Early Detection Of Sepsis Research Articles

Early Detection of Late Onset Sepsis in Premature Infants Using Visibility Graph Analysis of Heart Rate Variability.

This study was designed to test the diagnostic value of visibility graph features derived from the heart rate time series to predict late onset sepsis (LOS) in preterm infants using machine learning. The heart rate variability (HRV) data was acquired from 49 premature newborns hospitalized in neonatal intensive care units (NICU). The LOS group consisted of patients who received more than five days of antibiotics, at least 72 hours after birth. The control group consisted of infants who did not receive antibiotics. HRV features in the days prior to the start of antibiotics (LOS group) or in a randomly selected period (control group) were compared against a baseline value calculated during a calibration period. After automatic feature selection, four machine learning algorithms were trained. All the tests were done using two variants of the feature set: one only included traditional HRV features, and the other additionally included visibility graph features. Performance was studied using area under the receiver operating characteristics curve (AUROC). The best performance for detecting LOS was obtained with logistic regression, using the feature set including visibility graph features, with AUROC of 87.7% during the six hours preceding the start of antibiotics, and with predictive potential (AUROC above 70%) as early as 42h before start of antibiotics. These results demonstrate the usefulness of introducing visibility graph indexes in HRV analysis for sepsis prediction in newborns. The method proposed the possibility of non-invasive, real-time monitoring of risk of LOS in a NICU setting.

Heparin Binding Protein as a Predictive Marker for Sepsis and Septic Shock in Critically Ill Patients: A Cross Sectional Study

Background: Sepsis is a serious inflammatory condition caused by infection, accompanied by high morbidity and mortality. An early effective diagnostic and predictive tool is needed to improve the management and prognosis of patients. Objectives: This study was aimed to assess the role of serum levels of Heparin Binding Proteinin early detection of sepsis and circulatory failure in critically ill patients, compared with the currently used biomarkers. Subjects and methods: This is a cross-sectional study of 66 patients with sepsis, carried out in emergency ICU and surgical ICU in Zagazig University Hospitals. Patients’ data were collected at admission, after 48 and 72 hours from medical records, bedside sheets, and radiographic reports. The concentration of heparin Binding Protein (HBP) is determined and compared to that of procalcitonin )PCT(, lactate, and total leucocytic count. Results: Median range level of HBP in sepsis (11.8, 9.8 and 8.083 ng/ml), septic shock (20, 22.6 and 19.25 ng/ml), and in uncomplicated infections (1.57, 0.9 and 7.3 ng/ml) on admission, after 48 and 72 hours respectively. HBP had 90.3% sensitivity, 62.9% specificity and 75.8% accuracy at cut of value ≥13.35 ng/ml AUC= 0.822, in diagnosis of septic shock on admission. Conclusion: serum heparin binding protein level is positively correlated with the severity of sepsis and septic shock and showed a good performance as an early diagnostic marker in patients with suspected sepsis and septic shock.

Determining procalcitonin at point-of-care; A method comparison study of four commercial PCT assays

ObjectiveProcalcitonin (PCT) testing adds value in the early detection of infection and sepsis, as well as in management of antibiotic therapy. We determined the analytical and diagnostic performance of four PCT assays at POC. MethodsPCT assays on AQT90 FLEX, Getein 1100, mLabs, and Finecare POC analyzers, in whole blood and plasma, were analyzed for repeatability, linearity, accuracy and concordance, by comparing with our reference PCT assay on the Cobas E602 system. ResultsFor all assays precision was found higher in plasma than in whole blood. AQT90 showed good performance in all analytical and diagnostic areas, irrespective of test matrix and PCT concentration. The other POC assays demonstrated at least one analytical weakness. The Getein assay showed adequate precision only in plasma at high PCT levels, the mLabs assay only in plasma at low PCT levels. Accuracy, as demonstrated by Bland-Altman and Passing-Bablok analysis, was found adequate only for the AQT90 FLEX and Getein 1100 assay. Diagnostic concordance at 0.5 ​ng/mL was found excellent for the AQT90 FLEX, Getein 1100, and Finecare assays, much lower for the mLabs assay. At 0.25 ​ng/mL, only the AQT90 FLEX and Finecare assays showed excellent concordance with the reference assay. ConclusionsThe AQT90 FLEX PCT assay demonstrated excellent analytical performance and diagnostic agreement with the Cobas E602 assay, allowing both stand-alone and side-by-side testing. The other assays demonstrated some analytical deficiencies, potentially limiting their diagnostic use. Any clinical use of PCT results should always be in combination with all other clinical signs and diagnostic information.

