Detection Of Sepsis Research Articles

Role of Serum Lactate Clearance as a Predictor of Mortality and Morbidity in Neonatal Sepsis: A Prospective Cohort Study

Introduction: Neonatal sepsis is a global healthcare concern, which is more prevalent in developing countries. However, surprisingly, biomarkers with good sensitivity and specificity to predict mortality and morbidity are lacking. Higher levels of serum lactate are observed in patients exposed to an inflammatory response, but its practical use remains limited. Therefore, the author aimed to investigate the relationship between serum lactate measurements and the severity of neonatal sepsis. Aim: To assess the role of serum lactate clearance as a marker to predict mortality and morbidity in neonatal sepsis. Additionally, the secondary aim was to evaluate the demographic profile of neonatal sepsis and understand the relationship between C-reactive Protein (CRP), Procalcitonin, and lactate clearance in neonatal sepsis. Materials and Methods: A prospective cohort study was conducted in the Sick Newborn Care Unit (SNCU) and Neonatal Intensive Care Unit (NICU) of Chittaranjan Seva Sadan, College of Obstetrics, Gynaecology, and Child Health in Kolkata, India, from June 2020 to May 2021. A total of 93 confirmed cases of neonatal sepsis were included in the study. Serum lactate levels were measured at the time of sepsis diagnosis and 48 hours after the first sample. Lactate clearance was calculated, and the neonates were followed till discharge or death. Various parameters, including complete blood count, CRP, Procalcitonin, culture, and Cerebrospinal Fluid (CSF), were assessed. The data obtained were statistically analysed using paired t-test, one-way Analysis of Variance (ANOVA), and Pearson’s Chi-square test. Results: A total of 93 neonates were included after meeting the inclusion and exclusion criteria. Among them, 10 neonates died, while 83 were successfully discharged. It was found that death was significantly associated (p<0.0001) with lactate clearance. Total 9 (90%) of the deceased neonates had negative lactate clearance, while only 21 (25.3%) discharged neonates had negative lactate clearance. Lactate clearance was also significantly associated with the duration of hypoglycaemia (p=0.008), duration of Nil Per Mouth (NPM) (p=0.01), and need for reintubation (p=0.05). However, no association was found with the duration of hospital stay, duration of fluid therapy, duration of oxygen requirement, and need for ventilation. Conclusion: Lactate clearance showed a significant association with the risk of mortality in patients with neonatal sepsis. Therefore, lactate clearance can be used as a prognostic marker to identify sepsis. Early detection of sepsis can aid in proper management and subsequently reduce mortality

Advancing Early Detection of Sepsis With Temporal Convolutional Networks Using ECG Signals

Advancing Early Detection of Sepsis With Temporal Convolutional Networks Using ECG Signals

Role of cell-free DNA levels in the diagnosis and prognosis of sepsis and bacteremia: A systematic review and meta-analysis.

Sepsis remains a major cause of mortality in intensive care units (ICUs). Prompt diagnosis and effective management are imperative for better outcomes. In this systematic review and meta-analysis, we explore the potential of circulating cell-free DNA (cfDNA), as a promising tool for early sepsis detection and prognosis assessment, aiming to address limitations associated with traditional diagnostic methods. Following PRISMA guidelines, we collected relevant literature from thirteen databases. Studies were included if they analyzed quantitative diagnostic or prognostic cfDNA levels in humans in case of sepsis. We collected data on basic study characteristics, baseline patient demographics (e.g. age and sex), and cfDNA levels across different stages of sepsis. Pooled SMD with 95%-CI was calculated, and Comprehensive Meta-Analysis (CMA) software facilitated meta-analysis. Receiver operating characteristic (ROC) curves were generated to assess cfDNA's combined sensitivity and specificity in diagnostics and prognostics. We included a final of 44 studies, of which, only 32 with 2950 participants were included in the meta-analysis. cfDNA levels were higher in septic patients compared to healthy controls (SMD = 3.303; 95%-CI [2.461-4.145], p<0.01). Furthermore, cfDNA levels were higher in non-survivors than survivors (SMD = 1.554; 95%-CI [0.905-2.202], p<0.01). Prognostic studies demonstrated a pooled sensitivity and specificity of 0.78, while diagnostic studies showed a sensitivity of 0.81 and a specificity of 0.87. These findings show that cfDNA levels are significantly higher in sepsis patients compared to control groups and non-survivors in comparison to survivors among both adult and pediatric populations.

