Patent Publication Number: US-11644464-B2

Title: Sepsis infection determination systems and methods

Description:
CROSS-REFERENCES TO RELATED APPLICATIONS 
     This application is a nonprovisional of and claims the benefit of priority to U.S. Provisional Patent Application No. 62/660,795, filed Apr. 20, 2018, entitled “SEPSIS INFECTION DETECTION SYSTEMS AND METHODS,” the entire contents of which are herein incorporated by reference. 
     This application is related by subject matter to PCT Patent Application No. PCT/US17/14708, titled “Infection Detection and Differentiation Systems and Methods,” filed Jan. 24, 2017, which claims the benefit of U.S. Provisional Patent Application No. 62/288,091, titled “Infection Detection and Differentiation Systems and Methods,” filed Jan. 28, 2016, each of which is hereby incorporated by reference in its entirety. 
    
    
     BACKGROUND 
     Sepsis is an uncontrolled systemic inflammatory response to infection that may rapidly progress to a life-threatening condition that can lead to shock and organ failure (i.e., septic shock and severe sepsis) if not treated immediately. A patient admitted to a medical facility may show clinical features of systemic inflammation. A medical professional may then attempt to determine if the inflammation is caused by an infection, leading to a diagnosis of sepsis, or some other causes, leading to a diagnosis of systemic inflammatory response syndrome (SIRS). In some cases, a patient may have no obvious signs of systemic inflammation, which may mean that the patient may not be considered at risk for sepsis. 
     If undetected, sepsis may lead to severe sepsis or septic shock, which has a mortality rate of about 60%. A large fraction of hospital deaths are associated with sepsis. Diagnosing sepsis is challenging because of the lack of an accurate biomarker. Additionally, clinical criteria that may indicate sepsis, such as hypothermia, hyperthermia, tachycardia, tachypnea, may not distinguish sepsis from SIRS or other conditions. These criteria may be associated with non-infectious etiologies that may be present in a hospital emergency room, including trauma, burns, pancreatitis, sickle cell crisis, and other inflammatory disorders. These similarities between sepsis and inflammation may make diagnosing sepsis challenging and time-consuming. For example, obtaining blood culture results to confirm an infection and/or identify a pathogen responsible for the infection may take several days. During the time it takes to complete conventional diagnostic testing, the patient&#39;s condition could deteriorate, possibly to a degree that the patient requires extraordinary clinical support or can no longer be treated effectively. For these and additional reasons, improved or new systems and methods for assessing the likelihood of systemic infection, including sepsis, are desired. 
     BRIEF SUMMARY 
     Embodiments of the present disclosure may allow for an efficient and accurate way to assess whether an individual has sepsis, including an individual who may exhibit symptoms or clinical criteria similar to inflammation. Embodiments include using a laboratory test that may be routinely ordered. Individuals to be tested may be in an emergency room. Systems and methods to assess the likelihood of sepsis may have a sensitivity and specificity above the currently recognized standard of care values of 0.60 to 0.70. Embodiments of the present invention improve upon diagnostic, biological, and medical related technologies or technical fields by providing a fast, simple, and accurate determination of the sepsis status. Based on the sepsis status, treatment may be started quickly, thereby preventing complications, including organ failure and death, of not treating sepsis fast enough. The sepsis status may include an indication that the patient is at high risk of developing sepsis, rather than a diagnosis of sepsis. 
     The sepsis status may indicate sepsis is indicated in the blood sample or that sepsis is likely to develop based on characteristics measured in the blood sample. Sepsis being likely in the blood sample may indicate that a treatment for sepsis in the individual is recommended or needed. Sepsis results from an uncontrolled systemic response to an infection. Sepsis may result from any infection in the body. For example, a simple skin infection may trigger a septic event. A post-surgical infection may lead to sepsis as the post-surgical infection may include infection and system inflammation. Predicting which infectious insult may result in a septic event is difficult and not always possible. Clinicians desire an early detection or indication that a patient may become septic. 
     In a first aspect, embodiments may include an automated method for evaluating a sepsis status associated with a blood sample obtained from an individual. Methods may include determining, using a first module, a monocyte volume measure, including a standard deviation of monocyte volume, associated with the blood sample in order to detect likelihood of a sepsis infection. The standard deviation of monocyte volume may also be called the monocyte distribution width (MDW). 
     Furthermore, methods may include evaluating, using a data processing module, the sepsis status associated with the blood sample. The data processing module may include a processor and a tangible non-transitory computer readable medium. The computer readable medium may be programmed with a computer application. This computer application, when executed by the processor, may cause the processor to compare the standard deviation of monocyte volume to a first cutoff value to provide a first comparison. What is more, the computer application may cause the processor to evaluate the sepsis status associated with the blood sample based on the first comparison. 
     The processor may compare the standard deviation of the monocyte volume to the cutoff value by determining whether the standard deviation of the monocyte volume exceeds the first cutoff value. The automated method may include evaluating that the sepsis status is that the probability of sepsis is elevated upon determining the standard deviation exceeds the first cutoff value. The cutoff value may be 20. This is a standard deviation of the volume component of a monocyte population derived from a differential dataplot. It has no reporting unit. In some embodiments, the first cutoff value may be a value from 19 to 20, from 19.5 to 20.5, from 19 to 21, from 18 to 22, from 20 to 25, or greater than 20. The blood sample indicating sepsis may include an indication that sepsis is present in the individual or that the individual is at risk of developing sepsis, including that the risk is high enough to warrant preventative treatment for sepsis. 
     Methods of evaluating the infection status may have a specificity for an infection greater than 0.55. The specificity may describe the ability of the test to correctly identify those patients with the disease or condition. The specificity may be 0.55 or higher, 0.60 or higher, 0.65 or higher, 0.70 or higher, 0.75 or higher, 0.80 or higher, 0.85 or higher, 0.90 or higher, or 0.95 or higher in embodiments. The area under the curve (AUC) in a receiver operating characteristic (ROC) curve may be 0.79 or higher, 0.82 or higher, 0.85 or higher, 0.89 or higher, 0.90 or higher, 0.91 or higher, 0.92 or higher, 0.93 or higher, 0.94 or higher, 0.95 or higher, 0.96 or higher, 0.97 or higher, 0.98 or higher, or 0.99 or higher in embodiments. 
     Methods of evaluating the infection status may have a sensitivity for an infection greater than 0.55. The sensitivity may describe the ability of a diagnostic test to correctly identify those patients with the disease or condition. A false negative may describe when the method indicates that the blood status shows no infection when in fact infection is present. The sensitivity may be 0.55 or higher, 0.60 or higher, 0.65 or higher, 0.70 or higher, 0.75 or higher, 0.80 or higher, 0.85 or higher, 0.90 or higher, or 0.95 or higher in embodiments. 
     The automated method may include determining, using a second module, a white blood cell count (WBC) associated with the blood sample. The computer application may further cause the processor to compare the white blood cell count to a second cutoff value to provide a second comparison. The white blood cell count may be an absolute number or a concentration. The computer application may also cause the processor to evaluate the sepsis status associated with the blood sample based on the first comparison and the second comparison. 
     The second cutoff value may be a value from 3,000 cells/μL to 15,000 cells/μL, including 3,000, 4,000, 5,000, 10,000, 11,000, 12,000, 13,000, 14,000, or 15,000 cells/μL. Exceeding the second cutoff value may mean being either greater than the second cutoff value or being less than the second cutoff value. For example, if the second cutoff value is 4,000 cells/μL, exceeding the second cutoff value may mean that the white blood cell count is less than 4,000 cells/μL. And if the second cutoff value is 12,000 cells/μL, exceeding the second cutoff value may mean that the white blood cell count is greater than 12,000 cells/μL. The reason exceeding the second cutoff value may be either greater than or less than the second cutoff value is that the second cutoff value may represent one end of a range of normal values for white blood cell count. For example, a normal white blood cell count may be from 4,000 to 12,000 cells/μL. The range may be from any count described herein to any other count described herein. A white blood cell count outside this range may be used to evaluate that the blood sample indicates sepsis. Accordingly, the automated method may also include comparing to a third cutoff value, the third cutoff value being the other end of a range of values as the second cutoff value. Stated differently, if the normal range for WBC is 4,000 to 12,000 cells/μL, then the second cutoff value would be 4,000 cells/μL, and a value less than or equal to 4,000 cells/μL would increase suspicion of sepsis. In this example, the third cutoff value would be 12,000 cells/μL, and a value greater than or equal to 12,000 cells/μL would increase suspicion of sepsis. In both cases, the abnormal WBC value would increase suspicion of sepsis in the context of an MDW value above the MDW value cutoff. The normal range for WBC may vary contextually, e.g., in specialized clinical environments, using different means of measuring WBC, etc., and the range considered normal might be expanded (e.g., to 3,000 cells/μL to 13,000 cells/μL) or reduced (e.g., to 5,000 cells/μL to 11,000 cells/μL) to favor errors that tend to produce false negatives or errors that tend to produce false positives, respectively, based on the desired risk profile, clinical context and/or analytical context for the evaluation. 
     The automated method may further include evaluating that the sepsis status is that the blood sample does not indicate sepsis upon determining the standard deviation of the monocyte volume does not exceed the first cutoff value and upon determining the white blood cell count does not exceed the second cutoff value. In addition, the automated method may include evaluating that the sepsis status is that the blood sample indicates sepsis upon determining the standard deviation of the monocyte volume exceeds the first cutoff value and upon determining the white blood cell count exceeds the second cutoff value. In some embodiments, the automated method may include evaluating the sepsis status as undetermined when one of either the standard deviation of the monocyte volume exceeds the first cutoff or the white blood cell count exceeds the second cutoff, with the other parameter not exceeding the appropriate cutoff value. 
     Other than the white blood cell count and the standard deviation of monocyte volume, evaluating the sepsis status associated with the blood sample may exclude using a cell count or concentration in the blood sample or a statistical value thereof. For example, evaluating the sepsis status may not include using a mean corpuscular volume, a platelet concentration, a mean neutrophil volume, a standard deviation of neutrophil volume, or a mean monocyte volume. Put another way, evaluating the sepsis status may exclude one or more of these measures. These measures may be excluded because the measures may not improve the confidence in the evaluation of the sepsis status, or because using the additional measures may complicate the evaluation without a significant improvement in accuracy or predictive value of the assessment. In some cases, a measure may not be much better in evaluating the sepsis status than a random selection of the sepsis status. The measure may be a statistical value, such as a mean, median, mode, standard deviation, or percentile of a cell count or cell concentration. Other values may be measured, obtained and/or reported, but are not used to evaluate sepsis status. The method may also exclude using a biomarker. For example, sepsis has no known, reliable biomarker. Even if sepsis did have a reliable biomarker, embodiments described herein may be used to decide whether to run a biomarker test on a patient, or might be used before a biomarker reaches peak expression in the course of the immune dysregulation associated with sepsis, or might be used if the biomarker is subject to interference or inconsistent interpretation (e.g., the biomarker is associated with patient conditions other than sepsis, even if those conditions are rare). 
     The first cutoff value or the second cutoff value may be calculated by maximizing an estimated value of sensitivity for an infection for a given value of specificity for an infection. In some embodiments, the values of sensitivity and specificity may be adjusted depending on priorities. In other words, the specificity or sensitivity may be chosen to be a value, with the other accuracy measure adjusted to optimize the overall accuracy. The cutoff values may be calculated or selected based on other criteria. For example, the cutoff value may be selected to prioritize ruling-out infection over ruling-in infection in an individual. 
     If the sepsis status indicates sepsis, methods may include performing appropriate medical procedures related to an individual with sepsis. Methods may include treating sepsis, including, for example, prescribing and administering antibiotics. The treatment for sepsis may be prophylactic. Methods may include treating an individual with sepsis or suspected sepsis with supportive care and/or symptom management, potentially in anticipation of developing sepsis symptoms. The individual may not show definitive symptoms of sepsis, but the treatment may be prescribed to prevent or mitigate the onset of sepsis. Methods may also include additional testing to diagnose sepsis. Additional testing may include culture analysis from a biological sample of the individual. If the sepsis status indicates sepsis, the reporting process for the measurement results may be modified. For example, whereas a routine blood test with results that do not indicate sepsis might be automatically transmitted to a laboratory information system, health information system, or the like after the results are released by the laboratory, a blood test with a sepsis indication might be held and/or flagged for review by the analysis operator, e.g., with instructions to call the physician, hand-deliver results, initiate additional testing if there is adequate sample remaining (such as biomarker testing or microbiology cultures), or otherwise take some proactive step in addition to or instead of merely releasing the results. 
