Wang Bin, Chen Zhenxing, Zhao Ru, Zhang Li, Zhang Ye
Department of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
PeerJ. 2021 Nov 4;9:e12366. doi: 10.7717/peerj.12366. eCollection 2021.
Postoperative pulmonary complications (PPCs) after thoracoscopic surgery are common. This retrospective study aimed to develop a nomogram to predict PPCs in thoracoscopic surgery.
A total of 905 patients who underwent thoracoscopy were randomly enrolled and divided into a training cohort and a validation cohort at 80%:20%. The training cohort was used to develop a nomogram model, and the validation cohort was used to validate the model. Univariate and multivariable logistic regression were applied to screen risk factors for PPCs, and the nomogram was incorporated in the training cohort. The discriminative ability and calibration of the nomogram for predicting PPCs were assessed using C-indices and calibration plots.
Among the patients, 207 (22.87%) presented PPCs, including 166 cases in the training cohort and 41 cases in the validation cohort. Using backward stepwise selection of clinically important variables with the Akaike information criterion (AIC) in the training cohort, the following seven variables were incorporated for predicting PPCs: American Society of Anesthesiologists (ASA) grade III/IV, operation time longer than 180 min, one-lung ventilation time longer than 60 min, and history of stroke, heart disease, chronic obstructive pulmonary disease (COPD) and smoking. With incorporation of these factors, the nomogram achieved good C-indices of 0.894 (95% confidence interval (CI) [0.866-0.921]) and 0.868 (95% CI [0.811-0.925]) in the training and validation cohorts, respectively, with well-fitted calibration curves.
The nomogram offers good predictive performance for PPCs after thoracoscopic surgery. This model may help distinguish the risk of PPCs and make reasonable treatment choices.
胸腔镜手术后的术后肺部并发症（PPCs）很常见。这项回顾性研究旨在开发一种列线图，以预测胸腔镜手术中的PPCs。
总共随机纳入905例行胸腔镜检查的患者，并按80%:20%分为训练队列和验证队列。训练队列用于开发列线图模型，验证队列用于验证该模型。采用单因素和多因素逻辑回归筛选PPCs的危险因素，并将列线图纳入训练队列。使用C指数和校准图评估列线图预测PPCs的判别能力和校准情况。
在这些患者中，207例（22.87%）出现PPCs，其中训练队列中有166例，验证队列中有41例。在训练队列中，使用赤池信息准则（AIC）对临床重要变量进行向后逐步选择，纳入以下七个变量来预测PPCs：美国麻醉医师协会（ASA）III/IV级、手术时间超过180分钟、单肺通气时间超过60分钟、以及中风、心脏病、慢性阻塞性肺疾病（COPD）和吸烟史。纳入这些因素后，列线图在训练队列和验证队列中的C指数分别达到良好的0.894（95%置信区间（CI）[0.866 - 0.921]）和0.868（95%CI [0.811 - 0.925]），校准曲线拟合良好。
该列线图对胸腔镜手术后的PPCs具有良好的预测性能。该模型可能有助于区分PPCs的风险并做出合理的治疗选择。
