# Prognostic nomogram for heat stroke patients based on rapidly accessible clinical indicators

## TL;DR

This study created a quick and accurate tool to predict the risk of death in heat stroke patients using easily available clinical data, helping doctors make faster decisions in emergencies.

## Contribution

The study introduces a novel prognostic nomogram for heat stroke mortality prediction using rapidly accessible clinical indicators.

## Key findings

- The nomogram achieved an AUC of 0.794 in predicting in-hospital mortality in heat stroke patients.
- The model outperformed the SOFA score and was comparable to APACHE II in accuracy.
- Four key clinical indicators (Cr, GCS, OI, and FDP) were identified as independent predictors.

## Abstract

To develop and validate a rapid-assessment scoring system for predicting in-hospital mortality in heat stroke (HS) patients, thereby facilitating early identification and intervention for critical cases.

We conducted a retrospective cohort analysis of HS patients admitted to emergency department (ED) of 13 hospitals in southwest of China between July 1, 2022 and December 31, 2024. Clinical parameters including demographic data, initial vital signs, and major organ function biomarkers were systematically collected. Patients were further divided into a training cohort and a validation cohort at a 7:3 ratio. The primary endpoint was all-cause in-hospital mortality. Through rigorous variable selection using Least Absolute Shrinkage and Selection Operator (LASSO) regression followed by multivariable logistic regression modeling, we developed a prognostic nomogram. Model performance was assessed via receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA), and clinical impact curve (CIC) evaluation, with comparative benchmarking against established scoring systems [Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation II (APACHE II)].

A total of 307 patients were included in the study. 114 experienced in-hospital mortality, while 193 survived. Non-survivors exhibited significantly altered baseline values across multiple physiological domains: reduced Glasgow Coma Scale (GCS), impaired oxygenation index (OI), elevated fibrin degradation products (FDP), D-dimer, activated partial thromboplastin time (APTT), and serum creatinine (Cr) (all p < 0.0001). Through LASSO regression followed by multivariate logistic regression analysis, 27 initially significant variables were refined to four independent prognostic indicators: Cr, GCS, OI, and FDP. These predictors were subsequently integrated into a multivariate prognostic nomogram demonstrating discriminative capacity for mortality risk stratification in both training (AUC 0.811, 95% CI 0.751–0.871) and validation cohorts (AUC 0.766, 95% CI 0.706–0.826). DCA revealed superior net benefit across clinically relevant probability thresholds. The AUC of the nomogram in the entire cohort (0.794) was significantly superior to the SOFA score (0.703, DeLong’s test, p = 0.0008) and comparable to the APACHE II score (0.765, DeLong’s test, p = 0.3581).

We developed and validated a prognostic tool utilizing routinely available parameters in ED to predict in-hospital mortality in HS patients. This clinically implementable model demonstrates comparable accuracy to established intensive care scoring systems while offering distinct advantages in rapid bedside application, potentially enabling time-critical therapeutic decisions in emergency settings.