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. 2025 May 1;8(5):e2513149.
doi: 10.1001/jamanetworkopen.2025.13149.

Machine Learning for Predicting Critical Events Among Hospitalized Children

Sierra Strutz  1 , Huan Liang  1 , Kyle Carey  2 , Fereshteh Bashiri  3 , Priti Jani  4 , Emily Gilbert  5 , Julie L Fitzgerald  6 , Nicholas Kuehnel  7 , Maya Dewan  8 , L Nelson Sanchez-Pinto  9 , Dana Edelson  2 , Majid Afshar  1   3 , Matthew Churpek  1   3 , Anoop Mayampurath  1   3
Affiliations

Machine Learning for Predicting Critical Events Among Hospitalized Children

Sierra Strutz et al. JAMA Netw Open. .

Abstract

Importance: Unrecognized deterioration among hospitalized children is associated with a high risk of mortality and morbidity. The current approach to pediatric risk stratification is fragmented, as each hospital unit (emergency, ward, or intensive care) uses different tools for predicting specific outcomes.

Objective: To develop a machine learning model for the early detection of deterioration across all units, thereby enabling a unified risk assessment throughout the patient's hospital stay.

Design, setting, and participants: This retrospective cohort study used data from pediatric (age <18 years) admissions to inpatient and intensive care units at 3 tertiary care academic hospitals. Data were analyzed from January 2024 to March 2025.

Main outcomes and measures: The primary outcome was critical events, defined as invasive mechanical ventilation, administration of vasoactive medications, or death within 12 hours of an observation.

Results: The cohort included 135 621 patients (mean [SD] age, 7 [6] years; 60 376 [44.5%] female). Patient age, hospital unit, vital signs, laboratory results, and prior comorbidities were used to derive a regression-based model, an extreme gradient-boosted machine (XGB) model, and 2 deep learning models. Data from 2 hospitals were used as a derivation cohort, while patients in the third hospital constituted the hold-out external test cohort. The XGB model was the best-performing machine learning model, outperforming 2 existing ward-focused models in terms of discrimination (C statistic: XGB, 0.86; ward-focused models, 0.82 [P < .001] and 0.70 [P < .001]) and the number needed to alert (at an example 80% sensitivity: XGB, 6 ward-focused models: 9 and 11). The deep learning models did not exhibit improved performance. The XGB model performed better or equivalent to models trained for a specific hospital unit.

Conclusions and relevance: This retrospective cohort study describes the development of a novel hospitalwide model for continuously predicting the risk of critical events through the entirety of a child's stay. The model facilitated a unified framework for risk assessment in a pediatric hospital.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Gilbert reported receiving grants from the National Institutes of Health (NIH) during the conduct of the study. Dr Sanchez-Pinto reported receiving grants from the NIH during the conduct of the study. Dr Edelson reported receiving personal fees from AgileMD in the form of equity and employment outside the submitted work; in addition, Dr Edelson had a patent for US-11410777-B2 issued to the University of Chicago. Dr Churpek reported receiving grants from National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, and Department of Defense outside the submitted work; in addition, Dr Churpek had a patent for a machine learning model (eCART) issued to the University of Chicago received royalties from the University of Chicago related to the eCART patent. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. The Top 20 Most Important Variables, as Assessed Using Information Gain, From the pCREST Model for Predicting Critical Events
Mental status was measured using the alert-verbal-pain-unresponsive (AVPU) scale.
Figure 2.
Figure 2.. The Number Needed to Alert for Various Sensitivity Values for the Pediatric Critical Event Risk Evaluation and Scoring Tool (pCREST), Pediatric Calculated Assessment of Risk and Triage (pCART), and Bedside Pediatric Early Warning System (Bedside PEWS) in the External Test Cohort at University of Wisconsin-Madison
See this image and copyright information in PMC

Comment in

  • doi: 10.1001/jamanetworkopen.2025.13156

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