# Factors influencing spontaneous hypothermia after emergency trauma and the construction of a predictive model

## TL;DR

This study identifies factors that lead to spontaneous hypothermia in trauma patients and builds a model to predict it, helping with prevention.

## Contribution

A novel predictive model for spontaneous hypothermia in trauma patients is developed with high accuracy.

## Key findings

- Trauma severity, posture, clothing dampness, and warming measures are significant risk factors for hypothermia.
- The predictive model achieved 84.29% accuracy with high sensitivity and specificity.
- Prothrombin time was lower in hypothermic patients compared to controls.

## Abstract

This study aimed to investigate spontaneous hypothermia among emergency trauma patients and develop a predictive model. A cohort of 162 emergency trauma patients was categorized into hypothermic (n = 61) and control (n = 101) groups, with trauma severity assessed using the modified Glasgow Coma Scale (GCS). Univariate analysis revealed significant differences between the groups in trauma severity, posture, garment wetness, warming measures, pre-hospital fluid resuscitation, and modified GCS scores (P < 0.05). The hypothermic group exhibited lower prothrombin time compared to the control group (P < 0.05). A logistic regression model was constructed, expressed as Y = 25.76 − 1.030X
1 + 0.725X
2 + 0.922X
3 − 0.750X
4 − 0.57X
6, and its fit was evaluated using the Hosmer–Lemeshow test. The receiver operating characteristic curve demonstrated an area under the curve of 0.871, with 81.2% sensitivity and 79.5% specificity. The Youden index identified the optimal predictive cut-off at its highest (0.58). Validation results included 86.21% sensitivity, 82.93% specificity, and 84.29% accuracy. Risk factors for spontaneous hypothermia after emergency trauma encompassed trauma severity, posture during consultation, clothing dampness upon admission, warming measures during transfer, pre-hospital fluid resuscitation, and modified GCS scores. The risk prediction model demonstrated high accuracy, enabling effective assessment of spontaneous hypothermia risk in emergency trauma patients and facilitating preventive measures.