units, provides a means for detecting and measuring UMALS, LMALS, LALS, MALS, and ALL.

[0046] Wires or other transmission or connection mechanisms can transmit signals from the electrode assembly (e.g., electrodes 334, 336), light scatter detector unit 350A, and / or light scatter and transmission detector unit 350B to the analysis system 304 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 304 for data processing. In some examples, the analysis system 304 can include computer processing capabilities and / or one or more modules or components, such as those described herein for the system shown in FIG. 4, that can evaluate the measured parameters, identify and enumerate constituents of the biological sample, and correlate a subset of data characterizing the elements of the biological sample with one or more features or parameters of interest. As shown herein, the cellular analysis system 300 can generate or output a report 306 presenting measurements or calculated parameters made for the sample, such as UMALS averages for WBC, MDW, or NNRBC. In some examples, excess biological sample from the transducer module 310 can be directed to an external (or alternatively internal) waste system 308. In some examples, the cellular analysis system 300 can include one or more features of transducer modules or blood analysis instruments such as those described in previously incorporated U.S. Patent Nos. 5,125,737; 6,228,652; 8,094,299; 8,189,187; and 9,939,453.

[0047] FIG. 4 is a simplified block diagram of an exemplary module system broadly illustrating how individual system elements for the module system 600 may perform separately or in a more integrated manner. The module system 600 may be part of or connected to the cellular analysis system 200. The module system 600 is well suited for generating data or receiving inputs related to cellular analysis. In some examples, the module system 600 includes hardware elements that are electrically coupled via a bus subsystem 602, including one or more processors 604, one or more input devices 606, such as user interface input devices, and / or one or more output devices 608, such as user interface output devices. In some examples, the system 600 includes a network interface 610 and / or a diagnostic system interface 640 that can receive signals from and / or send signals to a diagnostic system 642. In some examples, system 600 includes software elements, such as programs configured to perform one or more aspects of the techniques disclosed herein, shown herein as currently resident in working memory 612 of memory 614, operating system 616, and / or other code 618. Memory 614 can be non-transitory and / or implemented in a tangible medium such as hardware.

[0048] In some embodiments, the module system 600 may include a storage subsystem 620 that can store basic programming and data constructs that provide the functionality of the various techniques disclosed herein. For example, software modules that perform the functionality of aspects of the methods as disclosed herein may be stored in the storage subsystem 620. These software modules may be executed by one or more processors 604. In a distributed environment, software modules may be stored in multiple computer systems and executed by processors of multiple computer systems. The storage subsystem 620 may include a memory subsystem 622 and a file storage subsystem 628. The memory subsystem 622 may include several memories, including a primary random access memory (RAM) 626 for storage of instructions and data during program execution, and a read-only memory (ROM) 624 for storing fixed instructions. The file storage subsystem 628 may provide persistent (non-volatile) storage for program and data files, and may include tangible storage media that may implement patient, treatment, assessment, or other data as needed. The file storage subsystem 628 may include hard disk drives, floppy disk drives with associated removable media, compact digital read-only memory (CD-ROM) drives, optical drives, DVDs, CD-Rs, CD-RWs, solid-state removable memory, other removable media cartridges or disks, and the like. One or more, or all, of the drives may be located at remote locations of the computer that connect to other at other locations coupled to the module system 600. In some examples, the system may include a computer-readable or tangible storage medium that stores one or more sequences of instructions that, when executed by one or more processors, cause the one or more processors to perform any aspect of the techniques or methods disclosed herein. One or more modules that perform the functionality of the techniques disclosed herein may be stored by the file storage subsystem 628.In some embodiments, the software or code provides a protocol that allows the module system 600 to communicate with a communications network 630. If desired, such communications may include dial-up or internet connection communications.

[0049] System 600 can be configured to perform various aspects of the disclosed methods. For example, processor component or module 604 can be a microprocessor control module configured to receive cellular parameter signals from a sensor input device or module 632, from a user interface input device or module 606, and / or from a diagnostic system 642 via a diagnostic system interface 640, and / or a network interface 610 and a communication network 630, as appropriate. In some examples, the sensor input device can be included in or be part of a cellular analysis system equipped to obtain detection parameters of multiple light angles, such as a Beckman Coulter DxH™ hematology analyzer. In some examples, the user interface input device 606 and / or the network interface 610 can be configured to receive cellular parameter signals generated by a cellular analysis system equipped to obtain detection parameters of multiple light angles, such as a Beckman Coulter DxH™ hematology analyzer. In some examples, diagnostic system 642 can include or be part of a cellular analysis system equipped to obtain detection parameters of multiple light angles, such as a Beckman Coulter DxH™ hematology analyzer.

