Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou, China.
Department of Physiology, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, 511495, China.
BMC Surg. 2023 Sep 25;23(1):290. doi: 10.1186/s12893-023-02150-z.
Spontaneous ventilation-video-assisted thoracoscopic surgery (SV-VATS) has been applied to non-small cell lung cancer (NSCLC) patients in many centers. Since it remains a new and challenging surgical technique, only selected patients can be performed SV-VATS. We aim to conduct a retrospective single-center study to develop a clinical decision-making model to make surgery decision between SV-VATS and MV (mechanical ventilation) -VATS in NSCLC patients more objectively and individually.
Four thousand three hundred sixty-eight NSCLC patients undergoing SV-VATS or MV-VATS in the department of thoracic surgery between 2011 and 2018 were included. Univariate and multivariate regression analysis were used to identify potential factors influencing the surgical decisions. Factors with statistical significance were selected for constructing the Surgical Decision-making Scoring (SDS) model. The performance of the model was validated by area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis (DCA).
The Surgical Decision-making Scoring (SDS) model was built guided by the clinical judgment and statistically significant results of univariate and multivariate regression analyses of potential predictors, including smoking status (p = 0.03), BMI (p < 0.001), ACCI (p = 0.04), T stage (p < 0.001), N stage (p < 0.001), ASA grade (p < 0.001) and surgical technique (p < 0.001). The AUC of the training group and the testing group were 0.72 and 0.70, respectively. The calibration curves and the DCA curve revealed that the SDS model has a desired performance in predicting the surgical decision.
This SDS model is the first clinical decision-making model developed for an individual NSCLC patient to make decision between SV-VATS and MV-VATS.
自发性通气-电视辅助胸腔镜手术（SV-VATS）已在许多中心应用于非小细胞肺癌（NSCLC）患者。由于它仍然是一种新的具有挑战性的手术技术，只有选择的患者可以进行 SV-VATS。我们旨在进行一项回顾性单中心研究，以开发一种临床决策模型，使 NSCLC 患者的 SV-VATS 和 MV（机械通气）-VATS 手术决策更加客观和个体化。
纳入 2011 年至 2018 年胸外科行 SV-VATS 或 MV-VATS 的 4368 例 NSCLC 患者。采用单因素和多因素回归分析确定影响手术决策的潜在因素。选择有统计学意义的因素构建手术决策评分（SDS）模型。通过接受者操作特征曲线（AUC）下面积、校准曲线和决策曲线分析（DCA）验证模型的性能。
SDS 模型是在临床判断的指导下，根据单因素和多因素回归分析潜在预测因素的统计学结果构建的，包括吸烟状况（p=0.03）、BMI（p<0.001）、ACCI（p=0.04）、T 分期（p<0.001）、N 分期（p<0.001）、ASA 分级（p<0.001）和手术技术（p<0.001）。训练组和测试组的 AUC 分别为 0.72 和 0.70。校准曲线和 DCA 曲线表明，SDS 模型在预测手术决策方面具有良好的性能。
这是第一个为 NSCLC 患者制定 SV-VATS 和 MV-VATS 手术决策的个体化临床决策模型。
