目的 探讨急性Stanford A型主动脉夹层患者(acute type A aortic dissection,ATAAD)术后机械通气时间延长影响因素,构建预测模型并验证,为早期识别高危患者和制订干预措施提供参考。方法 回顾性分析2020年1月至2025年8月于山东省某三级甲等医院心血管外科就诊的765例ATAAD患者的病例资料,以7:3的比例分为建模组和验证组。采用Lasso回归筛选预测变量,Logistic回归确定独立影响因素并构建预测模型。采用Hosmer-Lemeshow 拟合优度检验、受试者工作特征(receiver operating characteristic,ROC)曲线下面积(area under curve,AUC)及临床决策曲线评估模型拟合优度及预测效能。结果 765例ATAAD患者术后机械通气时间延长发生率为26.54%。手术时长、深低温停循环时间、发病至手术时间、年龄是ATAAD患者术后机械通气时间延长的独立危险因素(均P<0.05)。建模组AUC为0.749,验证组AUC为0.779。ROC曲线、校准曲线和决策曲线验证模型预测效能较优。结论 构建的列线图预测模型具有良好的区分度和预测效能,可为临床评估机械通气时间延长风险提供参考。

Objective To explore the influencing factors of prolonged postoperative mechanical ventilation time in patients with acute Stanford type A aortic dissection (ATAAD),construct and validate a prediction model,so as to provide references for early identification of high-risk patients and formulation of intervention measures.Methods A retrospective analysis was conducted on the clinical data of 765 patients with ATAAD admitted to the Department of Cardiovascular Surgery of a tertiary A hospital in Shandong Province from January 2020 to August 2025.The patients were divided into the modeling group and the validation group at a ratio of 7:3.Lasso regression was used to screen predictive variables,and Logistic regression was adopted to identify independent influencing factors and establish the prediction model.The Hosmer-Lemeshow goodness-of-fit test,the area under the receiver operating characteristic (ROC) curve (AUC) and clinical decision curve analysis were used to evaluate the goodness of fit and predictive efficacy of the model.Results The incidence of prolonged postoperative mechanical ventilation time among 765 ATAAD patients was 26.54%.Operation duration,deep hypothermic circulatory arrest time,time from onset to operation,and age were independent risk factors for prolonged postoperative mechanical ventilation time in ATAAD patients (all P<0.05).The AUC of the modeling group was 0.749,and that of the validation group was 0.779.ROC curve,calibration curve and decision curve verified that the model had favorable predictive performance.Conclusions The constructed nomogram prediction model possesses good discrimination ability and predictive efficacy,which can provide a reference for clinical risk assessment of prolonged mechanical ventilation time.

R473