Development and evaluation of a predictive nomogram for survival in heat stroke patients: a retrospective cohort study

BACKGROUND: This study aimed to establish an effective nomogram to predict the survival of heat stroke (HS) based on risk factors. METHODS: This was a retrospective, observational multicenter cohort study. We analyzed patients diagnosed with HS, who were treated between May 1 and September 30, 2018 at 15 tertiary hospitals from 11 cities in Northern China. RESULTS: Among the 175 patients, 32 patients (18.29%) died before hospital discharge. After the univariate analysis, mechanical ventilation, initial mean arterial pressure <70 mmHg, maximum heart rate, lab results on day 1 (white blood cell count, alanine aminotransferase, creatinine), and Glasgow admission prediction score were included in multivariate analysis. Multivariate Cox regression showed that invasive ventilation, initial mean arterial pressure <70 mmHg (1 mmHg=0.133 kPa), and Glasgow admission prediction score were independent risk factors for HS. The nomogram was established for predicting 7-d and 14-d survival in the training cohort. The nomogram exhibited a concordance index (C-index) of 0.880 (95% confidence interval [95% CI] 0.831-0.930) by bootstrapping validation (B=1,000). Furthermore, the nomogram performed better when predicting 14-d survival, compared to 7-d survival. The prognostic index cut-off value was set at 2.085, according to the operating characteristic curve for overall survival prediction. The model showed good calibration ability in the internal and external validation datasets. CONCLUSION: A novel nomogram, integrated with prognostic factors, was proposed; it was highly predictive of the survival in HS patients.