[0050] The processor component or module 604 can also be configured to transmit the cellular parameter signal, optionally processed by any of the techniques disclosed herein or known to those skilled in the art, to a sensor output device or module 636, a user interface output device or module 608, a network interface device or module 610, a diagnostic system interface 640, or any combination thereof. Each of the devices or modules according to the embodiments of the present disclosure can include one or more software modules of a computer readable medium processed by a processor, or hardware modules, or any combination thereof. Any of a variety of commonly used platforms, such as Windows, MacIntosh, and Unix, can be used with any of a variety of commonly used programming languages ​​to execute the embodiments of the present disclosure.

[0051] The user interface input devices 606 may include, for example, pointing devices such as touchpads, keyboards, mice, trackballs, graphic tablets, scanners, joysticks, touch screens integrated into displays, voice recognition systems, voice input devices such as microphones, and other types of input devices. The user input devices 606 may also download computer executable code from a tangible storage medium or a communications network 630, the code implementing any of the methods disclosed herein or aspects thereof. It will be understood that the software of the terminal may be updated from time to time and downloaded to the terminal as appropriate. In general, use of the term "input device" is intended to include a variety of conventional dedicated devices and methods of inputting information into the module system 600.

[0052] The user interface output device 606 may include, for example, a display subsystem, a printer, a facsimile machine, or a non-visual display such as an audio output device. The display subsystem may be a flat panel device such as a cathode ray tube (CRT), a liquid crystal display (LCD), a projection device, or the like. The display subsystem may also provide a non-visual display, such as via an audio output device. In general, use of the term "output device" is intended to include a variety of conventional dedicated devices and methods for outputting information from the module system 600 to a user. In some examples, the cellular analysis system does not directly include a user interface output device, but instead transfers data to a network, a computer processor, or a non-transitory computer-readable storage medium, while displaying the data for a human user occurs in conjunction with that device or a device that further transfers the data from the cellular analysis system after the initial transfer. When data is transferred from the analyzer without being displayed, the transferred data may be raw sensor data, or processed data, or a combination of raw and processed data.

[0053] Bus subsystem 602 provides a mechanism for allowing the various components and subsystems of module system 600 to communicate with one another as intended or desired. The various subsystems and components of module system 600 need not be in the same physical location, but may be distributed at various locations within a distributed network. Although bus subsystem 602 is shown generally as a single bus, alternative embodiments of the bus subsystem may utilize multiple buses.

[0054] The network interface 610 can provide an interface to an external network 630 or other devices. The external communication network 630 can be configured to communicate with other systems as needed or desired. Thus, it can receive electronic packets from the module system 600 and forward any information to the module system 600 as needed or desired. As shown herein, the communication network 630 and / or the diagnostic system interface 642 can transmit information to or receive information from a diagnostic system 642 equipped to obtain detection parameters for multiple light angles, such as a Beckman Coulter DxH™ cellular analysis system. As non-limiting examples, the external communication network 630 can be used to transmit data between the cellular analysis system and a research database, a laboratory information system (LIS), an electronic medical record (EMR), and the like. In some examples, the communication can be one-way with information flowing from the cellular analysis system to other systems. In some examples, communication may be one-way, with information (e.g., orders for certain measurements made or population parameters calculated) flowing from an external system, which may be remote to the cellular analysis system or physically adjacent to the cellular analysis system. In some examples, communication may be two-way. In some examples, information communicated to the cellular analysis system by an external device may include patient information useful for assessing the significance of the cellular measurements. For example, some reference ranges for hematology parameters may differ in the general adult population versus the pediatric population or specific patient subpopulations, and the cellular analysis system may take into account the patient information when determining whether to flag the analysis results for further review.

[0055] In addition to providing such infrastructure communications links within the system, the communications network system 630 may also provide connections to other networks, such as the Internet, and may include wired, wireless, modem, and / or other types of interface connections.

