Objective:To establish an early warning model to assess the mortality risk of patients with heat stroke disease.Methods:The case data of patients diagnosed with heat stroke disease admitted to the comprehensive ICU of Shanshan County from January 2016 to December 2020 were selected. According to the short-term outcome (28 days) of patients, they were divided into death group (20 cases) and survival group (53 cases) . The relevant indicators with statistically significant differences between groups within 24 hours after admission were selected. By drawing the subject work curve (ROC) and calculating the area under the curve, the relevant indicators with the area under the curve greater than 0.7 were selected, Fisher discriminant analysis was used to establish an assessment model for the death risk of heat stroke disease. The data of heat stroke patients from January 1, 2021 to December 2022 in the comprehensive ICU of Shanshan County were collected for external verification. Results There were significant differences in age, cystatin C, procalcitonin, platelet count, CKMB, CK, CREA, PT, TT, APTT, heart rate, respiratory rate and GLS score among the groups. Cystatin C, CKMB, CREA, PT, TT, heart rate AUC area at admission was greater than 0.7. Fisher analysis method is used to build a functional model.Results:The diagnostic sensitivity, specificity and AUC area of the functional model were 95%, 83% and 0.937 respectively. The external validation results showed that the accuracy of predicting survival group was 85.71%, the accuracy of predicting death group was 88.89%.Conclusion:The early warning model of heat stroke death constructed by ROC curve analysis and Fisher discriminant analysis can provide objective reference for early intervention of heat stroke.

Objective:To establish an early warning model to assess the mortality risk of patients with heat stroke disease.Methods:The case data of patients diagnosed with heat stroke disease admitted to the comprehensive ICU of Shanshan County from January 2016 to December 2020 were selected. According to the short-term outcome (28 days) of patients, they were divided into death group (20 cases) and survival group (53 cases) . The relevant indicators with statistically significant differences between groups within 24 hours after admission were selected. By drawing the subject work curve (ROC) and calculating the area under the curve, the relevant indicators with the area under the curve greater than 0.7 were selected, Fisher discriminant analysis was used to establish an assessment model for the death risk of heat stroke disease. The data of heat stroke patients from January 1, 2021 to December 2022 in the comprehensive ICU of Shanshan County were collected for external verification. Results There were significant differences in age, cystatin C, procalcitonin, platelet count, CKMB, CK, CREA, PT, TT, APTT, heart rate, respiratory rate and GLS score among the groups. Cystatin C, CKMB, CREA, PT, TT, heart rate AUC area at admission was greater than 0.7. Fisher analysis method is used to build a functional model.Results:The diagnostic sensitivity, specificity and AUC area of the functional model were 95%, 83% and 0.937 respectively. The external validation results showed that the accuracy of predicting survival group was 85.71%, the accuracy of predicting death group was 88.89%.Conclusion:The early warning model of heat stroke death constructed by ROC curve analysis and Fisher discriminant analysis can provide objective reference for early intervention of heat stroke.