Wu Xianning, Xu Shibin, Ke Li, Fan Jun, Wang Jun, Xie Mingran, Jiang Xianliang, Xu Meiqing
Department of Thoracic Surgery, The First Affiliated Hospital of University of Science and Technology of China (Anhui Provincial Hospital), Hefei 230001, China.
Zhongguo Fei Ai Za Zhi. 2017 Dec 20;20(12):827-832. doi: 10.3779/j.issn.1009-3419.2017.12.06.
Prolonged air leak (PAL) after anatomic lung resection is a common and challenging complication in thoracic surgery. No available clinical prediction model of PAL has been established in China. The aim of this study was to construct a model to identify patients at increased risk of PAL by using preoperative factors exclusively.
We retrospectively reviewed clinical data and PAL occurrence of patients after anatomic lung resection, in department of thoracic surgery, Anhui Provincial Hospital Affiliated to Anhui Medical University, from January 2016 to October 2016. 359 patients were in group A, clinical data including age, body mass index (BMI), gender, smoking history, surgical methods, pulmonary function index, pleural adhesion, pathologic diagnosis, side and site of resected lung were analyzed. By using univariate and multivariate analysis, we found the independent predictors of PAL after anatomic lung resection and subsequently established a clinical prediction model. Then, another 112 patients (group B), who underwent anatomic lung resection in different time by different team, were chosen to verify the accuracy of the prediction model. Receiver-operating characteristic (ROC) curve was constructed using the prediction model.
Multivariate Logistic regression analysis was used to identify six clinical characteristics [BMI, gender, smoking history, forced expiratory volume in one second to forced vital capacity ratio (FEV1%), pleural adhesion, site of resection] as independent predictors of PAL after anatomic lung resection. The area under the ROC curve for our model was 0.886 (95%CI: 0.835-0.937). The best predictive P value was 0.299 with sensitivity of 78.5% and specificity of 93.2%.
CONCLUSIONS: Our prediction model could accurately identify occurrence risk of PAL in patients after anatomic lung resection, which might allow for more effective use of intraoperative prophylactic strategies. .
解剖性肺切除术后的持续性漏气（PAL）是胸外科常见且具有挑战性的并发症。我国尚未建立可用的PAL临床预测模型。本研究的目的是仅使用术前因素构建一个模型，以识别PAL风险增加的患者。
我们回顾性分析了安徽医科大学附属安徽省立医院胸外科2016年1月至2016年10月解剖性肺切除术后患者的临床资料和PAL发生情况。A组有359例患者，分析其年龄、体重指数（BMI）、性别、吸烟史、手术方式、肺功能指标、胸膜粘连、病理诊断、切除肺的侧别和部位等临床资料。通过单因素和多因素分析，我们发现了解剖性肺切除术后PAL的独立预测因素，并随后建立了一个临床预测模型。然后，选择另外112例由不同团队在不同时间进行解剖性肺切除的患者（B组）来验证预测模型的准确性。使用该预测模型构建受试者操作特征（ROC）曲线。
采用多因素Logistic回归分析确定了六个临床特征[BMI、性别、吸烟史、一秒用力呼气容积与用力肺活量比值（FEV1%）、胸膜粘连、切除部位]为解剖性肺切除术后PAL的独立预测因素。我们模型的ROC曲线下面积为0.886（95%CI：0.835 - 0.937）。最佳预测P值为0.299，敏感性为78.5%，特异性为93.2%。
我们的预测模型可以准确识别解剖性肺切除术后患者发生PAL的风险，这可能有助于更有效地使用术中预防策略。