[0056] It will be apparent to one skilled in the art that substantial variations can be used according to particular requirements. For example, customized hardware can also be used, and / or particular elements can be implemented in hardware, software (including portable software such as applets), firmware, or a combination thereof. Additionally, connections to other computing devices, such as network input / output devices, can be utilized. The modular terminal system 600 itself can be of various types, including computer terminals, personal computers, portable computers, workstations, network computers, or any other data processing systems. Due to the ever-changing nature of computers and networks, the description of the modular system 600 shown in FIG. 4 is intended as a specific example only, for purposes of illustrating one or more embodiments of the present disclosure. Many other configurations of the modular system 600 may have more or fewer components than the modular system shown in FIG. 4. Any of the modules or components of the modular system 600, or any combination of such modules or components, can be configured to be coupled to or integrated with any of the embodiments of the cellular analysis system disclosed herein, or be in connectivity with any of the embodiments. Relatedly, any of the hardware and software components described above may be configured to integrate with or interface with other medical evaluation or treatment systems for use in other locations.

[0057] In some embodiments, the module system 600 can be configured to receive one or more cellular analysis parameters of a patient at an input module. The cellular analysis parameter data can be transmitted to an evaluation module that further processes and / or evaluates the raw sensor data or partially analyzed sensor data with respect to additional information, which may include previous laboratory test results of the same patient; laboratory test results from analysis on other types of analyzers or laboratory tests; non-laboratory laboratory data about the patient, such as patient distress, diagnostic history, vitals, or physical examination findings, or a combination thereof. Cellular analyses, such as WBC, MDW, NNRBC UMALS average, and other cell population parameters, can be output to a system user via an output module. In some examples, the module system 600 can determine an initial or guided treatment protocol, or an adjusted treatment protocol, for the patient based on one or more cellular analysis parameters and / or the predicted sepsis status, for example by using a treatment module. The treatment can be output to a system user via an output module. If desired, certain aspects of the treatment can be determined by an output device and transmitted to a treatment system or a sub-device of the treatment system. Any of a variety of data regarding the patient can be entered into the module system, including age, weight, sex, treatment history, medical history, etc. Parameters of a treatment regimen or diagnostic evaluation can be determined based on such data.

[0058] The analysis system 304 of the transducer module 300 or other code programs 618 of the module system 600, or both, may include one or more algorithms for processing the sensor data produced by the transducer module 300. The one or more algorithms may process the sensor data to identify and count cells. For example, individual cells may be identifiable based on light scattering and / or light transmission data that provides an indication of the size and certain surface properties of the cells. As another example, electrical impedance may provide an indication of the size of the cells. Radio frequency conductivity may provide an indication of the components of the cells that may be useful in distinguishing granular cells and nucleated versus non-nucleated cells. Markers, stains, image analysis, or other measurement techniques may be used to identify cells as, for example, NNRBCs, WBCs, monocytes, neutrophils, etc. The algorithm may count the number of signals consistent with a given cell type in an interrogation period, which may be defined by the time through the flow cell or imaging time, or by the volume of bodily fluid tested, or both.

[0059] In some cases, the signal produced is a continuous or ordinal value, and the magnitude or other characteristics of the signal can be further analyzed. For example, higher electrical impedance values ​​typically indicate larger cells, which can be useful for identifying specific cells. Electrical impedance values ​​also correlate with cell volume, and thus the magnitude of the signal across many cells in a sample can also convey useful information about subpopulations of cells. For example, electrical impedance values ​​can help identify monocytes and the characteristics of the volume distribution for monocytes, as subpopulations of cells can convey information about immune responses to infection.

[0060] 7 is a flow chart for an exemplary algorithm of potential use in the practice of the present disclosure. As shown, a single algorithm 700 encompasses all of the operations shown, although different algorithms or even different software may be used, in particular, various operations may be performed by different algorithms or different software that may reside on or be processed by different hardware. The algorithm 700 identifies 705 a cell type from the sensor signal. The algorithm 700 may calculate 710 one or more cell population parameters. The algorithm 700 may compare 715 the calculated cell population parameters to a reference range for each of the cell population parameters. The algorithm 700 classifies 720 the cell population parameters as normal or abnormal with respect to the respective reference range. Based at least in part on the classification of the cell population parameters as normal or abnormal, the algorithm 700 may characterize 725 an immune response to an infection as normal or abnormal. Based at least in part on the classification of the cell population parameters as normal or abnormal, the algorithm 700 may characterize 730 a septic state of the patient. The sepsis status may be binary, for example, yes or no. The sepsis status may be presented as a probability. The sepsis status may be presented as a qualitative flag, for example, an indication that one or more cell population parameters are consistent with sepsis. The sepsis status may be presented as a risk level, for example, low risk, medium risk, or high risk of sepsis.