Artificial Intelligence in Laboratory Technologies for Early Detection and Prognostication of Sepsis: A Systematic Review

Background: Sepsis: a complex clinical syndrome--represents life-threatening organ dysfunction instigated by an infection's dysregulated host response. Early detection and accurate prognostication of sepsis are crucial; they pave the way for timely intervention, ultimately enhancing patient outcomes. The rise in interest towards Artificial Intelligence (AI) applications within laboratory technologies is directly related to its potential for improving early detection and prognosis forecasting in sepsis cases; this interest comes as AI continues its advancement. Methods: We conducted a systematic review of studies utilizing AI algorithms in laboratory settings for early sepsis detection and prognostication: our methods entailed searching relevant databases for research published until October 2023. Our inclusion criteria spanned original articles; these applied machine learning (ML) and deep learning (DL) techniques to laboratory data--with the aim being sepsis prediction. We assessed the quality of the studies, extracted and synthesized data on AI model performance metrics - including: area under receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy. Results: The review encompassed eight studies meeting the inclusion criteria; AI models showcased exceptional predictive capabilities--evidenced by a range of AUROC values from 0.799 to 0.9213, signifying noticeably acceptable performance. However, there was wide variation in sensitivity and specificity among these analyses: an indicator of heterogeneity in model performance. Superior prognostic accuracy and potential for real-time monitoring of patients' early sepsis signs emerged in several models: notably, within the first 12 hours of patient admission - their highest predictive period. The models frequently outperformed traditional scoring systems. Conclusion: Laboratory technology's AI applications significantly promise sepsis' early detection and prognostication. Reviewed studies suggest AI models may surpass traditional methods, offering potential integration into clinical workflows for rapid sepsis identification aid. Nevertheless, we also acknowledged both the variability in model performance and necessity of additional validation across diverse clinical settings. Future research: it must concentrate on two key aspects--the refinement of AI algorithms to enhance sensitivity and precision; furthermore, it should delve into evaluating the clinical impact of tools for sepsis prediction that are assisted by AI.

Utilizing machine learning to create a blood-based scoring system for sepsis detection

Introduction and aim. Sepsis, a disease caused by inflammation as a response to infection, often goes undiagnosed due to its heterogeneity and lack of a single diagnostic test. Current sepsis detection scoring systems have low sensitivity and utilize biomarkers that are difficult to obtain from a single test. The goal of this research is to create a scoring system that outperforms current industry standards by utilizing blood-based biomarkers readily available in hospital settings. Material and methods. Machine learning algorithms were run through Google Colab using Extreme Gradient Boost classifier. The dataset was obtained from NCBI website containing electronic hospital records of intensive care patients. A multivariate linear regression was applied to the dataset to determine statistically significant biomarkers in the detection of sepsis, and their β coefficients. Then, validation testing was performed, and the performance was compared to other scoring systems. Results. This experiment reveals that a sepsis detection system that utilizes procalcitonin, white blood cells, C-reactive protein, neutrophil to lymphocyte ratio, and albumin can outperform other biomarkers and scoring systems with high sensitivity at a recall score of 0.7922. Conclusion. These results demonstrate the potential of utilizing a blood-based scoring system for sepsis detection within hospital settings.

Complete Blood Count and MDW-based Machine Learning Algorithms for Sepsis Detection: a Multicentric Development and External Validation Study (Preprint)

Complete Blood Count and MDW-based Machine Learning Algorithms for Sepsis Detection: a Multicentric Development and External Validation Study (Preprint)

Monocyte Distribution Width Predicts Sepsis, Respiratory Failure, and Death in COVID-19.

