Detection of Sepsis Using Hematology Parameters - Patent application

Abstract

The present disclosure describes a method and system for screening for sepsis or septic shock in a patient, and a method for ruling out sepsis or septic shock in a patient, using white blood cell count (WBC), monocyte cell population parameters, or neutrophil-to-lymphocyte ratio (NLR), or a combination thereof, in a blood sample from the patient. In one aspect, the present disclosure describes a method for screening for sepsis or septic shock in a patient.

Description

[Technical field]

[0001] Government Funds This invention was made with Government support under HS026640-02 awarded by the U.S. Department of Health and Human Services. The U.S. Government has certain rights in the invention. [Background technology]

[0002] background Sepsis is the number one cause of morbidity and mortality worldwide, accounting for 1.5 million hospitalizations and 250,000 deaths annually in the U.S. Early initiation of targeted treatment of sepsis improves patient outcomes and reduces costs, but reliable identification of sepsis remains difficult. Summary of the Invention [Means for solving the problem]

[0003] Summary of the Invention In one aspect, the present disclosure describes a method for screening for sepsis or septic shock in a patient. The method includes calculating the neutrophil-to-lymphocyte ratio (NLR) of a blood sample from the patient; characterizing the white blood cell count (WBC) of the blood sample; calculating a monocytic cell population parameter of the blood sample; comparing the NLR with a first predefined threshold set, the WBC with a second predefined threshold set, and the monocytic cell population parameter with a third predefined threshold set; and identifying the patient as being at low to high risk of sepsis or septic shock. In some embodiments, the method includes reporting the identified risk of the patient to a clinician on a screen display in an electronic medical record (EMR).

[0004] The terms "preferred" and "preferably" refer to embodiments or aspects of the invention that may provide 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 terms "comprises" and variations thereof do not have a limiting meaning where these terms appear in the description and claims. Such terms are understood to imply the inclusion of a described 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] "Consisting of" means including, but not limited to, whatever follows the phrase "consisting of". Thus, the phrase "consisting of" indicates that the recited elements are required or essential, and that other elements may not be present. "Consisting essentially of" means including any elements recited after the phrase, but limited to other elements that do not interfere with or contribute to the activity or action specified in the disclosure of the recited elements. Thus, the phrase "consisting essentially of" indicates that the recited elements are required or essential, but that other elements are optional and may or may not be present, whether or not they substantially affect the activity or action of the recited 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 used in its ordinary sense, including "and / or," unless the context 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] Similarly, herein the recitations of numerical ranges by endpoints include all numbers subsumed within that range (eg, 1 to 5 includes 1, 1.5, 2, 2.75, 3, 3.80, 4, 5, etc.).

[0011] As used herein, a reference to "up to" a number (eg, up to 50) includes that number (eg, 50).

[0012] The terms "in the range" or "within the range" (and similar descriptions) include the endpoints of the ranges described.

[0013] For any method disclosed herein that includes distinct steps, the steps may be performed in any feasible order, and, if desired, any combination of two or more steps may be performed 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 specifically stated.

[0015] References throughout this specification to "one embodiment," "one aspect," "embodiment," "aspect," "a particular embodiment," "a particular aspect," "some embodiments," or "some aspects" and the like mean that a particular feature, form, composition, or characteristic described in connection with that embodiment is included in at least one embodiment of the disclosure. As such, the appearance of such phrases in various places throughout this specification do not necessarily refer to the same embodiment of the disclosure. Furthermore, a particular feature, form, composition, or characteristic may be combined with one or more embodiments or aspects in any suitable manner.

[0016] Unless otherwise indicated, all numbers expressing quantities of components, molecular weights, and the like used in the present specification and claims should be understood to be modified in all instances by the term "about". When used herein in connection with a measured quantity, the term "about" refers to that variation in the measured quantity that would be expected by a person skilled in the art who makes the measurement and practices the treatment at a level commensurate with the purpose of the measurement and the precision of the measuring device used. Thus, unless otherwise indicated to the contrary, the numerical parameters described in the present specification and claims are approximations that may vary depending on 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 interpreted 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, however, every numerical range inherently contains certain ranges necessarily resulting from the standard deviation found in their respective testing measurements.

[0018] The above summary of the invention is not intended to describe each disclosed embodiment or every implementation of the present invention. The following description more particularly illustrates exemplary 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.

[0019] Exemplary embodiments of the present invention are described in detail below with reference to the accompanying drawings. [Brief description of the drawings]

[0020] [Figure 1] FIG. 1 shows a study flow diagram for the study described in Example 1.

[0021] [Diagram 2] Figure 2 shows a comparison of monocyte size distribution width, white blood cell count, and neutrophil-to-lymphocyte ratio by group. The dashed lines represent the cutoff values ​​for each test.

[0022] [Figure 3A] FIG. 3A shows lactate by group compared to white blood cell measurements of the same subpopulations, and FIG. 3B shows C-reactive protein. The dashed lines in each panel represent the cutoff values ​​for the respective tests. Sepsis and septic shock are defined according to the Sepsis-3 consensus definition. Abbreviations: CRP: c-reactive protein; MDW: [Figure 3B] FIG. 3A shows lactate by group compared to white blood cell measurements of the same subpopulations, and FIG. 3B shows C-reactive protein. The dashed lines in each panel represent the cutoff values ​​for the respective tests. Sepsis and septic shock are defined according to the Sepsis-3 consensus definition. Abbreviations: CRP: c-reactive protein; MDW:

[0023] [Figure 4] Figure 4 shows a comparison of monocyte size distribution width, white blood cell count, and neutrophil to lymphocyte ratio by group in subgroups of immunosuppressed patients. The dashed lines in each panel represent the cutoff values ​​for the respective tests. Subgroups of immunosuppressed patients were identified by having neutropenia or activity problems that met the criteria for immunocompromise.

[0024] [Diagram 5] FIG. 5 is a schematic diagram of an example operating environment according to an embodiment of the present disclosure.

[0025] [Figure 6] FIG. 6 is a schematic diagram of an example analyzer according to an embodiment of the present disclosure.

[0026] [Figure 7] FIG. 7 is a schematic diagram of an example analyzer process according to an embodiment of the present disclosure.

[0027] [Figure 8] FIG. 8 is a schematic diagram of an example analysis engine according to an embodiment of the present disclosure.

[0028] [Figure 9] FIG. 9 shows a flow chart of an exemplary method for assessing sepsis or septic shock from a blood sample according to an embodiment of the present disclosure.

[0029] [Figure 10] FIG. 10 illustrates an example conversion of blood sample parameters into indices that identify patients as being at low to high risk of sepsis and septic shock, according to an embodiment of the present disclosure.

[0030] [Figure 11] FIG. 11 illustrates an example process and clinical test output for visualization to a clinician according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0031] Detailed Description The present disclosure describes a method and system for screening for sepsis or septic shock in a patient, and a method for ruling out sepsis or septic shock in a patient using white blood cell count (WBC), monocyte cell population parameters, or neutrophil-to-lymphocyte ratio (NLR), or a combination thereof, in a blood sample from the patient. In some embodiments, the method can be used to screen patients, preferably in the emergency department (ED).

[0032] Current tools for sepsis screening in the ED are limited. A clinical scoring system that relies on routinely available information is advantageous due to its universal applicability. The original systemic inflammatory response syndrome (SIRS) criteria are still widely used to screen for sepsis but lack specificity; patients with acute non-infectious illnesses often screen positive (Sprung et al. Intensive Care Med. 2006;32(3):421-427; Bone et al. Chest. 1992;101(6):1644-1655). The SIRS criteria were removed from the consensus sepsis definition, third edition (SEP-3), but SEP-3 introduced the rapid sequential organ failure assessment score (qSOFA), which has been applied to screen for sepsis and determine prognosis (Singer et al. JAMA. 2016;315(8):801-810). Unfortunately, qSOFA has proven to lack sensitivity in the ED, where patients often present before showing overt signs of organ failure (Serafim et al. Chest. 2018;153(3):646-655). Biomarkers can serve as adjuncts for screening and diagnosing sepsis. However, currently available biomarkers, including lactate, c-reactive protein, and procalcitonin, may exhibit suboptimal performance due to limited diagnostic accuracy, delayed detectable signal, and / or lack of widespread availability in clinical settings (Al Jalbout et al. The Journal of Applied Laboratory Medicine. 2019;3(4):724-729; Pierrakos et al. Crit Care. 2020;24(1):287).

[0033] Although research leveraging automated algorithms to support the treatment of sepsis in the ED is on the rise, a single simple biomarker that could enable early identification in an undifferentiated population would be invaluable. To date, none has been found. Lactate, CRP, and procalcitonin are commonly used for risk stratification of sepsis, but none show optimal diagnostic performance when used alone (Lippi, Clinical Chemistry and Laboratory Medicine (CCLM). 2019;57(9):1281-1283). Lactate, the only biomarker whose use is recommended by consensus guidelines, is a nonspecific marker of cellular dysfunction whose elevation does not occur until late in the disease course, and its use is recommended for prognosis and to monitor response to treatment, rather than for case identification (Rhodes et al. Intensive Care Medicine. 2017;43(3):304-377;Casserly et al. Critical Care Medicine. 2015;43(3)). CRP has been shown to lack sensitivity and specificity for sepsis in undifferentiated populations and to be particularly unreliable in the ED setting (Wasserman et al. Medicine (Baltimore). 2019;98(2);Wu et al. Ann Intensive Care. 2017;7). Procalcitonin may have a role for distinguishing bacterial from viral infections, but recent data suggest that its sensitivity for invasive infections is unacceptably low to warrant its use as a screening tool (Goodlet et al. Open Forum Infect Dis. 2020;7(4);Gregoriano et al. J Thorac Dis. 2020;12(Suppl 1):S5-S15). The utility of these biomarkers for early sepsis screening is further undermined by their availability, which depends on clinical suspicion and is subject to practice variation.For example, lactate, CRP, and procalcitonin are not in clinical use in all EDs.

[0034] The complete blood count (CBC), a test that counts the cells that make up blood, is the most commonly ordered clinical laboratory panel worldwide (Horton et al. Am J Clin Pathol. 2019;151(5):446-451), and the CBC is ordered for most patients arriving at the emergency department. Although the CBC has been suggested as a useful source of significant amounts of information in patients with sepsis, the typical practice is to overlook most of this data, focusing only on a single component: the white blood cell count (WBC) (Farkas, J Thorac Dis. 2020;12(Suppl 1):S16-S21). Little support is available to clinicians regarding the use of CBC results in the count of sepsis detection.

[0035] WBC was incorporated into the original sepsis consensus criteria but has been excluded from more recent definitions of sepsis due to its poor diagnostic accuracy when used alone (Bone et al. Chest. 1992;101(6):1644-1655). Other components of the CBC, including neutrophil count, lymphocyte count, and neutrophil-to-lymphocyte ratio (NLR), have been suggested as possible sepsis markers (Ljungstrom et al. PLoS One. 2017;12(7):e0181704;Martins et al. Rev Bras Ter Intensiva. 2019;31(1):64-70), but their correlation with more severely ill patient populations is limited. Traditional screening tools such as the Systemic Inflammatory Response Syndrome (SIRS) criteria, the New Early Warning Score (NEWS), the Modified Early Warning Score (MEWS), the Rapid Sequential Organ Failure Assessment (qSOFA), and the Sequential Organ Failure Assessment (SOFA) criteria also show poor diagnostic accuracy. Islam et al. Comput Methods Programs Biomed 2019;170:1-9.

[0036] When CBC is obtained from blood sample, analyzer such as hematology analyzer can be used on the same blood sample to provide data on the abundant cell subpopulations, much more than the mere number or percentage of those cells compared to other cell subpopulations in the sample.For example, monocyte cell population parameters can be obtained that reflect monocyte activation.One such monocyte parameter is monocyte size distribution width (herein referred to as MDW, also referred to as monocyte anisotropy).MDW represents the volume distribution of monocyte population in blood sample; thus, this morphometric parameter reflects the variation of monocyte cell volume.

[0037] Morphological changes in monocyte cell volume occur early as a result of monocyte activation induced by pathogen recognition, and thus MDW changes early in the disease course. MDW has demonstrated the ability to identify patients with sepsis in high-risk populations (Crouser et al. Crit Care Med. 2019;47(8):1018-1025; Crouser et al. Chest. 2017;152(3):518-526; Crouser et al. Intensive Care. 2020;8:33). Furthermore, a combination of MDW and WBC has been proposed for the assessment of septic conditions (see, e.g., PCT Application PCT / US2019 / 028486).

[0038] The present disclosure describes that, as detailed herein (including, for example, Example 1), when MDW is considered together with WBC and NLR, a surprisingly good predictability of both sepsis and septic shock is achieved. For example, for both sepsis and septic shock, AUC reaches 0.86 (Table 2), and a sensitivity of 92.2% for sepsis and 97.7% for septic shock is achieved. Moreover, the results suggest that MDW has an important additive role with WBC and NLR, and the combination of the three parameters provides a substantially better discrimination of patient outcomes than WBC, NLR, or MDW alone, or any two combinations of these parameters. The finding that the combination of three separate parameters from a single clinical test panel (CBC classification) can achieve such high diagnostic performance was unexpected. CBC has been used in routine practice for several decades, including as a screening clinical test for infectious diseases. However, the use of this combination of CBC parameters has never been described before, and no previous study has reported this level of discrimination for sepsis and septic shock using any combination of CBC parameters. Similarly, it was unexpected that the sensitivity achieved using the parameter combination was synergistic rather than additive.

[0039] Methods for screening for sepsis or septic shock In some embodiments, the present disclosure describes a method of screening for sepsis or septic shock in a patient. The method includes calculating a neutrophil-to-lymphocyte ratio (NLR) of a blood sample from the patient; characterizing a white blood cell count (WBC) of the blood sample; and calculating a monocytic cell population parameter of the blood sample. The method further includes comparing the WBC, the monocytic cell population parameter, and the NLR to a threshold or threshold range. For example, the NLR may be compared to a first predefined threshold, the WBC to a predefined threshold range, and the monocytic cell population parameter to a second predefined threshold. At least one of these comparisons is used to determine whether the patient has an increased risk of sepsis or septic shock. In some embodiments, the method is an in vitro method.

[0040] In some embodiments, screening for sepsis or septic shock includes diagnosing or detecting sepsis, but may additionally or alternatively include predicting the onset of sepsis or septic shock within 6 hours, 12 hours, or 24 hours.