Comparison of the accuracy of some laboratory parameters with blood culture for early detection of neonatal sepsis at Zagazig university children hospital.

Comparison of the accuracy of some laboratory parameters with blood culture for early detection of neonatal sepsis at Zagazig university children hospital.

Multiple organ dysfunction syndrome: an extended patient care report

This report describes and reflects on the intensive nursing care provided to a patient with multiple organ dysfunction syndrome (MODS) and systemic inflammatory response syndrome (SIRS). Veterinary nurses are at the forefront of supportive care for these critical patients and nursing interventions should focus on close and frequent monitoring for early detection of sepsis. An understanding of the physiological processes that occur with these diseases can increase awareness of the potential life-threatening complications associated with MODS, thus, allowing early recognition and intervention to maximise successful patient outcomes.

Vasoactive-inotropic Score for Early Detection and Mortality Prediction of Sepsis in Children

BACKGROUND: Early detection and treatment of sepsis can prevent septic shock and reduce mortality rate. Troponin can become a prognostic factor in sepsis. However, not all health facilities are equipped to assess troponin levels. Vasoactive-inotropic score (VIS) is a simpler and more accessible method to describe hemodynamic status. The aim of this study was to assess the suitability of VIS score as early prognosis and mortality predictor of sepsisMETHODS: A retrospective study was conducted to determine the correlation between VIS and troponin levels for sepsis cases in Pediatric Intensive Care Unity (PICU) Haji Adam Malik Hospital, Medan from January 2018 to December 2019. VIS score at 48 hours, maximum VIS score, pediatric logistic organ dysfunction-2 (PELOD-2) score, cardiac troponin levels at 48 hours were taken from medical records.RESULTS: There were 54 samples analyzed. VIS scores were positively correlated (p<0.001) to troponin T and troponin I levels at 48 hours (r=0.670 and r=0.606, respectively). VIS at 48 hours and maximum VIS were related to mortality (p=0.001 and p<0.001, respectively). VIS score at 48 hours could be used as a predictive factor for mortality (area under the curve (AUC): 79.7%, p<0.001) with a cut-off point at 11 (74.4% sensitivity and 80% specificity). High VIS at 48 hours indicated poor outcomes of sepsis in children with odd ratio (OR) value: 1.99 (95% confidence interval (CI): 1.25-3.19).CONCLUSION: Vasoactive-inotropic score was suitable as an alternative to cardiac troponin T and troponin I levels at 48 hours to early detect myocardial dysfunctions and mortality in children.KEYWORDS: troponin, vasoactive-inotropic score, sepsis, children, mortality

Mean platelet volume to platelet count ratio as a predictor of mortality in septic patients