Introduction Sepsis is the leading cause of hospital mortality nationwide. Early recognition has been shown to improve outcomes. This research investigates the use of monocyte distribution width's (MDW) ability to detect sepsis and clinically correlate to outcomes in COVID-19 infection. Methods This is a retrospective, single-center cohort study of adult patients with confirmed COVID-19 requiring hospital admission over a 14-month period (September 2020 to November 2021). MDW was evaluated as a cytomarker to predict disease severity, mortality, and determination of sepsis in patients with COVID-19. Additionally, MDW was compared to existing inflammatory markers, including procalcitonin, D-dimer, ferritin, and lactic acid. Results MDW was able to predict sepsis in patients with COVID-19. The average MDW was found to be significantly higher in the detection of sepsis (25.50 ± 5.93) vs. patients without (23.13 ± 4.46) (p < 0.01). MDW was able to correlate with clinical outcomes or respiratory failure/hypoxia and death. An MDW value of 24.9 was shown to be the best cut-off value in determining fatal outcomes; receiver operating characteristic curve analysis revealed an area under the curve value of 0.69 (95% CI: 0.55-0.71) with a sensitivity of 83% and specificity of 71%. A chi-square test was performed, which detected a significant association between MDW values and the final clinical outcome of COVID-19 (OR = 3.52, 95% CI: 1.78-7.11, p < 0.001). Additionally, the mean MDW of patients with hypoxia or respiratory failure was significantly higher (22 vs. 25, p < 0.1). MDW did not correlate with any of the existing inflammatory markers. Conclusion MDW is a novel and reliable cytomarker for identifying sepsis in patients with COVID-19 infection. High MDW values are associated with clinical outcomes of respiratory failure and death with a mortality rate or absolute risk of 25%. MDW is easily obtained from routine laboratory evaluation in the emergency room and has the potential to be a useful tool in the triage of COVID-19 patients.

Rapid Detection of Early Sepsis in Intensive Care Unit: Comparison of Biomarkers - Procalcitonin, Serum Lactate and C - Reactive Protein

Background: Intensivists would benefit from an accurate and easily accessible biomarker for identifying patients with different source of infections who require special attention in critical care units due to their higher risk of death. Objectives: The aim of the study was to determine the rapid detection of early sepsis in intensive care unit: comparison of biomarkers - procalcitonin, serum lactate and C - reactive protein. Methods: Prospective, observational study was carried out in the Department of Critical care unit, Evercare Hospital Dhaka, Dhaka, Bangladesh during January 2021 to June 2021. A total of 60 patients were participated in the study. Statistical analyses of the results were be obtained by using window-based Microsoft Excel and Statistical Packages for Social Sciences (SPSS-24), where required. Results: Patients admitted in Intensive Care Unit of different disciplines, of different adult ages 20-60 years. In this present series it was observed that 36.7% patients were blood culture positive among 60 cases, 21.7% were Urine culture positive among 60 cases, 28.0% were Tracheal Aspirate positive among 25 cases, and finally 25.0% were Wound Swab culture positive among 12 cases. Conclusion: Blood culture positivity was the most common, followed by urine culture positivity, tracheal aspirate positivity, and wound swab positivity. Procalcitonin had the highest sensitivity, accuracy, and positive predictive value for predicting sepsis, followed by C-Reactive protein and serum lactate.