     Embodiments may include evaluating that sepsis is not present even when the individual has systemic inflammatory response syndrome (SIRS). In other words, embodiments may be able to distinguish between when an individual has SIRS only or when the individual has sepsis (a combination of inflammation and infection). In some embodiments, methods may be able to distinguish between sepsis and other types of infection (e.g., non-systemic, localized infections). 
     Methods may also include delivering a hydrodynamically focused stream of the biological sample toward a cell interrogation zone of an optical element. In some embodiments, methods may include measuring, with an electrode assembly, current (DC) impedance of cells of the biological sample passing individually through the cell interrogation zone. The monocyte volume measure may be based on the DC impedance measurement from cells of the blood sample. 
     Embodiments may include assigning a sepsis indication to the blood sample based on evaluating the sepsis status. For example, the sepsis indication may include a label of not septic, septic, needing treatment, not needing treatment, or undetermined. The sepsis indication may also include a degree of certainty based on the comparisons. For example, the sepsis indication may include possibly septic, likely septic, or almost certainly septic. A standard deviation of monocyte volume or white blood cell count that far exceeds the applicable cutoff values may be associated with a higher degree of certainty. The magnitude of the difference between the standard deviation of monocyte volume or white blood cell count and the corresponding cutoff values may indicate the severity of the infection. For example, a high standard deviation of monocyte volume may be more likely associated with severe sepsis or septic shock. 
     Embodiments may include outputting the sepsis status. For example, the sepsis status may be outputted on a display of a computer, a mobile device, a smart watch, a terminal, a laboratory information system, a health information system, an electronic medical record, or other digital devices. In some embodiments, the sepsis status may be outputted into a physical form, such as paper. 
     In some embodiments, evaluating the sepsis status of the blood sample of the individual may include predicting whether the individual has sepsis, assessing the likelihood of the individual having sepsis, or determining whether the individual has sepsis. 
     The blood sample may be obtained from the individual using a syringe or any suitable instrument using accepted medical protocols. A physician, nurse, or other medical professional may obtain the blood sample from the individual. 
     In another aspect, embodiments may include an automated system for evaluating a sepsis status associated with a blood sample obtained from an individual. The system may also include a first module. The first module may include an electrode assembly configured to measure direct current (DC) impedance of cells of the blood sample passing individually through a cell interrogation zone. Systems may also include a data processing module connected with the first module. The data processing module may include a processor and a tangible non-transitory computer readable medium. The computer readable medium may be programmed with a computer application that, when executed by the processor, causes the processor to calculate compare a standard deviation of monocyte volume measure to a first cutoff value to provide a first comparison. The standard deviation of monocyte volume may be determined using the DC impedance measurement. The computer application may also cause the processor to evaluate the sepsis status associated with the blood sample based on the first comparison. Testing of the sample at the first module may take less than one minute. 
     In some embodiments, the computer application may also cause the processor to compare the standard deviation of monocyte volume to the first cutoff value by determining whether the standard deviation of the monocyte volume exceeds the first cutoff value. The comparison may be any comparison described herein. 
     The sepsis status may have a sensitivity for sepsis greater than 0.70 and a specificity for the infection greater than 0.70. The specificity and sensitivity may be any specificity and sensitivity described herein. 
     In some embodiments, the system may include a second module configured to determine the cell count or concentration of the blood sample. The second module may be configured to determine the white blood cell count. The second module may be in connectivity with the data processing module. The computer application, when executed by the processor, may cause the processor to compare the white blood cell count determined by the second module to a second cutoff value to provide a second comparison, and to evaluate the sepsis status associated with the blood sample based on the first comparison and the second comparison. The second comparison may be any comparison for white blood cell count described herein. 
     A better understanding of the nature and advantages of embodiments of the present invention may be gained with reference to the following detailed description and the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates aspects of blood cell analysis, according to embodiments of the present invention. 
         FIG.  2    schematically depicts aspects of a cellular analysis system, according to embodiments of the present invention. 
         FIG.  3    provides a system block diagram illustrating aspects of a cellular analysis system according to embodiments of the present invention. 
         FIG.  4    illustrates aspects of an automated cellular analysis system for assessing a likelihood of infection in an individual, according to embodiments of the present invention. 
         FIG.  4 A  shows aspects of an optical element of a cellular analysis system, according to embodiments of the present invention. 
         FIG.  5    depicts aspects of an exemplary method for evaluating an infection status of an individual, according to embodiments of the present invention. 
         FIG.  6    provides a simplified block diagram of an exemplary module system, according to embodiments of the present invention. 
         FIG.  7    depicts an example screen shot of a differential count screen, according to embodiments of the present invention. 
         FIG.  7 A  schematically shows a technique for obtaining blood cell parameters, according to embodiments of the present invention. 
         FIG.  8    illustrates aspects of a method for assessing likelihood of infection based on a biological sample obtained from an individual, according to embodiments of the present invention. 
         FIG.  9    shows a method of evaluating a sepsis status of a blood sample, according to embodiments of the present invention. 
         FIG.  10    shows receiver operating characteristic (ROC) curve for evaluating a sepsis status using monocyte distribution width (MDW), according to embodiments of the present invention. 
         FIG.  11    shows ROC curves for evaluating a sepsis status using MDW and white blood cell (WBC) count, according to embodiments of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     Diagnostic markers for sepsis have been researched for many years. Even so, there has not been a clear diagnostic test or biomarker for determining sepsis available. It was previously believed that a series of seven blood-cell-related factors could be reviewed in order to determine possible septic infection. See, for example, Park et al., “Screening of sepsis using leukocyte cell population data from the Coulter automatic blood cell analyzer DxH800,” International Journal of Laboratory Hematology, 2011, 33, 391-399 at 397-98. As recently as a few years ago, it was believed that parameters or indices based on a calculation including at least two of the seven factors were needed in order to make a septic infection prediction. The present inventors have determined that, rather than requiring a formula to calculate a parameter or index using the parameters, it is unexpectedly possible to predict onset of a possible septic infection through simple and efficient comparisons with cutoff values. In other words, a binary analysis of one or two measures may be used to determine sepsis without calculating an index based on two or more factors and then evaluating sepsis status based on a non-binary value of the index. 
     Embodiments of the present invention thus include systems and methods that assess the likelihood of infection, including sepsis, in a patient using cell count and cell population data. Data about the cell population, such as the standard deviation of the monocyte volume or monocyte distribution width (MDW) may be compared to a cutoff value for determining if an individual has sepsis. Data about the white blood cell count may then be used as a secondary check to the initial determination/testing. Evaluation of MDW sequentially with WBC (before or after, but as distinct, binary evaluations), may provide a clinical indicator with sensitivity and specificity about 0.80. The effectiveness of this simple, sequential evaluation is surprising at least in part because WBC has consistently been identified in the literature as a non-specific observation. That is, irregular (low or elevated) WBC measures may be associated with a variety of conditions, many of which have no relation to sepsis or SIRS. It was therefore unforeseen that a sequential evaluation of routine WBC cut-offs for normal/abnormal WBC measurements would have any bearing on whether a high MDW observation is indicative or predictive of sepsis. 
     The definition of sepsis itself has changed, illustrating additional difficulties in conclusively diagnosing sepsis. Under the Sepsis-2 definition, sepsis was defined based on systemic inflammatory response syndrome (SIRS).” SIRS may refer to a clinical syndrome that results from a dysregulated inflammatory response to a noninfectious insult, such as an autoimmune disorder, pancreatitis, vasculitis, thromboembolism, burns, or surgery. SIRS criteria include temperature, heart rate, respiratory rate, and white blood cell count. SIRS criteria are described in Kaukonen et al., “Systemic Inflammatory Response Syndrome Criteria in Defining Severe Sepsis,”  New England J. of Med.,  372:1629-38 (2015) (doi: 10.1056/NEJMoa1415236) and the Supplementary Appendix, the contents of both of which are incorporated herein by reference for all purposes. “Sepsis” may be the clinical syndrome that results from a dysregulated inflammatory response to an infection. Under Sepsis-2, sepsis includes two SIRS criteria and infection. “Severe sepsis” may refer to sepsis-induced tissue hypoperfusion or organ dysfunction resulting from infection. “Septic shock” may refer to a condition of severe sepsis plus hypotension persisting despite adequate fluid resuscitation, which may be defined as infusion of 20-30 mL/kg of crystalloids. 
     In 2016, Sepsis-3 updated the definition of sepsis, which is described in Singer et al., “The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3),” JAMA, 315(8):801-810 (2016) (doi: 10.1001/jama.2016.0287). Sepsis-3 defines sepsis as a life-threatening organ dysfunction caused by a dysregulated host response to infection. Organ dysfunction can be identified using a Sequential [Sepsis-related] Organ Failure Assessment (SOFA) score. The SOFA “score grades abnormality by organ system and accounts for clinical interventions.” “Septic shock” is considered a subset of sepsis, when “underlying circulatory and cellular/metabolic abnormalities are profound enough to substantially increase mortality.” There is no “severe sepsis” in Sepsis-3. As Sepsis-2 and Sepsis-3 definitions are not identical, even defining “sepsis” is challenging. Nonetheless, certain patient sample measurements, alone or in combination, may identify patients who meet the criteria for Sepsis-2 and/or Sepsis-3, or are at elevated risk of meeting the criteria for Sepsis-2 and/or Sepsis-3 in the near future (e.g., within 24 hours, or within 48 hours, of sample testing), as described herein. 
     As sepsis is defined based on a set of clinical signs and symptoms, sepsis is not detectable in the blood the way a parasite or a low hemoglobin concentration may be detected. Methods and systems described herein may enable a clinician to identify or determine sepsis when clinical conditions are vague or non-specific (e.g., flu-like symptoms, which may be symptoms of sepsis). If “detecting” or a form of the word is used herein with sepsis, the term should be understood to mean determining, diagnosing, or assessing sepsis, rather than measuring a specific component definitively indicating sepsis. 
     Conventional systems and methods for diagnosing sepsis may be inefficient and/or time consuming. In current practice, clinical criteria may be used to diagnose sepsis by detecting systemic inflammation that accompanies sepsis. The clinical criteria, however, may be common to both sepsis and SIRS, which may be associated with non-infectious conditions. An individual who may have sepsis may undergo laboratory tests, including but not limited to a test to generate a complete blood count (CBC) with differential (CBC-diff); measurements of C-reactive protein (CRP), serum lactate, erythrocyte sedimentation rate (ESR), and Procalcitonin (PCT); and cultures for bacteria. These technologies may result in poor sensitivity and/or specificity when used to diagnose sepsis. Other systems and methods may be limited to leukocyte cell population data (CPD) and may still be lacking in sensitivity and/or specificity. Some conventional methods may use CPD parameter(s) (e.g., monocyte volume) that lack the sensitivity and/or specificity of CPD parameters used herein. In some cases, conventional methods may require the use of multiple CPD parameters to show an increased sensitivity or specificity. Some of these tests may be expensive and may not be run routinely on individuals, and as a result, individuals who are infected and potentially septic but not yet symptomatic may not be diagnosed promptly or not diagnosed at all. The lack of an efficient and accurate method and system to evaluate the infection status may lead to a clinician administering antibiotics as a precautionary measure, resulting in overuse of antibiotics. Adverse drug events, adverse treatment interactions or side effects that might be easily managed in a healthy patient can present significant problems in a patient with SIRS, sepsis or similarly severe clinical conditions. Medicating all potentially septic patients with antibiotics, therefore, is not an ideal clinical strategy. 
