ArticleBMC medical informatics and decision making2020
Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 4 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 4 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- AI-Based Sepsis Prediction in Hospitalized Adults: Systematic Review, Subgroup Meta-Analysis, and Contextual Analysis of Clinical Burden.Journal of medical Internet research · 2026Pooled it
- Revolutionizing sepsis diagnosis using machine learning and deep learning models: a systematic literature review.BMC infectious diseases · 2025Pooled it
- Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.Frontiers in medicine · 2024Pooled 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
- Interpretable Machine Learning Model for Fungal Infection Prediction: A Real-World Study.Health care science · 2026Article
- 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
- A methodological systematic review of validation and performance of sepsis real-time prediction models.NPJ digital medicine · 2025Article
- Be-SNAP: the Belgian Sepsis National Action Plan.Frontiers in public health · 2025Article
- The Synergy of Machine Learning and Epidemiology in Addressing Carbapenem Resistance: A Comprehensive Review.Antibiotics (Basel, Switzerland) · 2024Review
- Digital health delivery in respiratory medicine: adjunct, replacement or cause for division?European respiratory review : an official journal of the European Respiratory Society · 2024Review
- Parsimonious Waveform-derived Features consisting of Pulse Arrival Time and Heart Rate Variability Predicts the Onset of Septic Shock.Biomedical signal processing and control · 2024Article
- Machine Learning Approach with Harmonized Multinational Datasets for Enhanced Prediction of Hypothyroidism in Patients with Type 2 Diabetes.Diagnostics (Basel, Switzerland) · 2024Article
- Machine Learning Approach for Improved Longitudinal Prediction of Progression from Mild Cognitive Impairment to Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2023Article
- Application Prospect of the SOFA Score and Related Modification Research Progress in Sepsis.Journal of clinical medicine · 2023Review
- Artificial intelligence in perioperative medicine: a narrative review.Korean journal of anesthesiology · 2022Review
- Evaluating machine learning models for sepsis prediction: A systematic review of methodologies.iScience · 2022Article
- Machine Learning Model to Identify Sepsis Patients in the Emergency Department: Algorithm Development and Validation.Journal of personalized medicine · 2021Article
- Machine Learning and Syncope Management in the ED: The Future Is Coming.Medicina (Kaunas, Lithuania) · 2021Review
- Trends in US Pediatric Hospital Admissions in 2020 Compared With the Decade Before the COVID-19 Pandemic.JAMA network open · 2021Article
- Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective.Frontiers in medicine · 2021Article
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundSevere sepsis and septic shock are among the leading causes of death in the United States and sepsis remains one of the most expensive conditions to diagnose and treat. Accurate early diagnosis and treatment can reduce the risk of adverse patient outcomes, but the efficacy of traditional rule-based screening methods is limited. The purpose of this study was to develop and validate a machine learning algorithm (MLA) for severe sepsis prediction up to 48 h before onset using a diverse patient dataset.
methodsRetrospective analysis was performed on datasets composed of de-identified electronic health records collected between 2001 and 2017, including 510,497 inpatient and emergency encounters from 461 health centers collected between 2001 and 2015, and 20,647 inpatient and emergency encounters collected in 2017 from a community hospital. MLA performance was compared to commonly used disease severity scoring systems and was evaluated at 0, 4, 6, 12, 24, and 48 h prior to severe sepsis onset.
results270,438 patients were included in analysis. At time of onset, the MLA demonstrated an AUROC of 0.931 (95% CI 0.914, 0.948) and a diagnostic odds ratio (DOR) of 53.105 on a testing dataset, exceeding MEWS (0.725, P < .001; DOR 4.358), SOFA (0.716; P < .001; DOR 3.720), and SIRS (0.655; P < .001; DOR 3.290). For prediction 48 h prior to onset, the MLA achieved an AUROC of 0.827 (95% CI 0.806, 0.848) on a testing dataset. On an external validation dataset, the MLA achieved an AUROC of 0.948 (95% CI 0.942, 0.954) at the time of onset, and 0.752 at 48 h prior to onset.
conclusionsThe MLA accurately predicts severe sepsis onset up to 48 h in advance using only readily available vital signs extracted from the existing patient electronic health records. Relevant implications for clinical practice include improved patient outcomes from early severe sepsis detection and treatment.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.