Evaluation of haematological scoring system as a screening tool for early detection of neonatal sepsis

Background: Early diagnosis of neonatal septicemia is a vexing problem because of its nonspecific clinical picture. Hence the timely diagnosis of sepsis in neonates is critical as the illness can be rapidly progressive and, in some instances, fatal. The current study was undertaken to assess the significance of the hematological scoring system (HSS) for early detection of neonatal sepsis. Objectives were to assess the effectiveness of HSS in early detection of neonatal sepsis by comparing it with blood culture result, in neonates who were clinically suspected to have bacterial infection Methods: This is a descriptive study done in TDMCH, Vandanam for a period of 1 year. All babies who were at risk of developing sepsis were studied after applying inclusion and exclusion criteria. Blood samples were collected and subjected to sepsis work up. HSS scoring was done. Sensitivity, specificity, positive predictive value (PPV), Negative predictive value (NPV) of HSS and each of its components were calculated. Results: Among the 85 neonates studied, 39 (45.9%) were blood culture positive, 42 (49.4%) had HSS score of 5 or more. HSS score had sensitivity of 92.31%, specificity of 86.96%, PPV of 85.71% and NPV of 93.02%. Conclusions: HSS score of ≥5 had high NPV and would play a major role in ruling out neonatal sepsis.

Development and validation of a machine learning model integrated with the clinical workflow for early detection of sepsis

BackgroundSepsis is a life-threatening condition caused by a dysregulated response to infection, affecting millions of people worldwide. Early diagnosis and treatment are critical for managing sepsis and reducing morbidity and mortality rates.Materials and methodsA systematic design approach was employed to build a model that predicts sepsis, incorporating clinical feedback to identify relevant data elements. XGBoost was utilized for prediction, and interpretability was achieved through the application of Shapley values. The model was successfully deployed within a widely used Electronic Medical Record (EMR) system.ResultsThe developed model demonstrated robust performance pre-operations, with a sensitivity of 92%, specificity of 93%, and a false positive rate of 7%. Following deployment, the model maintained comparable performance, with a sensitivity of 91% and specificity of 94%. Notably, the post-deployment false positive rate of 6% represents a substantial reduction compared to the currently deployed commercial model in the same health system, which exhibits a false positive rate of 30%.DiscussionThese findings underscore the effectiveness and potential value of the developed model in improving timely sepsis detection and reducing unnecessary alerts in clinical practice. Further investigations should focus on its long-term generalizability and impact on patient outcomes.

Comparing artificial intelligence strategies for early sepsis detection in the ICU: an experimental study

Sepsis is a life-threatening condition whose early recognition is key to improving outcomes for patients in intensive care units (ICUs). Artificial intelligence can play a crucial role in mining and exploiting health data for sepsis prediction. However, progress in this field has been impeded by a lack of comparability across studies. Some studies do not provide code, and each study independently processes a dataset with large numbers of missing values. Here, we present a comparative analysis of early sepsis prediction in the ICU by using machine learning (ML) algorithms and provide open-source code to the community to support future work. We reviewed the literature and conducted two phases of experiments. In the first phase, we analyzed five imputation strategies for handling missing data in a clinical dataset (which is often sampled irregularly and requires hand-crafted preprocessing steps). We used the MIMIC-III dataset, which includes more than 5,800 ICU hospital admissions from 2001 to 2012. In the second phase, we conducted an extensive experimental study using five ML methods and five popular deep learning models. We evaluated the performance of the methods by using the area under the precision-recall curve, a standard metric for clinical contexts. The deep learning methods (TCN and LSTM) outperformed the other methods, particularly in early detection tasks more than 4 hours before sepsis onset. The motivation for this work was to provide a benchmark framework for future research, thus enabling advancements in this field.Graphical

Role of interleukins in the detection of neonatal sepsis: a network meta-analysis.

