ArticleScientific reports2026
Development and external validation of a machine learning-based Cox model for predicting in-hospital survival in classic heatstroke: a multicenter retrospective study.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Classical heatstroke (CHS) is a life-threatening condition necessitating accurate prognostic tools for risk stratification. The potential of machine learning to predict clinical outcomes in CHS remains largely unexplored. Our objective was to develop and externally validate machine learning-based Cox model for predicting survival in hospitalized CHS patients. This retrospective multicenter study analyzed data from 538 CHS patients admitted to eight hospitals in western China between June 2022 and September 2023, with four institutions constituting the training cohort and four geographically distinct hospitals forming the external validation cohort. An innovative machine learning framework integrating nine algorithms with 54 combinatorial implementations was employed to identify robust prognostic features. Significant predictors from the optimal algorithm combination were incorporated into a multivariable Cox regression model, visualized as a clinical nomogram. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and calibration curves, while survival differences were assessed with Kaplan-Meier analysis. The least absolute shrinkage and selection operator (Lasso) + supervised principal components (SuperPC) combination yielded the highest C-indexes (0.921 training; 0.811 validation). The nomogram achieved AUCs of 0.91, 0.85, and 0.86 for 10-, 20-, and 30-day survival in the training set, and 0.710, 0.80, and 0.80 in external validation. Calibration curves indicated strong agreement between predicted and observed 10-day survival probabilities, and Kaplan-Meier analyses confirmed significant survival stratification between risk groups. We developed and validated an accurate machine learning integration-based prognostic tool for CHS inpatients. This approach supports personalized clinical decision-making and offers new avenues for intelligent prognosis monitoring in critical care.
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