[0041] In some embodiments, determining whether a patient has an increased risk of sepsis or septic shock comprises determining whether at least one of WBC, monocyte cell population parameter, and NLR is greater than a predefined threshold or outside a predefined threshold range. For example, the method may comprise determining whether NLR is greater than a first predefined threshold, WBC is outside a predefined threshold range, or monocyte cell population parameter is greater than a second predefined threshold, or a combination thereof. In some embodiments, determining whether a patient has an increased risk of sepsis or septic shock comprises determining whether NLR is greater than a first predefined threshold, and further determining whether monocyte cell population parameter is greater than a predefined threshold or WBC is outside a predefined threshold range, or both. In some embodiments, determining whether a patient has an increased risk of sepsis or septic shock comprises determining whether NLR is greater than a first predefined threshold, and further determining whether monocyte cell population parameter is greater than a predefined threshold.

[0042] In some embodiments, determining whether the patient has an increased risk of sepsis or septic shock comprises determining that the NLR is greater than a first predetermined threshold, that the WBC is outside a predetermined threshold range, and that the monocytic cell population parameter is greater than a second predetermined threshold.

[0043] Each of the first predefined threshold, the second predefined threshold, and the predefined threshold range can be selected by a person skilled in the art, including, for example, a clinician. The normal range values ​​are known in the art and may vary slightly depending on the laboratory (e.g., by the test method or the processing of the specimen). In some embodiments, the threshold value may include multiple threshold values. In some embodiments, the threshold value range may include multiple ranges. For example, for each parameter, different threshold values ​​may be provided for sepsis versus septic shock.

[0044] In some embodiments, the monocyte cell population parameter reflects monocyte activation. For example, the monocyte cell population parameter may preferably include monocyte size distribution width (MDW). When the monocyte cell population parameter includes MDW, the second predefined threshold value may be 19, 20, 21, 22, 23, or 24. In such an embodiment, determining whether the patient has an increased risk of sepsis or septic shock comprises determining that the MDW is greater than the second predefined threshold value. For example, the second predefined threshold value may be 20, and determining whether the patient has an increased risk of sepsis or septic shock may comprise determining that the MDW is greater than 20.

[0045] In some embodiments, the lower limit of the WBC threshold range is 3.4×10 cells 9 cells / L, cells 4×10 9 cells / L, cells 4.5×10 9 cells / L, or 5 x 10 cells 9 In some embodiments, the upper limit of the WBC threshold range is 9.6×10 cells / L. 9 pcs / L, cells 10×10 9 cells / L, cells 11×10 9 cells / L, or 12 x 10 cells 9 An exemplary threshold range is, for example, 4×10 cells / L. 9 pcs / L~12×10 9 cells / L, cells 4.5×10 9 pcs / L~cells 11×10 9 cells / L, cells 4×10 9 pcs / L~cells 11×10 9 cells / L, cells 5×10 9 pcs / L~cells 10×10 9 In such an embodiment, determining whether the patient has an increased risk of sepsis or septic shock may include determining that the WBC is outside a threshold range. For example, the threshold range for WBC is 4×10 cells / L, etc. 9 pcs / L~cells 12×10 9 determining whether a patient has sepsis or an increased risk of septic shock may be based on a WBC count of 4 x 10 cells / L. 9cells / L or less than 12 × 10 9 The method may include determining that the number of cells per 100 is greater than 1 / L.

[0046] In some embodiments, the first predefined threshold is 6, 7, 8, 9, 10, 11, or 12. In such embodiments, determining whether the patient has an increased risk of sepsis or septic shock can include determining that the NLR is greater than the first predefined threshold. For example, the first predefined threshold can be 10, and determining whether the patient has an increased risk of sepsis or septic shock can include determining that the NLR is greater than 10.

[0047] In some embodiments, the method is an automated method. When the method is an automated method, the method can include using a data processing module to determine whether the patient has an increased risk of sepsis or septic shock. The data processing module can include a processor and a tangible non-transitory computer readable medium. The computer readable medium can be programmed with any suitable computer application. For example, in some embodiments, the computer readable medium is programmed with a computer application that, when executed by the processor, causes the processor to compare NLR with a first predefined threshold to provide a first comparison, compare monocyte cell population parameters with a second predefined threshold to provide a second comparison, compare WBC with a third predefined threshold and a fourth predefined threshold to provide a third comparison, and determine the risk of sepsis or septic shock based on the first comparison, the second comparison, and the third comparison. In such embodiments, determining the risk of sepsis or septic shock indicates a suspicion of sepsis or septic shock if the first comparison is higher than a first predefined threshold, the second comparison is higher than a second predefined threshold, and the third comparison is lower than a third predefined threshold or higher than a fourth predefined threshold. In some embodiments, the first predefined threshold (i.e., NLR threshold) is 10. In some embodiments, the monocyte cell population parameter comprises a standard deviation of monocyte volume, e.g., monocyte size distribution width (MDW), and the second predefined threshold (i.e., MDW threshold) is 20. In some embodiments, the third predefined threshold is 12×10 cells. 9 cells / L, and the fourth predefined threshold is 4 x 10 cells. 9 cells / L (i.e., the threshold range for WBC is 4 x 10 cells 9 pcs / L~cells 12×10 9cells / L). In some embodiments, the automated method may further include indicating a suspicion of sepsis or septic shock in a test report of the sample. In some embodiments, the automated method may include directing a hydrodynamically focused stream of the blood sample toward a cell interaction region of the optical element; and measuring, with an electrode assembly, a current (DC) impedance of cells of the blood sample passing individually through the cell interaction region; the module determines a standard deviation of the monocyte volume based on the DC impedance measurement of the cells of the blood sample.

[0048] In some aspects, the patient may present with malaise. In some aspects, the patient may present with symptoms of a systemic inflammatory condition.

[0049] In some aspects, the patient may preferably present to an emergency department.

[0050] In some aspects, the method further includes administering a treatment for sepsis. Exemplary treatments for sepsis include administering an antibiotic preparation, intravenous fluids, a vasopressor, a corticosteroid, insulin, an analgesic, a sedative, or an immune enhancing therapy, or a combination thereof.

[0051] In some aspects, the methods may further include determining whether a treatment for sepsis has been administered or determining the success of the treatment for sepsis.

[0052] How to rule out sepsis or septic shock In some embodiments, the present disclosure describes a method for ruling out sepsis or septic shock in a patient. The method includes calculating a neutrophil-to-lymphocyte ratio (NLR) of a blood sample from the patient; characterizing a white blood cell count (WBC) of the blood sample; and calculating a monocytic cell population parameter of the blood sample. The method further includes comparing the WBC, the monocytic cell population parameter, and the NLR to a threshold or threshold range. For example, the NLR may be compared to a first predefined threshold, the WBC to a predefined threshold range, and the monocytic cell population parameter to a second predefined threshold. At least one of these comparisons is used to determine whether the patient has an increased risk of sepsis or septic shock. In some embodiments, the method is an in vitro method.

[0053] In some embodiments, determining whether a patient is at risk for sepsis or septic shock comprises determining whether at least one of WBC, monocyte cell population parameter, and NLR is below a predefined threshold or within a predefined threshold range. In some embodiments, determining whether a patient is at risk for sepsis or septic shock comprises determining that NLR is below a first predefined threshold, and further determining that WBC is within a predefined threshold range, or that monocyte cell population parameter is below a second predefined threshold, or both. In some embodiments, determining whether a patient is at risk for sepsis or septic shock comprises determining that NLR is below a first predefined threshold, and further determining that monocyte cell population parameter is below a second predefined threshold.

[0054] In some embodiments, determining whether the patient is not at risk for sepsis or septic shock comprises determining that the NLR is below a first predetermined threshold, that the WBC is within a predetermined threshold range, and that the monocyte cell population parameter is below a second predetermined threshold.

[0055] Each of the first predefined threshold, the second predefined threshold, and the predefined threshold range can be selected by a person skilled in the art, including, for example, a clinician.The normal range values ​​are known in the art and may vary slightly depending on the laboratory (e.g., due to the test method or specimen processing).In some embodiments, the threshold value may include multiple threshold values.In some embodiments, the threshold value range may include multiple ranges.

[0056] In some embodiments, the monocyte cell population parameter reflects monocyte activation. For example, the monocyte cell population parameter may include monocyte volume measurement. In such embodiments, determining whether the patient is not at risk for sepsis or septic shock comprises determining that the monocyte cell population parameter is less than a second predefined threshold. In one example, the monocyte cell population parameter may preferably include monocyte size distribution width (MDW). When the monocyte cell population parameter includes MDW, the second predefined threshold may be 19, 20, 21, 22, 23, or 24. In such embodiments, determining whether the patient is not at risk for sepsis or septic shock comprises determining that the MDW is less than a second predefined threshold. For example, the second predefined threshold may be 20, and determining whether the patient is not at risk for sepsis or septic shock comprises determining that the MDW is less than 20.

[0057] In some embodiments, the lower limit of the WBC threshold range is 3.4×10 cells 9 cells / L, cells 4×10 9 cells / L, cells 4.5×10 9 cells / L, or 5 x 10 cells 9 In some embodiments, the upper limit of the WBC threshold range is 9.6×10 cells / L. 9 pcs / L, cells 10×10 9 cells / L, cells 11×10 9 cells / L, or 12 x 10 cells 9 An exemplary threshold range is, for example, 4×10 cells / L. 9 pcs / L~cells 12×10 9 cells / L, cells 4.5×10 9 pcs / L~cells 11×109 cells / L, cells 4×10 9 pcs / L~cells 11×10 9 cells / L, cells 5×10 9 pcs / L~cells 10×10 9 In such an embodiment, determining whether the patient is not at risk for sepsis or septic shock may include determining that the WBC is within a threshold range. For example, the threshold range for WBC is 4×10 cells / L, etc. 9 pcs / L~cells 12×10 9 cells / L, and determining whether a patient is at risk for sepsis or septic shock is based on a WBC count of 4 x 10 cells / L. 9 > 12 x 10 cells / L 9 The method may include determining that the number of cells per liter is less than 100.

[0058] In some embodiments, the first predefined threshold is 6, 7, 8, 9, 10, 11, or 12. In such embodiments, determining whether the patient is not at risk for sepsis or septic shock comprises determining that the NLR is less than the first predefined threshold. For example, the first predefined threshold may be 10, and determining whether the patient is not at risk for sepsis or septic shock comprises determining that the NLR is less than 10.

[0059] In some aspects, the patient may present with malaise. In some aspects, the patient may present with symptoms of a systemic inflammatory condition.

[0060] In some aspects, the patient may preferably present to an emergency department.

[0061] Operating environment and example analyzer Turning to FIG. 5, an example operating environment 500 is illustrated in accordance with some aspects described herein. In general, the example operating environment 500 includes a system capable of facilitating diagnostic, prognostic, and medical intervention actions as described herein. The operating environment 500 is an example of a suitable environment and system configuration for implementing embodiments of the present disclosure. Some embodiments may be implemented as a system including one or more computers and associated networks and devices on which a method or computer software application is executed. Thus, aspects of the present disclosure may take the form of an embodiment that combines software and hardware aspects, which may all be generally referred to herein as "modules" or "systems." Additionally, the methods of the present disclosure may take the form of a computer application embodied in a computer readable medium having machine readable application software embodied thereon. In this regard, a machine readable storage medium may be any tangible medium capable of containing or storing a software application for use by a computing device.

[0062] Some embodiments of the 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 attributes 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 characterization; or mass spectrometry and / or immunological measurements. An analyzer, as used herein, includes a work cell or modular system in which two or more types of measurements are performed; for example, a work cell including blood chemistry and immunoassays. In some embodiments, the analyzer may include a hematology analyzer.

[0063] Some embodiments of the example operating environment 500 include a network 504. The network 504 generally facilitates communication between the analyzer 502 and any other devices communicatively connected to the network 504. As such, the network 504 may include access points, routers, switches, or any other network components generally understood to facilitate communication among devices. By way of example, the network 504 may 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, the network 504 may include multiple networks, or networks of networks, but is depicted in a simplified form so as not to obscure the embodiments described herein.

[0064] Some embodiments of the example operating environment 500 include a remote device 506. The remote device 506 may take a variety of forms, such as a personal computer (PC), a smartphone, 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 depicted devices, or any other device capable of directly or indirectly communicating with an analyzer (e.g., the analyzer 502) and / or a data store (e.g., the data store 508). For example, in certain embodiments, the remote device 506 includes a workstation PC capable of executing a local client application. The local client application may be communicatively connected to the analyzer 502, the data store 508, or both. For example, the local client application may be an application that facilitates user interaction with the analyzer 502. The local client application. In another example, the local client application may be an electronic medical record system application that facilitates user interaction with an electronic medical record system maintained by the data store.

[0065] Some embodiments of the example operating environment 500 include one or more data stores 508. The data stores 508 typically store, maintain, and communicate data over a network 504. The data stores 508 may include any combination of hardware, software, and firmware. For example, the data stores 508 may include an electronic medical record (EMR) system. An EMR system may store medical information (e.g., demographic, physical, biological, and other) about multiple individuals. In other words, an EMR is a real-time, comprehensive collection of patient data including medical history, physician notes, diagnoses, medications, allergies, immunizations, lab test results, and vital signs. An EMR system stores and maintains multiple EMRs.

[0066] In another example, the data store 508 may include a laboratory information system (LIS). The LIS is a software system that stores, processes, and manages laboratory analyzer data and information about individuals, including sample measurements. Laboratory test results from individual biological samples, such as WBC and MDW, may also be entered into the LIS manually, by a laboratory technician, indirectly through laboratory middleware connected to one or more analyzers, or directly from the analyzers. In some embodiments, the LIS system can add or modify patient data stored in the EMR system,

[0067] Turning to FIG. 6, a depiction of an example analyzer 600 consistent with embodiments described herein is provided. Analyzer 600 illustrates components in a system that may be used to perform measurements on a sample, such as a blood sample. As will be appreciated by those skilled in the art, analyzers are available that operate on many principles, including electrical impedance, dyed fluorescent analysis, cell image analysis, and light scattering analysis. In particular, many commercially available blood analyzers use a combination of these methodologies. For example, the Beckman Coulter DxH™ 900 blood analyzer uses electrical impedance (also called DC current) to measure and count cell size, and radio frequency (RF), light loss, and light scattering to evaluate cell morphology and further differentiate subpopulations of cells. Exemplary systems and methods are described, for example, in U.S. Pat. No. 5,125,737. As will be appreciated from the disclosure of U.S. Pat. No. 5,125,737, there is more than one way to differentiate cells in a blood sample. For example, cells may be differentiated based on volume (often measured by impedance), or by light scattering, or by a combination of parameters. When distinguishing by light scatter, different angles of light scatter may be used, such as small angle light scatter (LALS), axial light loss (ALL), high-medium angle light scatter (UMALS), and others. In some cases, cells with similar size and morphology may be best distinguished using a combination of different measurements, which may use plots (e.g., one measurement on the x-axis and another on the y-axis) or formulas, such as ratios or sums. As an example, eosinophils have several light scatter measurements similar to neutrophils and may be difficult to distinguish based on any single measurement. However, by examining a combination of medium angle light scatter (MALS), UMALS, and low-medium angle light scatter (LMALS), eosinophils can be unambiguously distinguished from neutrophils as well as monocytes, lymphocytes, and basophils.

