Development and validation of a risk prediction model for multiple organ dysfunction syndrome secondary to severe heat stroke based on immediate assessment indicators on ICU admission.

INTRODUCTION

Early prediction of multiple organ dysfunction syndrome (MODS) secondary to severe heat stroke (SHS) is crucial for improving patient outcomes. This study aims to develop and validate a risk prediction model for those patients based on immediate assessment indicators on ICU admission.

METHODS

Two hundred eighty-four cases with SHS in our hospital between July 2009 and April 2024 were retrospectively reviewed, and categorized into non-MODS and MODS groups. Logistic regression analyses were performed to identify risk factors for MODS, and then to construct a risk prediction model, which was visualized by a nomogram. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow (HL) test, calibration curve, and decision curve analysis (DCA). Finally, the AUCs of the prediction model was compared with other scoring systems.

RESULTS

Acute gastrointestinal injury (AGI), heart rate (HR) >100 bpm, a decreased Glasgow Coma Scale (GCS) score, and elevated total bilirubin (TBil) within the first 24 h of ICU admission are identified as independent risk factors for the development of MODS in SHS patients. The model demonstrated good discriminative ability, and the AUC was 0.910 (95% : 0.856-0.965). Applying the predictive model to the internal validation dataset demonstrated good discrimination with an AUC of 0.933 (95% : 0.880-0.985) and good fit and calibration. The DCA of this model showed a superior clinical net benefit.

DISCUSSION

The risk prediction model based on AGI, HR, GCS, and TBil shows robust predictive performance and clinical utility, which could serve as a reference for assessing and screening the risk of MODS in SHS patients.