The purpose of the network meta-analysis was to make a more comprehensive comparison of different interleukins in the detection of neonatal sepsis and to pose clues in the field of clinical practice. Electronic databases of PubMed, Web of Science and Embase were systematically searched. Eligible studies included diagnostic tests utilizing interleukins to detect neonatal sepsis. We calculated pooled sensitivity, specificity, positive Likelihood Ratio (PLR) and negative Likelihood Ratio (NLR), diagnostic odds ratio (DOR), and superiority index. Fifteen studies including 1,369 neonates diagnosed of sepsis were included in this meta-analysis. For the detection of early-onset sepsis in neonates, the pooled sensitivity was 0.91 (95% CI: 0.81, 0.97; I2 = 0%, p = 0.946) and the pooled specificity was 0.98 (95% CI: 0.87, 0.97; I2 = 46.3%, p = 0.172) for IL-8. For the detection of late-onset sepsis in neonates. the sensitivity was 0.96 (95% CI: 0.85, 1.00; I2 = NA, p = NA) and the pooled specificity was 1.00 (95% CI: 0.92, 1.00; I2 = NA, p = NA) for IL-27. Results of ANOVA model revealed that the superiority index of IL-6, IL-8, IL-10, and IL-27 were 1.20 (0.14, 5.00), 5.14 (0.33, 7.00), 0.75 (0.14, 5.00), and 1.31 (0.14, 5.00) in the detection of early-onset neonatal sepsis. Superiority index of IL-8, IL-10, and IL-27 were 1.84 (0.20, 5.00), 1.04 (0.20, 5.00), and 2.21 (0.20, 5.00) in the detection of late-onset neonatal sepsis. Findings of this network meta-analysis suggest that interleukins including IL-6, IL-8, IL-10, and IL-27 may have favorable performance in the detection of neonatal sepsis. IL-8 was more accurate in the detection of early-onset sepsis in neonates. IL-27 was more accurate in the detection of late-onset neonatal sepsis.

How digital health translational research is prioritised: a qualitative stakeholder-driven approach to decision support evaluation

ObjectivesDigital health is now routinely being applied in clinical care, and with a variety of clinician-facing systems available, healthcare organisations are increasingly required to make decisions about technology implementation and...

Diagnostic suspicion bias and machine learning: Breaking the awareness deadlock for sepsis detection.

Many early warning algorithms are downstream of clinical evaluation and diagnostic testing, which means that they may not be useful when clinicians fail to suspect illness and fail to order appropriate tests. Depending on how such algorithms handle missing data, they could even indicate "low risk" simply because the testing data were never ordered. We considered predictive methodologies to identify sepsis at triage, before diagnostic tests are ordered, in a busy Emergency Department (ED). One algorithm used "bland clinical data" (data available at triage for nearly every patient). The second algorithm added three yes/no questions to be answered after the triage interview. Retrospectively, we studied adult patients from a single ED between 2014-16, separated into training (70%) and testing (30%) cohorts, and a final validation cohort of patients from four EDs between 2016-2018. Sepsis was defined per the Rhee criteria. Investigational predictors were demographics and triage vital signs (downloaded from the hospital EMR); past medical history; and the auxiliary queries (answered by chart reviewers who were blinded to all data except the triage note and initial HPI). We developed L2-regularized logistic regression models using a greedy forward feature selection. There were 1164, 499, and 784 patients in the training, testing, and validation cohorts, respectively. The bland clinical data model yielded ROC AUC's 0.78 (0.76-0.81) and 0.77 (0.73-0.81), for training and testing, respectively, and ranged from 0.74-0.79 in four hospital validation. The second model which included auxiliary queries yielded 0.84 (0.82-0.87) and 0.83 (0.79-0.86), and ranged from 0.78-0.83 in four hospital validation. The first algorithm did not require clinician input but yielded middling performance. The second showed a trend towards superior performance, though required additional user effort. These methods are alternatives to predictive algorithms downstream of clinical evaluation and diagnostic testing. For hospital early warning algorithms, consideration should be given to bias and usability of various methods.

Clinical value of neutrophil-to-lymphocyte ratio and prognostic nutritional index on prediction of occurrence and development of diabetic foot-induced sepsis.