[0068] The DxH system measures multiple parameters for each cellular event using high-speed, high-resolution analog-to-digital conversion by digital signal processing (DSP) circuitry. DSP algorithms digitally analyze the cellular data, providing cellular definition and resolution. Differential precision and flagging techniques are obtained by combining additional light scatter measurements with data analysis techniques to further define and separate cell populations. For example, in some cases, MALS is converted to modified rotational MALS (RMALS) parameters through mathematical transformations to remove overlaps and create distinct neutrophil, lymphocyte, monocyte, and eosinophil populations, allowing optimal analysis and visual confidence in the results. Current internal visual differential data transformations include RMALS (rotational MALS), opacity (conductivity minus size aspect), SOP (opacity at elongation), and nonlinear AL2 transformation. The DxH system reports "VCS parameters."

[0069] Other blood analyzers, such as those manufactured by Abbott, Sysmex, and Mindray, use the Cluster of Differentiation (CD) system to classify white blood cell populations. CD molecules can act in a variety of ways, often acting as receptors or ligands, which initiate signal cascades and change 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 nomenclature of the CD system used to identify cell markers generally thus allows a cell to be defined based on which molecules are present on its surface. More than 350 CD molecules have been identified for humans. For example, monocytes can be identified as CD45+ and CD14+. Cells can be sorted using these cell markers using fluorescently labeled antibodies. Fluorescence-activated cell sorting (FACS) provides a method to sort a heterogeneous mixture of cells, one cell at a time, into two or more containers based on the specific light scattering and fluorescence characteristics of each cell. Fluorescence flow cytometry or FACS can also provide information about the cellular composition of the labeled cells. For example, information about cell density or complexity may be obtained by measuring the light scattered by the cells, and information about cell size and internal structure may be obtained by measuring the fluorescent signal intensity of the cells. Fluorescence flow cytometry or FACS-based systems report "FACS parameters."

[0070] There are thousands of possible combinations of sensor readings and calculated relationships that may correlate with specific characteristics of the blood sample, and once a subpopulation of cells has been identified, the specific subpopulation of cells may be further characterized by one or more sensor readings (e.g., LALS, ALL, UMALS, LMALS, MALS, impedance, etc.) in addition to or instead of cytochemical stains, marker affinities, or other cell identification techniques. That is, blood analyzers can often provide data regarding cell subpopulations that is much more abundant than the mere count or percentage of those cells compared to other subpopulations of cells in the sample. One example is the monocyte size distribution width (MDW), which is the calculation of the standard deviation of cell volume within a subpopulation of monocytes within a blood sample. This characterization of monocyte populations is relevant to sepsis, as described, for example, in PCT Applications PCT / US2017 / 014708, PCT / US2020 / 041535, PCT / US2019 / 28486, PCT / US2020 / 041541, PCT / US2020 / 41548, and PCT / US2019 / 028487. The MDW may be determined by measuring the volume of individual cells passing through a measurement module based on passing an electric current through a blood sample and measuring the amplitude of the resulting impedance measurement (e.g., in the flow cell 630 of the system as shown in FIG. 6). This volume may be measured by a system that transmits light through the blood sample and measures the resulting light scattering to determine cell volume. In some cases, more than one characterization of, or relationships between, subpopulations of cells may be indicative of the same or related conditions, such as viral infection, sepsis, anemia, leukemia, etc.

[0071] As shown in FIG. 6, the analyzer 600 includes a transducer module 610 having a light or radiation source, such as a laser 612 that emits a light beam 614. The laser 612 can be, for example, a 635 nm, 5 mW, solid-state laser. In some cases, the analyzer 600 can include a focus alignment system 620 that adjusts the light beam 614 so that the resulting light beam 622 is focused and positioned at a cell interaction zone 632 of the flow cell 630. In some examples, the flow cell 630 receives a sample aliquot from the preparation system 602. Various fluidic mechanisms and techniques can be used for hydrodynamic focusing of the sample aliquot within the flow cell 630.

[0072] In some examples, the aliquot generally flows through the cell communication zone 632 such that its components pass through the cell communication zone 632 one at a time. In some cases, the analyzer 600 may include a transducer module or cell communication zone or other features of a hematology analyzer, such as those described in U.S. Patent 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 communication zone 632 may be defined by a rectangular cross-section measuring approximately 50×50 microns and having a length (measured in the direction of flow) of approximately 65 microns. The 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 cells passing through the cell communication zone 632. Signals from the electrodes 634, 636 may be transmitted to the analysis system 604. The electrode assembly may use low frequency and high frequency currents to analyze the volume and conductivity characteristics of cells, respectively. For example, low frequency DC impedance measurements may be used to analyze the volume of each individual cell passing through the cell communication zone. High frequency RF current measurements may be used to determine the conductivity of cells passing through the cell communication zone. Because the cell walls act as conductors for high frequency current, high frequency currents may be used to detect differences in the insulating properties of cell components as current passes through the cell walls and through each cell interior. High frequency currents may be used to characterize the nuclear and granule constituents and chemical composition inside the cells.

[0073] Although the light source in FIG. 6 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 a combination of the same or different types of lamps (e.g., a multi-LED lamp, or at least one LED lamp and at least one xenon lamp). As shown in FIG. 6, for example, an incident light beam 622 illuminates cells passing through a cell interaction zone 632, resulting in light propagation within an angular range α (e.g., scatter, transmission) emanating from the interaction zone 632. An exemplary system includes a sensor assembly capable of detecting light within 1, 2, 3, 4, 5, or more angular ranges within the angular range α, including light associated with measuring light extinction or axial light loss. As shown, light propagation 640 may be detected by a light detection assembly 650 having a light scatter detector unit 650A and a light scatter and / or light transmission detector unit 650B as needed. In some examples, the light scatter detector unit 650A includes a photoactive region or sensor area for detecting and measuring high-medium angle light scatter (UMALS), e.g., light scattered or otherwise propagated at angles in the range of 20-42 degrees relative to the beam axis. In some examples, UMALS corresponds to light propagated within an angle range of 20-43 degrees relative to the incident beam axis that illuminates cells flowing through the communication zone. The light scatter detector unit 650A may also include a photoactive region or sensor area for detecting and measuring low-medium angle light scatter (LMALS), e.g., light scattered or otherwise propagated at angles in the range of 10-20 degrees relative to the beam axis. In some examples, LMALS corresponds to light propagated within an angle range of 9-19 degrees relative to the incident beam axis that illuminates cells flowing through the communication zone.

[0074] The combination of UMALS and LMALS is defined as Medium Angle Light Scatter (MALS), which can be light scattering or propagation at angles between 9 degrees and 43 degrees relative to the incident beam axis that illuminates cells flowing through the interaction zone. One of skill in the art will understand that these angles (and other angles described herein) can vary somewhat based on the configuration of the interaction, sensing, and analysis systems.

[0075] As shown in FIG. 6, the light scatter detector unit 650A may include an aperture 651 that allows small angle light scatter or propagation 640 to pass beyond the light scatter detector unit 650A and thereby reach and be detected by the light scatter and transmission detector unit 650B. According to some embodiments, the light scatter and transmission detector unit 650B may include a photoactive region or sensor area for detecting and measuring small angle light scatter (LALS), for example light scattered or propagated at an angle of less than 5.1 degrees relative to the illuminating beam axis. In some examples, LALS corresponds to light propagated at an angle of less than 9 degrees relative to the incident beam axis that illuminates cells flowing through the communication zone. In some examples, LALS corresponds to light propagated at an angle of less than 10 degrees relative to the incident beam axis that illuminates cells flowing through the communication zone. In some examples, LALS corresponds to light propagated at an angle of 1.9 degrees ±0.5 degrees relative to the incident beam axis that illuminates cells flowing through the communication zone. In some examples, the LALS corresponds to light propagated at an angle of 3.0 degrees ± 0.5 degrees relative to the incident beam axis illuminating cells flowing through the communication zone. In some examples, the LALS corresponds to light propagated at an angle of 3.7 degrees ± 0.5 degrees relative to the incident beam axis illuminating cells flowing through the communication zone. In some examples, the LALS corresponds to light propagated at an angle of 5.1 degrees ± 0.5 degrees relative to the incident beam axis illuminating cells flowing through the communication zone. In some examples, the LALS corresponds to light propagated at an angle of 7.0 degrees ± 0.5 degrees relative to the incident beam axis illuminating cells flowing through the communication zone. In each example, the LALS may correspond to light propagated at an angle of 1.0 degree or greater. That is, the LAL can correspond to light propagating at an angle 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.

[0076] According to some embodiments, the light scattering and transmission detector unit 650B may include a photoactive region or sensor area for detecting and measuring light transmitted axially through the cell or light propagated from the illuminated cell at an angle of 0 degrees to the incident light beam axis. In some cases, the photoactive region or sensor area may detect and measure light propagated axially from the cell at an angle of less than 1 degree to the incident light beam axis. In some cases, the photoactive region or sensor area may detect and measure light propagated axially from the cell at an angle of less than 0.5 degrees to the incident light beam axis. Such measurements of axially transmitted or propagated light 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, a portion of the incident light changes direction through a scattering process (i.e., light scattering) and a portion of the light is absorbed by the particle. Both of these processes remove energy from the incident light beam. When viewed along the incident axis of the light beam, the light loss may 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 through column 6, line 4.

[0077] As such, analyzer 600 provides a means for obtaining light propagation measurements, including light scatter and / or light transmission, and for obtaining light emanating from irradiated cells of a biological sample at any of a variety of angles or within any of a variety of angle 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.

[0078] Wires or other transmission or connection mechanisms can transmit signals from the electrode assembly (e.g., electrodes 634, 636), the light scattering detector unit 650A, and / or the light scattering and transmission detector unit 650B to the analysis system 604 for processing. For example, measured DC impedance, RF conductivity, light transmission, and / or light scattering parameters can be provided or transmitted to the analysis system 604 for data processing. In some examples, the analysis system 604 can include computer processing features and / or one or more modules or components that can evaluate the measured parameters, identify and enumerate biological sample constituents, and correlate subsets of data characterizing elements of the biological sample to one or more properties or parameters of interest. Some embodiments of the analysis system 604 include an analysis engine as described in connection with FIG. 8.

[0079] Additionally or alternatively, as shown in FIG. 6, the analyzer 600 may generate or output a report 606 presenting the measured or calculated parameters performed on the sample. The measured or calculated parameters performed on the sample may include UMALS, LMALS, LALS, MALS, ALL, WBC, MDW, % of monocytes, absolute lymphocyte count (ALC), % of lymphocytes, % of eosinophils, absolute neutrophil count (ANC), % of neutrophils, or any combination thereof. As further described herein, in some embodiments, it may be particularly advantageous for the parameters to include MDW, WBC, absolute lymphocyte count (ALC), absolute neutrophil count (ANC), and the absolute lymphocyte count (ALC) and absolute neutrophil count (ANC) are used to calculate the neutrophil to lymphocyte ratio.

[0080] In some examples, excess biological sample from the transducer module 610 can be directed to an external (or alternatively internal) waste system 608. In some examples, the analyzer 600 can include one or more features of a transducer module or hematology analyzer, 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.

[0081] FIG. 7 outlines an exemplary analyzer process 700 that may optionally utilize, for example, the analyzer 600 of FIG. 6. In this embodiment, in 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 sample preparation is complete in step 704, the sample may pass through one or more measurement modules in step 706. The measurement modules in step 706 may include a conductivity module, a light scattering module, an RF module, or any combination thereof. Other modules may be used instead of or in addition to the conductivity module or the light scattering module. For example, the blood 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. The measurement of the sample may then be evaluated by a data processing module in step 708. In some aspects, once the measurement of the sample is completed, the measurement may be displayed by a reporting module in step 710. Additionally or alternatively, once the measurement of the sample is completed, the measurement may be communicated to an analysis engine for further processing, such as the example analysis engine of FIG. 8.

[0082] 8 illustrates an example analysis engine 800 according to embodiments described herein. Embodiments of the analysis engine 800 may be incorporated into the processing features and / or modules or components of an analyzer (e.g., analysis system 604 shown in FIG. 6), an application executed by a remote device (e.g., remote device 506 shown in FIG. 5), or may operate as an independent component of an operating environment (e.g., operating environment 500 shown in FIG. 5).

[0083] In general, the analytical engine 800 evaluates a set of measurements or parameters, identifies and enumerates biological sample constituents, and correlates a subset of data characterizing the elements of the biological sample with one or more properties or parameters of interest. To that end, the analytical engine 800 includes a receiver module 804, an analyzer module 806, and a communicator module 808.

[0084] A receiver, such as receiver 802, typically collects measurements made or parameters calculated based on an analysis of an individual's sample. Data (e.g., measurements made or parameters calculated) may be received directly from an analyzer subsystem or data store in some embodiments. Receiver 802 may use any data collection technique known in the art.

[0085] The data analyzer 806 includes modules that include logical expressions for evaluation of measurements and parameters received by the analysis engine 800. The logical expressions may include linear or parallel processes that evaluate measurements or parameters calculated by a blood analyzer, such as the analyzer 502 described in connection with FIG 5 or the analyzer 600 described in connection with FIG 6. The data analyzer 806 may include at least one of an urgency analyzer 810a, a decision rule analyzer 810b, and a risk analyzer 810c.

[0086] The Urgency Analyzer 810a includes a library of rules, models, and logical expressions, in any combination, that facilitate determining the probability and / or risk of one or more outcomes based on one or more parameters or characteristics of a blood sample. A potential outcome may, in some embodiments, be associated with a recommendation, treatment, or intervention. For example, if an individual's outcome corresponds to a risk of shock, a recommendation to transfer the individual to an intensive care unit / critical care unit may be associated therewith.

[0087] The decision rule analyzer 810b includes a library of decision rules. A decision rule is a logical formula that compares an individual's parameters or characteristics of a blood sample to a threshold value. The decision rule analyzer 810b assembles one or more decision rules from the library to build a logical formula that the analytical engine can evaluate. In one embodiment, the analyzer 810b can utilize a linear combination or two or more parameters. When combined, the decision rules can be used to determine the probability that an individual associated with the blood sample currently has a condition, e.g., an infectious disease, including a viral infection.

[0088] The risk analyzer 810c may include rules, models, and logical expressions in any combination configured to predict a medical condition. For example, the risk analyzer module may characterize information received from the analyzer to determine the risk of an individual developing sepsis. Additionally, some embodiments of the risk analyzer module may characterize information received from the analyzer to determine the probability of the severity of sepsis.

