摘要
目的:探讨肺部手术重症患者出现术后心房颤动(POAF)的危险因素并构建列线图预测模型。方法:纳入首都医科大学附属北京朝阳医院重症医学科2018年1月至2021年12月收治的接受肺部手术患者213例。根据术后7天内是否出现POAF分为POAF组(42例)和非POAF组(171例)。采用 logistic回归分析患者出现POAF的危险因素,并根据危险因素构建列线图预测模型。 结果:POAF的发生率为19.7%。二尖瓣反流( OR=4.270,95% CI:1.380~13.213, P=0.012)、术中使用西地兰( OR=14.619,95% CI:2.913~73.373, P=0.001)、术中使用儿茶酚胺( OR=3.244,95% CI:1.144~9.203, P=0.027)、心包切开( OR=6.079,95% CI:1.362~27.128, P=0.009)及系统淋巴结清扫( OR=5.460,95% CI:1.770~16.846, P=0.003)是肺部手术重症患者术后7天内出现POAF的独立危险因素。基于上危险因素构建POAF的列线图, ROC曲线下面积为0.801(95% CI:0.721~0.881, P<0.001)。 结论:肺部手术重症患者术后7天内出现POAF的危险因素包括二尖瓣反流、术中应用西地兰、术中使用儿茶酚胺类药物、心包切开、系统淋巴结清扫。据此构建的列线图预测模型可以量化患者出现POAF的风险,预测能力优于既往的评分系统。
ObjectiveTo identify the risk factors of postoperative atrial fibrillation(POAF)in critically ill lung surgery patients and establish a nomogram.Methods213 critically ill lung surgery patients were collected in Beijing Chaoyang Hospital from January 2018 to December 2021.Logistic analysis was used to analyze the risk factors of POAF.A nomogram was developed based on the verified risk factors.ResultsThe independent risk factors associated with POAF was mitral regurgitation(OR=4.270,95%CI:1.380-13.213,P=0.012),cedilanid(OR=14.619,95%CI:2.913-73.373,P=0.001),catecholamine(OR=3.244,95%CI:1.144-9.203,P=0.027),pericardiotomy(OR=6.079,95%CI:1.362-27.128,P=0.009),systematic lymph node dissection(OR=5.460,95%CI:1.770-16.846,P=0.003).Nomogram model showed the ROC was 0.801(95%CI:0.721-0.881,P<0.001).ConclusionThe risk factors of POAF in critically ill lung surgery patients are mitral regurgitation,cedilanid,catecholamine,pericardiotomy and systematic lymph node dissection.The nomogram predicted POAF better than other scoring systems.
作者
蒋怡佳
张进
王静怡
刘薇
Jiang Yijia;Zhang Jin;Wang Jingyi;Liu Wei(Department of Surgical Intensive Critical Unit,Beijing Chao-yang Hospital,Capital Medical University,Beijing 100020,China)
出处
《中华胸心血管外科杂志》
CSCD
北大核心
2023年第6期352-359,共8页
Chinese Journal of Thoracic and Cardiovascular Surgery
关键词
肺部手术
重症
术后心房颤动
危险因素
预测模型
Lung surgery
Critically ill
Postoperative atrial fibrillation
Risk factors
Prediction model