ArticleBMC emergency medicine2016
Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department.
Article in BMC emergency medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 7 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Artificial Intelligence-Based Predictive Modeling for Early Detection of Sepsis in Hospitalized Patients: A Systematic Review and Meta-Analysis.Critical care explorations · 2025Pooled it
- Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysis.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Pooled it
- APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for Clinical Decision Support.JAMA network open · 2023Pooled it
- Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- Randomized Controlled Trials of Artificial Intelligence in Clinical Practice: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Sepsis Alerts in Emergency Departments: A Systematic Review of Accuracy and Quality Measure Impact.The western journal of emergency medicine · 2020Pooled it
- Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy.Intensive care medicine · 2020Pooled it
- Challenges in early detection and prognostication of sepsis: new approaches from the emergency department and intensive care unit.EClinicalMedicine · 2026Review
- Artificial intelligence and robotic surgery in emergency gastrointestinal procedures: a review of current evidence and future directions.Journal of robotic surgery · 2026Review
- A novel, standardised approach to balancing effectiveness, efficiency and utility of surveillance AI prediction models for hospitalised patients using sepsis prediction as an exemplar.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Artificial intelligence for improving decision-making in bacterial infection management: a narrative review.The Journal of antimicrobial chemotherapy · 2026Review
- Review
- Validation of an artificial intelligence-based algorithm for predictive performance and risk stratification of sepsis using real-world data from hospitalised patients: a prospective observational study.BMJ health & care informatics · 2025Observational
- The role of artificial intelligence in sepsis in the Emergency Department: a narrative review.Annals of translational medicine · 2025Review
- Development and validation of an ensemble learning risk model for sepsis after abdominal surgery.Archives of medical science : AMS · 2025Article
- Review
- Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review.Journal of clinical medicine · 2023Review
- Implementation frameworks for end-to-end clinical AI: derivation of the SALIENT framework.Journal of the American Medical Informatics Association : JAMIA · 2023Review
- Identifying the Risk of Sepsis in Patients With Cancer Using Digital Health Care Records: Machine Learning-Based Approach.JMIR medical informatics · 2022Article
- Computerized Clinical Decision Support Systems for the Early Detection of Sepsis Among Adult Inpatients: Scoping Review.Journal of medical Internet research · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundSepsis is an often-fatal syndrome resulting from severe infection. Rapid identification and treatment are critical for septic patients. We therefore developed a probabilistic model to identify septic patients in the emergency department (ED). We aimed to produce a model that identifies 80 % of sepsis patients, with no more than 15 false positive alerts per day, within one hour of ED admission, using routine clinical data.
methodsWe developed the model using retrospective data for 132,748 ED encounters (549 septic), with manual chart review to confirm cases of severe sepsis or septic shock from January 2006 through December 2008. A naïve Bayes model was used to select model features, starting with clinician-proposed candidate variables, which were then used to calculate the probability of sepsis. We evaluated the accuracy of the resulting model in 93,733 ED encounters from April 2009 through June 2010.
resultsThe final model included mean blood pressure, temperature, age, heart rate, and white blood cell count. The area under the receiver operating characteristic curve (AUC) for the continuous predictor model was 0.953. The binary alert achieved 76.4 % sensitivity with a false positive rate of 4.7 %.
conclusionsWe developed and validated a probabilistic model to identify sepsis early in an ED encounter. Despite changes in process, organizational focus, and the H1N1 influenza pandemic, our model performed adequately in our validation cohort, suggesting that it will be generalizable.
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