Background: Sepsis is a major cause of mortality among critically ill patients. The complex pathophysiology involves infection with systemic inflammatory response. Early detection of sepsis, helps not only is stratification of risk, but also for monitoring its progression and efficacy of therapeutic interventions. At present there are only few reliable prognostic tools to predict severity and mortality in sepsis. This study evaluates the usefulness of mean platelet value in prognosticating patient admitted with sepsis. Methods: We enrolled 200 patients prospectively, that have been admitted to our Medical ICU for sepsis over 2 years from 2018–2020. Changes in platelet indices, including mean platelet volume to platelet count ratio, were compared between survivors and non-survivors by using student’s t test. The prognostic value of mean platelet volume to platelet count ratio for 28-day mortality was ascertained by multivariate logistic regression. Results: Total 39 (19.5 %) patients expired within 28 days of ICU admission. MPV increased during the first 72 hours of hospital stay for both survivor and non survivors. Mean platelet volume to platelet count ratio was significantly higher in non survivors (P<0.001) as compared to survivors. In multivariate cox regression, mean platelet volume to platelet count ratio was an independent predictor of 28-day mortality, after adjusting for plausible confounders. Conclusions: Mean platelet volume to platelet count ratio is an independent risk factor for poor clinical outcomes. Hence monitoring mean platelet volume can prove as a simple tool to stratify the risk of mortality in septic patients.

English

BACKGROUND Sepsis remains an important cause of hospitalisation and mortality worldwide among patients admitted in intensive care units (ICU). Due to increasing mortality and morbidity of sepsis early diagnosis and treatment are essential. This can be achieved with the help of biomarkers–haematological and inflammatory i.e., C-reactive protein (CRP) and pro calcitonin (PCT). They are found to be highly sensitive and useful markers for early detection and management of sepsis. METHODS This is a cross-sectional study conducted from May 2019 to March 2020 in the Department of Biochemistry, ESI Hospital, Okhla. A total of 104 subjects were recruited for the study. Of them, 52 subjects were healthy controls, and 52 cases were ICU patients. Samples were taken to evaluate differential leucocyte count (DLC), red cell distribution width standard deviation (RDW SD), platelet count (PLT), absolute neutrophil count (ANC) and platelet crit as haematological markers whereas Creactive protein, fibrinogen and pro calcitonin indices were chosen as inflammatory. markers. RESULTS There was significant increase in the TLC (21583.51 ± 2240.36), total neutrophil count (7889.48 ± 1482.83), immature neutrophil (2.97 ± 1.67), fibrinogen levels (518.07 ± 66.74), CRP (0.048 ± 0.01) and pro calcitonin levels (0.062 ± 0.22) of the cases as compared to the healthy controls. The platelet count was observed to be higher in healthy subjects as compared to the patients in ICU. CONCLUSIONS The study results showed that clinical examination along with laboratory testing which include hematologic and inflammatory biomarkers is the most effective combination of parameters for rapidly and accurately predicting or diagnosing sepsis in a critically ill patient. KEY WORDS Sepsis, Haematological, Inflammatory, CRP, PCT, Fibrinogen

HeMA: A hierarchically enriched machine learning approach for managing false alarms in real time: A sepsis prediction case study

Early detection of sepsis can be life-saving. Machine learning models have shown great promise in early sepsis prediction when applied to patient physiological data in real-time. However, these existing models often under-perform in terms of positive predictive value, an important metric in clinical settings. This is especially the case when the models are applied to data with less than 50% sepsis prevalence, reflective of the incidence rate of sepsis on the floor or in the ICU. In this study, we develop HeMA, a hierarchically enriched machine learning approach for managing false alarms in real time, and conduct a case study for early sepsis prediction. Specifically, we develop a two-stage framework, where a first stage machine learning model is paired with statistical tests, particularly Kolmogorov-Smirnov tests, in the second stage, to predict whether a patient would develop sepsis. Compared with machine learning models alone, the framework results in an increase in specificity and positive predictive value, without compromising F1 score. In particular, the framework shows improved performance when applied to data with 50% and 25% sepsis prevalence, collected from a large hospital system in the US, resulting in up to 18% and 7% increase in specificity and positive predictive value, respectively. Despite the significant improvements observed, and although F1 score is not negatively affected, because of the up to 6% decrease in sensitivity, further improvements and pilot studies may be necessary before deploying the framework in a clinical setting. Finally, external validation conducted using a publicly available dataset produces similar results, validating that the proposed framework is generalizable.

Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective.