Diabetic foot-induced sepsis is a serious complication associated with increased disability and mortality in hospitalized patients. Early prediction of admission and detection effectively improve treatment options and prevent further deterioration. This study aims to evaluate the clinical value of the neutrophil-to-lymphocyte ratio (NLR) and prognostic nutritional index (PNI) to predict the risk of sepsis in patients with diabetic foot ulcers (DFU). Retrospective analysis was performed on 216 patients who were admitted to the Fujian Medical University Union Hospital between January 2015 and December 2022. Patients with DFU were divided into the non-sepsis (n = 166) and the DFU-induced sepsis (n = 50) groups. The independent factors of DFU-induced sepsis were determined by univariate and multivariate logistic regression analyses. A receiver operating characteristic (ROC) curve was performed to compare the area under the curves (AUC) of PNI and NLR. Multivariate logistic regression analysis revealed that the PNI, NLR, international normalized ratio (INR), thrombin time (PT), and C-reactive protein (CRP) were independent prognostic factors for DFU-induced sepsis. After adjusting for potential confounders, the adjusted odds ratios of NLR for DFU-induced sepsis were 1.121 (1.072-1.172), 1.132 (1.077-1.189), and 1.080 (1.022-1.142), while those of PNI were 0.912 (0.873-0.953), 0.902 (0.856-0.950), and 1.004 (1.001-1.006). Moreover, the AUC of NLR was significantly greater than that of CRP (0.790, 95% CI: 0.689-0.891, p < 0.001 vs. 0.780, 95% CI: 0.686-0.873, p < 0.001). NLR and PNI have been regarded as readily and independently predictive markers in patients with DFU-induced sepsis. NLR is critical for the early detection and effective treatment of DFU-induced sepsis and is superior to CRP.

Simultaneous detection of C-reactive protein and lipopolysaccharide based on a dual-channel electrochemical biosensor for rapid Gram-typing of bacterial sepsis

Sepsis is a life-threatening multi-organ failure syndrome, with bacterial infections being the most common cause. Rapid Gram-typing is imperative to assist in antibiotic intervention. C-reactive protein (CRP) and lipopolysaccharide (LPS) are effective biomarkers for discerning the Gram type of bacteria but differ by several orders of magnitude in clinical detection, thereby impeding their simultaneous detection. And two independent methods are time-consuming and laborious. In this study, a dual-channel electrochemical biosensor was developed for simultaneous detection of LPS and CRP. Under optimal conditions, linear ranges of LPS (0.5–1000 pg/mL) and CRP (0.1–20 μg/mL) were obtained in line with the clinical evaluation scopes. In simulated sample tests, Gram-positive, Gram-negative, and healthy plasma samples were clearly distinguished by the developed biosensors, and these results were consistent with that of enzyme-linked immunosorbent assay (ELISA). In addition, the results of the plasma samples tested by the electrochemical biosensor matched those derived from blood cultures in the laboratory. Collectively, the electrochemical biosensor was expected to provide the scientific basis for the rapid Gram-typing and point-of-care detection of bacterial sepsis.

Monocyte distribution width as an early predictor of short-term outcome in adult patients with sepsis.

Monocyte distribution width (MDW) is a quantitative measurement of monocyte anisocytosis and has been proposed as an efficient marker for early sepsis detection. This study aimed to assess the prognostic potential of MDW in septic patients. In this study, a total of 252 adult septic patients were enrolled. Demographic, clinical, and laboratory finding including MDW and traditional inflammatory biomarkers detected at three time points (day 1, day 3 and day 6) after admission were collected and compared between 28-day survivors and non-survivors. Receiver operating characteristic (ROC) curves, Kaplan-Meier survival curve and Cox regression analyses were performed to assess and compare their predictive values. Group-based trajectory modeling was applied to identify MDW trajectory endotypes. Basic characteristics and 28-day outcomes were compared between the trajectories. ROC curve analysis showed that MDW levels measured on day 3 after admission (D3-MDW) had moderate prognostic value and was independently associated with 28-day mortality in patients with sepsis. A D3-MDW value of 26.20 allowed discrimination between survivors and non-survivors with a sensitivity of 77.8 % and a specificity of 67.6 %. However, the prognostic accuracy of D3-MDW was diminished in immune-compromised patients and patients who already received antibiotics before admission. Group-based trajectory modeling indicated that excessively elevated and delayed decreased MDW levels during the first week after admission inversely correlated with prognosis. MDW values detected on day 3 after admission and its kinetic change might be potential markers for predicting short-term outcome in adult septic patients.