[0089] In some embodiments, the data analysis engine 800 can incorporate the operation of one or more analyzer modules to generate an output. For example, the decision rules maintained by the decision rule analyzer 810b can be used to determine whether an individual currently has a condition, such as an infection. In response to a determination that an individual has a probability of infection above a certain threshold, some embodiments of the data analysis engine 800 can activate the risk analyzer 810c to facilitate a determination of whether the individual is at an elevated risk of developing sepsis or septic shock. In response to a determination that an individual is at an elevated risk of developing sepsis or septic shock, some embodiments of the data analysis engine 800 can activate the urgency analyzer 810a to facilitate a determination of a recommended level of treatment or disposition. In alternative embodiments, the Urgency Analyzer 810a may first identify individuals at risk of needing intensive care and / or at risk of in-hospital mortality, for example within 48 hours of obtaining a blood sample, and then one or more of the Decision Rule Analyzer 810b and / or the Risk Analyzer 810c may be utilized for further determinations.

[0090] The communicator 808 typically communicates the results of the analysis engine 800 to at least one predefined target. In some embodiments, the predefined target may include a local client of a laboratory information system, or a remote device running a local client of an electronic medical record system (e.g., remote device 506 described in FIG. 5). In such embodiments, the results may include a treatment recommendation, a discharge recommendation, a diagnosis recommendation, or the presentation of a visual display or an audible signal providing a warning that the individual corresponding to the analyzed sample may develop sepsis or another severe condition.

[0091] In some embodiments, the predefined target may include a data store that maintains a laboratory information system or an electronic medical record system (e.g., data store 508 described in connection with FIG. 5). In such embodiments, the communicated result may include entering instructions for the individual associated with the medical record of the analyzed sample. For example, the instructions may include transferring the individual to an intensive care unit, increasing monitoring of the individual by a physician or device, or a particular test or standard of care protocol.

[0092] As described in more detail above, the data analysis engine 800 includes at least one analyzer having measurements or parameters provided to the analysis engine. The process may include rules, models, logical expressions in any combination configured to detect and / or predict a medical condition. For example, some embodiments of a decision rule analyzer (e.g., decision rule analyzer 810b described in connection with FIG. 8) may include a program that characterizes information received from the analyzer to determine a probability that an individual has or may develop sepsis. In particular, an example method 900 for assessing sepsis or septic shock is shown in FIG. 9 in accordance with some embodiments described herein. The method 900 may be generally described as a "decision rule" approach in which individual parameters or features of a blood sample are considered against thresholds for each parameter or feature.

[0093] As described below in connection with FIG. 10, additional or alternative embodiments of methods performed by a data analysis engine (eg, data analysis engine 800) include weighted scoring methods of parameters or features of a blood sample.

[0094] Hematology Evaluation of Sepsis and Septic Shock As described above, the analyzer may count and classify various cells contained in a blood sample. Some embodiments of the method 900 include characterizing blood cells as part of a complete blood count (CBC) in a blood sample at block 902. The CBC characterization may include characterizing a variety of different parameters. In one example, in certain embodiments of block 902, the method 900 includes determining the number (or count) of white blood cells present per liter of blood. In another example, in certain embodiments of block 902, the method 900 includes determining the percentage of WBCs that are lymphocytes or the absolute number of white blood cells that are lymphocytes. In a further example, in certain embodiments of block 902, the method 900 includes determining the percentage of WBCs that are neutrophils or the absolute number of WBCs that are neutrophils. In yet another example, in certain embodiments of block 902, the method 900 includes determining monocyte parameters including, for example, monocyte size distribution width (MDW). Based on the collected information, the analyzer may determine, for example, a white blood cell count (WBC), lymphocyte percentage, neutrophil percentage, lymphocyte count, neutrophil count, neutrophil to lymphocyte ratio (NLR), or monocyte size distribution width (MDW), or any combination thereof. For example, at block 902, the method may further include determining, by the analyzer, an absolute neutrophil count and an absolute lymphocyte count, and these values ​​may be used to determine the NLR. In an alternative aspect of block 902, the analyzer engine may query the individual's medical record for a data value corresponding to the most recent value of the white blood cell count (WBC), lymphocyte percentage, neutrophil percentage, lymphocyte count, neutrophil count, neutrophil to lymphocyte ratio (NLR), or monocyte size distribution width (MDW), or any combination thereof.

[0095] At decision block 904, the analyzer further determines whether the NLR determined at block 902 is greater than a first predefined threshold (e.g., including 6, 7, 8, 9, 10, 11, or 12). In some embodiments, if the NLR is greater than the first predefined threshold (e.g., 10 as shown in FIG. 9), the test report of the sample may indicate suspected sepsis or septic shock, as shown at block 912.

[0096] Additionally or alternatively (if the NLR is determined to be less than the first predefined threshold), the method may proceed to decision block 906, which involves determining whether the patient's white blood cell count per liter of blood is within a threshold range. The lower limit of the WBC threshold range may be, for example, 3.4×10 cells. 9 cells / L, cells 4×10 9 cells / L, cells 4.5×10 9 cells / L, or 5 x 10 cells 9 cells / L; the upper limit of the threshold range is 9.6 x 10 cells 9 pcs / L, cells 10×10 9 cells / L, cells 11×10 9 cells / L, or 12 x 10 cells 9 An exemplary threshold range can be, for example, 4×10 cells / L. 9 pcs / L~cells 12×10 9 cells / L, cells 4.5×10 9 pcs / L~cells 11×10 9 cells / L, cells 4×10 9 pcs / L~cells 11×10 9 cells / L, cells 5×10 9 pcs / L~cells 10×10 9 In some embodiments, the white blood cell count (WBC) may include a threshold range lower limit (e.g., 4×10 cells / L, as shown in FIG. 9 ). 9 cells / L) or below the upper limit of the threshold range (e.g., 12 x 10 cells as shown in Figure 9). 9 cells / L), a sample test report (such as that described in FIG. 11) may indicate suspected sepsis or septic shock, as shown in block 912.

[0097] Additionally or alternatively (if the NLR is determined to be below the first predefined threshold and the WBC is determined to be outside the threshold range), the method may proceed to decision block 908. In decision block 908, the method includes determining whether the monocyte size distribution width (MDW) value in the blood sample is greater than a second predefined threshold (e.g., including 19, 20, 21, 22, 23, or 24). In other words, the analyzer determines whether the standard deviation of the distribution of monocyte volume, as reported by the MDW value determined in block 902, is greater than a second predefined threshold in the monocytes counted by the analyzer. In some embodiments, if the MDW is greater than the second predefined threshold (e.g., 20 as shown in FIG. 9), the sample test report may indicate a suspicion of sepsis or septic shock, as shown in block 912.

[0098] If it is determined that the NLR is greater than a first predefined threshold, the WBC is outside the threshold range, and the MDW is less than a second predefined threshold, the method 900 may proceed to block 910. In block 910, the analyzer may determine that sepsis or septic shock is unlikely.

[0099] Additionally or alternatively, although not illustrated in FIG. 9, the sample test report may indicate suspicion of sepsis or septic shock (as shown in block 912 of FIG. 9) if more than one (e.g., two or more parameters) are greater than a predefined threshold or threshold range.

[0100] At block 912, the analyzer may generate a message of suspicion. In some examples, the message of suspicion may include a flag, message, or other signal on the test report to indicate possible sepsis or septic shock to a clinician or researcher. In some embodiments, the message of suspicion may include an audio or visual message transmitted to a remote device indicating that the individual associated with the sample has a possible viral infection. The indication may be provided on a screen, such as a display of the blood analyzer, a laboratory information system (LIS), or an electronic medical record (EMR), or may be provided on a printout, fax, email, or other digital or hard copy report of the blood test results.

[0101] In some aspects, a message of suspected sepsis or septic shock may prompt further testing of the patient, including, for example, additional standard of care sepsis testing.

[0102] In some embodiments, the suspected sepsis or septic shock message may facilitate treatment 914 of an individual with possible sepsis or septic shock. In some embodiments, the suspected sepsis or septic shock message informs a clinician-user that a patient is at risk for sepsis or septic shock. Exemplary treatments for sepsis or septic shock may include administering antibiotic preparations, intravenous fluids, vasopressors, corticosteroids, insulin, analgesics, sedatives, or immune-enhancing therapies, or combinations thereof.

[0103] As described further herein, it may be advantageous for the analyzer to further include other clinical data. Such clinical data may be incorporated through the use of the same analyzer or additional analyzers. The incorporation of other clinical data may further include the use of algorithms. Exemplary additional clinical data may include patient demographics, medical history, complaints, and vital signs, etc. In some embodiments, the clinical data may be clinical data that is preferably available to the clinician and / or analyzer before the CBC results.

[0104] Additional or alternative embodiments of the methods performed by the data analysis engine (e.g., data analysis engine 800) may include weighted scoring methods of parameters or features of a blood sample. For example, the data analysis engine may execute operations or processes to implement one or more steps of method 1000 shown in FIG. 10. Similar to method 900, aspects of method 1000 include characterizing blood cells as part of a complete blood count (CBC) in a blood sample at block 1002. The CBC characterization may include characterizing a variety of different parameters. In one example, in certain aspects of block 1002, method 1000 includes determining the number (or count) of white blood cells present per liter of blood. Alternatively or additionally, in certain aspects of block 1002, method 1000 includes determining the percentage of WBCs that are lymphocytes or the absolute number of white blood cells that are lymphocytes. In a further example, in a particular embodiment of block 1002, method 1000 includes determining the percentage of WBCs that are neutrophils or the absolute number of WBCs that are neutrophils. In yet another example, in a particular embodiment of block 1002, method 1000 includes determining monocyte parameters, including, for example, monocyte size distribution width (MDW). Based on the collected information, the analyzer can determine, for example, white blood cell count (WBC), lymphocyte percentage, neutrophil percentage, lymphocyte count, neutrophil count, neutrophil to lymphocyte ratio (NLR), or monocyte size distribution width (MDW), or any combination thereof. For example, in block 1002, the method can further include determining, by the analyzer, the absolute neutrophil count and absolute lymphocyte count, and using these values ​​to determine the NLR. In an alternative embodiment of block 1002, the analyzer engine may query the individual's medical record for data values ​​corresponding to recent values ​​of white blood cell count (WBC), lymphocyte percentage, neutrophil percentage, lymphocyte count, neutrophil count, neutrophil-to-lymphocyte ratio (NLR), or monocyte size distribution width (MDW), or combinations thereof.

[0105] In certain embodiments of method 1000, the data analysis engine discretizes one or more of the values ​​(or counts) determined during the characterization of the blood sample. For example, the MDW is discretized at block 1004, the NLR is discretized at block 1006, and the WBC is discretized at block 1008. For example, the discretization of the WBC or any other parameter may be a rounding down, rounding up, or other approximation of the measurement to a discrete count, for example, using Euler-Maruyama, zero-order hold, or any other suitable method. As shown in FIG. 10, each discretized value is analyzed by a set of decision rules 1010 associated with each of the discretized parameters. The decision rules 1010 show example parameter ranges for the determination of the MDW, NLR, and WBC values. For example, MDW values ​​comprised between 0 and 20 are scored by the weighted scoring rule of the data analysis engine as 0 (zero), values ​​greater than 20 and less than or equal to 25 are scored as 2, and values ​​greater than 25 are scored as 4. In another example, NLR values ​​comprised between 0 and 6 are scored by the weighted scoring rule of the data analysis engine as 0 (zero), values ​​greater than 6 and less than or equal to 10 are scored as 2, and values ​​greater than 10 are scored as 4. In another example, WBC values ​​comprised between 0 and 4 are scored by the weighted scoring rule of the data analysis engine as 1, values ​​greater than 4 and less than or equal to 12 are scored as 0, values ​​greater than 12 and less than or equal to 15 are scored as 1, and values ​​greater than 15 are scored as 2. While other weighted scoring rules are contemplated, this discretization was determined based on univariate evaluation of the relationship between multiple observational test parameters and sepsis outcomes (e.g., descriptive statistics and graphical plots). Logistic regression analysis was performed with sepsis (sepsis-3 criteria) as dependent variable and discretized white blood cell parameters as independent variables. Odds ratios for each of the discretized categories of white blood cell parameters were identified and the weights of the scoring system were defined based on the odds ratios. For example, the aim is to convert these odds ratios (weights) into a simple scoring system between 0 and a set maximum value. In the example shown, the maximum points were set to a value of 10.Thus, the set of odds ratios are summed (e.g., 2.77+5.39+2.36+4.52+1.10+1.89+2.97=12.9) and scaled to the desired maximum value (e.g., 10 / 12.9) to obtain a conversion factor of 0.78. Each odds ratio is multiplied by the conversion factor (0.78) and rounded to one decimal place to generate the index points shown in decision rule 1010.

[0106] In some embodiments of the method 1000, the analyzer (e.g., data analysis engine 800) classifies the risk associated with the blood sample based on a decision rule. The risk classification includes a number of potential classes based on the sum of index points calculated for each of the analyzed parameters. As shown in FIG. 10, a low risk class 1012 may include patients having blood samples with a cumulative score less than or equal to 3; a moderate risk class 1014 may include patients having blood samples with a cumulative score greater than 3 and less than or equal to 7; and a high risk class 1016 may include patients having blood samples with a cumulative score greater than 7.

[0107] Similar to method 900, method 1000 includes generating a message of suspicion. In some examples, the message of suspicion may include a flag, message, or other signal on a test report to indicate possible sepsis or septic shock to a clinician or researcher. In some embodiments, the message of suspicion may include an audible or visual message communicated to a remote device indicating that the individual associated with the sample has a possible viral infection. The indication may be provided on a screen, for example, on the display of a blood analyzer, a laboratory information system (LIS), or an electronic medical record (EMR), or may be provided on a printout, fax, email, or other digital or hard copy report of the blood test results.

[0108] In some aspects, a message of suspected sepsis or septic shock may prompt further testing of the patient, including, for example, additional standard of care sepsis testing.

[0109] In some aspects, the suspected sepsis or septic shock message may facilitate treatment of an individual with possible sepsis or septic shock. In some aspects, the suspected sepsis or septic shock message may inform a clinician-user that a patient is at risk for sepsis or septic shock. Exemplary treatments for sepsis or septic shock may include administering antibiotic preparations, intravenous fluids, vasopressors, corticosteroids, insulin, analgesics, sedatives, or immune-enhancing therapies, or combinations thereof.

[0110] Analyzers and Algorithms As described above, the analysis engine may include a risk analyzer configured to process the measurements or parameters provided to the analysis engine. The risk analyzer may include rules, models, logical expressions in any combination configured to detect and / or predict a medical condition. The data analyzer (e.g., data analyzer 806 of FIG. 8) may include machine learning algorithms, including, for example, a classifier based on a decision tree, a simple logical classifier, a neural network, a logistic regression algorithm, or the like. The processing may be used to help screen for or rule out sepsis or septic shock in a patient. Additionally or alternatively, machine learning algorithms may be used to calculate parameters and rules of a weighted scoring method (e.g., as described with reference to method 1000 of FIG. 10).

