aceP-WIN deployment: real-time personalized machine learning to predict critical deterioration in a pediatric intensive care unit
Early recognition of critical deterioration events (CDEs) in pediatric intensive care units (PICUs) is crucial to improving outcomes, but the real-time performance of machine-learning (ML)-based alert systems is rarely assessed. We prospectively evaluated the performance, operational utility, and deployment feasibility of the PICU Warning Index (P-WIN), a personalized ML alert system predicting CDE in the PICU. In this prospective cohort study at a quaternary-care children's hospital, P-WIN estimated deteriorati…
In a prospective PICU cohort, the P-WIN machine-learning alert system detected 73% of critical deterioration events 1-24 hours in advance, far more than the existing warning tool or clinician huddles.
The study was conducted at a single quaternary-care children's hospital over a three-month window, and the reported results cover detection and alert rates rather than patient outcomes.
Evidence
- Peer-reviewedJAMIA Open2026-10-19
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Truvace Impact Record TRV-2026-2127, v1: “P-WIN deployment: real-time personalized machine learning to predict critical deterioration in a pediatric intensive care unit.” Truvace, 2026-10-21. /record/TRV-2026-2127 (accessed at citation time). sha256 f13d62243f4fac1a…
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