Predictive Score for Dengue Infection with Complete Blood Count Parameters, Including the Monocyte Distribution Width: A Retrospective Single-Center Derivation and Validation Study.

Early detection of dengue virus infection will lead to proper management and reduction in morbidity/mortality. Monocyte distribution width (MDW) was recently approved for use in the early detection of sepsis. Because monocytes are involved in the innate immune system against viral infection, we sought to determine changes in MDW to develop and validate a new predictive score for dengue viral infection. This study included patients who presented with symptoms or signs related to dengue infection and who had a complete blood count and dengue investigation performed during September 2019 to May 2020. The proportion of dengue infection was 29.5% in the current study. The MDW was significantly higher in dengue infection (median, 29.7 versus 24.2; P < 0.001). We then randomly separated patients into training and validation cohorts. Independent predictive factors of dengue infection were white blood cells < 4 × 109/L (score 1), platelets < 100 × 109/L (score 1), and MDW > 24 (score 1). Clinical features were not significantly predictive of dengue infection. The areas under the receiver operating characteristic curve (95% CI) of the prognostic score were 0.839 (0.779-0.899) in the training cohort and 0.742 (0.674-0.811) in the validation cohort. With a cutoff score ≥ 1, the sensitivity and specificity of the scores were 92.2% and 40.8% in the training cohort and 88.9% and 44.1% in the validation cohort, respectively. We concluded that MDW increases with dengue infection and MDW could easily be incorporated in the predictive scores for dengue infection.

Validating the performance of 3 sepsis screening tools in patients with clinical chorioamnionitis

Maternal sepsis is a leading cause of maternal death in the United States. Approximately two-thirds of maternal deaths because of sepsis are related to delayed recognition or treatment. New early warning systems using a 2-step approach have been developed for the early recognition of sepsis in obstetrics; however, their performance has not been validated. This study aimed to assess the performance of 3 primary screening tools introduced by the Society of Obstetric Medicine Australia and New Zealand and the California Maternal Quality Care Collaborative for use in the first step of their 2-step early warning systems. The obstetrically modified quick Sequential (sepsis-related) Organ Failure Assessment score tool, the obstetrically modified Systemic Inflammatory Response Syndrome tool, and the obstetrically modified Systemic Inflammatory Response Syndrome 1 tool were evaluated for the early detection of sepsis in patients with clinically diagnosed chorioamnionitis. This was a retrospective cohort study using prospectively collected clinical data at a tertiary care center and an affiliated healthcare system. The electronic health records were searched to identify and verify cases with clinically diagnosed chorioamnionitis between November 2017 and September 2022. The flow sheet for every patient was reviewed to determine when criteria were met for any of the 3 tools. The performance of these tools was analyzed using their sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic curve for the identification of sepsis. There were 545 cases that had the requisite data for inclusion in the analysis. Of note, 11 patients met the criteria for sepsis. Both the obstetrically modified Systemic Inflammatory Response Syndrome and obstetrically modified Systemic Inflammatory Response Syndrome 1 tools had overall similar test characteristics, which were notably different from the obstetrically modified quick Sequential Organ Failure Assessment tool. The screen-positive rate of the obstetrically modified quick Sequential Organ Failure Assessment tool (1.5%; 95% confidence interval, 0.6%-2.9%) was lower than that of the obstetrically modified Systemic Inflammatory Response Syndrome tool (60.0%; 95% confidence interval, 55.7%-64.1%) and the obstetrically modified Systemic Inflammatory Response Syndrome 1 tool (50.0%; 95% confidence interval, 45.8%-54.3%). The sensitivities of the obstetrically modified Systemic Inflammatory Response Syndrome tool (100.0%; 95% confidence interval, 71.5%-100.0%) and the obstetrically modified Systemic Inflammatory Response Syndrome 1 tool (100.0%; 95% confidence interval, 71.5%-100.0%) were higher than that of the obstetrically modified quick Sequential Organ Failure Assessment tool (18.0%; 95% confidence interval, 2.3%-51.8%). All 3 tools had high negative predictive values; however, their positive predictive values were poor. This study demonstrated that all 3 tools had limitations in screening for sepsis among patients with a clinical diagnosis of chorioamnionitis. The obstetrically modified quick Sequential Organ Failure Assessment tool missed more than half of the sepsis cases and, thus, had poor performance as a primary screening tool for sepsis. Both the obstetrically modified Systemic Inflammatory Response Syndrome and obstetrically modified Systemic Inflammatory Response Syndrome 1 tools captured all sepsis cases; however, they tended to overdetect sepsis.