[0111] In some embodiments, the machine learning algorithm processes the input to provide output data. The input may include WBC, monocyte cell population parameters, and / or NLR (or if WBC, monocyte cell population parameters, and / or NLR exceed a threshold or threshold range), and the output may include information indicating whether the patient has an increased risk of sepsis or septic shock. In some embodiments, the input may include all of WBC, monocyte cell population parameters, and NLR. In some embodiments, the input may include information regarding whether any of WBC, monocyte cell population parameters, and NLR exceed a threshold or threshold range. The output data may include a numerical value designating the risk of sepsis or septic shock. The output data may further include a rationale for the numerical value designating the risk of sepsis or septic shock. The justification may include a text-based message that identifies a risk of sepsis associated with the patient based on the output data and includes contextual information regarding the relative severity of the risk (e.g., “low”, “low risk”, “moderate”, “medium risk”, “high”, “high risk”) based on a predefined risk classification (e.g., obtained by performing method 1000 described above and shown in FIG. 10).

[0112] In some embodiments, the output data may undergo additional transformation, for example described by a fixed function, to provide an index score. In some embodiments, the index score may designate the probability that the patient has an increased risk of sepsis or septic shock. Alternatively, the index score may designate the probability that the patient does not have an increased risk of sepsis or septic shock. In some embodiments, the index score may be the result of a weighted scoring method. In some embodiments, the index score may be the result of comparing a parameter (e.g., monocytic cell population parameter and / or NLR) to more than one threshold value, or comparing a parameter (e.g., WBC) to more than one threshold range.

[0113] Rapid, simultaneous interpretation of WBC, NLR, and MDW is difficult in the fast-paced ED environment, and processing on at least one analyzer may provide more immediate interpretation to the provider. Additionally, additional accuracy may be achieved through algorithms that incorporate other clinical data such as patient demographics, medical history, complaints, and vital signs, all available prior to the CBC results (Levin et al. Ann Emerg Med. 2018; 71(5):565-574.e2).

[0114] For example, some embodiments of an acuity analyzer (e.g., acuity analyzer 810a described in connection with FIG. 8) may include programs that characterize information received from the analyzer to determine an acuity of an individual. In some embodiments, the acuity analyzer may include programs that characterize information received from the analyzer to identify an individual for discharge from the hospital and / or assess whether an individual is responding to treatment. In certain embodiments, the acuity analyzer may include programs that characterize information received from the analyzer to assess the severity of an infection and / or determine whether an individual is at risk for sepsis or shock. In some embodiments, the urgency analyzer may include a program that characterizes the information received from the analyzer to stratify the risk associated with a febrile newborn, assess or predict the risk of a systemic infection, assess or predict whether an individual is at risk for exhibiting an acute inflammatory condition, identify whether an individual suspected of infection has a viral infection, identify whether an individual suspected of infection has a bacterial infection, identify whether antibiotic treatment should be provided, identify whether an individual is experiencing respiratory exacerbations associated with inflammation and / or infection, e.g., cystic fibrosis, assess the severity of an infection in an immunocompromised individual, or a combination thereof.

[0115] In certain embodiments, systems and methods are provided that are directed to assessing the urgency of individuals. In certain systems, the majority of individuals arriving at the emergency department (ED) have an uncertain clinical course. For example, in certain scenarios, the majority of individuals admitted to the ED appear to be stable, and the ED requires multiple types of resources, such as laboratory tests and / or imaging, to examine or treat the individuals. In certain examples, such individuals with an uncertain clinical course may be initially classified as emergency department urgency level 3. The time required to examine individuals with an uncertain clinical course may result in extended waiting and dwell times, which may cause inefficiencies in the ED or treatment. Furthermore, diagnosing individuals seeking treatment at the ED may be difficult due to overlapping symptoms of various illnesses, including the presence or absence of conditions related to infection. Long waiting times and long times to diagnosis may result in adverse outcomes.

[0116] The systems and methods disclosed herein can alleviate one or more of the above problems. For example, in certain embodiments, the systems and methods disclosed herein can assess the urgency of an individual. In such embodiments, assessing or assessing the urgency of an individual can identify individuals who may require more treatment early, such as individuals with sepsis or septic shock, or individuals who have a risk or increased risk of sepsis or septic shock. These individuals may then be transferred to the appropriate care unit, such as an intensive care unit (e.g., an intensive care unit (ICU)), more efficiently. In the same or alternative embodiments, by identifying individuals with sepsis or risk of severe sepsis early via the methods disclosed herein, additional standard of care sepsis testing can then be indicated.

[0117] In various embodiments, the systems and methods disclosed herein can identify individuals for discharge. For example, in certain embodiments, MDW values ​​(alone or in combination with WBC and / or NLR) may be compared to one or more predefined criteria to identify individuals as candidates for discharge. In various embodiments, parameter values ​​for multiple blood samples may be obtained over a treatment or observation period. In such embodiments, identifying individuals for discharge may help free up hospital resources and / or allocate hospital resources more efficiently.

[0118] For example, as shown in FIG. 11 and as mentioned in some references in FIGS. 5-10, the analyzer 1102 may process and analyze a biological sample (e.g., blood) and generate output data. The analyzer 1102 may be a hematology instrument (e.g., analyzer 600) or any other clinical testing device configured to determine measurements or parameters related to blood or its components. The output data from the analyzer 1102 may include UMALS, LMALS, LALS, MALS, ALL, WBC, MDW, monocyte%, ALC, lymphocyte%, eosinophil%, ANC, neutrophil%, neutrophil to lymphocyte ratio, or any combination thereof. A data analyzer (e.g., data analyzer 806) incorporated into the analyzer 1102 or executed remotely accesses the output data. The data analyzer may analyze data (e.g., as described in connection with FIGS. 7-10). For example, the data analyzer 806 generates a sepsis risk score based on a rule set regarding the parameters and measurements determined by the analyzer 1102 using a weighted scoring methodology. A communicator (e.g., communicator 808 of FIG. 8) transmits, among other things, the risk score generated by the data analyzer to a data store (e.g., data store 508 of FIG. 5) that maintains an electronic medical record 1104 for the patient associated with the biological sample. The risk score may be added to the electronic medical record 1104 in a machine understandable format with executable instructions that define a maximum score, a reference range, or both.

[0119] In certain embodiments, the executable instructions include conditional instructions that are triggered in response to a predefined action. For example, the remote device 506 may execute a local or remote application that provides access to the electronic medical record 1104 stored by the data store 508. In response to the application requesting a patient's complete blood count (CBC) (e.g., report data 1106), the instructions may trigger the insertion of a risk score identification 1108, a risk score 1108a, and a reference range 1108b.

[0120] In another embodiment, the executable instructions trigger an automated message on a remote device 506 running an application that provides access to the electronic medical record 1104 stored by the data store 508 in response to a risk score exceeding a predetermined threshold.

[0121] Computer implementation method In a first aspect, the present disclosure provides a computer-implemented method including obtaining, by a computer device, subject value data from a patient. The subject value data includes at least one of a neutrophil-to-lymphocyte ratio (NLR) of a blood sample from the patient, a white blood cell count (WBC) of a blood sample from the patient, and a monocyte cell population parameter of a blood sample from the patient. The computer-implemented method further includes determining, by the computer device, whether the patient has an increased risk of sepsis or septic shock by using the subject value data of the patient.

[0122] In a second aspect, the present disclosure provides a computer-implemented method comprising obtaining, by a computer device, subject value data for a patient. The subject value data comprises at least one of a neutrophil-to-lymphocyte ratio (NLR) of a blood sample from the patient, a white blood cell count (WBC) of a blood sample from the patient, and a monocyte cell population parameter of a blood sample from the patient. The computer-implemented method further comprises determining, by the computer device, whether the patient is not at risk for sepsis or septic shock.

[0123] In some embodiments, the subject value data includes a neutrophil-to-lymphocyte ratio (NLR) of a blood sample from the patient, a white blood cell count (WBC) of a blood sample from the patient, and a monocyte cell population parameter of a blood sample from the patient.

[0124] In some embodiments, the monocyte cell population parameter preferably comprises the monocyte size distribution width (MDW).

[0125] The patient's subject value data may further include one or more additional parameters including, for example, the patient's vitals, such as the patient's blood pressure, the patient's blood oxygen level, the patient's heart rate or the like. Alternatively or in addition, the patient's subject value data may further include one or more of lymphocyte percentage, neutrophil percentage, lymphocyte count, and neutrophil count.

[0126] Determining by the computer device whether the patient has an increased risk of sepsis or septic shock may be performed by using at least one trained machine learning algorithm. Determining by the computer device whether the patient is not at risk of sepsis or septic shock may be performed by using at least one machine learning algorithm. Thus, the method according to the first aspect and / or the method according to the second aspect of the present invention may be at least partially implemented as a trained machine learning algorithm or any other artificial intelligence based algorithm.

[0127] The machine learning algorithm may include an algorithm based on trained artificial intelligence.For example, the machine learning algorithm may include at least one decision tree, at least one neural network, at least one gradient boosting algorithm, and / or at least one logistic regression algorithm.In some embodiments, the machine learning algorithm is comprised of a decision tree, a neural network, a gradient boosting algorithm, or a logistic regression algorithm.

[0128] The obtained subject value data of the patient can be processed by the trained classifier of the computer device.For example, the computer device can include a classifier or a classifier circuit, which can include a trained machine learning algorithm, a trained gradient boosting algorithm, or any other AI-based algorithm or circuit.The classifier can be part of the control circuit of the computer device, or can be implemented as a separate classifier circuit in the computer device.

[0129] Exemplarily, determining whether a patient has an increased risk of sepsis comprises processing the patient's target value data by using a machine learning algorithm. For example, the patient's obtained target value data may be processed by a trained classifier of a computing device. According to an embodiment of the second aspect of the present disclosure, determining whether a patient is free of risk of sepsis or septic shock comprises processing the target value data for the patient by using a machine learning algorithm. Thus, in particular, according to the present disclosure, the patient's target value may be the input of the trained machine learning algorithm.

[0130] In some embodiments of the first aspect of the present disclosure, determining whether the patient has an increased risk of sepsis or septic shock includes generating output data. According to some embodiments of the second aspect of the present disclosure, determining whether the patient is free of risk of sepsis or septic shock includes generating output data. In particular, according to the present disclosure, generating output data may be performed by processing the patient's target value data by using a machine learning algorithm. In some embodiments of the first aspect of the present disclosure, the output data includes information indicating whether the patient has an increased risk of sepsis or septic shock. According to some embodiments of the second aspect of the present disclosure, the output data includes information indicating whether the patient is free of risk of sepsis or septic shock.

[0131] In accordance with the present disclosure, the output data may include a numerical value. For example, the numerical value may be a value between 0 and 1, or between 0 and 10, or may be included in any other normalized range, designating a risk of sepsis or septic shock, with higher numerical values ​​corresponding to a higher risk of sepsis or septic shock. Alternatively, the numerical value may designate a risk of sepsis or septic shock, with higher numerical values ​​corresponding to a lower risk of sepsis or septic shock.

[0132] The output data may further include a numerical justification designating the risk of sepsis or septic shock. The justification may include, for example, a text-based message.

[0133] According to the present disclosure, the output data may undergo additional transformation, for example described by a fixed function, to provide an index score. According to the first aspect of the present disclosure, the index score may designate the probability that the patient has an increased risk of sepsis or septic shock. In some embodiments of the second aspect of the present disclosure, the index score may designate the probability that the patient does not have the risk of sepsis or septic shock. In some aspects, the index score may be the result of a weighted scoring method. In some aspects, the index score may be the result of comparing a parameter (e.g., a monocytic cell population parameter and / or NLR) to more than one threshold value, or comparing a parameter (e.g., WBC) to more than one threshold range.

[0134] In particular, the machine learning algorithm can be trained by using a reference data set that shows the reference object value associated with one or more reference patients.The reference data set can include a plurality of reference object value data.For example, each of the plurality of reference object value data includes at least one of the neutrophil-to-lymphocyte ratio (NLR) of the blood sample from each reference object, the white blood cell count (WBC) of the blood sample from each reference object, and the monocyte cell population parameter of the blood sample from each reference object.

[0135] In some embodiments, each referent value data of the plurality of referent value data comprises a neutrophil-to-lymphocyte ratio (NLR) of the blood sample from the respective reference subject, a white blood cell count (WBC) of the blood sample from the respective reference subject, and a monocyte cell population parameter of the blood sample from the respective reference subject.

[0136] Reference data set may be used to train and / or be used as the training data set of, for example, machine learning algorithm, artificial intelligence-based algorithm, and / or classifier of computer device.For example, the trained machine learning algorithm, AI-based algorithm, and / or classifier may include multiple parameters, the values ​​of said parameters are determined during training by using reference data set.For example, computer device may be trained using raw reference data related to one or more reference subjects.In addition or alternatively, machine learning algorithm may be used to calculate parameters and rules of weighting scoring method (for example, obtained by performing method 1000 described above and shown in FIG. 10).

[0137] Each of the plurality of referent value data may include sepsis information about the referent. According to the first aspect of the present disclosure, the sepsis information of the referent indicates whether each of the referent has an increased risk of sepsis or septic shock. In some embodiments of the second aspect of the present disclosure, the referent indicates whether each of the referent is free of risk of sepsis or septic shock includes generating output data. During training, the sepsis information constituting at least a portion of the plurality of referent value data may be used to evaluate a loss function used during training the machine learning algorithm, for example, a loss function that is minimal.

[0138] According to the present disclosure, the method may include determining and / or deriving at least a portion of the reference data set by using raw reference data related to one or more reference patients. The raw reference data may include NLR of a blood sample from a reference subject, WBC of a blood sample from a reference subject, monocyte cell population parameters of a blood sample from a reference subject, and sepsis information of a reference subject.

[0139] In accordance with the present disclosure, "obtaining data by a computing device" or "obtaining subject value data by a computing device" may include evaluating the data or subject value data by a computing device. Alternatively, "obtaining data by a computing device" or "obtaining subject value data by a computing device" or "obtaining subject value data for a patient by a computing device" may include generating the data or subject value data, i.e., creating the data or subject value data based on one or more inputs. In yet another example, "obtaining data by a computing device" or "obtaining subject value data for a patient by a computing device" may include accessing a first portion of the data or subject value data and generating a second portion of the data or subject value data from the first portion of the data or subject value data.

[0140] In the present disclosure, "accessing data by a computing device" may include, for example, obtaining data from at least one memory of the computing device, from a memory of another computing device, or from another remote data storage (database, secondary memory, cloud storage, or the like). Thus, in some examples, obtaining data may include downloading data. Illustratively, the computing device may obtain data in response to a user command and / or a notification sent by another computing device. Additionally or alternatively, "accessing data by a computing device" may include, for example, obtaining data from a user or another computing device. The two options are not mutually exclusive. For example, "accessing data by a computing device" may include receiving data, storing data in a memory of the computing device, and obtaining data by accessing the memory.

