ArticleNPJ digital medicine2025
A methodological systematic review of validation and performance of sepsis real-time prediction models.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Impact of manual sepsis screening in hospitalized adult patients: A systematic review.Journal of hospital medicine · 2026Pooled it
- Explainable Retrospective Sepsis Classification Based on Elixhauser Comorbidity Groups Using Machine and Deep Learning on MIMIC-IV.Journal of clinical medicine · 2026Article
- Clinical Performance of BIO-S and BIO-SC Composite Bioscores for 28-Day Mortality Stratification in Adults with Sepsis and Septic Shock.Biomedicines · 2026Article
- Intelligent documentation in medical education: can AI replace manual case logging?JAMIA open · 2026Article
- Machine Learning Model for Sepsis Prediction in Prolonged and Chronic Critical Illness: Development and Validation Using Retrospective Real-World ICU Data.Journal of clinical medicine · 2026Article
- Advancing precision triage in strangulated small bowel obstruction: from static scores to dynamic multiparametric models.Frontiers in surgery · 2026Review
- Hyper-inflammation and immunosuppression: redefining sepsis therapy using modern approaches.Frontiers in pharmacology · 2026Review
- A 2004-2025 Bibliometric Study of Genetic Variation and Multiomics Biomarkers in Sepsis Based on 940 Publications.Human mutation · 2026Review
- Improving sepsis care with Artificial Intelligence. What would Illich say?Critical care science · 2026Article
- Harnessing artificial intelligence to address substance use disorders in critically ill adolescents: a synergistic approach.Annals of intensive care · 2025Article
- IL-10 to lymphocyte ratio (ILR) and lactate in the prognosis prediction and risk stratification of sepsis: a pilot study.Frontiers in medicine · 2025Article
- Antimicrobial resistance at a turning point: microbial drivers, one health, and global futures.Frontiers in microbiology · 2025Review
- Article
- Artificial intelligence-assisted phenotyping of sepsis: Research progress, clinical challenges, and translational prospects.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Sepsis real-time prediction models (SRPMs) provide timely alerts and may improve patient outcomes but face limited clinical adoption due to inconsistent validation methods and potential biases. Comprehensive evaluation, including external full-window validation with model- and outcome-level metrics, is crucial for real-world effectiveness, yet performance evidence remains scarce. This study systematically reviewed SRPM performance across validation methods, analyzing 91 studies from multiple databases. Only 54.9% applied full-window validation with both metric types. Performance decreased under external and full-window validation, with median AUROCs of 0.886 and 0.861 at 6- and 12-hours pre-onset, dropping to 0.783 in full-window external validation. Median Utility Scores declined from 0.381 in internal to -0.164 in external validation. Combining AUROC and Utility Score identified top-performing SRPMs in 18.7% of studies. Hand-crafted features significantly improved performance. Future research should focus on multi-center datasets, hand-crafted features, multi-metric full-window validation, and prospective trials to support clinical implementation.
Identifiers
What OpenQuestion holds
Registered trials
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.