     On the other hand, waiting for test results may endanger an individual&#39;s life. Analyzing a blood culture to definitively diagnose sepsis may take three to four days. In that time, an individual can develop sepsis, develop organ failure, be past the point of recovery, and eventually die. A quick and accurate method to evaluate sepsis would improve patient outcomes and save lives. Any time saved in identifying sepsis or potential sepsis may improve patient outcomes. 
     Generally, total white blood cell count and absolute neutrophil count increase with bacterial infection. Neutrophil percentage of white blood cells may also increase with infection. Even so, a significant proportion, up to 40%, of patients, may not exhibit these increases. As a result, WBC may not be a sensitive or specific marker for sepsis. Additionally, elevated white blood cell count (WBC) may be associated with conditions other than sepsis (e.g., trauma, burns, and inflammatory disorders), and accurately differentiating between sepsis and the other conditions would not be possible with WBC. 
     Other tests may also be inadequate. CRP may not be specific to bacterial and viral infections. Serum lactate may not be specific to sepsis and may be used more as a prognostic biomarker in sepsis instead of a diagnostic biomarker. ESR may represent physical properties associated with inflammatory processes but has poor specificity for infection. Blood cultures may be too time consuming to allow physicians to make immediate or timely treatment decisions. Additionally, antibiotic drugs and/or fastidious pathogens may limit the sensitivity of blood cultures. PCT, lacking sufficient sensitivity and specificity in symptomatic patients, may not reliably differentiate sepsis from other non-infectious causes of SIRS in critically ill patients. Furthermore, because PCT may be a separate test that may be performed only upon clinician request, the test may not be administered early and may not be an early identifier of septic patients. 
     Conventional systems may include computers, which are not able to evaluate the infection status with sufficient sensitivity and specificity even if the computer had all the information provided from a blood sample. Embodiments of the present invention may improve computer-related technology by allowing the computer to perform evaluation of the infection status, including the evaluation of a sepsis status. Embodiments of the present invention may also decrease programming complexity, processing power requirements, storage requirements, and bandwidth requirements. Embodiments of the present invention may not include computational complexity beyond a determination of cell population data parameters in the sample and a comparison of two numbers, thereby increasing computational efficiency and lowering cost. 
     Embodiments of the present invention include comparing the standard deviation of monocyte volume (SD-V-MO) to a cutoff value. Monocytes are a subset of white blood cells, so the use of a parameter related to monocytes was not expected to improve sensitivity and specificity for sepsis. These results may be double-checked using white blood cell count (WBC) data. WBC has been shown to increase in some cases with sepsis. Without intending to be bound by theory, it is thought that dissemination of infection leads to activation of circulating immune cells, such as the monocyte. The activation of circulating immune cells may be associated with a change in cell volume. Activated monocytes may play a role in the pathophysiology of sepsis. WBC may increase with SIRS in addition to sepsis, and thus, has low specificity for sepsis. SD-V-MO may be used alone to diagnose sepsis, however, the combination of WBC with SD-V-MO may lead to significant improvements in the determination of sepsis. 
     Embodiments of the present invention may evaluate the sepsis status. The sepsis status may indicate that an individual has sepsis. If an individual is evaluated to have sepsis, clinical criteria may be used to confirm whether the individual has sepsis. Clinical criteria may include heart rate, body temperature, presence of a fever, and mental status. Individuals diagnosed with sepsis may receive closer monitoring, hospital admission, aggressive IV fluids, repeated blood cultures, and prioritized diagnoses and treatment. 
     Analysis Techniques and Systems 
     Turning to the figures,  FIG.  1    illustrates aspects of an example analysis technique. As shown here, and as discussed elsewhere herein, a whole blood sample  100  may include cells such as platelets, white blood cells (WBCs), and red blood cells (RBCs), including nucleated red blood cells (NRBCs). Various RBC, WBC, and NRBC parameters, obtained from channel processing mechanisms such as a CBC module  110  or transducer  120 , can be evaluated to assess the infection status of an individual. The transducer may obtain current data for blood samples as the sample passes through an aperture. The aperture may part of a flow cell. 
       FIG.  2    schematically depicts a cellular analysis system  200 . As shown here, system  300  includes a preparation system  210 , a transducer module  220 , and an analysis system  230 . While system  200  is herein described at a very high level, with reference to the three core system blocks ( 210 ,  220 , and  230 ), one of skill in the art would readily understand that system  200  includes many other system components such as central control processor(s), display system(s), fluidic system(s), temperature control system(s), user-safety control system(s), and the like. In operation, a whole blood sample (WBS)  240  can be presented to the system  200  for analysis. In some instances, WBS  240  is aspirated into system  200 . Exemplary aspiration techniques are known to the skilled artisan. After aspiration, WBS  240  can be delivered to a preparation system  210 . Preparation system  210  receives WBS  240  and can perform operations involved with preparing WBS  240  for further measurement and analysis. For example, preparation system  210  may separate WBS  240  into predefined aliquots for presentation to transducer module  220 . Preparation system  210  may also include mixing chambers so that appropriate reagents may be added to the aliquots. For example, where an aliquot is to be tested for differentiation of white blood cell subset populations, a lysing reagent (e.g. ERYTHROLYSE, a red blood cell lysing buffer) may be added to the aliquot to break up and remove the RBCs. Preparation system  210  may also include temperature control components to control the temperature of the reagents and/or mixing chambers. Appropriate temperature controls can improve the consistency of the operations of preparation system  210 . 
     In some instances, predefined aliquots can be transferred from preparation system  210  to transducer module  220 . As described in further detail below, transducer module  220  can perform direct current (DC) impedance, radiofrequency (RF) conductivity, light transmission, and/or light scatter measurements of cells from the WBS passing individually therethrough. Measured DC impedance, RF conductivity, and light propagation (e.g. light transmission, light scatter) parameters can be provided or transmitted to analysis system  230  for data processing. In some instances, analysis system  230  may include computer processing features and/or one or more modules or components such as those described herein with reference to the system depicted in  FIG.  6    and described further below, which can evaluate the measured parameters, identify and enumerate the WBS constituents, and correlate a subset of data characterizing elements of the WBS with an infection status. As shown here, cellular analysis system  200  may generate or output a report  250  containing the evaluated infection status and/or a prescribed treatment regimen for the individual. In some instances, excess biological sample from transducer module  220  can be directed to an external (or alternatively internal) waste system  260 . 
       FIG.  3    illustrates in more detail a transducer module and associated components in more detail. As shown here, system  300  includes a transducer module  310  having a light or irradiation source such as a laser  310  emitting a beam  314 . The laser  312  can be, for example, a 635 nm, 5 mW, solid-state laser. In some instances, system  300  may include a focus-alignment system  320  that adjusts beam  314  such that a resulting beam  322  is focused and positioned at a cell interrogation zone  332  of a flow cell  330 . In some instances, flow cell  330  receives a sample aliquot from a preparation system  302 . As described elsewhere herein, various fluidic mechanisms and techniques can be employed for hydrodynamic focusing of the sample aliquot within flow cell  330 . 
     In some instances, the aliquot generally flows through the cell interrogation zone  332  such that its constituents pass through the cell interrogation zone  332  one at a time. In some cases, a system  300  may include a cell interrogation zone or other feature of a transducer module or blood analysis instrument such as those described in U.S. Pat. Nos. 5,125,737; 6,228,652; 7,390,662; 8,094,299; and 8,189,187, the contents of which are incorporated herein by references. For example, a cell interrogation zone  332  may be defined by a square transverse cross-section measuring approximately 50×50 microns, and having a length (measured in the direction of flow) of approximately 65 microns. Flow cell  330  may include an electrode assembly having first and second electrodes  334 ,  336  for performing DC impedance and RF conductivity measurements of the cells passing through cell interrogation zone  332 . Signals from electrodes  334 ,  336  can be transmitted to analysis system  304 . The electrode assembly can analyze volume and conductivity characteristics of the cells using low-frequency current and high-frequency current, respectively. For example, low-frequency DC impedance measurements can be used to analyze the volume of each individual cell passing through the cell interrogation zone. Relatedly, high-frequency RF current measurements can be used to determine the conductivity of cells passing through the cell interrogation zone. Because cell walls act as conductors to high frequency current, the high frequency current can be used to detect differences in the insulating properties of the cell components, as the current passes through the cell walls and through each cell interior. High frequency current can be used to characterize nuclear and granular constituents and the chemical composition of the cell interior. 
     Incoming beam  322  travels along beam axis AX and irradiates the cells passing through cell interrogation zone  332 , resulting in light propagation within an angular range a (e.g. scatter, transmission) emanating from the zone  332 . Exemplary systems are equipped with sensor assemblies that can detect light within three, four, five, or more angular ranges within the angular range a, including light associated with an extinction or axial light loss measure as described elsewhere herein. As shown here, light propagation  340  can be detected by a light detection assembly  350 , optionally having a light scatter detector unit  350 A and a light scatter and transmission detector unit  350 B. In some instances, light scatter detector unit  350 A includes a photoactive region or sensor zone for detecting and measuring upper median angle light scatter (UMALS), for example light that is scattered or otherwise propagated at angles relative to a light beam axis within a range from about 20 to about 42 degrees. In some instances, UMALS corresponds to light propagated within an angular range from between about 20 to about 43 degrees, relative to the incoming beam axis which irradiates cells flowing through the interrogation zone. Light scatter detector unit  350 A may also include a photoactive region or sensor zone for detecting and measuring lower median angle light scatter (LMALS), for example light that is scattered or otherwise propagated at angles relative to a light beam axis within a range from about 10 to about 20 degrees. In some instances, LMALS corresponds to light propagated within an angular range from between about 9 to about 19 degrees, relative to the incoming beam axis which irradiates cells flowing through the interrogation zone. 
     A combination of UMALS and LMALS is defined as median angle light scatter (MALS), which is light scatter or propagation at angles between about 9 degrees and about 43 degrees relative to the incoming beam axis which irradiates cells flowing through the interrogation zone. 
     As shown in  FIG.  3   , the light scatter detector unit  350 A may include an opening  351  that allows low angle light scatter or propagation  340  to pass beyond light scatter detector unit  350 A and thereby reach and be detected by light scatter and transmission detector unit  350 B. According to some embodiments, light scatter and transmission detector unit  350 B may include a photoactive region or sensor zone for detecting and measuring lower angle light scatter (LALS), for example light that is scattered or propagated at angles relative to an irradiating light beam axis of about 5.1 degrees. In some instances, LALS corresponds to light propagated at an angle of less than about 9 degrees, relative to the incoming beam axis which irradiates cells flowing through the interrogation zone. In some instances, LALS corresponds to light propagated at an angle of less than about 10 degrees, relative to the incoming beam axis which irradiates cells flowing through the interrogation zone. In some instances, LALS corresponds to light propagated at an angle of about 1.9 degrees±0.5 degrees, relative to the incoming beam axis which irradiates cells flowing through the interrogation zone. In some instances, LALS corresponds to light propagated at an angle of about 3.0 degrees±0.5 degrees, relative to the incoming beam axis which irradiates cells flowing through the interrogation zone. In some instances, LALS corresponds to light propagated at an angle of about 3.7 degrees±0.5 degrees, relative to the incoming beam axis which irradiates cells flowing through the interrogation zone. In some instances, LALS corresponds to light propagated at an angle of about 5.1 degrees±0.5 degrees, relative to the incoming beam axis which irradiates cells flowing through the interrogation zone. In some instances, LALS corresponds to light propagated at an angle of about 7.0 degrees±0.5 degrees, relative to the incoming beam axis which irradiates cells flowing through the interrogation zone. 
