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Item refines Peer-reviewed Added

NEWS early warning score and machine learning give similar net benefit for 24 h mortality

A gradient-boosting model flagged at most 1·9 more 24 h deaths per 10 000 patients than NEWS, in Danish hospitals.

This retrospective cohort compared the National Early Warning Score (NEWS) with simplified NEWS, a demographic score and a gradient-boosting model. It covered 2·08 million adult encounters at 15 Danish hospitals, 2018–2023, with 24 h mortality as the outcome. Treatment within 24 h, such as ICU admission or an operation, can avert predicted deaths and bias validation. This intervention bias was handled by setting treated encounters aside and weighting the rest by propensity scores. Overall, the model added at most 1·9 correct identifications per 10 000 over NEWS, and NEWS 1·5 over simplified NEWS. The design is retrospective, and unmeasured factors behind treatment decisions may bias the weighted estimates.

Why it is interesting: Most validations report discrimination, but this one counts extra deaths correctly flagged. The gain over NEWS was under 2 per 10 000 overall, but 78·1 when initial NEWS was 7 or more.

Source
The Lancet Digital Health, 5 November 2026
Paper
Development and validation of early warning scores predicting 24 h mortality addressing intercurrent medical interventions in Denmark: a multicentre retrospective cohort study of 2·08 million hospital encounters
Authors
Katsiferis A, Scheidwasser N, Nguyen TL, Lange T, Khurana MP, Nielsen PB et al.
DOI
10.1016/j.landig.2026.101067
Full text
Open copy (submitted version, before peer review, repository)
Type
Peer-reviewed article
Design
Multicentre retrospective cohort, 15 Danish hospitals, 2,080,765 encounters / 825,200 patients, Oct 2018–Oct 2023; propensity-weighted net benefit, AUROC, calibration
Verdict
Refines prior evidence
Driver
AI in medicine

Other drivers in Data, models and systems