[0141] In the present disclosure, obtaining subject value data for a patient may include calculating the NLR of a blood sample from the patient, characterizing the WBC of a blood sample from the patient, and / or calculating a monocyte cell population parameter of a blood sample from the patient. Obtaining subject value data for a patient may additionally or alternatively include obtaining data that provides information regarding the relationship between the NLR of the blood sample from the patient, the WBC of the blood sample from the patient, and / or the monocyte cell population parameter of the blood sample from the patient, and corresponding thresholds or threshold ranges.

[0142] The present invention is defined in the claims. However, below is provided a non-exhaustive list 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 other example embodiments or aspects described herein.

[0143] Exemplary Aspects Embodiment A1 is a method of screening for sepsis or septic shock in a patient, the method comprising: calculating a neutrophil-to-lymphocyte ratio (NLR) of a blood sample from the patient; characterizing a white blood cell count (WBC) of the blood sample; calculating a monocytic cell population parameter of the blood sample; comparing the NLR to a first predetermined threshold, the WBC to a predetermined threshold range, and the monocytic cell population parameter to a second predetermined threshold; and determining whether the patient has an increased risk of sepsis or septic shock.

[0144] Embodiment A2 is the method of embodiment A1, wherein determining whether the patient has an increased risk of sepsis or septic shock comprises determining that the NLR is greater than a first predefined threshold, that the WBC is outside a predefined threshold range, or that a monocyte cell population parameter is greater than a second predefined threshold, or a combination thereof.

[0145] Embodiment A3 is a method according to embodiment A1 or A2, wherein determining whether the patient has an increased risk of sepsis or septic shock comprises determining that the NLR is greater than a first predefined threshold, that the WBC is outside a predefined threshold range, and that the monocyte cell population parameter is greater than a second predefined threshold.

[0146] Embodiment A4 is a method according to any of embodiments A1 to A3, wherein the monocyte cell population parameter reflects monocyte activation.

[0147] Embodiment A5 is a method according to any of embodiments A1 to A4, wherein the monocyte cell population parameter comprises monocyte size distribution width (MDW).

[0148] Embodiment A6 is the method of embodiment A5, wherein the second predefined threshold is 19, 20, 21, 22, 23, or 24, and determining whether the patient has an increased risk of sepsis or septic shock comprises determining that the MDW is greater than the second predefined threshold.

[0149] Embodiment A7 is the method of embodiment A6, wherein the second predetermined threshold is 20, and determining whether the patient has an increased risk of sepsis or septic shock comprises determining that the MDW is greater than 20.

[0150] Embodiment A8 has a lower limit of the WBC threshold range of 3.4×10 cells. 9 cells / L, cells 4×10 9 cells / L, cells 4.5×10 9 cells / L, or 5 x 10 cells 9 cells / L; the upper limit of the threshold range is 9.6 x 10 cells 9 pcs / L, cells 10×10 9 cells / L, cells 11×10 9 cells / L, or 12 x 10 cells 9 The method of any of aspects A1-A7, wherein the WBC is outside a threshold range and determining whether the patient has an increased risk of sepsis or septic shock comprises determining that the WBC is outside a threshold range.

[0151] Embodiment A9 has a threshold range of 4×10 cells 9 pcs / L~cells 12×10 9 cells / L and determining whether a patient has sepsis or an increased risk of septic shock is performed when the WBC is greater than or equal to 4 x 10 cells / L. 9 cells / L or less than 12 × 10 9 The method of embodiment A8 comprising determining that the number of cells per 100 is greater than 100 / L.

[0152] Embodiment A10 is a method according to any of embodiments A1-A7, wherein the first predefined threshold is 6, 7, 8, 9, 10, 11, or 12, and determining whether the patient has an increased risk of sepsis or septic shock comprises determining that the NLR is greater than the first predefined threshold.

[0153] Embodiment A11 is a method according to embodiment A10, wherein the first predetermined threshold is 10, and determining whether the patient has an increased risk of sepsis or septic shock comprises determining that the NLR is greater than 10.

[0154] Aspect A12 is a method according to any one of aspects A1 to A11, wherein the patient has a generalized complaint.

[0155] Embodiment A13 is a method according to any of embodiments A1-A12, wherein the patient is symptomatic of a systemic inflammatory condition.

[0156] Embodiment A14 is a method according to any of embodiments A1-A13, wherein the patient presents to an emergency department.

[0157] Embodiment A15 is a method according to any one of embodiments A1 to A14, which is an in vitro method.

[0158] Embodiment A16 is a method according to any of embodiments A1-A15, further comprising administering a treatment for sepsis.

[0159] Embodiment A17 is a method according to embodiment A16, wherein administering treatment for sepsis comprises administering an antibiotic preparation, intravenous fluids, a vasopressor, a corticosteroid, insulin, analgesics, sedatives, or immune enhancing therapy, or a combination thereof.

[0160] Embodiment A18 is a method according to embodiment A16 or A17, further comprising determining the success of treating sepsis.

[0161] Embodiment A19 is a method according to any of embodiments A1-A18, further comprising discretizing one or more of the NLR, WBC, and monocyte cell population parameters to provide one or more discretized values ​​of the NLR, WBC, and monocyte cell population parameters; analyzing the one or more discretized values ​​of the NLR, WBC, and monocyte cell population parameters with a set of decision rules; defining weights for the scoring system based on odds ratios; and generating index points that may be summed to provide an index score.

[0162] Embodiment B1 is a method of ruling out sepsis or septic shock in a patient, the method comprising: calculating a neutrophil-to-lymphocyte ratio (NLR) of a blood sample from the patient; characterizing a white blood cell count (WBC) of the blood sample; calculating a monocytic cell population parameter of the blood sample; comparing the NLR to a first predetermined threshold, the WBC to a predetermined threshold range, and the monocytic cell population parameter to a second predetermined threshold; and determining whether the patient is at risk for sepsis or septic shock.

[0163] Embodiment B2 is a method according to embodiment B1, wherein determining whether the patient is not at risk for sepsis or septic shock comprises determining that the NLR is below a first predefined threshold; and that the WBC is within a predefined threshold range or that a monocytic cell population parameter is below a second predefined threshold, or both.

[0164] Embodiment B3 is a method according to embodiment B1 or B2, wherein determining whether the patient is not at risk for sepsis or septic shock comprises determining that the NLR is below a first predefined threshold, that the WBC is within a predefined threshold range, and that the monocyte cell population parameter is below a second predefined threshold.

[0165] Embodiment B4 is the method according to any of embodiments B1 to B3, wherein the monocyte cell population parameter reflects monocyte activation.

[0166] Embodiment B5 is the method of any of embodiments B1-B4, wherein the monocyte cell population parameter comprises monocyte size distribution width (MDW).

[0167] Embodiment B6 is the method of embodiment B5, wherein the second predefined threshold is 19, 20, 21, 22, 23, or 24, and determining whether the patient is not at risk of sepsis or septic shock comprises determining that the MDW is less than the second predefined threshold.

[0168] Embodiment B7 is the method of embodiment B6, wherein the second predetermined threshold is 20, and determining whether the patient is not at risk for sepsis or septic shock comprises determining that the MDW is less than 20.

[0169] Embodiment B8 has a lower limit of the WBC threshold range of 3.4×10 cells 9 cells / L, cells 4×10 9 cells / L, cells 4.5×10 9 cells / L, or 5 x 10 cells 9 cells / L; the upper limit of the threshold range is 9.6 x 10 cells 9 pcs / L, cells 10×10 9 cells / L, cells 11×10 9 cells / L, or 12 x 10 cells 9 The method of any of aspects B1-B7, wherein the WBC is within a threshold range and the determining whether the patient is not at risk for sepsis or septic shock comprises determining that the WBC is within a threshold range.

[0170] Embodiment B9 is a method for determining whether the WBC threshold range is greater than 4×10 cells. 9 pcs / L~cells 12×10 9 cells / L, and determining whether a patient is at risk for sepsis or septic shock is possible when the WBC count is 4 x 10 cells / L. 9 More than 12 x 10 cells / L 9 The method of embodiment B8, comprising determining that the number of cells / L is less than 1.

[0171] Embodiment B10 is a method according to any of embodiments B1-B9, wherein the first predefined threshold is 6, 7, 8, 9, 10, 11, or 12, and determining whether the patient is not at risk of sepsis or septic shock comprises determining that the NLR is less than the first predefined threshold.

[0172] Embodiment B11 is the method of embodiment B10, wherein the first predetermined threshold is 10, and determining whether the patient is not at risk for sepsis or septic shock comprises determining that the NLR is less than 10.

[0173] Aspect B12 is the method according to any one of aspects B1 to B11, wherein the patient has a generalized complaint.

[0174] Embodiment B13 is the method of any of embodiments B1-B12, wherein the patient is symptomatic of a systemic inflammatory condition.

[0175] Embodiment B14 is the method of any of embodiments B1-B13, wherein the patient presents to the emergency department.

[0176] Aspect B15 is an in vitro method, which is a method according to any one of aspects B1 to B14.

[0177] Embodiment B16 is the method of embodiment B1, further comprising: discretizing one or more of the NLR, WBC, and monocyte cell population parameters to provide one or more discretized values ​​of the NLR, WBC, and monocyte cell population parameters; analyzing the one or more discretized values ​​of the NLR, WBC, and monocyte cell population parameters by a set of decision rules; defining weights for the scoring system based on odds ratios; and generating index points that may be summed to provide an index score.

[0178] Embodiment C1 is a method comprising an automated method for screening for sepsis or septic shock in a patient, the method comprising: calculating a neutrophil-to-lymphocyte ratio (NLR) of a blood sample from the patient; characterizing a white blood cell count (WBC) of the blood sample; calculating a monocytic cell population parameter of the blood sample; and determining whether the patient has an increased risk of sepsis or septic shock using a data processing module, the data processing module comprising a processor and a tangible, non-transitory computer readable medium, the computer readable medium being programmed with a computer application that, when executed by the processor, causes the processor to compare the NLR to a first predetermined threshold to provide a first comparison, compare the monocytic cell population parameter to a second predetermined threshold to provide a second comparison, compare the WBC to a third predetermined threshold and a fourth predetermined threshold to provide a third comparison, and determine the risk of sepsis or septic shock based on the first comparison, the second comparison, and the third comparison.

[0179] Embodiment C2 is the method of embodiment C1, wherein determining the risk of sepsis or septic shock indicates a suspicion of sepsis or septic shock if the first comparison is higher than a first predetermined threshold, the second comparison is higher than a second predetermined threshold, and the third comparison is lower than a third predetermined threshold or higher than a fourth predetermined threshold.

[0180] Embodiment C3 is a method according to embodiment C1 or C2, wherein the first predetermined threshold is 6, 7, 8, 9, 10, 11, or 12.

[0181] Embodiment C4 is the method of embodiment C3, wherein the first predetermined threshold is 10.

[0182] Embodiment C5 is the method of any of embodiments C1 to C4, wherein the monocyte cell population parameter comprises standard deviation of monocyte volume.

[0183] Embodiment C6 is the method of embodiment C5, wherein the standard deviation of monocyte volume comprises a monocyte size distribution width (MDW) and the second pre-defined threshold is 19, 20, 21, 22, 23, or 24.

[0184] Embodiment C7 is the method of embodiment C6, wherein the standard deviation of monocyte volume comprises a monocyte size distribution width (MDW) and the second pre-defined threshold is 20.

[0185] Embodiment C8 has a third predetermined threshold of 3.4×10 cells. 9 cells / L, cells 4×10 9 cells / L, cells 4.5×10 9 cells / L, or 5 x 10 cells 9 cells / L; the fourth predefined threshold is 9.6 x 10 cells / L. 9 pcs / L, cells 10×10 9 cells / L, cells 11×10 9 cells / L, or 12 x 10 cells 9 The method according to any one of aspects C1 to C5, wherein the number of particles per liter is 100 / L.

[0186] Embodiment C9 has a third predetermined threshold of 12×10 cells. 9 cells / L, and the fourth predefined threshold is 4 x 10 cells. 9 The method according to embodiment C8, wherein the number of cells per liter is 100 / L.

[0187] Embodiment C10 is the method of any of embodiments C1-C9, further comprising altering the test reporting process based on the assessment of the sepsis status.

[0188] Embodiment C11 is a method according to any of embodiments C1-C10, further comprising treating the individual from whom the blood sample was obtained if the septic condition is indicative of sepsis.

[0189] Embodiment C12 is a method described in any of embodiments C1 to C11, further comprising: delivering a hydrodynamically focused flow of the blood sample toward a cell interaction zone of the optical element; and measuring, by the electrode assembly, the current (DC) impedance of cells of the blood sample that individually pass through the cell interaction zone, wherein the module determines the standard deviation of monocyte volume based on the DC impedance measurement of the cells of the blood sample.

[0190] Embodiment D1 is the method of any of embodiments A1-A17, wherein determining whether a patient has an increased risk of sepsis or septic shock includes generating output data by processing input data by using a trained machine learning algorithm, wherein the input data includes an NLR of a blood sample from the patient, a WBC of the blood sample from the patient, and a monocyte cell population parameter of the blood sample from the patient, and wherein the output data includes information indicating whether the patient has an increased risk of sepsis or septic shock.

[0191] Aspect D2 is the method of any of aspects B1-B14, wherein determining whether a patient has an increased risk of sepsis or septic shock includes generating output data by processing input data by using a trained machine learning algorithm, wherein the input data includes an NLR of a blood sample from the patient, a WBC of the blood sample from the patient, and a monocyte cell population parameter of the blood sample from the patient, and wherein the output data includes information indicating whether the patient is at risk of sepsis or septic shock.

[0192] Embodiment D3 is the method of embodiment D1 or embodiment D2, wherein the output data includes a numerical value designating the risk of sepsis or septic shock.

[0193] Embodiment D4 is the method of embodiment D3, wherein the output data further includes a numerical basis for designating the risk of sepsis or septic shock.

[0194] Embodiment D5 is a method according to any of embodiments D1-D4, further comprising: discretizing one or more of the NLR, WBC, and monocyte cell population parameters to provide one or more discretized values ​​of the NLR, WBC, and monocyte cell population parameters; analyzing the one or more discretized values ​​of the NLR, WBC, and monocyte cell population parameters by a set of decision rules; defining weights for a scoring system based on odds ratios; and generating index points that may be summed to provide a numerical value designating the risk of sepsis or septic shock.

[0195] Embodiment E1 is a computer-implemented method that includes obtaining, by a computing device, subject value data for a patient, subject value data including a neutrophil-to-lymphocyte ratio (NLR) of a blood sample from the patient, a white blood cell count (WBC) of the blood sample from the patient, and a monocyte cell population parameter of the blood sample from the patient; and determining, by the computing device, whether the patient has an increased risk of sepsis or septic shock by using the patient's subject value data.

