Wang Yaxuan, Xie Shiyang, Liu Jiayun, Wang He, Yu Jiangang, Li Wenya, Guan Aika, Xu Shun, Cui Yong, Tan Wenfei
Department of Anesthesiology, the First Hospital of China Medical University, China.
Department of Radiation Oncology, the First Hospital of China Medical University, China.
Ann Med. 2025 Dec;57(1):2487636. doi: 10.1080/07853890.2025.2487636. Epub 2025 Apr 7.
Reducing postoperative cardiovascular and neurological complications (PCNC) during thoracic surgery is the key to improving postoperative survival.
We aimed to investigate independent predictors of PCNC, develop machine learning models, and construct a predictive nomogram for PCNC in patients undergoing thoracic surgery for lung cancer.
This study used data from a previous retrospective study of 16,368 patients with lung cancer (training set: 11,458; validation set: 4,910) with American Standards Association physical statuses I-IV who underwent surgery. Postoperative information was collected from electronic medical records to help build models based on cause-and-effect and statistical data, potentially revealing hidden dependencies between factors and diseases in a big data environment. The optimal model was analyzed and filtered using multiple machine-learning models (Logistic regression, eXtreme Gradient Boosting, Random forest, Light Gradient Boosting Machine and Naïve Bayes). A predictive nomogram was built and receiver operating characteristics were used to assess the validity of the model. The discriminative power and clinical validity were assessed using calibration and decision-making curve analyses.
Multivariate logistic regression analysis revealed that age, surgery duration, intraoperative intercostal nerve block, postoperative patient-controlled analgesia, bronchial blocker use and sufentanil use were independent predictors of PCNC. Random forest was identified as the optimal model with an area under the curve of 0.898 in the training set and 0.752 in the validation set, confirming the excellent prediction accuracy of the nomogram. All the net benefits of the five machine-learning models in the training and validation sets demonstrated excellent clinical applicability, and the calibration curves showed good agreement between the predicted and observed risks.
The combination of machine-learning models and nomograms may contribute to the early prediction and reduction in the incidence of PCNC.
降低胸外科手术期间的术后心血管和神经并发症（PCNC）是提高术后生存率的关键。
我们旨在研究PCNC的独立预测因素，开发机器学习模型，并为接受肺癌胸外科手术的患者构建PCNC预测列线图。
本研究使用了先前一项对16368例接受手术的美国麻醉医师协会身体状况分级为I-IV级的肺癌患者的回顾性研究数据（训练集：11458例；验证集：4910例）。从电子病历中收集术后信息，以帮助基于因果关系和统计数据构建模型，可能揭示大数据环境中因素与疾病之间隐藏的依赖关系。使用多种机器学习模型（逻辑回归、极端梯度提升、随机森林、轻梯度提升机和朴素贝叶斯）对最佳模型进行分析和筛选。构建预测列线图，并使用受试者工作特征曲线评估模型的有效性。使用校准和决策曲线分析评估判别力和临床有效性。
多因素逻辑回归分析显示，年龄、手术时长、术中肋间神经阻滞、术后患者自控镇痛、支气管封堵器使用和舒芬太尼使用是PCNC的独立预测因素。随机森林被确定为最佳模型，训练集曲线下面积为0.898，验证集为0.752，证实了列线图具有出色的预测准确性。训练集和验证集中五个机器学习模型的所有净效益均显示出出色的临床适用性，校准曲线显示预测风险与观察到的风险之间具有良好的一致性。
机器学习模型和列线图的结合可能有助于早期预测并降低PCNC的发生率。