[0061] The algorithm 700 can use a single cell population parameter to characterize the immune response to infection and / or the septic state. Alternatively, the algorithm 700 can use two or more cell population parameters to characterize the immune response to infection and / or the septic state. In some embodiments, the algorithm 700 can use as many as 26 cell population parameters to characterize the immune response to infection and / or the septic state. For any number of cell population parameters considered, if all of the appropriate cell population parameters are normal based on their respective reference ranges, the immune response to infection is normal for this purpose and the patient is not identified as septic. Then, if one or more of the NNRBC-UMALS-SD, MDW, and WBC are abnormal relative to their respective reference ranges, the overall result is abnormal for this purpose and the algorithm can apply a global or local decision rule. A global decision rule is a threshold or action that is applied uniformly to all data processed by the algorithm 700. In contrast, local decision rules can be appreciated to allow different facilities or different specialists to establish different rules for identifying patients as having an abnormal immune response to infection or as being septic or at high risk of developing sepsis. Local decision rules allow facilities to adapt the specificity (ability to comprehensively identify many or all possible cases of sepsis) and sensitivity (ability to exclude many or all non-septic cases) of the algorithm to reduce false negatives or false positives, respectively. In many or all cases, it is envisioned that if all of the appropriate cell population parameters are abnormal, the result is flagged as abnormal, and if the result is used to identify sepsis, the patient is identified as having sepsis or at high risk of developing sepsis. If the results are neither all normal nor all abnormal, the decision rules determine whether to flag the result as a whole as abnormal with respect to the immune response to infection and / or the septic state.In some examples, the decision rule may weight different cell population parameters based on observed correlations to sepsis conditions in prior clinical cases (e.g., clinical trials). The overall result (e.g., across multiple appropriate cell population parameters of interest) may be flagged as abnormal if the weighted majority or overwhelming majority of the appropriate cell population parameters are abnormal. The observed correlations may be based on the correlations of individual cell population parameters to sepsis conditions, or the multivariate model may be based on multiple cell population parameters. The overall result may be flagged as abnormal if any appropriate cell population parameter is abnormal. The overall result may be flagged as abnormal if a particular appropriate cell population parameter is abnormal, even if the others are normal. For example, the overall result may be flagged as abnormal if the NNRBC UMALS measurements (e.g., mean or standard deviation), MDW, and WBC are abnormal, even if other cell population parameters appropriate for immune response or sepsis are normal. That is, a selected cell population parameter or combination of cell population parameters may be sufficient to treat the overall result as abnormal. The overall result can be flagged as abnormal only if certain appropriate cell population parameters or combinations of cell population parameters are abnormal, even if others are normal. That is, selected cell population parameters may be required to treat the overall result as abnormal. For example, the overall result can be considered normal if the WBC is normal, even if the MDW, NNRBC UMALS measurements, or others are abnormal.

[0062] If the overall result is identified by the algorithm 700 as abnormal, this may be presented as a separate analysis result (e.g., did it indicate sepsis? Yes / No, or measurements of immune response: normal / abnormal) or may be presented as a flag for review by the laboratory technician and / or clinician (e.g., a letter or other symbol or indicator in the report indicating that the results indicate an abnormal immune response to infection and / or indicate possible sepsis). Of course, in some instances, the algorithm 700 may not apply any decision rules and defer to the laboratory technician and / or clinician to interpret the results of the cellular analysis.

[0063] As mentioned above, a relatively new use of cell analysis is the assessment of the possibility that a patient has sepsis or is at high risk of developing sepsis (compared to healthy people of similar age) for a short period of time (one week or less). Such assessments have so far focused on white blood cells, or subpopulations of white blood cells, such as monocytes or immature granulocytes. However, different measurements of white blood cells, and different subpopulations of white blood cells (e.g., monocytes, lymphocytes, eosinophils, basophils, neutrophils, granulocytes, immature granulocytes, or combinations thereof) are more indicative of immune response dysfunction to infection and / or sepsis than others. Furthermore, the inventors have surprisingly found that there may be measurable distinctions in heterogeneous populations of circulating blood cells, such as NNRBC, when a patient may have or develop a dysfunction of immune response to infection or sepsis.