A-151 Evaluation of Monocyte Distribution Width as an Early Marker for Diagnosis of Sepsis

Abstract Background Since sepsis has a high mortality rate, early diagnosis and treatment can increase the survival rate and improve prognosis. However, it is difficult to diagnose sepsis accurately in actual clinical practice. Determination of Procalcitonin or C-reactive protein (CRP) levels, which are conventionally used for diagnosing sepsis, are time-consuming. Recent studies have shown that monocyte distribution width (MDW), which is observed simultaneously during the complete blood count (CBC)/diff test in Beckman Coulter DxH900, is a potential marker for the early detection of patients with or developing sepsis. This study evaluated MDW for early detection of sepsis in patients visiting the emergency department. Methods This study enrolled a total of 1167 patients (&amp;gt;18 and ≤80 years of age) who visited the emergency department of a single institution from August 4, 2020 to March 31, 2021, whose initial evaluation included CBC with differential tests and other blood tests (CRP and Procalcitonin). Samples for CBC and MDW were collected in K3-EDTA tubes and analyzed in a UniCel DxH 900 analyzer (Beckman Coulter, Inc., USA). Sepsis was diagnosed according to the sepsis-3 definition through a medical record review. All patients were grouped into no sepsis, sepsis, and septic shock. The diagnostic performance of MDW and other biomarkers for the detection of sepsis was evaluated through statistical analysis. The subgroup analysis for diagnostic performance was performed in subjects with malignancy or without malignancy. All statistical analyses were carried out using Analyze-it, and P &amp;lt; 0.05 was considered statistically significant. Results Of the 1167 patients enrolled, 156 patients (13.4%) were diagnosed with sepsis (135 (11.6%) sepsis and 21 (1.8%) septic shock). Among sepsis patients, those with positive culture results were 46.8%, and the rest either had negative culture results (43.6%) or did not proceed with culture testing (9.6%). The MDWs of the no sepsis group, sepsis group, and septic shock group were 19.70, 27.20, and 31.10, respectively, CRPs were 1.82 mg/dL, 8.84 mg/dL, and 20.44 mg/dL, and Procalcitonins were 0.19 ng/mL, 1.72 ng/mL, and 18.20 ng/mL (median, P &amp;lt; 0.0001). All biomarkers showed a higher value in the sepsis group compared to the no sepsis group. Median MDW showed an increasing trend in patients with malignancy and without malignancy. The AUC of MDW, WBC, CRP, and Procalcitonin for the prediction of sepsis were 0.869, 0.604, 0.773, and 0.868, respectively (P &amp;lt; 0.0001), thus MDW and Procalcitonin showed similar diagnostic performance. At the cutoff of MDW (21.5), the sensitivity was 91.0% and the negative predictive value (NPV) was 98%. The diagnostic performance of MDW showed similar results in cancer patients and cancer-negative patients (AUC 0.873 vs 0.869). Conclusions MDW, which is automatically measured in Beckman Coulter DxH900 hematology analyzer without the requirement of additional reagents during CBC/diff test, has high accuracy in detecting patients with sepsis and could be a reliable tool for early detection of patients with sepsis in the emergency department compared to results of culture or other biomarkers which take longer time to get the result. Furthermore, high NPV may help clinicians rule out sepsis.