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.
目的： 建立早期评估热射病患者死亡风险的预警模型。 方法： 于2022年6月，选取2016年1月至2020年12月鄯善县人民医院重症加强护理病房（ICU）收治的确诊为热射病的患者病例资料，根据患者短期结局（28 d），分为死亡组（20例）和幸存组（53例），选取患者入院后24 h内组间差异有统计学意义的相关指标，通过绘制受试者工作曲线（ROC）并计算曲线下面积，选取曲线下面积>0.7的相关指标，并采用Fisher判别分析建立热射病死亡风险的评估模型。收集2021年1月1日至2022年12月鄯善县综合ICU热射病患者数据进行外部验证。 结果： 年龄、胱抑素C、降钙素原测定、血小板计数、肌酸激酶同工酶（CKMB）、肌酸激酶（CK）、肌酐（CREA）、凝血酶原时间（PT）、凝血酶时间（TT）、活化部分凝血酶时间（APTT）、心率、呼吸频率、GLS评分组间差异有统计学意义（P<0.05）。胱抑素C、CKMB、CREA、PT、TT、入院时心率AUC面积>0.7。采用Fisher分析方法构建函数模型。通过回代性检验，该函数模型诊断的灵敏度为95%，特异度为83%，AUC面积为0.937。外部验证结果为预测幸存组准确率为85.71%，预测死亡组准确率为88.89%。 结论： 应用ROC曲线分析及Fisher判别分析构建的热射病死亡早期预警模型，可以为热射病早期干预提供客观参考依据。.
Keywords: Death risk; Early warning model; Fisher discriminant analysis; Heat stroke disease; Multiple organ dysfunction.