[0064] Figure 5 is a heatmap highlighting markers that were differentially expressed between septic and non-septic patients. This analysis, along with Figure 6, is derived from data collected in a pivotal clinical trial involving adult patients aged 18-89 years who had been hospitalized for at least 12 hours with a differential complete blood count performed upon presentation to the emergency department (ED). A total of 2,158 subjects were enrolled and classified as control (n=1,088), systemic inflammatory response syndrome (SIRS) (n=441), infected (n=244), sepsis (n=385) for the Sepsis-2 criteria; and control (n=1,529), infected (n=386), sepsis (n=243) for the Sepsis-3 criteria. All 385 septic cases and 385 randomly selected non-septic cases were used for the analysis. Each column (line) in the heatmap represents a sample, and samples were grouped into septic and non-septic patients. Each row represents a parameter. The columns were further grouped into clusters and differentiated by color and dendrogram. Each gradient represents a cluster group and the height of the dendrogram represents the negative strength of the relationship between the markers. All values ​​were normalized by marker to show the relative importance of the values ​​or the deviation of the values ​​from the corresponding mean. The gradient represents the magnitude of low / high values ​​and black represents the missing value of the corresponding marker. The heatmap shows that many of the markers are highly discriminative between sepsis and non-sepsis. To recognize the significantly discriminative markers and the magnitude of significance, the following statistical analysis was performed.

[0065] Two types of statistical analyses were performed to explore further promising markers of sepsis. First, the area under the curve (AUC) as well as the sensitivity and specificity were calculated for each marker, and the top marker with the highest AUC was presented as shown in Figure 6. For each marker, the AUC was calculated using a one-predictor logistic regression model, and the cutoff values ​​as well as the corresponding sensitivity and specificity were calculated using Youden's J statistic. The Youden's J statistic (also known as the Youden index) is a single statistic that captures the performance of a dichotomous diagnostic test.

[0066] Secondly, multiple hypothesis weighted covariate was performed to identify important markers that were differentially expressed between sepsis and non-sepsis. Because the markers are highly correlated, multiple hypotheses were necessary to simultaneously evaluate a series of statistical inferences / tests. Evaluating multiple inferences may induce erroneous conclusions due to chance. Therefore, a stricter significance threshold for each comparison is used to compensate for the number of inferences made. Covariate weighting is one of the best ways to adjust the significance threshold, taking the (statistically independent of the test) covariates, calculating the weights, and as a result, adjusting the original p-value divided by the weights, and then comparing it to a given significance level. For this analysis, the data was transformed to a log scale by skewness, and then the p-values ​​were calculated using t-statistics to test the differences between sepsis and non-sepsis (Table 1). The weights were calculated using the covariate rank weighting method, using the standard deviation of each marker as a covariate. Table 1: 20 statistically significant markers. [Table 1-1] [Table 1-2]

[0067] NNRBC_MALS_MEAN is the mean of MALS light scatter measurements for cells identified as nonnucleated red blood cells (NNRBC). NNRBC_UMALS_MEAN is the mean of UMALS light scatter measurements for cells identified as NNRBC. NNRBC_ALL_MEAN is the mean of ALL light transmittance measurements for cells identified as NNRBC. MO_DC_MEAN is the mean of DC measurements for cells identified as monocytes. NE_DC_MEAN is the mean of DC measurements for cells identified as neutrophils. EGC_LMALS_MEAN is the mean of LMALS light scatter measurements for cells identified as early granular cells. NNRBC_DC_SD is the standard deviation of DC measurements for cells identified as NNRBC. NNRBC_UMALS_SD is the standard deviation of UMALS light scatter measurements for cells identified as NNRBC. NNRBC_MALS_SD is the standard deviation of the MALS light scatter measurements for cells identified as NNRBC. NNRBC_LMALS_SD is the standard deviation of the LMALS light scatter measurements for cells identified as NNRBC. MDW is the volume distribution width measurement for cells identified as monocytes. MO_DC_SD is the standard deviation of the DC measurements for cells identified as monocytes. NE_DC_SD is the standard deviation of the DC measurements for cells identified as neutrophils. LY_PC is the percentage of WBC cells identified as lymphocytes. MO_ALL_SD is the standard deviation of the ALL light transmission measurements for cells identified as monocytes. WBC is the number of white blood cells. WDOP is the estimate of white blood cells (corrected) from the DIFF optical channel. WNOP is the estimate of white blood cells (corrected) from the NRBC optical channel. NE_NO is the number of WBC cells identified as neutrophils. BA_PC is the percentage of WBC cells identified as basophils.

[0068] To the best of our knowledge, there are no previous studies that have examined changes in light scattering parameters in heterogeneous cell populations of circulating cells from septic populations, such as NNRBC, compared to controls. Previous studies have used hematological analyzers to examine changes in light scattering in specific cell types during sepsis, such as specific lymphocyte, monocyte, or neutrophil cell populations (reviewed in Zonneveld R, Molema G, Plotz FB: Analyzing neutrophil morphology, mechanics, and motility in sepsis: options and challenges for novel bedside technologies. Crit Care Med 2016; 44: 218-228). Regarding cell surface granulation status, there are no hypothesis-driven studies that demonstrate a specific correlation between sepsis and the cell surface granulation status of any cell type.

