Abstract
BACKGROUND: One of the challenges of physical training in extreme condition is frostbite, especially in Northeast China. In this study, we aimed to construct a risk prediction model for frostbite among soldiers in Northeast China, and verify its effect. METHODS: 698 participants were selected via convenience sampling from Northeast China from December 2021 to January 2022 (winter). They were randomly divided into a training set (N = 479) and a testing set (N = 202) in a ratio of 7:3. All participants completed a researcher-made questionnaire on frostbite. The prediction model was constructed through the use of Logistic regression analysis, which was used to predict the independent risk factors for frostbite formation and screen significant indicators. The model's performance was assessed using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA) to evaluate the prediction efficiency and goodness of fit. RESULTS: The incidence of frostbite in the training set was 19.83% (95 people), all of which were first-degree frostbite. Among them, frostbite in multiple parts was the most common (58.95%), followed by singular body parts like hands (24.21%), ears (11.58%) and feet (5.26%). Single factor logistic regression analyses showed that ambient temperature, ambient wind speed, outdoor stationary time, stationary status, and history of frostbite are independent risk factors that affect the occurrence of frostbite. Furthermore, we constructed the frostbite risk prediction model for soldiers in the northeastern region of China. The area under the receiver operating characteristic curve (AUC) for the risk of frostbite in the training set and testing set was 0.816 (95% CI, 0.770 ~ 0.862) and 0.787 (95% CI, 0.713 ~ 0.860), respectively. The Hosmer-Lemeshow test of the model showed χ(2) = 11.328 and P = 0.184 (> 0.05). The DCA curve indicated that most of the clinical net benefits of the model are greater than 0, demonstrating good clinical usefulness. CONCLUSION: The constructed frostbite prediction model can effectively identify soldiers with a higher risk of frostbite. It provided theoretical support for commanders to take preventive measures to reduce the incidence of frostbite among soldiers and was of great clinical guiding significance.