     According to some embodiments, light scatter and transmission detector unit  350 B may include a photoactive region or sensor zone for detecting and measuring light transmitted axially through the cells, or propagated from the irradiated cells, at an angle of 0 degrees relative to the incoming light beam axis. In some cases, the photoactive region or sensor zone may detect and measure light propagated axially from cells at angles of less than about 1 degree relative to the incoming light beam axis. In some cases, the photoactive region or sensor zone may detect and measure light propagated axially from cells at angles of less than about 0.5 degrees relative to the incoming light beam axis less. Such axially transmitted or propagated light measurements correspond to axial light loss (ALL or AL2). As noted in previously incorporated U.S. Pat. No. 7,390,662, when light interacts with a particle, some of the incident light changes direction through the scattering process (i.e. light scatter) and part of the light is absorbed by the particles. Both of these processes remove energy from the incident beam. When viewed along the incident axis of the beam, the light loss can be referred to as forward extinction or axial light loss. Additional aspects of axial light loss measurement techniques are described in U.S. Pat. No. 7,390,662 at column 5, line 58 to column 6, line 4. 
     As such, the cellular analysis system  300  provides means for obtaining light propagation measurements, including light scatter and/or light transmission, for light emanating from the irradiated cells of the biological sample at any of a variety of angles or within any of a variety of angular ranges, including ALL and multiple distinct light scatter or propagation angles. For example, light detection assembly  350 , including appropriate circuitry and/or processing units, provides a means for detecting and measuring UMALS, LMALS, LALS, MALS, and ALL. 
     Wires or other transmission or connectivity mechanisms can transmit signals from the electrode assembly (e.g. electrodes  334 ,  336 ), light scatter detector unit  350 A, and/or light scatter and transmission detector unit  350 B to analysis system  304  for processing. For example, measured DC impedance, RF conductivity, light transmission, and/or light scatter parameters can be provided or transmitted to analysis system  304  for data processing. In some instances, analysis system  304  may include computer processing features and/or one or more modules or components such as those described herein with reference to the system depicted in  FIG.  6   , which can evaluate the measured parameters, identify and enumerate biological sample constituents, and correlate a subset of data characterizing elements of the biological sample with an infection status of the individual. As shown here, cellular analysis system  300  may generate or output a report  306  containing the evaluated infection status and/or a prescribed treatment regimen for the individual. In some instances, excess biological sample from transducer module  310  can be directed to an external (or alternatively internal) waste system  308 . In some instances, a cellular analysis system  300  may include one or more features of a transducer module or blood analysis instrument such as those described in previously incorporated U.S. Pat. Nos. 5,125,737; 6,228,652; 8,094,299; and 8,189,187. 
       FIG.  4    illustrates aspects of an automated cellular analysis system for evaluating the infection status in an individual, according to embodiments of the present invention. In particular, the infection status can be evaluated based on a biological sample obtained from blood of the individual. As shown here, an analysis system or transducer  400  may include an optical element  410  having a cell interrogation zone  412 . The transducer also provides a flow path  420 , which delivers a hydrodynamically focused stream  422  of a biological sample toward the cell interrogation zone  412 . For example, as the sample stream  422  is projected toward the cell interrogation zone  412 , a volume of sheath fluid  424  can also enter the optical element  410  under pressure, so as to uniformly surround the sample stream  422  and cause the sample stream  422  to flow through the center of the cell interrogation zone  412 , thus achieving hydrodynamic focusing of the sample stream. In this way, individual cells of the biological sample, passing through the cell interrogation zone one cell at a time, can be precisely analyzed. 
     Transducer module or system  400  also includes an electrode assembly  430  that measures direct current (DC) impedance and radiofrequency (RF) conductivity of cells  10  of the biological sample passing individually through the cell interrogation zone  412 . The electrode assembly  430  may include a first electrode mechanism  432  and a second electrode mechanism  434 . As discussed elsewhere herein, low-frequency DC measurements can be used to analyze the volume of each individual cell passing through the cell interrogation zone. In some instances, the standard deviation of the volume of monocytes may be derived with low-frequency DC measurements. Relatedly, high-frequency RF current measurements can be used to determine the conductivity of cells passing through the cell interrogation zone. Such conductivity measurements can provide information regarding the internal cellular content of the cells. For example, high frequency RF current can be used to analyze nuclear and granular constituents, as well as the chemical composition of the cell interior, of individual cells passing through the cell interrogation zone. 
     The system  400  also includes a light source  440  oriented to direct a light beam  442  along a beam axis  444  to irradiate the cells  10  of the biological sample individually passing through the cell interrogation zone  412 . Relatedly, the system  400  includes a light detection assembly  450  optically coupled with the cell interrogation zone, so as to measure light scattered by and transmitted through the irradiated cells  10  of the biological sample. The light detection assembly  450  can include a plurality of light sensor zones that detect and measure light propagating from the cell interrogation zone  412 . In some instances, the light detection assembly detects light propagated from the cell interrogation zone at various angles or angular ranges relative to the irradiating beam axis. For example, light detection assembly  450  can detect and measure light that is scattered at various angles by the cells, as well as light that is transmitted axially by the cells along the beam axis. The light detection assembly  450  can include a first sensor zone  452  that measures a first scattered or propagated light  452   s  within a first range of angles relative to the light beam axis  444 . The light detection assembly  450  can also include a second sensor zone  454  that measures a second scattered or propagated light  454   s  within a second range of angles relative to the light beam axis  444 . As shown here, the second range of angles for scattered or propagated light  454   s  is different from the first range of angles for scattered or propagated light  452   s . Further, the light detection assembly  450  can include a third sensor zone  456  that measures a third scattered or propagated light  456   s  within a third range of angles relative to the light beam axis  444 . As shown here, the third range of angles for scattered or propagated light  456   s  is different from both the first range of angles for scattered or propagated light  452   s  and the second range of angles for scattered or propagated light  454   s . The light detection assembly  450  also includes a fourth sensor zone  458  that measures axial light  458   t  transmitted through the cells of the biological sample passing individually through the cell interrogation zone  412  or propagated from the cell interrogation zone along the axis beam. In some instances, each of the sensor zones  452 ,  454 ,  456 , and  458  are disposed at a separate sensor associated with that specific sensor zone. In some instances, one or more of the sensor zones  452 ,  454 ,  456 , and  458  are disposed on a common sensor of the light detection assembly  450 . For example, the light detection assembly may include a first sensor  451  that includes first sensor zone  452  and second sensor zone  454 . Hence, a single sensor may be used for detecting or measuring two or more types (e.g. low angle, medium angle, or high angle) of light scatter or propagation. 
     Automated cellular analysis systems may include any of a variety of optical elements or transducer features. For example, as depicted in  FIG.  4 A , an optical element  410   a  of a cellular analysis system transducer may have a square prism shape, with four rectangular, optically flat sides  450   a  and opposing end walls  436   a . In some instances, the respective widths W of each side  450   a  are the same, each measuring about 4.2 mm, for example. In some instances, the respective lengths L of each side  450   a  are the same, each measuring about 6.3 mm, for example. In some instances, all or part of the optical element  410   a  may be fabricated from fused silica, or quartz. A flow passageway  432   a  formed through a central region of optical element  410   a  may be concentrically configured with respect to a longitudinal axis A passing through the center of element  410   a  and parallel to a direction of sample-flow as indicated by arrow SF. Flow passageway  432   a  includes a cell interrogation zone Z and a pair of opposing tapered bore holes  454   a  having openings in the vicinity of their respective bases that fluidically communicate with the cell interrogation zone. In some instances, the transverse cross-section of the cell interrogation zone Z is square in shape, the width W′ of each side nominally measuring 50 microns±10 microns. In some instances, the length L′ of the cell interrogation zone Z, measured along axis A, is about 1.2 to 1.4 times the width W′ of the interrogation zone. For example, the length L′ may be about 65 microns±10 microns. As noted elsewhere herein, DC and RF measurements can be made on cells passing through the cell interrogation zone. In some instances, the maximum diameter of the tapered bore holes  454   a , measured at end walls  436   a , is about 1.2 mm. An optical structure  410   a  of the type described can be made from a quartz square rod containing a 50×50 micron capillary opening, machined to define the communicating bore holes  454   a , for example. A laser or other irradiation source can produce a beam B that is directed through or focused into the cell interrogation zone. For example, the beam may be focused into an elliptically shaped waist located within the interrogation zone Z at a location through which the cells are caused to pass. A cellular analysis system may include a light detection assembly that is configured to detect light which emanates from the optical element  410   a , for example light P that is propagated from the cell interrogation zone Z which contains illuminated or irradiated cells flowing therewithin. As depicted here, light P can propagate or emanate from the cell interrogation zone Z within an angular range a, and thus can be measured or detected at selected angular positions or angular ranges relative to the beam axis AX. Relatedly, a light detection assembly can detect light scattered or axially transmitted in a forward plane within various angular ranges with respect to an axis AX of beam B. As discussed elsewhere herein, one or more light propagation measurements can be obtained for individual cells passing through the cell interrogation zone one at a time. In some cases, a cellular analysis system may include one or more features of a transducer or cell interrogation zone such as those described in U.S. Pat. Nos. 5,125,737; 6,228,652; 8,094,299; and 8,189,187, the contents of which are incorporated herein by reference. 
       FIG.  5    depicts aspects of an exemplary method  500  for evaluating an infection status associated with a blood sample obtained from an individual. Method  500  includes introducing a blood sample into a blood analysis system, as indicated by step  510 . As shown in step  520 , the method may also include preparing the blood sample by dividing the sample into aliquots and mixing the aliquot samples with appropriate reagents. In step  530 , the samples can be passed through a flow cell in a transducer system such that sample constituents (e.g. blood cells) pass through a cell interrogation zone in a one by one fashion. The constituents can be irradiated by a light source, such as a laser. In step  540 , any combination RF conductivity  541 , DC impedance  542 , first angular light propagation  543  (e.g. LALS), second angular light propagation  544  (e.g. AL2), third angular light propagation  545  (e.g. UMAL), and/or fourth angular light propagation  546  (e.g. LMALS) may be measured. As depicted by step  547 , the third and fourth angular light propagation measurements can be used to determine a fifth angular light propagation measurement (e.g. MALS). Alternatively, MALS can be measured directly. In some examples, step  540  may include DC impedance  542  and may exclude any combination of the other measurements. In step  550 , the white blood cell count in a blood sample may be measured. The blood sample may be a second blood sample from the individual or may be the same blood sample that is flowed through the flow cell. As discussed elsewhere herein, certain measurements or combinations of measurements can be processed, as indicated by step  560 , so as to provide a likelihood of infection. Optionally, methods may also include determining a treatment regime based on the predicted likelihood of infection. 
     A cellular analysis system may be configured to correlate a subset of DC impedance, RF conductivity, angular light measurements (e.g. first scattered light, second scattered light), the axial light measurements from the cells of the biological sample with an infection status, which may include sepsis status. As discussed elsewhere herein, in some instances at least a portion of the correlation can be performed using one or more software modules executable by one or more processors, one or more hardware modules, or any combination thereof. Processors or other computer or module systems may be configured to receive as an input values for the various measurements or parameters and automatically output the predicted evaluated infection status. In some instances, one or more of the software modules, processors, and/or hardware modules may be included as a component of a hematology system that is equipped to obtain multiple light angle detection parameters, such as Beckman Coulter&#39;s UniCel® DxH™ 800 Cellular Analysis System. In some instances, one or more of the software modules, processors, and/or hardware modules may be includes as a component of a stand-alone computer that is in operative communication or connectivity with a hematology system that is equipped to obtain multiple light angle detection parameters, such as Beckman Coulter&#39;s UniCel® DxH™ 800 System. In some instances, at least a portion of the correlation can be performed by one or more of the software modules, processors, and/or hardware modules that receive data from a hematology system that is equipped to obtain multiple light angle detection parameters, such as Beckman Coulter&#39;s UniCel® DxH™ 800 System remotely via the internet or any other over wired and/or wireless communication network. Relatedly, each of the devices or modules according to embodiments of the present invention can include one or more software modules on a computer readable medium that is processed by a processor, or hardware modules, or any combination thereof. 