[0069] It is well established that sepsis causes several changes in circulating cells. Without wishing to be bound by theory, changes in membrane protein and lipid composition, changes in Na / Cl pump concentration, changes in cell type ratios, and changes in immune cell activation state may be potential causes of changes in cell granularity that may affect light scattering. Any of these potential biological mechanisms or combinations of them may cause the observed light scattering differences in NNRBC parameters. Nevertheless, obtaining light scattering measurements and calculating specific cell population parameters such as NNRBC-UMALS-SD involves a process that does not occur in nature. The inventors have not shown that a human-performed visual examination of NNRBC granularity, for example via review of blood smear slides, is useful for distinguishing between septic and non-septic patients. Granularity can be assessed via review of blood smears, but is subjective and not standardized. Standard deviation cannot be assessed by visual inspection. In this study, mean NNRBC UMALS measurements were no more effective at identifying patients with sepsis than NNRBC-UMALS-SD, suggesting that human impressions or estimates of granularity in relatively small samples of cells are unreliable for this purpose.

[0070] Those skilled in the art will understand that the reference ranges and thresholds for assessing normality or abnormality for any given cell population parameter will vary based on the method used to measure or observe the cells, as well as the specific hardware (e.g., light source, sensing hardware) used to make the measurements. The units of measurement as well as the reference ranges for these parameters can vary based on the design of the transducer module used. Once the cell population parameters have been identified as appropriate, it is routine to determine the appropriate reference ranges for a particular analyzer configuration. Using two or more of these criteria can increase the sensitivity and / or specificity of the cell analysis for predicting sepsis.

[0071] The cell population parameters described herein can be used alone or in combination with other cellular analyses, in conjunction with current standards of care, such as qSOFA by the clinician and physical examination (e.g., looking for fever, altered mental status, tachycardia, tachypnea, hypotension, or other symptoms that may not be detectable or may be uncertain from cellular analyses, blood chemistries, immunoassays, or other laboratory tests). Using the consensus definition of "sepsis-2," the standard of care includes evaluation of the patient for SIRS. A patient is considered to have SIRS if they meet two or more of the following criteria: temperature greater than 38° C. or less than 36° C., heart rate greater than 90 beats per minute (bpm), or respiratory rate greater than 20 breaths per minute (breaths / min), and a blood glucose level greater than 4,000 per microliter of blood (4,000 / mm ). 3 ) or a white blood cell count (WBC) of less than 12,000 / mm 3A higher white blood cell count (WBC) (leukocytosis). Under Sepsis-2, a patient is considered septic if they have at least two SIRS and a persistent infection (bacterial, viral, or fungal) or suspected infection. Using the common definition of "Sepsis-3," the standard of care includes a Sequential Organ Failure Assessment (SOFA) or rapid SOFA (qSOFA). The qSOFA is scored on a scale of 0 to 3, with 1 point for each symptom that results in a positive test. These symptoms are a respiratory rate greater than 22 breaths / min, a systolic arterial blood pressure less than or equal to 100 mmHg, and altered mental status. It has been determined that patients with a qSOFA score of at least 2 have a 24% hospital mortality rate, and 3% of patients have a qSOFA score of less than 2. Typically, if the qSOFA score is at least 2, then the patient is evaluated with a full SOFA exam. The SOFA test involves scoring on a scale of 0-24 points and evaluating specific organ systems (respiratory, cardiovascular, liver, kidney, blood coagulation, and central nervous system). If the SOFA score is also greater than or equal to 2 points, then the patient is considered septic under sepsis-3. In some embodiments, a single cell population parameter can be used with or without other cellular analysis parameters such as WBC and / or MDW to provide further insight into the patient's septic state when the standard of care is not clearly diagnostic. For example, under sepsis-2 criteria, inability to definitively identify infection using, for example, blood cultures, or inability to wait for a test to confirm infection, for example when the patient is too sick to wait several days for blood culture results, one or more cell population parameters with a minimum acceptable specificity and sensitivity, such as greater than 70% or greater than 80% or greater than 90%, can give the clinician confidence in initiating preventative measures or increased patient monitoring (e.g., hospitalization for specialized monitoring) by asserting other indicators of sepsis or developing sepsis.