[0196] Embodiment E2 is the method of embodiment E1, wherein determining whether the patient has an increased risk of sepsis is performed by using at least one machine learning algorithm.

[0197] Embodiment E3 is the method of embodiment E2, wherein determining whether the patient has an increased risk of sepsis includes processing the patient's subject value data by using a machine learning algorithm.

[0198] Embodiment E4 is a computer-implemented method that includes obtaining, by a computing device, subject value data for a patient, the subject value data including a neutrophil-to-lymphocyte ratio (NLR) of a blood sample from the patient, a white blood cell count (WBC) of the blood sample from the patient, and a monocyte cell population parameter of the blood sample from the patient; and determining, by the computing device, whether the patient is at risk for sepsis or septic shock.

[0199] Embodiment E5 is the method of embodiment E4, wherein determining whether the patient is at risk for sepsis or septic shock is performed by using at least one machine learning algorithm.

[0200] Embodiment E6 is the method of embodiment E5, wherein determining whether the patient is at risk for sepsis or septic shock includes processing the patient's subject value data by using a machine learning algorithm.

[0201] Embodiment E7 is a method according to any one of embodiments E2, E3, E5, and E6, wherein the machine learning algorithm is trained with a reference dataset indicative of referent values ​​associated with one or more reference patients; the reference dataset comprises a plurality of referent value data, each referent value data of the plurality of referent value data comprising at least one of: a neutrophil-to-lymphocyte ratio (NLR) of a blood sample from the respective reference subject; a white blood cell count (WBC) of the blood sample from the respective reference subject, and a monocyte cell population parameter of the blood sample from the respective reference subject.

[0202] Embodiment E8 is a method according to any one of embodiments E1 to E7, wherein obtaining subject value data for the patient comprises calculating an NLR of a blood sample from the patient, characterizing WBCs of a blood sample from the patient, and / or calculating monocyte cell population parameters of a blood sample from the patient.

[0203] Embodiment E9 is a method according to any of embodiments E1-E8, further comprising discretizing one or more of the NLR, WBC, and monocyte cell population parameters to provide one or more discretized values ​​of the NLR, WBC, and monocyte cell population parameters; analyzing the one or more discretized values ​​of the NLR, WBC, and monocyte cell population parameters with a set of decision rules; defining weights for the scoring system based on odds ratios; and generating index points that may be summed to provide subject value data for a patient.

[0204] The present invention is illustrated by one or more values ​​of NLR, WBC, and monocyte cell population parameters, and the specific examples, materials, amounts, and procedures should be construed broadly in accordance with the scope and spirit of the invention described herein. EXAMPLES

[0205] Example 1 This example shows that a CBC typing panel including MDW, white blood cell count (WBC), and neutrophil-to-lymphocyte ratio (NLR) demonstrated strong performance characteristics in a broad ED population, suggesting practical value as a rapid screen for sepsis and septic shock.

[0206] This example describes a study evaluating the performance of monocyte size distribution width (MDW) alone and in combination with other routine CBC parameters as a screen for sepsis and septic shock in ED patients. A prospective cohort analysis of adult patients with CBCs collected in an urban ED from January 2020 to July 2021. The performance of MDW, white blood cell count (WBC), and neutrophil-to-lymphocyte ratio (NLR) to detect sepsis and septic shock (Sepsis-3 criteria) was evaluated using diagnostic performance measures.

[0207] 7952 ED patients were included in the cohort, 180 fulfilled criteria for sepsis; 43 with septic shock and 137 without shock. MDW was highest in patients with septic shock (median 24.8 U, IQR 22.0-28.1) and tended to decrease in patients with sepsis without shock (23.9 U, IQR 20.2-26.8), infection (20.4 U, IQR 18.2-23.3), and then controls (18.6 U, IQR 17.1-20.4). Alone, MDW detected sepsis and septic shock with AUC 0.80 (95% CI 0.77-0.84) and 0.85 (95% CI 0.80-0.91), respectively. When combined with WBC and NLR, optimal performance was achieved for detection of sepsis (AUC 0.86, 95% CI 0.83–0.89) and septic shock (0.86, 95% CI 0.80–0.92).

[0208] material and method Study design and setting: A prospective cohort study was conducted in the Johns Hopkins Hospital ED in Baltimore, MD, between January 21, 2020 and July 14, 2021. The study was approved by the institutional review board (IRB) and followed the Standards for Reporting of Diagnostic Accuracy Studies (STARD) guidelines.

[0209] Participant Selection. All adult patients (age 18 and older) had a CBC collected within 6 hours of ED arrival as part of routine clinical care and were eligible for the study. Patients were enrolled consecutively for the period during which members of the study team were present. Patients missing a valid MDW (e.g., low sample volume or poor sample quality), patients whose MDW sample analysis was performed more than 2 hours after blood collection, and patients missing other CBC parameters (WBC, neutrophils, lymphocytes) within 6 hours of arrival were excluded. Repeated ED visits by the same patient during the study period were also excluded.

[0210] Measurements: Demographics, clinical data (complaints, comorbidities, vital signs, laboratory tests), and hospital utilization data were collected from the electronic health record (EHR) system. Complaints were entered from the ED triage pick list, and comorbidities were sought by classifying diagnosis codes (ICD-10) for active problems available in the EHR upon patient arrival (World Health Organization, International Classification of Diseases (ICD), available online at www.who.int / standards / classifications / classification-of-diseases (last accessed May 26, 2021); Levin et al. Ann Emerg Med. 2018;71(5):565-574.e2; Agency for Healthcare Research in Quality. AHRQ QI ICD-10-CM / PCS Specification Version 7 Patient Safety Indicators Appendices, available online at www.qualityindicators.ahrq.gov / Downloads / Modules / PSI / V70 / TechSpecs / PSI_Appendix_I.pdf (last accessed May 26, 2021)). Rapid Sequential Organ Failure Assessment (qSOFA) scores (range, 0–3 points) were estimated at the time of triage using the first measurement of systolic blood pressure (<100 mmHg = 1 point), respiratory rate (>22 breaths / min = 1 point), and altered mental status as indicated by complaints related to altered mental status (Levin et al. Ann Emerg Med. 2018;71(5):565-574.e2) (1 point), or Glasgow Coma Score (GCS) <15 reported within 6 hours of ED arrival (Seymour et al. JAMA 2016;315(8):762). Mortality was defined as in-hospital mortality or transfer to hospice. Direct admission to intensive care unit (ICU), inpatient admission, and length of stay (from ED presentation to discharge) were reported as well.

[0211] MDW was analyzed in K2 EDTA tubes with a UniCel DxH900 analyzer (Beckman Coulter,Inc), software version 1.0. A cutoff value of >20 units was defined as abnormal (Sprung et al. Intensive Care Med. 2006;32(3):421-427; Bone et al. Chest. 1992;101(6):1644-1655; Singer et al. JAMA. 2016;315(8):801-810). MDW measurements were performed by study team members blinded to the patients' clinical information. MDW was not reported to the EHR; clinicians were unaware of MDW values ​​while providing care to enrolled patients. Other CBC parameters (WBC and NLR) were measured with a separate hematology analyzer used for routine clinical practice, which was available to the attending physician. Abnormal WBC was defined as >4×10 cells. 9 < 12 x 10 cells / L 9 A normal NLR was defined as greater than 10 cells / L (Bone et al. Chest. 1992;101(6):1644-1655; Levy et al. Intensive Care Medicine. 2003;29(4):530-538), and an abnormal NLR was defined as greater than 10 (Farkas J Thorac Dis. 2020;12(Suppl 1):S16-S21). Measurements of lactate (abnormal defined as >2.0 mmol / L) (Singer et al. JAMA. 2016;315(8):801-810) and C-reactive protein (CRP) (abnormal defined as >10 mg / L) (Laboratory Tests Reference Ranges. Available online at www.abim.org / Media / bfijryql / laboratory-reference380ranges.pdf) were both performed at the request of the treatment team and were included in the analysis as comparators if performed within 6 hours of ED presentation. A subgroup of immunosuppressed patients were neutropenic (absolute neutrophil count >1.5 × 10 cells when measured within 6 hours of ED arrival). 9We defined patients as those with active problems who met criteria for immunosuppression (< or equal to cells / L) or immunosuppression (Agency for Healthcare Research in Quality. AHRQ QI ICD-10-CM / PCS Specification Version 7 Patient Safety Indicators Appendices, available online at www.qualityindicators.ahrq.gov / Downloads / Modules / PSI / V70 / TechSpecs / PSI_Appendix_I.372 pdf (last accessed May 26, 2021)).

[0212] Outcomes. The primary outcome was presentation of sepsis with or without shock within 12 hours of CBC collection. For analysis, patients were assigned to four mutually exclusive groups based on previously validated criteria; control, infection, sepsis (without shock), or septic shock (Singer et al. JAMA. 2016;315(8):801-810; Liu et al. J Biomed Inform. 2021;121:103879). Patients met criteria for infection if they either (a) initiated a novel antibiotic within a 4-day course (from the date of first dose to the date of last dose) and blood cultures were ordered within 48 hours of ED arrival, or (b) met ICD-10 coded diagnosis criteria for infection (Liu et al. J Biomed Inform. 2021;121:103879). Patients on shorter antibiotic durations were eligible if death occurred within 4 days of treatment initiation. The Sepsis-3 definition using the Sequential Organ Failure Assessment (SOFA) (Singer et al. JAMA. 2016;315(8):801-810) was used as the reference standard to define the sepsis group (sepsis without shock and septic shock). Patients met the criteria for infection and met the following SOFA criteria within 12 hours of CBC collection: (a) initiation of vasopressors, (b) initiation of mechanical ventilation, (c) doubling of serum creatinine level, (d) 50% decrease in estimated glomerular filtration rate compared to baseline, (e) bilirubin level >2.0 mg / dL and doubling from baseline, (f) platelet count >100×10 9 < cells / L and a decrease of more than 50% from baseline (baseline is 100 × 10 9 Patients were assigned to the sepsis group if they met at least one of the following criteria: (i) infection, (ii) blood pressure, (iii) blood glucose level, (iv) lactate level >2.0 mmol / L; or (g) lactate >2.0 mmol / L. Patients who met the sepsis criteria, initiated vasopressors, and had a lactate level >2 mmol / L were assigned to the septic shock group. Patients who did not meet the infection or sepsis criteria were included in the control group.

[0213] Patient outcome classification was assigned by an automated algorithm applied to EHR data. Two physicians from the study team performed an unblinded chart review of a subset of 100 patients with sepsis (including septic shock) and 100 patients without sepsis (infection or control) (Liu et al. J Biomed Inform. 2021;121:103879). Adjudicated review was performed to assess the reliability and accuracy of the algorithm with respect to the above definitions. The review resulted in confirmation of a reliable classification of sepsis with a positive predictive value (PPV) of 99% and a negative predictive value (NPV) of 100%.

[0214] Analysis. Continuous variables were expressed as medians with interquartile ranges (IQR) and compared using the Mann-Whitney U test. Categorical variables were expressed as numbers and percentages and compared using the chi-square test. Correlation coefficients were calculated using the Spearman rank correlation method. Diagnostic performance was evaluated using binary classification measures. The area under the receiver operating characteristic curve (AUC) was calculated using a logistic regression model with sepsis (sepsis without shock or septic shock) and septic shock as separate response variables. White blood cell parameters were modeled as continuous variables alone (single predictor) and in combination (multiple predictors). Comparison of AUCs and their CIs were evaluated using the DeLong method (DeLong et al. Biometrics 1988;44(3):837-845). Sensitivity, specificity, positive and negative predictive values, and likelihood ratios were calculated using clinical test cutoffs with dichotomous definitions as normal or abnormal. Patients with missing lactate or CRP measurements were excluded from the respective subgroup analyses. No imputation or interpolation methods were applied to any of the clinical data used to derive sepsis outcomes. All analyses were performed in Python version 3. [Table 1-1] [Table 1-2]

[0215] [Table 2-1] [Table 2-2]

[0216] result Characteristics of Study Population. Overall, 8,915 patients who had MDW measured within 6 hours of ED arrival were included in the study. Patients were excluded due to delays of more than 2 hours between blood draw and MDW analysis (570 patients), invalid MDW measurements (171 patients), and lack of correlation with WBC, neutrophil, or lymphocyte counts (222 patients), as seen in Chart 100 in Figure 1. This resulted in a final cohort of 7,952 patients, consisting of 6,597 (83.0%) controls, 1,175 (14.8%) patients with infection, 137 (1.7%) patients who met criteria for sepsis without shock, and 43 (0.5%) patients who met criteria for septic shock. Patients who met the infection outcome criteria were older and more likely to have comorbidities such as cancer, heart failure, and renal disease, as seen in Table 1. Patients were also more likely to meet qSOFA criteria of 2 or greater at presentation, require hospitalization or ICU admission, and die.

[0217] Main Results. The distribution of MDW, WBC, and NLR is displayed in Figure 2. As seen in Figure 2, MDW was highest in patients with septic shock (median: 24.8 U, IQR 22.0-28.1), tended to decrease in sepsis (23.9 U, IQR 20.2-26.8) and infection (20.4 U, IQR 18.2-23.3), and the lowest values ​​were found in control patients (18.6 U, IQR 17.1-20.4). WBC and NLR differentiated sepsis from non-septic (control and infection) groups, but had poor discrimination between sepsis (without shock) and septic shock groups when compared with MDW (P=0.048). Furthermore, MDW demonstrated low positive correlations with WBC (rho = 0.09, 95% CI [0.07-0.11]) and NLR (rho = 0.19, 95% CI [0.17-0.22]), whereas WBC and NLR were moderately correlated (rho = 0.52, 95% CI [0.50-0.53]).

[0218] The diagnostic performance of qSOFA and white blood cell parameters (MDW, WBC, NLR) for the sepsis group are displayed in Table 2. MDW detected sepsis with an AUC of 0.80 (95% CI 0.77-0.84) and septic shock with an AUC of 0.85 (95% CI 0.79-0.91). In comparison, WBC had an AUC of 0.77 (95% CI 0.73-0.81) and 0.79 (95% CI 0.71-0.87), and NLR had an AUC of 0.84 (95% CI 0.81-0.87) and 0.81 (95% CI 0.73-0.88) for sepsis and septic shock, respectively. Using a cutoff of 20 U or higher for MDW as a test for sepsis shows a sensitivity of 77.8% (95% CI 71.1-83.9) and a specificity of 66.8% (95% CI 65.8-67.9%). The overall diagnostic performance (AUC) of MDW and NLR alone was superior to qSOFA for sepsis (P<0.05). When MDW, WBC, and NLR were combined, the overall diagnostic performance increased to AUC 0.86 (95% CI 0.83-0.89) for sepsis and 0.86 (95% CI 0.80-0.92) for septic shock, as seen in Table 2.