       FIG.  6    is a simplified block diagram of an exemplary module system that broadly illustrates how individual system elements for a module system  600  may be implemented in a separated or more integrated manner. Module system  600  may be part of or in connectivity with a cellular analysis system for evaluating the infection status according to embodiments of the present invention. Module system  600  is well suited for producing data or receiving input related to evaluate the infection status. In some instances, module system  600  includes hardware elements that are electrically coupled via a bus subsystem  602 , including one or more processors  604 , one or more input devices  606  such as user interface input devices, and/or one or more output devices  608  such as user interface output devices. In some instances, system  600  includes a network interface  610 , and/or a diagnostic system interface  640  that can receive signals from and/or transmit signals to a diagnostic system  642 . In some instances, system  600  includes software elements, for example shown here as being currently located within a working memory  612  of a memory  614 , an operating system  616 , and/or other code  618 , such as a program configured to implement one or more aspects of the techniques disclosed herein. Each of the calculations or operations described herein may be performed using a computer or other processor having hardware, software, and/or firmware. The various method steps may be performed by modules, and the modules may comprise any of a wide variety of digital and/or analog data processing hardware and/or software arranged to perform the method steps described herein. The modules optionally comprising data processing hardware adapted to perform one or more of these steps by having appropriate machine programming code associated therewith, the modules for two or more steps (or portions of two or more steps) being integrated into a single processor board or separated into different processor boards in any of a wide variety of integrated and/or distributed processing architectures. These methods and systems will often employ a tangible media embodying machine-readable code with instructions for performing any one or more of the method or process steps described herein. 
     In some embodiments, module system  600  may include a storage subsystem  620  that can store the basic programming and data constructs that provide the functionality of the various techniques disclosed herein. For example, software modules implementing the functionality of method aspects, as described herein, may be stored in storage subsystem  620 . These software modules may be executed by the one or more processors  604 . In a distributed environment, the software modules may be stored on a plurality of computer systems and executed by processors of the plurality of computer systems. Storage subsystem  620  can include memory subsystem  622  and file storage subsystem  628 . Memory subsystem  622  may include a number of memories including a main random access memory (RAM)  626  for storage of instructions and data during program execution and a read only memory (ROM)  624  in which fixed instructions are stored. File storage subsystem  628  can provide persistent (non-volatile) storage for program and data files, and may include tangible storage media which may optionally embody patient, treatment, assessment, or other data. File storage subsystem  628  may include a hard disk drive, a floppy disk drive along with associated removable media, a Compact Digital Read Only Memory (CD-ROM) drive, an optical drive, DVD, CD-R, CD RW, solid-state removable memory, other removable media cartridges or disks, and the like. One or more of the drives may be located at remote locations on other connected computers at other sites coupled to module system  600 . In some instances, systems may include a computer-readable storage medium or other tangible storage medium that stores one or more sequences of instructions which, when executed by one or more processors, can cause the one or more processors to perform any aspect of the techniques or methods disclosed herein. One or more modules implementing the functionality of the techniques disclosed herein may be stored by file storage subsystem  628 . In some embodiments, the software or code will provide protocol to allow the module system  600  to communicate with communication network  630 . Optionally, such communications may include dial-up or internet connection communications. 
     It is appreciated that system  600  can be configured to carry out various aspects of methods of the present invention. For example, processor component or module  604  can be a microprocessor control module configured to receive cellular parameter signals from a sensor input device or module  632 , from a user interface input device or module  606 , and/or from a diagnostic system  642 , optionally via a diagnostic system interface  640  and/or a network interface  610  and a communication network  630 . In some instances, sensor input device(s) may include or be part of a cellular analysis system that is equipped to obtain multiple light angle detection parameters, such as Beckman Coulter&#39;s UniCel® DxH™ 800 Cellular Analysis System. In some instances, user interface input device(s)  606  and/or network interface  610  may be configured to receive cellular parameter signals generated by a cellular analysis system that is equipped to obtain multiple light angle detection parameters, such as Beckman Coulter&#39;s UniCel® DxH™ 800 Cellular Analysis System. In some instances, diagnostic system  642  may include or be part of a cellular analysis system that is equipped to obtain multiple light angle detection parameters, such as Beckman Coulter&#39;s UniCel® DxH™ 800 Cellular Analysis System. 
     Processor component or module  604  can also be configured to transmit cellular parameter signals, optionally processed according to any of the techniques disclosed herein, to sensor output device or module  636 , to user interface output device or module  608 , to network interface device or module  610 , to diagnostic system interface  640 , or any combination thereof. Each of the devices or modules according to embodiments of the present invention can include one or more software modules on a computer readable medium that is processed by a processor, or hardware modules, or any combination thereof. Any of a variety of commonly used platforms, such as Windows, Mac, and Unix, along with any of a variety of programming languages, may be used to implement embodiments of the present invention. 
     User interface input devices  606  may include, for example, a touchpad, a keyboard, pointing devices such as a mouse, a trackball, a graphics tablet, a scanner, a joystick, a touchscreen incorporated into a display, audio input devices such as voice recognition systems, microphones, and other types of input devices. User input devices  606  may also download a computer executable code from a tangible storage media or from communication network  630 , the code embodying any of the methods or aspects thereof disclosed herein. It will be appreciated that terminal software may be updated from time to time and downloaded to the terminal as appropriate. In general, use of the term “input device” is intended to include a variety of conventional and proprietary devices and ways to input information into module system  600 . 
     User interface output devices  606  may include, for example, a display subsystem, a printer, a fax machine, or non-visual displays such as audio output devices. The display subsystem may be a cathode ray tube (CRT), a flat-panel device such as a liquid crystal display (LCD), a light-emitting diode (LED) display, an organic light-emitting diode (OLED) display, a projection device, or the like. The display subsystem may also provide a non-visual display such as via audio output devices. In general, use of the term “output device” is intended to include a variety of conventional and proprietary devices and ways to output information from module system  600  to a user. The results of any method or operation described herein (e.g. an infection status) may be displayed on an output device. 
     Bus subsystem  602  provides a mechanism for letting the various components and subsystems of module system  600  communicate with each other as intended or desired. The various subsystems and components of module system  600  need not be at the same physical location but may be distributed at various locations within a distributed network. Although bus subsystem  602  is shown schematically as a single bus, alternate embodiments of the bus subsystem may utilize multiple busses. 
     Network interface  610  can provide an interface to an outside network  630  or other devices. Outside communication network  630  can be configured to effect communications as needed or desired with other parties. It can thus receive an electronic packet from module system  600  and transmit any information as needed or desired back to module system  600 . As depicted here, communication network  630  and/or diagnostic system interface  642  may transmit information to or receive information from a diagnostic system  642  that is equipped to obtain multiple light angle detection parameters, such as such as Beckman Coulter&#39;s UniCel® DxH™ 800 Cellular Analysis System. 
     In addition to providing such infrastructure communications links internal to the system, the communications network system  630  may also provide a connection to other networks such as the internet and may comprise a wired, wireless, modem, and/or other type of interfacing connection. 
     It will be apparent to the skilled artisan that substantial variations may be used in accordance with specific requirements. For example, customized hardware might also be used and/or particular elements might be implemented in hardware, software (including portable software, such as applets), or both. Further, connection to other computing devices such as network input/output devices may be employed. Module terminal system  600  itself can be of varying types including a computer terminal, a personal computer, a portable computer, a workstation, a network computer, or any other data processing system. Due to the ever-changing nature of computers and networks, the description of module system  600  depicted in  FIG.  6    is intended only as a specific example for purposes of illustrating one or more embodiments of the present invention. Many other configurations of module system  600  are possible having more or less components than the module system depicted in  FIG.  6   . Any of the modules or components of module system  600 , or any combinations of such modules or components, can be coupled with, or integrated into, or otherwise configured to be in connectivity with, any of the cellular analysis system embodiments disclosed herein. Relatedly, any of the hardware and software components discussed above can be integrated with or configured to interface with other medical assessment or treatment systems used at other locations. 
     In some embodiments, the module system  600  can be configured to receive one or more cellular analysis parameters of a patient at an input module. Cellular analysis parameter data can be transmitted to an assessment module where an infection status is evaluated, predicted, analyzed, or determined. The infection status can be output to a system user via an output module. In some cases, the module system  600  can determine an initial treatment or induction protocol for the patient, based on one or more cellular analysis parameters and/or the evaluated infection status, for example by using a treatment module. The treatment can be output to a system user via an output module. Optionally, certain aspects of the treatment can be determined by an output device, and transmitted to a treatment system or a sub-device of a treatment system. Any of a variety of data related to the patient can be input into the module system, including age, weight, sex, treatment history, medical history, and the like. Parameters of treatment regimens or diagnostic evaluations can be determined based on such data. 
     Relatedly, in some instances a system includes a processor configured to receive the cell population data as input. Optionally, a processor, storage medium, or both, may be incorporated within a hematology or cellular analysis machine. In some instances, the hematology machine may generate cell population data or other information for input into the processor. In some instances, a processor, a storage medium, or both, can be incorporated within a computer, and the computer can be in communication with a hematology machine. In some instances, a processor, a storage medium, or both, can be incorporated within a computer, and the computer can be in remote communication with a hematology machine via a network. 
     Cell Population Data 
     In addition to a differential count, once the WBC sub-populations are formed, the mean (MN) and standard deviation (SD) values for the grades of various morphologic parameters (e.g. volume, conductivity, and angles of light scatter or propagation) can be calculated separately for leukocytes and other blood cells. For example, a WBC differential channel can provide measurement data for neutrophils, lymphocytes, monocytes, eosinophils, and/or basophils and an nRBC channel can provide measurement data for non-nucleated red blood cells or a non-nucleated red blood cell parameter, as described elsewhere herein. As a result, a vast amount of data directly correlating to blood cell morphology can be generated. This information can be called collectively “Cell Population Data” (CPD). Table 1 depicts a variety of Cell Population Data parameters which may be obtained based on a biological sample of an individual. SD-V-MO may be a parameter used in embodiments. Embodiments may exclude any subset of the parameters listed in Table 1. Embodiments may include or exclude any parameters for basophils. Additionally, embodiments may include any subset of the parameters listed in Table 1. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Cell Population Data parameters 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                   
                 Non-nucleated 
               