Sepsis is a major cause of death worldwide. Over the past years, prediction of clinically relevant events through machine learning models has gained particular attention. In the present perspective, we provide a brief, clinician-oriented vision on the following relevant aspects concerning the use of machine learning predictive models for the early detection of sepsis in the daily practice: (i) the controversy of sepsis definition and its influence on the development of prediction models; (ii) the choice and availability of input features; (iii) the measure of the model performance, the output, and their usefulness in the clinical practice. The increasing involvement of artificial intelligence and machine learning in health care cannot be disregarded, despite important pitfalls that should be always carefully taken into consideration. In the long run, a rigorous multidisciplinary approach to enrich our understanding in the application of machine learning techniques for the early recognition of sepsis may show potential to augment medical decision-making when facing this heterogeneous and complex syndrome.

Diagnostic performance of biomarkers in maternal sepsis: A prospective observational study.

Maternal sepsis is a life-threatening condition. Biomarkers have been found to be useful in early detection of sepsis in the critical care setting. We aimed to determine the diagnostic performance of different biomarkers such as procalcitonin, C-reactive protein (CRP), absolute eosinophil count, and activated partial thromboplastin time (aPTT) in maternal sepsis. A total of 35 patients were enrolled in this prospective observational study. Patients with suspected sepsis were evaluated for multi-organ dysfunction. The blood samples for testing of these biomarker levels were obtained at the time of enrollment in the study (day 1), and on day 3 and day 7. Trends of each marker were followed and correlated with the clinical picture. Of 35 enrolled patients, 30 completed the study. Among these, 18 had sepsis and 12 were designated as without sepsis. Sensitivities of procalcitonin, CRP, aPTT, and absolute eosinophil count were 83.33%, 77.78%, 55.56%, and 58.82% whereas their specificities were 66.67%, 75.0%, 100%, and 75%, respectively. Area under the curve was highest for procalcitonin (0.813) followed in decreasing order by CRP (0.778), aPTT (0.731), and eosinophil count (0.642), respectively. Procalcitonin and CRP may be used as a valuable adjunct in the clinical stepwise approach for the prompt diagnosis of maternal sepsis.

Superiority of Sepsis-3 to Sepsis-2 in the Early Detection of Severe Early Postoperative Sepsis After Living Donor Liver Transplantation

BackgroundTo date, the utility of Sepsis-3 compared to Sepsis-2 in living donor liver transplantation (LDLT) recipients has not been evaluated. We assessed the utility of Sepsis-3 compared to Sepsis-2 and verified the following hypotheses: 1. Sepsis-3–based sepsis (S3BS) corresponds to Sepsis-2–based severe sepsis (S2BSS), and 2. S3BS enables earlier diagnosis of early postoperative sepsis (within 21 postoperative days; EPoS) compared to S2BSS. MethodsWe evaluated 66 LDLT recipients in our institution. Patients with EPoS, who were diagnosed with S3BS and S2BSS, were extracted, and the postoperative day of diagnosing S3BS and S2BSS was identified. ResultsEPoS was diagnosed in 14 patients with S3BS (21.2%) and in 15 with S2BSS (22.7%). All but 1 patient with S2BSS corresponded to those with S3BS, with 98.4% overlap. Among the overlapping 14 patients, the comparison between the postoperative days when S3BS and S2BSS occurred demonstrated that S3BS was diagnosed earlier in 7 patients (50%) and on the same day in 4 (28.6%), and S2BSS was diagnosed earlier in 3 (21.4%). Especially in cases with a change in the sequential organ failure assessment (SOFA) immediately after S3BS onset compared to before (ΔSOFA) of ≥ 4 points (n = 6), S3BS was diagnosed earlier in 5 cases (83.3%); in cases with ΔSOFA of 2 to 3 points (n = 8), S3BS was diagnosed earlier only in 2 cases (25.0%). Thus, early diagnosis of S3BS was significantly more common in cases with ≥ 4 points of ΔSOFA (P = .02). ConclusionsS3BS nearly corresponds to S2BSS and can enable earlier detection of EPoS, especially with a high ΔSOFA.

