The analysis of risk factors and the construction of prediction model for persistent acute kidney injury in heat stroke patients

Yu Ahong; Yin Bingling; Xu Chongxiao; Xiao Shenghua; Dong Jianguang; Lin Guodong

doi:10.3969/j.issn.1002-1949.2024.06.004

Abstract：Objective To analyze the risk factors and construct predictive model for persistent acute kidney injury(pAKI)in heat stroke patients.Methods A retrospective collection of 177 heat stroke patients admitted to the General Hospital of the Southern Theater Command of Chinese People's Liberation Army from January 2013 to December 2023 was conducted.Patients were divided into a pAKI group(n=70)and a control group(n=107)based on whether they had pAKI or not.The clinical characteristics of the two groups were compared,and the risk factors for pAKI in heat stroke patients were analyzed.Based on the relevant risk factors,a predictive model for the occurrence of pAKI in heat stroke patients was constructed by using R4.2.1 statistical software and validated by calibration curves,decision curves and clinical impact curves.Results There were significantly statistical differences in temperature,white blood cells,platelets,Glasgow coma scale(GCS),rhabdomyolysis,disseminated intravascular coagulation(DIC),creatinine,acute physiology and chronic health evaluation Ⅱ(APACHEⅡ),sequential organ failure assessment(SOFA),heart rate,mechanical ventilation,vasoactive drugs use(P＜0.05).Multivariate Logistic regression analysis showed that APACHEⅡ(AUC=0.854,95％CI 0.809-0.921)and DIC(AUC=0.744,95％CI 0.752-0.855)were independent risk factors for pAKI in the patients with heat stroke(P＜0.05).Based on APACHE Ⅱ score and DIC as variables,a pAKI nomogram model was successfully constructed,the validation results showed the model had good credibility and predictive value.Compared with the control group,the patients in the pAKI group had significantly higher in-hospital mortality and ICU hospitalization time(P＜0.05).Conclusions APACHE Ⅱ score and DIC are risk factors for severe heat stroke combined with pAKI,and this model can identify the patients at high risk of pAKI after heat stroke.