重症中暑的早期预警因素分析
Analysis of early warning factors in severe heat stroke patients

张友平.重症中暑的早期预警因素分析[J].内科急危重症杂志,2019,25(1):35-37

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DOI：10.11768/nkjwzzzz20190111

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作者	单位	E-mail
张友平	华中科技大学同济医学院附属同济医院	fangmhtj@163.com

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中文摘要:

目的:探索重症中暑的早期预警因素。方法：收集21例重症中暑患者的临床资料和APACHEⅡ评分数据。根据预后将患者分为好转组（10例）和恶化组（11例）。运用Logistic回归分析建立预测重症中暑不良预后的模型。通过描绘ROC曲线，比较预测模型与APACHEⅡ评分在预测重症中暑不良预后的效能。结果：机械通气（P=0.038）和血气pH（P=0.059）与重症中暑的结局独立相关，预测模型方程为：Y=186.976+5.505×机械通气-25.073×pH。预测模型AUC为0.955 (95% CI=0.874~1.000)， APACHEⅡ评分AUC0.818 (95% CI=0.613~1.000)。结论：该预测模型在预测重症中暑不良结局上优于APACHEⅡ评分，重症中暑早期需要机械通气，血气pH<7.3与不良预后密切相关。

英文摘要:

Objective: To explore early warning factors in severe heat stroke patients. Methods: The consecutive patients diagnosed as having severe heat stroke patients were studied. Those patients were divided into recovery group and deterioration group according to their outcome. Detailed clinical characteristics and Acute Physiology and Chronic Health Score (APACHEⅡ) were collected and analyzed. We established the forecasting model to predict the poor outcome of severe heat stroke patients by Multi-Logistic regression analysis. By describing receiver-operating characteristic (ROC) curves, we compared the forecasting model with APACHEⅡ in the prediction of severe heat stroke patients' poor outcome. Results: Mechanical ventilation (MV) and blood potential of hydrogen (PH) were found to be independently associated with poor outcome of severe heat stroke patients. Logistic regression equation (i.e. prediction model) was: 181.471+5.505×MV-25.073×pH. The area under the ROC curves (AUC) of the prediction model and APACHEⅡ was 0.955 (95% CI=0.874-1.000) and 0.881 (95% CI=0.613-1.000), respectively. Conclusion: Our forecasting model was much better than APACHEⅡ in predicting poor outcome of severe heat stroke patients. MV requiring and pH <7.3 in the early stage of heat stroke were closely associated with poor outcome.