Application of Critical Care Scores in Severely Burned Patients.

Early detection of sepsis is of crucial importance in patients with severe burn injuries. However, according to the S1-guideline, based on systemic inflammatory response syndrome (SIRS) criteria, the early diagnosis of sepsis in severely burned patients is difficult. The value of the new definition of sepsis based on sequential organ failure assessment (SOFA) according to S3-guidelines for patients with severe burn injuries is not described in detail in the literature. We analyzed retrospectively all data during the period 2014 to 2018 from the electronic patient information system. Using the receiver operating characteristic curve, the area under the curve was calculated for the diagnostic value of procalcitonin, SIRS, and SOFA score according to the burned total body surface area. Six hundred fifty-one patients with burn injuries were admitted to our burn unit, 315 of them had burn injuries affecting more than 10% body surface area with partial- to full-thickness burns (grade 2-4). In this group, 59 patients showed one or more septic events in the course of the intensive care treatment, defined by positive bloodstream infection. Both S1- and S3-guidelines were inappropriate to diagnose sepsis in patients with severe burn injuries. Due to pathophysiological changes of the body function in severely burned patients, which show features of both SIRS and pathological SOFA scores (>2 points) at the outset of burn injury, the diagnosis of sepsis associated with burns is intricate in this patient group. Assessing data for potential hallmarks of sepsis in burn patients we found procalcitonin to show a significant correlation with sepsis.

Association of ischemia modified albumin with mortality in qSOFA positive sepsis patients by sepsis-3 in the emergency department.

The early detection and treatment of sepsis and septic shock patients in emergency departments are critical. Ischemia modified albumin (IMA) is a biomarker produced by ischemia and oxygen free radicals which are related to the pathogenesis of sepsis-induced organ dysfunction. This study aimed to investigate whether IMA was associated with short-term mortality in quick sequential organ failure assessment (qSOFA)-positive sepsis or septic shock patients screened by the sepsis management program. From September 2019 to April 2020, patients who arrived at the emergency departments with qSOFA-positive sepsis or septic shock were included in this retrospective observational study. Among 124 patients analyzed, IMA was higher in the non-surviving group than in the surviving group (92.6±8.1 vs. 86.8±6.2U/mL, p<0.001). The area under the receiver operating characteristics curve was 0.703 (95% CI: 0.572-0.833, p<0.001). The optimal IMA cutoff was 90.45 (sensitivity 60.9%, specificity 79.2%). IMA values were independently associated with 28-day mortality in the multivariate Cox proportional hazard model (adjusted hazard ratio (aHR)=1.16, 95% CI: 1.06-1.27, p<0.01). In this study, we showed that IMA in the emergency departments was associated with 28-day mortality in qSOFA-positive sepsis and septic shock patients. Further studies are needed to evaluate the clinical value of IMA as a useful biomarker in large populations and multicenter institutions.

Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare

Sepsis is a leading cause of death in hospitals. Early prediction and diagnosis of sepsis, which is critical in reducing mortality, is challenging as many of its signs and symptoms are similar to other less critical conditions. We develop an artificial intelligence algorithm, SERA algorithm, which uses both structured data and unstructured clinical notes to predict and diagnose sepsis. We test this algorithm with independent, clinical notes and achieve high predictive accuracy 12 hours before the onset of sepsis (AUC 0.94, sensitivity 0.87 and specificity 0.87). We compare the SERA algorithm against physician predictions and show the algorithm’s potential to increase the early detection of sepsis by up to 32% and reduce false positives by up to 17%. Mining unstructured clinical notes is shown to improve the algorithm’s accuracy compared to using only clinical measures for early warning 12 to 48 hours before the onset of sepsis.

