Derivation of a model to predict mortality in urban patients with accidental hypothermia: a retrospective observational study

AIM: Accidental hypothermia in urban settings is associated with high mortality rates. However, the predictors of mortality remain under discussion. The purpose of this study was to evaluate prognostic factors and develop a prediction model in patients with accidental hypothermia in urban settings. METHODS: We retrospectively reviewed medical records in patients with hypothermia brought to our hospital by ambulance in a 7‐year study period. Patients’ records of survival discharge or in‐hospital death and clinical data were collected from medical records. We analyzed factors to predict in‐hospital death using multiple logistic regression analysis. Recursive partitioning analysis was used to construct a prediction model using predictors from multiple logistic regression analysis. RESULTS: In the study period, 192 patients were included in this study. Of them, 154 patients were discharged alive and 38 patients died. Multiple logistic regression analysis revealed that in‐hospital death was related to Glasgow Coma Scale (GCS) score, prothrombin time – international normalized ratio (PT‐INR) value, and fibrin degradation product (FDP). Recursive partitioning analysis revealed that patients with accidental hypothermia could be divided into four groups: very high risk (FDP ≥ 14 µg/mL, PT‐INR ≥ 1.4), high risk (FDP ≥ 14 µg/mL, PT‐INR < 1.4), moderate risk (FDP < 14 µg/mL, GCS < 10), and low risk (FDP < 14 µg/mL, GCS ≥ 10). CONCLUSION: High FDP and PT‐INR values and low GCS score on arrival at the emergency department were associated with in‐hospital mortality in urban patients with hypothermia. A simple prediction model for grouping risk was developed using these predictors.