PDF(Pubmed)
Abstract：
UNASSIGNED: Sleeve lobectomy is a challenging procedure with a high risk of postoperative complications. To facilitate surgical decision-making and optimize perioperative treatment, we developed risk stratification models to quantify the probability of postoperative complications after sleeve lobectomy.
UNASSIGNED: We retrospectively analyzed the clinical features of 691 non-small cell lung cancer (NSCLC) patients who underwent sleeve lobectomy between July 2016 and December 2019. Logistic regression models were trained and validated in the cohort to predict overall complications, major complications, and specific minor complications. The impact of specific complications in prognostic stratification was explored via the Kaplan-Meier method.
UNASSIGNED: Of 691 included patients, 232 (33.5%) developed complications, including 35 (5.1%) and 197 (28.5%) patients with major and minor complications, respectively. The models showed robust discrimination, yielding an area under the receiver operating characteristic (ROC) curve (AUC) of 0.853 [95% confidence interval (CI): 0.705-0.885] for predicting overall postoperative complication risk and 0.751 (95% CI: 0.727-0.762) specifically for major complication risks. Models predicting minor complications also achieved good performance, with AUCs ranging from 0.78 to 0.89. Survival analyses revealed a significant association between postoperative complications and poor prognosis.
UNASSIGNED: Risk stratification models could accurately predict the probability and severity of complications in NSCLC patients following sleeve lobectomy, which may inform clinical decision-making for future patients.
UNASSIGNED: We retrospectively analyzed the clinical features of 691 non-small cell lung cancer (NSCLC) patients who underwent sleeve lobectomy between July 2016 and December 2019. Logistic regression models were trained and validated in the cohort to predict overall complications, major complications, and specific minor complications. The impact of specific complications in prognostic stratification was explored via the Kaplan-Meier method.
UNASSIGNED: Of 691 included patients, 232 (33.5%) developed complications, including 35 (5.1%) and 197 (28.5%) patients with major and minor complications, respectively. The models showed robust discrimination, yielding an area under the receiver operating characteristic (ROC) curve (AUC) of 0.853 [95% confidence interval (CI): 0.705-0.885] for predicting overall postoperative complication risk and 0.751 (95% CI: 0.727-0.762) specifically for major complication risks. Models predicting minor complications also achieved good performance, with AUCs ranging from 0.78 to 0.89. Survival analyses revealed a significant association between postoperative complications and poor prognosis.
UNASSIGNED: Risk stratification models could accurately predict the probability and severity of complications in NSCLC patients following sleeve lobectomy, which may inform clinical decision-making for future patients.
摘要：
■袖状肺叶切除术是一项具有挑战性的手术，术后并发症的风险很高。为了便于手术决策和优化围手术期治疗，我们建立了风险分层模型来量化袖状肺叶切除术后并发症的发生概率.
■我们回顾性分析了2016年7月至2019年12月接受袖状肺叶切除术的691例非小细胞肺癌（NSCLC）患者的临床特征。在队列中对Logistic回归模型进行训练和验证，以预测总体并发症，主要并发症,和特定的轻微并发症。通过Kaplan-Meier方法探讨了特定并发症在预后分层中的影响。
■在691名患者中，232（33.5％）出现并发症，包括35例（5.1％）和197例（28.5％）有主要和次要并发症的患者，分别。模型显示出强大的辨别能力，受试者工作特征(ROC)曲线下面积(AUC)为0.853[95%置信区间(CI):0.705~0.885],用于预测术后总体并发症风险,尤其是0.751(95%CI:0.727~0.762).预测轻微并发症的模型也取得了良好的性能，AUC范围从0.78到0.89。生存分析显示，术后并发症与不良预后之间存在显着关联。
风险分层模型可以准确预测袖状肺叶切除术后NSCLC患者并发症的发生概率和严重程度，这可能为未来患者的临床决策提供信息。
■我们回顾性分析了2016年7月至2019年12月接受袖状肺叶切除术的691例非小细胞肺癌（NSCLC）患者的临床特征。在队列中对Logistic回归模型进行训练和验证，以预测总体并发症，主要并发症,和特定的轻微并发症。通过Kaplan-Meier方法探讨了特定并发症在预后分层中的影响。
■在691名患者中，232（33.5％）出现并发症，包括35例（5.1％）和197例（28.5％）有主要和次要并发症的患者，分别。模型显示出强大的辨别能力，受试者工作特征(ROC)曲线下面积(AUC)为0.853[95%置信区间(CI):0.705~0.885],用于预测术后总体并发症风险,尤其是0.751(95%CI:0.727~0.762).预测轻微并发症的模型也取得了良好的性能，AUC范围从0.78到0.89。生存分析显示，术后并发症与不良预后之间存在显着关联。
风险分层模型可以准确预测袖状肺叶切除术后NSCLC患者并发症的发生概率和严重程度，这可能为未来患者的临床决策提供信息。