The role of Hematological Scoring System (HSS) in early diagnosis of Neonatal Sepsis

Background: Timely diagnosis of neonatal sepsis is critical. Early diagnosis of neonatal sepsis is still a great challenge. Objectives: To determine the significance of the hematological scoring system (HSS) for early detection of neonatal sepsis. Study design: A case control study done at the neonatal ICU, Al-Azhar Assiut university Hospital. It included sixty newborn infants divided into three groups (20 newborn infant for each one), septic, probably septic and control groups. Perinatal history with emphasis on risk factors for neonatal sepsis, Neonatal physical examination and laboratory investigation, including complete blood picture with differential, blood film, HSS (hematological scoring system) and blood culture. Results: HSS among septic group had a sensitivity (96%), specificity (90%), PPV (86.7%) and NPV (86%) and area under Receiver Operating Characteristic curve (ROC curve) with [95 % CI] was 0.76 (0.599 – 0.881). Immature/total WBCs ratio had a sensitivity (80.3%), specificity (78.2%), PPV (66.3%) and NPV (84.7%) in early detection of neonatal sepsis. Immature/mature WBCs ratio had a sensitivity (78.3%), specificity (66.5%), PPV (42%) and NPV (60%) in early detection of neonatal sepsis. Total leukocytic count in the septic group was statistically significantly increased compared with the corresponding values in the probable septic and control groups (P= 0.021*). Conclusions: HSS is a useful test to distinguish the infected from non-infected infants. HSS is a simple, quick, readily available effective tool with high sensitivity and specificity in the early diagnosis of neonatal sepsis.

Early detection of sepsis using artificial intelligence: a scoping review protocol

BackgroundSepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. To decrease the high case fatality rates and morbidity for sepsis and septic shock, there is a need to increase the accuracy of early detection of suspected sepsis in prehospital and emergency department settings. This may be achieved by developing risk prediction decision support systems based on artificial intelligence.MethodsThe overall aim of this scoping review is to summarize the literature on existing methods for early detection of sepsis using artificial intelligence. The review will be performed using the framework formulated by Arksey and O’Malley and further developed by Levac and colleagues. To identify primary studies and reviews that are suitable to answer our research questions, a comprehensive literature collection will be compiled by searching several sources. Constrictions regarding time and language will have to be implemented. Therefore, only studies published between 1 January 1990 and 31 December 2020 will be taken into consideration, and foreign language publications will not be considered, i.e., only papers with full text in English will be included. Databases/web search engines that will be used are PubMed, Web of Science Platform, Scopus, IEEE Xplore, Google Scholar, Cochrane Library, and ACM Digital Library. Furthermore, clinical studies that have completed patient recruitment and reported results found in the database ClinicalTrials.gov will be considered. The term artificial intelligence is viewed broadly, and a wide range of machine learning and mathematical models suitable as base for decision support will be evaluated. Two members of the team will test the framework on a sample of included studies to ensure that the coding framework is suitable and can be consistently applied. Analysis of collected data will provide a descriptive summary and thematic analysis. The reported results will convey knowledge about the state of current research and innovation for using artificial intelligence to detect sepsis in early phases of the medical care chain.Ethics and disseminationThe methodology used here is based on the use of publicly available information and does not need ethical approval. It aims at aiding further research towards digital solutions for disease detection and health innovation. Results will be extracted into a review report for submission to a peer-reviewed scientific journal. Results will be shared with relevant local and national authorities and disseminated in additional appropriate formats such as conferences, lectures, and press releases.

Quick Sequential Organ Failure Assessment Versus Systemic Inflammatory Response System in Early Detection of Sepsis in Emergency Department