               
                   
                   
                   
                 Monocyte 
                   
                 red blood cell 
               
               
                   
                 Neutrophil 
                 Lymphocyte 
                 MO (mo or 
                 Eosinophil 
                 NNRBC (nnr or 
               
               
                   
                 NE (ne) 
                 LY (ly) 
                 mn) 
                 EO (eo) 
                 nnrbc) 
               
               
                   
               
               
                 Cell 
                 SD-C-NE 
                 SD-C-LY 
                 SD-C-MO 
                 SD-C-EO 
                 SD-C-NNRBC 
               
               
                 Conductivity 
                 MN-C-NE 
                 MN-C-LY 
                 MN-C-MO 
                 MN-C-EO 
                 MN-C-NNRBC 
               
               
                 (C) 
                   
                   
                   
                   
                   
               
               
                 high freq. 
                   
                   
                   
                   
                   
               
               
                 current 
                   
                   
                   
                   
                   
               
               
                 Cell Volume 
                 SD-V-NE 
                 SD-V-LY 
                 SD-V-MO 
                 SD-V-EO 
                 SD-V-NNRBC 
               
               
                 (V) 
                 MN- V-NE 
                 MN-V-LY 
                 MN-V-MO 
                 MN-V-EO 
                 MN-V-NNRBC 
               
               
                 low freq. 
                   
                   
                   
                   
                   
               
               
                 current 
                   
                   
                   
                   
                   
               
               
                 Axial light 
                 SD-AL2-NE 
                 SD-AL2-LY 
                 SD-AL2- 
                 SD-AL2-EO 
                 SD-AL2- 
               
               
                 loss or 
                 MN-AL2- 
                 MN-AL2- 
                 MO 
                 MN-AL2- 
                 NNRBC 
               
               
                 absorbed 
                 NE 
                 LY 
                 MN-AL2- 
                 EO 
                 MN-AL2- 
               
               
                 light (AL2 or 
                   
                   
                 MO 
                   
                 NNRBC 
               
               
                 ALL) 
                   
                   
                   
                   
                   
               
               
                 Low-angle 
                 SD-LALS- 
                 SD-LALS- 
                 SD-LALS- 
                 SD-LALS- 
                 SD-LALS- 
               
               
                 light scatter 
                 NE 
                 LY 
                 MO 
                 EO 
                 NNRBC 
               
               
                 (LALS) 
                 MN-LALS- 
                 MN-LALS- 
                 MN-LALS- 
                 MN-LALS- 
                 MN-LALS- 
               
               
                   
                 NE 
                 LY 
                 MO 
                 EO 
                 NNRBC 
               
               
                 Upper 
                 SD- 
                 SD- 
                 SD- 
                 SD- 
                 SD-UMALS- 
               
               
                 median-angle 
                 UMALS-NE 
                 UMALS-LY 
                 UMALS- 
                 UMALS-EO 
                 NNRBC 
               
               
                 light scatter 
                 MN- 
                 MN- 
                 MO 
                 MN- 
                 MN-UMALS- 
               
               
                 (UMALS) 
                 UMALS-NE 
                 UMALS-LY 
                 MN- 
                 UMALS-EO 
                 NNRBC 
               
               
                   
                   
                   
                 UMALS- 
                   
                   
               
               
                   
                   
                   
                 MO 
                   
                   
               
               
                 Lower 
                 SD- 
                 SD-LMALS- 
                 SD- 
                 SD- 
                 SD-LMALS- 
               
               
                 median-angle 
                 LMALS-NE 
                 LY 
                 LMALS- 
                 LMALS-EO 
                 NNRBC 
               
               
                 light scatter 
                 MN- 
                 MN- 
                 MO 
                 MN- 
                 MN-LMALS- 
               
               
                 (LMALS) 
                 LMALS-NE 
                 LMALS-LY 
                 MN- 
                 LMALS-EO 
                 NNRBC 
               
               
                   
                   
                   
                 LMALS- 
                   
                   
               
               
                   
                   
                   
                 MO 
                   
                   
               
               
                 Median- 
                 SD-MALS- 
                 SD-MALS- 
                 SD-MALS- 
                 SD-MALS- 
                 SD-MALS- 
               
               
                 angle light 
                 NE 
                 LY 
                 MO 
                 EO 
                 NNRBC 
               
               
                 scatter 
                 MN-MALS- 
                 MN-MALS- 
                 MN-MALS- 
                 MN-MALS- 
                 MN-MALS- 
               
