Development and validation of the predictive scoring system for 90-day mortality in critical ill patients with exertional heatstroke.

Abstract

Purpose: Despite rising incidence, exertional heatstroke (EHS) lacks validated prognostic scoring tools. This study aimed to developed and validated a 90-day prognostic model for EHS patients.

Methods: We conducted a retrospective cohort study of patients with EHS. Logistic regression analysis was utilized to identify the risk predictors associated with 90-day mortality. Using the mathematical transformation principle, the regression coefficients of each risk predictor were reassigned to develop a practical predictive scoring system. In this study, the predictive capability of the scoring model was validated via ROC curve analysis (AUC-based risk stratification), with model calibration further confirmed by the Hosmer-Lemeshow test.

Results: Among 273 EHS patients in this cohort, 24 (8.8%) experienced 90-day mortality. Logistic regression analysis revealed acute kidney injury (AKI), prolonged activated partial thromboplastin time (APTT), and low fibrinogen as independent risk predictors. A scoring system (0-5 points) was developed by reassigning each predictor according to the logistic regression coefficient: AKI 3 points, prolonged APTT (≥47 s) 1 point, and fibrinogen (<2 g/L) 1 point. Internal validation using 1000 bootstrapping samples demonstrated that the scoring system had a relatively high discriminative ability, with a C-index of 0.90 (95% CI: 0.90-0.93). Using receiver operating characteristic curve analysis, the composite index incorporating these three risk predictors demonstrated a sensitivity of 78.3% and specificity of 89.9% in predicting 90-day mortality (area under the curve: 0.90; 95% confidence interval (CI): 0.81-0.98; p < 0.001).

Conclusions: A predictive scoring system based on AKI, APTT, and fibrinogen can help predict the risk of 90-day mortality in patients with EHS.