[0072] The dimensions and values ​​disclosed herein should not be understood to be strictly limited to the exact numerical values ​​recited. Instead, unless otherwise specified, each such dimension is intended to mean both the recited value and a functionally equivalent range around that value that includes at least the variation due to the reproducibility of measurements obtained using the testing methods described herein or industry standard testing methods if no testing method is expressly disclosed.

[0073] All documents cited herein, including any cross-references or related patents or applications, are incorporated herein by reference in their entirety unless expressly excluded or otherwise limited. The citation of any document is not an admission that it is prior art with respect to any invention disclosed or claimed herein, or that it alone or in any combination with any other reference teaches, suggests, or discloses any such invention. Furthermore, to the extent that any meaning or definition of a term in this document conflicts with any meaning or definition of the same term in a document incorporated by reference, the meaning or definition given to the term in this document applies.

[0074] While particular embodiments of the present invention have been illustrated and described, it would be obvious to those skilled in the art that various other changes and modifications can be made without departing from the spirit and scope of the invention. It is therefore intended in the appended claims to cover all such changes and modifications that are within the scope of this invention.

Claims

1. 1. A method for characterizing an inflammatory response to an infection, comprising: flowing a body fluid sample through a flow cell; irradiating a plurality of cells in the bodily fluid sample within the flow cell; measuring light scatter and direct current impedance from individual cells of said plurality of cells; identifying the individual cell within the plurality of cells based at least in part on the light scatter measurements or the direct current impedance measurements; (i) one or more cell population parameters selected from the group consisting of: standard deviation of volume measurements for monocytes, standard deviation of volume measurements for neutrophils, mean volume measurements for neutrophils, number of neutrophils, percentage of leukocytes identified as lymphocytes, estimate of leukocytes from the nucleated red blood cell channel, mean volume measurements for monocytes, estimate of leukocytes from the differential optical channel, percentage of leukocytes identified as basophils, mean lower mid-angle light scatter for early granulocytes, standard deviation of volume measurements for nonnucleated red blood cells, and combinations thereof, based at least in part on said light scatter or said direct current impedance measurements; and (ii) based at least in part on the light scatter or DC impedance measurements of the body fluid sample, the standard deviation of the volume measurements for monocytes, the standard deviation of the volume measurements for neutrophils, the average of the volume measurements for neutrophils, the standard deviation of the granularity and / or nuclear structure measurements for nonnucleated red blood cells, the standard deviation of the axial light loss for monocytes, the number of neutrophils, the percentage of white blood cells identified as lymphocytes, the standard deviation of the medium angle light scatter for nonnucleated red blood cells, an estimate of white blood cells from the nucleated red blood cell channel, the standard deviation of the axial light loss for monocytes, the average of the volume measurements for neutrophils, the standard deviation of the granularity and / or nuclear structure measurements for nonnucleated red blood cells, the standard deviation of the axial light loss for monocytes, the number of neutrophils, the percentage of white blood cells identified as lymphocytes, the standard deviation of the medium angle light scatter for nonnucleated red blood cells, the standard deviation of the nucleated red blood cell channel ... one or more cell population parameters selected from the group consisting of: average of volume measurements for nonnucleated red blood cells, estimate of white blood cells from the differential optical channel, average of granularity and / or nuclear structure measurements for nonnucleated red blood cells, percentage of white blood cells identified as basophils, average of mid-angle light scatter for nonnucleated red blood cells, average of lower mid-angle light scatter for early granular cells, standard deviation of volume measurements for nonnucleated red blood cells, standard deviation of lower mid-angle light scatter for nonnucleated red blood cells, average axial light loss for nonnucleated red blood cells, and combinations thereof. analyzing two or more cell population parameters including: wherein the two or more cell population parameters are provided for characterizing risk of sepsis; Analysing the two or more cell population parameters includes comparing each analysed cell population parameter to a corresponding reference range, the method comprising using a processor to: i. applying a first global decision rule, said first decision rule indicating that the patient is at increased risk of sepsis if all of the analyzed cell population parameters are outside of corresponding non-sepsis reference ranges; ii. applying a second global decision rule, the second decision rule indicating that the patient is not at increased risk of sepsis if all of the analyzed cell population parameters are not outside of corresponding non-sepsis reference ranges; and iii. if at least one analyzed cell population parameter is outside a corresponding non-septic reference range and at least one analyzed cell population parameter is not outside a corresponding non-septic reference range, apply local decision rules to determine whether said patient is at increased risk of sepsis. Executing a set of instructions operable to A method comprising:

2. The method of claim 1 , wherein the bodily fluid sample is whole blood.

3. 3. The method of claim 1 or claim 2, wherein the cell population parameters are analyzed for cells from the plurality of cells classified as non-nucleated red blood cells.