               
                 (MALS) 
                 NE 
                 LY 
                 MO 
                 EO 
                 NNRBC 
               
               
                 [UMALS + 
                   
                   
                   
                   
                   
               
               
                 LMALS] 
               
               
                   
               
            
           
         
       
     
     CPD values can be viewed on the screen of an instrument, such as that depicted in  FIG.  7   , as well as automatically exported as an Excel file. Hence, white blood cells (WBCs) can be analyzed and individually plotted in tri-dimensional histograms, with the position of each cell on the histogram being defined by certain parameters as described herein. In some instances, systems or methods can grade the cell in a range from 1 to 256 points, for each of the parameters. 
     Because WBCs of the same sub-type, for example granulocytes (or neutrophils), lymphocytes, monocytes, eosinophils, and basophils, often have similar morphologic features, they may tend to be plotted in similar regions of the tri-dimensional histogram, thus forming cell populations. The number of events in each population can be used to generate a differential count.  FIG.  7    depicts an exemplary screen shot of a differential count screen. As illustrated here, the WBC sub-populations are in clearly separated groups at different locations on the histogram, and are defined by different colors. The histogram shown here provides cell size (volume) in the y axis and light scatter in the x axis. 
     By clicking on the “Additional Data” tab, users can view the CPD values. Such CPD values can correspond to the position of the population in the histogram, and to the morphology of the WBCs under the microscope. For example, monocytes are known to be the largest of all WBCs, and have the highest mean volume. Lymphocytes are known to be the smallest of all WBCs, and have the lowest mean volume. Lymphocytes also have the lowest level of cytoplasmic granularity and the least complex nuclear morphology, and have the lowest mean light scatter, called MALS). 
     CPD parameters can be used to analyze cellular morphology in a quantitative, objective, and automated manner, free from the subjectivity of human interpretation, which is also very time consuming, expensive, and has limited reproducibility. CPD parameters can be used for improving the value of the CBC-diff in the diagnosis of various medical conditions that alter the morphology of WBCs. 
       FIG.  7 A  illustrates aspects of a biological sample analysis system  700   a , according to embodiments of the present invention. As depicted here, infection status analysis techniques may include determining both a WBC count and a standard deviation of monocyte volume using VCS technology. Further, techniques may include determining an RBC count using an RBC aperture bath of a CBC module. 
     As shown here, the sample analysis system  700   a  includes a sample aspiration module  710   a , a CBC module  720   a  (which incorporates Coulter technology), and a VCS module  730   a  (which incorporates VCS technology). The CBC module  720   a  includes a blood sampling valve  721   a , which receives sample from aspiration module  710   a . Further, the CBC module  720   a  includes a WBC aperture bath  722   a  which receives sample from BSV  721   a  (and can be used to determine a WBC count) and an RBC aperture bath  723   a  which receives sample from BSV  721   a  (and can be used to determine an RBC count). The VCS module  730   a  includes a sample distribution valve  731   a , which receives sample from aspiration module  710   a , and which can be used to transfer sample to a reticulocyte chamber  732   a  for processing with a flow cell transducer  740   a . Sample distribution valve  731   a  can also be used to transfer sample to a WBC differential chamber  734   a  for processing with a flow cell transducer  740   a . What is more, sample distribution valve  731   a  can be used to transfer sample to an NRBC chamber  736   a  for processing with a flow cell transducer  740   a.    
     According to some embodiments, sample may or may not be lysed depending on where the sample is processed in the system. For example, in many instances, sample is lysed when processed using the WBC aperture bath  722   a , the WBC differential chamber  734   a , and the NRBC chamber  736   a . In contrast, in many instances, sample is not lysed when processed using the RBC aperture bath  723   a  or the reticulocyte chamber  732   a . Hence, as depicted in  FIG.  7 A , the uncorrected white blood cell count (UWBC) can be determined based on sample which is not lysed. The standard deviation of monocyte volume may be obtained from data from WBC differential chamber  734   a.    
     According to some embodiments, a CBC module can be used to determine both a WBC count (via a WBC aperture bath) and an RBC count (via an RBC aperture bath). The parameter from the CBC module which is used in  FIG.  7 A  is the WBC count  742 . In some instances, the parameter from the CBC module may be a neutrophil count or neutrophil percentage of the WBCs. In some examples, an RBC aperture bath of a CBC module may not be required. Some embodiments may compute NE % using Beckman Coulter&#39;s UniCel DxH™ 800 System and not use the WBC aperture bath. 
     As discussed herein, embodiments of the present invention encompass automated systems for evaluating an infection status in a biological sample, where the system includes a first analyzer module (e.g. implementing Coulter technology) configured to determine a white blood cell count  742  of the biological sample, a second analyzer module (e.g. implementing VCS technology) configured to determine a standard deviation of monocyte volume  744  of the biological sample, and a data processing module configured to evaluate the infection status based on the Coulter white blood cell count  742  and the VCS standard deviation of monocyte volume  744 . 
       FIG.  8    schematically illustrates a method  800  for evaluating an infection status according to embodiments of the present invention. As depicted here, the method includes obtaining blood samples from individuals (e.g. during routine examinations), as indicated by step  810 . Complete Blood Count (CBC) data, Volume Conductivity Scatter (VCS) data, or combinations thereof, can be obtained from these biological samples, using a cellular analysis system that is equipped to obtain cellular event parameters, such as Beckman Coulter&#39;s UniCel DxH™ 800 System, as indicated by step  820 . CBC parameters, VCS parameters, or combinations thereof from analyzed samples can be used to evaluate the sepsis status, as indicated by step  830 . As described herein, the WBC count and the standard deviation of monocyte volume may be the only parameters or derived parameters used from the CBC and VCS parameters. Methods may also include outputting an index of the likelihood of sepsis, as indicated in step  840 . 
       FIG.  9    shows an automated method  900  for evaluating a sepsis status associated with a blood sample obtained from an individual according to embodiments of the present invention. The method may include determining a standard deviation of monocyte volume associated with the blood sample (block  902 ). The determining operation may use a transducer module as described herein. The method may include determining a white blood cell count (WBC) associated with the blood sample (block  906 ). This determining operation may use a CBC module as described herein. The module may include evaluating, using a data processing module, the sepsis status associated with the blood sample (block  906 ). 
     Diagnostic Accuracy 
     Embodiments may involve different measures of diagnostic accuracy. Diagnostic accuracy involves the degree of agreement between a test and a reference method or clinical outcome measure. Diagnostic parameters of a test may not be intrinsic properties of the test and instead may depend on the clinical context of the test. 
     A test, compared to a reference method or clinical outcome measure, may have different outcomes: true positive, false positive, false negative, and true negative. Table 2 illustrates the relationship. 
     
       
         
           
               
             
               
                 TABLE 2 
               
             
            
               
                   
               
               
                 Outcomes of a test result compared to a reference method. 
               
            
           
           
               
               
            
               
                   
                 Reference Standard or Clinical Outcome Measure 
               
            
           
           
               
               
               
               
            
               
                   
                 Disease present 
                 Disease absent 
                   
               
               
                   
                 (Sepsis) 
                 (Non-Sepsis) 
                 Total 
               
               
                   
               
               
                 Test positive 
                 True positive (TP) 
                 False positive (FP) 
                 TP + FP 
               
               
                 Test negative 
                 False negative (FN) 
                 True negative (TN) 
                 TN + FN 
               
               
                 Total 
                 TP + FN 
                 TN + FP 
                 Total 
               
               
                   
               
            
           
         
       
     
     Sensitivity, or sometimes called “positivity in disease,” refers to the proportion of subjects who have the target condition (reference standard or clinical outcome measure shows that the disease is present) and give “test positive” results. As a formula, sensitivity can be expressed as the following: 
     
       
         
           
             Sensitivity 
             = 
             
               TP 
               
                 TP 
                 + 
                 FN 
               
             
           
         
       
     
     Specificity, or sometimes called “negativity in health,” refers to the proportion of subjects without the target condition (reference standard or clinical outcome measure shows that the disease is absent) and give “test negative” results. As a formula, specificity can be expressed as the following: 
     
       
         
           
             Specificity 
             = 
             
               TN 
               
                 TN 
                 + 
                 FP 
               
             
           
         
       
     
     Positive predictive value (PPV) refers to the proportion of positive results that are true positives. In other words, PPV may indicate a proportion that actually have the target condition. As a formula, PPV can be expressed as the following: 
     
       
         
           
             PPV 
             = 
             
               TP 
               
                 TP 
                 + 
                 FP 
               
             
           
         
       
     
     Negative predictive value (NPV) refers to the proportion of negative results that are true negatives. Put simply, NPV may indicate a fraction that do not have the target condition. As a formula, NPV can be expressed as the following: 
     
       
         
           
             NPV 
             = 
             
               TN 
               
                 TN 
                 + 
                 FN 
               
             
           
         
       
     
     Predictive values may vary depending upon the prevalence of the target condition in the population being studied, although sensitivity and specificity remain the same. 
     A cutoff point may be created to condition the values of sensitivity and specificity of the test. An ROC curve may be a way to graphically display true positives versus false positives across a range of cutoffs and may aid in selecting a desired cutoff to achieve a clinical utility of the test. Examples of ROC curves are shown in  FIG.  10    and  FIG.  11   , which will be discussed in detail in the examples below. An ROC curve may help determine cutoffs in determining the presence or absence of a target condition. The y-axis indicates the sensitivity of a test, and the x-axis indicates 1—specificity of the test. The area under curve (AUC) for an ROC curve may be used to compare test performance. The AUC may quantify the overall ability of the test to discriminate between individuals with the target condition and those without the target condition. A perfect test results in a curve that extended to the (0,1) point with an AUC of 1. A worthless test has an AUC of 0.5, indicating that the test may be no better than randomly determining whether an individual has the target condition. A line of y=x is shown in the graph to illustrate an AUC of 0.5. 
     Often, the distribution of test results indicating the presence of a target condition may overlap with the test results indicating the absence of the target condition. A cutoff may be set high so that the test may be unlikely to diagnose the target condition in someone who does not have the target condition (i.e., low false positive, high specificity). However, with a high cutoff, the test may be more likely to misdiagnose a person who has the target condition as someone who does not have the target condition (i.e., high false negative, low sensitivity). On the ROC curve, the choice of a high cutoff may be represented by a point near the origin. 
     If the cutoff is set too low, the test may diagnose correctly all or almost all the people with the target condition (i.e., high true positive, high sensitivity). However, a low cutoff may result in diagnosing the target condition in more people who do not have the target condition (i.e., high false positive, low specificity). On the ROC curve, the choice of low cutoff may be represented by a point near (1,1). 
     Examples 
     A blinded, prospective, observational, multicenter cohort study was conducted at three sites comprised of both academic and community hospital emergency departments in the United States. The study enrolled a total of 2,160 consecutive adult emergency-department patients (18 to 89 years of age) meeting inclusion criteria for:
         Having a CBC-DIFF performed upon presentation and   Subjects remaining in the hospital (emergency department or in-patient) for at least 12 hours.       