[0219] Subgroup analysis. Lactate was measured in a subgroup of 2,712 patients (34.1% of the total cohort) and CRP was measured in a subgroup of 542 patients (6.8%) during routine care in the ED. Figure 3A shows the distribution of lactate and Figure 3B shows the distribution of CRP compared to white blood cell parameters (MDW, WBC, NLR) for these respective subgroups. Lactate demonstrated reliable differences between sepsis and non-sepsis (control and infection) groups, as seen in Figure 3A, but only limited differentiation between sepsis (without shock) and septic shock groups. CRP showed a similar trend in fewer samples, but did not clearly differentiate between sepsis (without shock) and septic shock groups (Figure 3B). MDW maintained its upward trend between control, infection, sepsis, and septic shock in both of these subgroups when additional laboratory tests were performed. This trend also held for the group of 965 patients (12.2%) who met criteria for immunosuppression as seen in Supplementary Figure 4 .

[0220] Consideration Despite widespread recognition that early initiation of targeted treatment for sepsis is crucial for improved outcomes, rapid identification of patients with the condition remains a major challenge (Morr et al. BMC Emerg Med. 2017;17(1):11). Diagnosis of sepsis is complicated by ambiguous symptom manifestations and a lack of biomarkers or other ancillary tests that reliably include or exclude the disease (Al Jalbout et al. The Journal of Applied Laboratory Medicine. 2019;3(4):724-729;Pierrakos et al. Crit Care. 2020;24(1):287;Boushra et al. J Emerg Med. 2019;56(1):36-45). Screening for sepsis is particularly challenging in the ED because systemic inflammatory response syndrome (SIRS) and organ failure are often driven by non-infectious pathologies, and patients with subclinical infections may present presymptomatically with overt signs of sepsis (e.g., tachycardia, hypotension, altered mental status) that are detected by tools such as qSOFA that have been applied for screening (Singer et al. JAMA. 2016;315(8):801-810; Serafim et al. Chest. 2018;153(3):646-655; Anand et al. Chest. 2019;156(2):289-297).

[0221] This example shows that MDW may have concurrent utility as an ED-based sepsis screen and for disease severity stratification. Alone, MDW was the most sensitive marker tested for both sepsis and septic shock, surpassing qSOFA, WBC, and NLR (Table 2). Similarly, it demonstrated an NPV of 99.2% for sepsis and 99.9% for septic shock. These results suggest that MDW is a strong candidate for broad-spectrum screening aimed at identifying unsuspected cases of sepsis. In addition, MDW is unique in distinguishing disease severity (e.g., distinguishing septic shock from sepsis) compared to WBC, NLR, lactate, and CRP. For example, the NLR (AUC=0.84), which reported the highest discrimination power for sepsis in this cohort, showed limited ability to distinguish between sepsis without shock and septic shock, consistent with previous findings in higher-risk populations (e.g., ICU) (Farkas, J Thorac Dis. 2020;12(Suppl 1):S16-S21). Thus, the complementary clinical attributes of different leukocyte parameters (e.g., utility for screening vs. risk stratification) suggest that they may be most useful in combination.

[0222] When applied together, MDW, WBC, and NLR increased the AUC to 0.86 for both sepsis and septic shock (Table 2); sensitivity was achieved at 92.2% for sepsis and 97.7% for septic shock. These sensitivities translate to negative likelihood ratios of 0.14 and 0.04, respectively. For patients presenting to the ED with low pre-test probability (e.g., 20%), sepsis and septic shock would be effectively ruled out by not meeting threshold criteria for any of these three parameters (post-test probability of 3% and 1%, respectively). Furthermore, additional accuracy could potentially be achieved through algorithms that incorporate other clinical data such as patient demographics, medical history, complaints, and vital signs, all available prior to the CBC results (Levin et al. Ann Emerg Med. 2018;71(5):565-574.e2).

[0223] The availability of MDW, WBC, and NLR as part of the CBC classification should not be underestimated. Although other markers of sepsis (e.g., lactate, CRP, and procalcitonin) are commonly used for risk stratification of sepsis, none of them show optimal diagnostic performance when used alone, and they are not consistently utilized in clinical practice in all EDs. In contrast, the identification of disease-specific patterns within routinely used laboratory test panels has potential value for enabling recognition of clinically time-sensitive disease (sepsis) when unsuspected, and directing interventions to those at highest risk of missed or delayed diagnosis and adverse outcomes. In this study, MDW was comparable to lactate and CRP in a highly selected group of patients in whom these tests were ordered by the attending physician (Figure 3), but MDW was still available in the entire study cohort. WBC, NLR, and MDW showed predictive value and unique diagnostic performance characteristics alone (Figure 2 and Table 2). However, the lack of correlation between MDW and other CBC parameters indicates that MDW has an important additive role (Crouser et al. Chest. 2017;152(3):518-526) and may help optimize the use of CBC results (Farkas, J Thorac Dis. 2020;12(Suppl 1):S16-S21).

[0224] This example describes the first large-scale clinical trial of MDW to date. The findings support the results of several smaller studies showing that MDW alone has a fairly good accuracy for detecting sepsis in undifferentiated ED populations (Crouser et al. Crit Care Med. 2019;47(8):1018-1025;Crouser et al. Chest. 2017;152(3):518-526;Crouser et al. J Intensive Care. 2020;8:33;Agnello et al. Int J Lab Hematol. 2021;23(4):O183;Polilli et al. PLoS ONE. 2020;15(1):e0227300;Le et al. Critical Care Medicine. 2020;48(1):12). This study extends these findings by evaluating the performance of MDW alone and in combination with multiple routinely reported components of CBC to optimize sensitivity and specificity. This example further describes the performance of MDW compared to both lactate and CRP and evaluates its performance in a subpopulation of immunosuppressed patients. Although this subanalysis is limited by its small sample size, MDW was effective in distinguishing patients with infection from those with sepsis without infection, and similar trends in MDW signal were observed in this sample as in the larger population (Figure 4). These data strengthen the evidence supporting the use of MDW for broad sepsis screening and strengthen the argument for its incorporation into a comprehensive algorithm for sepsis diagnosis and disease severity assessment (Le et al. Critical Care Medicine. 2020;48(1):12).

[0225] The complete disclosures of all patents, patent applications, and publications cited herein, as well as electronically available materials, are incorporated herein by reference. In the event of any discrepancy between the disclosure of this application and the disclosure(s) of any document incorporated herein by reference, the disclosure of this application shall prevail. The foregoing detailed description and examples are given merely for clarity of understanding. No unnecessary limitations are to be understood therefrom. The present invention is not limited to the exact details shown and described, and variations obvious to one skilled in the art are included in the invention defined by the claims.

Claims

1. A method for obtaining neutrophil-to-lymphocyte ratio (NLR), white blood cell count (WBC), and monocyte population parameters as indicators for screening for sepsis or septic shock in a patient, Calculating the NLR of the blood sample from the patient; Characterizing the WBC of a blood sample; and To calculate the monocyte population parameter of the blood sample. Includes, Here, comparing the NLR to a first default threshold, the WBC to a default threshold range, and the monocyte population parameter to a second default threshold indicates whether the patient has an increased risk of sepsis or septic shock. method.

2. NLR is greater than the first predetermined threshold, The WBC is outside the aforementioned predetermined threshold range, or The monocyte population parameter is greater than the second default threshold. or a combination thereof The method according to claim 1, wherein determining whether the patient has an increased risk of sepsis or septic shock.

3. NLR is greater than the first predetermined threshold, The WBC is outside the aforementioned predetermined threshold range, and The monocyte population parameter is greater than the second default threshold. The method according to claim 1 or 2, wherein determining whether the patient has an increased risk of sepsis or septic shock.

4. The method according to claim 1 or 2, wherein the monocyte population parameter reflects monocyte activation.

5. The method according to claim 1 or 2, wherein the monocyte population parameter includes monocyte size distribution width (MDW).

6. The method according to claim 5, wherein the second default threshold is 20, and determining that the MDW is greater than 20 indicates whether the patient has an increased risk of sepsis or septic shock.

7. The aforementioned threshold range is for cells 4 × 10 ９ cells / L ~ cells 12×10 ９ The cell count is cells / L, and the WBC is 4 x 10⁶ cells. ９ If the number of cells / L is less than 12 × 10⁶ cells ９ The method according to claim 1 or 2, wherein determining that the values ​​are greater than 1 / L indicates whether the patient has an increased risk of sepsis or septic shock.

8. The method according to claim 1 or 2, wherein the first default threshold is 10, and determining that the NLR is greater than 10 indicates whether the patient has an increased risk of sepsis or septic shock.

9. The method according to claim 1 or 2, wherein the patient presents with nonspecific symptoms.

10. The method according to claim 1 or 2, wherein the patient exhibits symptoms of a systemic inflammatory state.

11. The method according to claim 1 or 2, wherein the patient is visiting the emergency department.

12. The method according to claim 1 or 2, wherein it is an in vitro method.

13. The method according to claim 1 or 2, wherein it is indicated that treatment for sepsis should be administered.

14. The method according to claim 13, wherein the treatment for sepsis includes administering an antibiotic preparation, intravenous fluid, vasopressor, corticosteroid, insulin, analgesic, sedative, or immunosuppressant therapy, or a combination thereof.

15. The method according to claim 13, further demonstrating the success of the treatment for sepsis.

16. To discretize one or more of the NLR, WBC, and the monocyte population parameters, and to provide one or more discretized values ​​of the NLR, WBC, and the monocyte population parameters; Analyze NLR, WBC, and one or more of the discretized values ​​of the monocyte population parameters using a set of determination rules; Defining the weights of the scoring system based on the odds ratio; and To generate one or more index points of NLR, WBC, and the monocyte population parameter that can be aggregated to provide an index score. The method according to claim 1 or 2, further comprising:

17. To discretize one or more of the NLR, WBC, and the monocyte population parameters to provide discretized values ​​or a set of discretized values; Analyzing the aforementioned discretized value or multiple discretized values ​​using a set of decision rules; Defining the weights of the scoring system based on the odds ratio; and To generate metric points that can be totaled to provide an metric score. The method according to claim 1 or 2, further comprising:

18. A method for obtaining neutrophil-to-lymphocyte ratio (NLR), white blood cell count (WBC), and monocyte population parameters as indicators for ruling out sepsis or septic shock in a patient, Calculating the NLR of the blood sample from the patient; Characterizing the WBC of the blood sample; and To calculate the monocyte population parameter of the blood sample. Includes, A method for indicating whether a patient is at risk of sepsis or septic shock, wherein NLR is compared to a first default threshold, WBC to a default threshold range, and the monocyte population parameter to a second default threshold.

19. NLR is less than a first predetermined threshold; and The WBC is within a predetermined threshold range, or the monocyte population parameter is below the second predetermined threshold, or both. The method according to claim 18, wherein determining that indicates the patient is not at risk of sepsis or septic shock.

20. NLR is less than a first predetermined threshold, The WBC is within the predetermined threshold range, and The monocyte population parameter is less than the second predetermined threshold. The method according to claim 18 or 19, wherein determining whether the patient is at risk of sepsis or septic shock.

21. The method according to claim 18 or 19, wherein the monocyte population parameter reflects monocyte activation.

22. The method according to claim 18 or 19, wherein the monocyte population parameter includes monocyte size distribution width (MDW).

23. The method according to claim 22, wherein determining that the second default threshold is 20 and that the MDW is less than 20 indicates whether the patient is at risk of sepsis or septic shock.

24. The threshold range for WBC is 4 × 10 cells. ９ cells / L ~ cells 12×10 ９ The cell count is cells / L, and the WBC is 4 x 10⁶ cells. ９ Larger than cells / L, and with 12 x 10 cells. ９ The method according to claim 18 or 19, wherein determining that the number of cells / L is less indicates whether or not the patient is at risk of sepsis or septic shock.

25. The method according to claim 18 or 19, wherein determining that the first default threshold is 10 and that the NLR is less than 10 indicates whether or not the patient is at risk of sepsis or septic shock.

26. The method according to claim 18 or 19, wherein the patient presents with nonspecific symptoms.

27. The method according to claim 18 or 19, wherein the patient exhibits symptoms of a systemic inflammatory state.

28. The method according to claim 18 or 19, wherein the patient is visiting the emergency department.

29. The method according to claim 18 or 19, wherein it is an in vitro method.

30. A computer implementation method including an automated method for screening patients for sepsis or septic shock, Calculate the neutrophil-to-lymphocyte ratio (NLR) of the blood sample from the aforementioned patient; Characterizing the white blood cell count (WBC) of the aforementioned blood sample; Calculating the monocyte population parameters of the blood sample; and A method comprising using a data processing module to determine whether the patient has an increased risk of sepsis or septic shock, wherein the data processing module comprises a processor and a tangible, non-temporary computer-readable medium, and the computer-readable medium is programmed by a computer application that, when executed by the processor, causes the processor to provide a first comparison by comparing the NLR to a first default threshold, a second comparison by comparing the monocyte population parameter to a second default threshold, a third comparison by comparing the WBC to a third default threshold and a fourth default threshold, and determines the risk of sepsis or septic shock based on the first comparison, the second comparison, and the third comparison.

31. Determining the aforementioned risk of sepsis or septic shock is The comparison in the first step is higher than the default threshold in the first step. The second comparison is higher than the second default threshold, If the third comparison is lower than the third default threshold or higher than the fourth default threshold, The computer implementation method according to claim 30, which indicates a suspected case of sepsis or septic shock.

32. The computer implementation method according to claim 30 or 31, wherein the first default threshold is 10.

33. The computer implementation method according to claim 30 or 31, wherein the monocyte population parameter includes the standard deviation of monocyte volume.

34. The computer implementation method according to claim 32, wherein the standard deviation of the monocyte volume includes the monocyte size distribution width (MDW), and the second default threshold is 20.

35. The third predetermined threshold is 12×10 cells / L, and the fourth predetermined threshold is 4×10 cells / L, the computer-implemented method according to claim 30 or 31. ９ The third predetermined threshold is 12×10 cells / L, and the fourth predetermined threshold is 4×10 cells / L, the computer-implemented method according to claim 30 or 31. ９ cells / L, the computer-implemented method according to claim 30 or 31.

36. The computer implementation method according to claim 30 or 31, further comprising changing the test reporting process based on the evaluation of the sepsis state.

37. The computer implementation method according to claim 30 or 31, wherein if the septic condition indicates sepsis, the individual from which the blood sample was obtained should be treated.

38. To deliver the hydrodynamically focused flow of the blood sample toward the cell interrogation area of ​​the optical element; and The electrode assembly measures the current (DC) impedance of the cells in the blood sample as they pass through the cell examination area individually. It further includes, The computer implementation method according to claim 34, wherein the module determines the standard deviation of the monocyte volume based on the DC impedance measurement of the cells in the blood sample.

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