4. 2. The method of claim 1, further comprising determining whether a volume distribution width (MDW) measured for a population of monocytes in the bodily fluid sample is within an MDW reference range.

5. The method of claim 1, wherein at least one of the analyzed cell population parameters is outside its corresponding reference range and the distribution width of the monocyte volume is greater than 19 channels, indicates an increased risk of sepsis.

6. 10. The method of claim 1, further comprising determining whether the white blood cell count (WBC) in the bodily fluid sample is within a normal reference range.

7. At least one of the analyzed cell population parameters is outside its corresponding reference range and the WBC is less than 4,000 cells / mm ３ or less than 12,000 cells / mm ３ The method of claim 6, wherein a higher value indicates an increased risk of sepsis.

8. 2. The method of claim 1, wherein analyzing two or more cell population parameters comprises analyzing one or more cell population parameters selected from the group consisting of: a number of leukocytes identified as neutrophils, a percentage of leukocytes identified as lymphocytes, an estimate of leukocytes from a nucleated red blood cell channel, an estimate of leukocytes from a differential optical channel, a percentage of leukocytes identified as basophils, an average lower mid-angle light scatter for cells identified as early granulocytes, and combinations thereof based on light scatter and / or direct current impedance measurements of the bodily fluid sample.

9. 1. A method for characterizing whether a patient is at increased risk for sepsis, comprising: flowing a body fluid sample through a flow cell; irradiating a plurality of cells in the bodily fluid sample within the flow cell; measuring light scatter and direct current impedance from individual cells of said plurality of cells; identifying the individual cell within the plurality of cells based at least in part on the light scatter measurements or the direct current impedance measurements; (i) one or more cell population parameters selected from the group consisting of: a number of white blood cells identified as neutrophils, a percentage of white blood cells identified as lymphocytes, an estimate of white blood cells from the nucleated red blood cell channel, an estimate of white blood cells from the differential optical channel, a percentage of white blood cells identified as basophils, an average of lower mid-angle light scatter for cells identified as early granular cells, a standard deviation of direct current measurements for cells identified as non-nucleated red blood cells, and combinations thereof, based on light scatter measurements of the body fluid sample; and (ii) based at least in part on the light scatter or DC impedance measurements of the body fluid sample, the standard deviation of the volume measurements for monocytes, the standard deviation of the volume measurements for neutrophils, the average of the volume measurements for neutrophils, the standard deviation of the granularity and / or nuclear structure measurements for nonnucleated red blood cells, the standard deviation of the on-axis light loss for monocytes, the number of neutrophils, the percentage of white blood cells identified as lymphocytes, the standard deviation of the medium angle light scatter for nonnucleated red blood cells, the estimate of white blood cells from the nucleated red blood cell channel, the average of the volume measurements for monocytes, the white blood cells from the differential optical channel, one or more cell population parameters selected from the group consisting of: an estimate of granularity, an average of granularity and / or nuclear structure measurements for nonnucleated red blood cells, a percentage of white blood cells identified as basophils, an average of mid-angle light scatter for nonnucleated red blood cells, an average of lower mid-angle light scatter for early granular cells, a standard deviation of volume measurements for nonnucleated red blood cells, a standard deviation of lower mid-angle light scatter for nonnucleated red blood cells, an average of on-axis light loss for nonnucleated red blood cells, a corrected white blood cell estimate from the nucleated red blood cell channel, a corrected white blood cell estimate from the differential optical channel, and combinations thereof. analyzing two or more cell population parameters including: wherein the two or more cell population parameters are provided for determining a risk of sepsis; The method, wherein analyzing the two or more cell population parameters comprises comparing each analyzed cell population parameter to a corresponding reference range.

10. a. a transducer module for measuring at least light scattering and DC impedance due to cells passing through the flow cell of the method of any one of claims 1 to 9; and b. A processor configured with instructions stored on a non-transitory computer-readable medium for performing the method of any one of claims 1 to 8. Including, the system.

11. The system of claim 10, wherein the transducer module includes a means for measuring RF conductivity, the means for measuring RF conductivity operable to measure the RF conductivity of cells passing through the flow cell of the method of any one of claims 1 to 9 and also operable to measure the RF conductivity of cells passing through a second flow cell included in the transducer module.

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