     The prevalence of sepsis as defined by the Sepsis-2 (ACCP/SCCM 2001 consensus criteria) was 17.8%. This prevalence is higher than the general prevalence of sepsis in emergency department patients; the septic population was likely enriched by the inclusion criteria. The emergency-department population demographics based upon presenting clinical status are summarized in Table 3. 
     
       
         
           
               
             
               
                 TABLE 3 
               
             
            
               
                   
               
               
                 Emergency Department Population Demographics 
               
               
                 Based on Presenting Diagnosis 
               
            
           
           
               
               
               
               
               
               
            
               
                 Site 
                 Case Control 
                 SIRS 
                 Infection 
                 Sepsis 
                 Total 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 1 
                 320 
                 111 
                 95 
                 139 
                 665 
               
               
                 2 
                 440 
                 222 
                 60 
                 115 
                 837 
               
               
                 3 
                 328 
                 108 
                 89 
                 131 
                 656 
               
               
                 Total 
                 1088 
                 441 
                 244 
                 385 
                 2158 
               
               
                   
               
            
           
         
       
     
     The subject categories included:
         Non-SIRS—case controls, subjects having 0 to 1 SIRS (Systemic Inflammatory Response Syndrome) criteria and no infection   SIRS—subjects having ≥2 SIRS criteria   Infection—subjects having suspected or confirmed infection   Sepsis—subjects having infection plus SIRS   Severe sepsis—subjects having sepsis with one or more organ failure   Septic shock—subjects having sepsis with severe hypotension.
 
In Table 3, sepsis includes sepsis, severe sepsis, and septic shock.
       

     Samples collected in K2 EDTA were analyzed within two hours of venipuncture. MDW results were compared to the clinical adjudication of sepsis per Sepsis-2 Criteria (defined as a documented or suspected infection together with two or more SIRS criteria). Sepsis criteria had to be met within 12 hours of presentation. Infection diagnosis was determined by retrospective chart review for microbiological, radiological, and molecular results available for up to five to seven days post-emergency-department presentation. Diagnosis was adjudicated at each site by two independent physicians and discordances were arbitrated by a third independent physician. 
     MDW Alone 
       FIG.  10    shows an ROC curve for determining sepsis using only monocyte distribution width (MDW). A cutoff value of 20.0 units for MDW effectively differentiated sepsis from all other conditions in emergency-department patients. The ROC analysis yielded an area under the curve (AUC) of 0.788 (95% CI 0.762 to 0.815). 
     Table 4 summarizes the sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios with their respective 95% confidence intervals based on a cutoff value of 20.0. 
     
       
         
           
               
             
               
                 TABLE 4 
               
             
            
               
                   
               
               
                 Performance of MDW for Sepsis 
               
               
                 MDW Cutoff at 20.0 
               
            
           
           
               
               
               
            
               
                   
                 Predictive Values 
                 Likelihood Ratios 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                 Sensitivity 
                 Specificity 
                 Positive 
                 Negative 
                 Positive 
                 Negative 
               
               
                   
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                 Estimate 
                 0.740 
                 0.720 
                 0.365 
                 0.927 
                 2.646 
                 0.361 
               
               
                 Lower 95% CI 
                 0.694 
                 0.699 
                 0.332 
                 0.912 
                 2.406 
                 0.304 
               
               
                 Upper 95% CI 
                 0.782 
                 0.741 
                 0.399 
                 0.940 
                 2.911 
                 0.428 
               
               
                   
               
            
           
         
       
     
     MDW values between 19.0 and 19.5 have a higher sensitivity for predicting sepsis, but lower specificity (for example, more false-positive results). The impact on sensitivity and specificity for early detection of sepsis at different MDW cut-offs is provided in Table 5. 
     
       
         
           
               
             
               
                 TABLE 5 
               
             
            
               
                   
               
               
                 Sensitivity and Specificity at Various MDW Cutoffs 
               
            
           
           
               
               
               
               
            
               
                   
                 95% 
                   
                 95% Confidence 
               
               
                   
                 Confidence Intervals 
                   
                 Intervals 
               
            
           
           
               
               
               
               
               
               
               
            
               
                 Cutoff 
                 Sensitivity 
                 Lower 
                 Upper 
                 Specificity 
                 Lower 
                 Upper 
               
               
                   
               
               
                 19.0 
                 0.821 
                 0.779 
                 0.856 
                 0.594 
                 0.571 
                 0.617 
               
               
                 19.5 
                 0.784 
                 0.741 
                 0.823 
                 0.661 
                 0.639 
                 0.683 
               
               
                 20.0 
                 0.740 
                 0.694 
                 0.782 
                 0.720 
                 0.699 
                 0.741 
               
               
                 20.5 
                 0.683 
                 0.635 
                 0.728 
                 0.771 
                 0.751 
                 0.790 
               
               
                   
               
            
           
         
       
     
     MDW and WBC 
       FIG.  11    shows an ROC curve for using MDW and WBC to determine sepsis. The AUC for using both parameters together was statistically greater than the AUC for WBC alone. The results are summarized in Table 6. 
     
       
         
           
               
             
               
                 TABLE 6 
               
             
            
               
                   
               
               
                 AUC for WBC and Combined WBC&amp;MDW 
               
            
           
           
               
               
               
            
               
                   
                 Standard 
                 95% Confidence Limits 
               
            
           
           
               
               
               
               
               
            
               
                 Parameter 
                 AUC 
                 Error 
                 Lower 
                 Upper 
               
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 WBC 
                 0.75 
                 0.02 
                 0.72 
                 0.78 
               
               
                 WBC &amp; MDW 
                 0.85 
                 0.01 
                 0.83 
                 0.88 
               
               
                 Difference 
                 0.10 
                 0.01 
                 0.08 
                 0.13 
               
               
                   
               
            
           
         
       
     
     WBC and MDW Decision Rules 
     The following decision rules were used to evaluate a sepsis status:
         WBC&gt;12×10 3  cells/μL is abnormal   WBC&lt;4×10 3  cells/μL is abnormal   MDW&gt;20 indicate for having or developing sepsis   (WBC&gt;12×10 3  cells/μL or WBC&lt;4×10 3  cells/μL) &amp; MDW&gt;20 strong indication for sepsis       

     Post-test probability (Pi) was calculated based on the positive likelihood ratio (LR) and pre-test probability. Pre-test predicted probability depends on the physical/clinical conditions of each patient. Prevalence can be used as the average pre-test probability (P 0 ). In this example, P 0 =17.8%. Post-test probability was calculated as 
     
       
         
           
             
               P 
               1 
             
             = 
             
               
                 
                   
                     P 
                     0 
                   
                   × 
                   LR 
                 
                 
                   1 
                   - 
                   
                     
                       P 
                       0 
                     
                     ⁡ 
                     
                       ( 
                       
                         LR 
                         - 
                         1 
                       
                       ) 
                     
                   
                 
               
               . 
             
           
         
       
     
     Positive likelihood ratios were calculated for WBC (abnormal; WBC&gt;12 or WBC&lt;4) and WBC (abnormal; WBC&gt;12 or WBC&lt;4) &amp; MDW (abnormal MDW&gt;20). 
     Pre-test and post-test probabilities are shown in Table 7. An abnormal WBC result increased the probability of a patient having or developing sepsis to 44.7% when the pre-test probability is 17.8%. The post-test probability increased to 63.5% when both WBC and MDW were abnormal. 
     Positive likelihood ratios were also calculated for WBC (normal; 4≤WBC≤12) and WBC (normal; 4≤WBC≤12) &amp; MDW (normal MDW&lt;20). 
     A normal test results reduced the probability of sepsis. Post-test probability of a normal WBC test was 7.9% but this value decreased to 2.9% if both WBC and MDW are normal. 
     
       
         
           
               
             
               
                 TABLE 7 
               
             
            
               
                   
               
               
                 Pre-Test and Post-Test Probabilities 
               
               
                 for Different Combination of Tests 
               
            
           
           
               
               
               
               
            
               
                   
                   
                 Likelihood 
                   
               
               
                   
                 Test 
                 Ratio 
                 Probability 
               
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 Pre-Test 
                   
                 17.8% 
               
               
                   
                 Post-Test: Positive test results 
                   
                   
               
               
                   
                 WBC abnormal 
                 3.72 
                 44.7% 
               
               
                   
                 WBC &amp; MDW abnormal 
                 8.01 
                 63.5% 
               
               
                   
                 Post Test: Negative test results 
                   
                   
               
               
                   
                 WBC normal 
                 0.39 
                 7.9% 
               
               
                   
                 WBC &amp; MDW normal 
                 0.14 
                 2.9% 
               
               
                   
               
            
           
         
       
     
     In the preceding description, for the purposes of explanation, numerous details have been set forth in order to provide an understanding of various embodiments of the present technology. It will be apparent to one skilled in the art, however, that certain embodiments may be practiced without some of these details, or with additional details, or in varied combinations or sub-combinations of features of the embodiments. 
     Having described several embodiments, it will be recognized by those of skill in the art that various modifications, alternative constructions, and equivalents may be used without departing from the spirit of the invention. Additionally, a number of well-known processes and elements have not been described in order to avoid unnecessarily obscuring the present invention. Additionally, details of any specific embodiment may not always be present in variations of that embodiment or may be added to other embodiments. 
     Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limits of that range is also specifically disclosed. Each smaller range between any stated value or intervening value in a stated range and any other stated or intervening value in that stated range is encompassed. The upper and lower limits of these smaller ranges may independently be included or excluded in the range, and each range where either, neither, or both limits are included in the smaller ranges is also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included. 
     As used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a method” includes a plurality of such methods and reference to “the transducer” includes reference to one or more transducers and equivalents thereof known to those skilled in the art, and so forth. The invention has now been described in detail for the purposes of clarity and understanding. However, it will be appreciated that certain changes and modifications may be practice within the scope of the appended claims.