Background: Sepsis is a life threating medical condition where infection leads to massive inflammatory response and eventually lead to organ dysfunction. It continues to pose a significant health threat despite remarkable developments in critical care medicine and extensive study of septic patients. Early recognition and treatment of sepsis in emergency department (ED) is important to reduce mortality, hospital length of stay and morbidity. Objective: This study was aimed to validate the performance of qSOFA scoring system and SIRS criteria in early sepsis diagnosis in the Emergency Department. Patient and method: This prospective observational clinical study was carried out in Emergency Department (ED) on 100 patients with suspected infection presented and admitted at o Mansoura University Emergency Hospital from February 2019 to February 2020. Patients were divided into two groups: infection group with qSOFA –ve criteria and sepsis group with qSOFA +ve group. We compare between qSOFA and SIRS scores in both groups. Results: The study demonstrated that organ dysfunction >2, 2ry to infection (according to sepsis definition by sepsis-3 task force) was more frequently reported among qSOFA + SIRS + group, compared to qSOFA - SIR – group. Moreover, both qSOFA and SIRS had comparable sensitivity (100%) in prediction of mortality within 1 week, while qSOFA demonstrated higher specificity (53.3%) in comparison with SIRS (20%). Both scores had comparable sensitivity for prediction ICU admission and of mechanical ventilation (86.67 and 88.9) whereas qSOFA demonstrated higher specificity than SIRS for ICU admission (94.29 versus 71.43) as well as mechanical ventilation (82.9 versus 63.41). Conclusion: It could be concluded that qSOFA is considered as specific not sensitive tool, while SIRS is more sensitive but not specific score for sepsis detection in emergency room.

To Catch A Killer: A Data-Driven Personalized and Compliance-Aware Sepsis Alert System

Sepsis affects more than 1.5 million people annually and contributes to as many as half of all hospital deaths in the United States. Early sepsis detection and timely treatment can significantly reduce sepsis-related mortality. For timely detection, healthcare providers increasingly leverage automated sepsis alerts. In this study, we develop an alert system that personalizes alerts to individual patients and accounts for caregivers' compliance behavior. Our alert system integrates predictive approaches with prescriptive ones in a Markov decision process framework to determine when a sepsis alert should be triggered. We characterize optimal alert policies that are easy to describe, follow, compute, and hence implement in the real world. We find that personalized alerts are essential for capturing the heterogeneity of sepsis risk among patients, while compliance-aware alerts are necessary when caregivers' compliance varies during a patient's hospital stay. Using data from a large hospital system in the U.S. with a typical alert implementation, we back test and validate our alert policy by evaluating its performance against that of the hospital system. On average, our alert system detects 22% more sepsis cases than the existing system. In cases for which both the existing system and ours triggered an alert, our system alerts, on average, 39 hours earlier (ranges 29-53 hours). This time difference matters, as every hour of delay in providing proper sepsis treatment can increase mortality by up to 8%. Our findings shed light on how and when personalization of alerts and incorporation of caregivers' behavior can improve sepsis-care quality. *The paper is available upon request from authors.

Towards early sepsis detection from measurements at the general ward through deep learning

BackgroundSepsis is one of the leading causes of death in the hospital. Several warning scores have been developed to categorize patients’ degrees of illness, with the purpose of recognizing sepsis onset at an early stage and consequently reducing time before starting treatment. The most accurate classification method, known as the SOFA score, is developed for use in the intensive care unit (ICU). ObjectiveSepsis is not exclusively developing in the ICU and may occur in any hospitalized patient. Therefore, a reliable method for sepsis recognition using routinely gathered information from the general ward is of major importance. MethodsRecently, the use of computational methods has been proposed for early sepsis prediction. Multiple sepsis classifiers have been devised using machine learning methods. We validated a linear classification model and improved upon it using a deep neural network trained on data from the MIMIC-III database. ResultsThe reference model approach yielded an Area Under Precision-Recall Curve (AUPRC) of 0.45 for a 3-h prediction time. Our newly constructed deep neural network outperformed the reference model, reaching an AUPRC of 0.49 for a 3-h prediction time. These results demonstrate to be robust for multiple prediction times, utilizing different datasets and multiple sepsis criteria. ConclusionsOur results are comparable to a high-resolution model, yet using only 8 simple and commonly performed measurements, instead of the complex set of dozens of measurements leveraged by other authors. Moreover, our method proves to adapt to different datasets and sepsis criteria, yielding a high accuracy. Therefore, sepsis onset prediction may also be viable in less monitored environments in the hospital, such as the general ward and the emergency room.