Ma Jiajia, Xue Bei, Zhang Zhengmin, Yao Liping, Liu Xiaoxin
Nursing Department, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shanghai Jiao Tong University School of Nursing, Shanghai, China.
J Thorac Dis. 2025 Jun 30;17(6):3702-3715. doi: 10.21037/jtd-2024-2276. Epub 2025 Jun 10.
The hierarchical operation management system is one of the core medical systems. Graded management based on the degree of surgical risk, difficulty, resource consumption, and ethical risks can help ensure the quality and safety of the surgery. With the progress of medical technology and the continuous development of medical standards, the proportion of lung cancer patients who underwent high-risk surgery was increasing rapidly. The purpose of this study is to explore the incidence, risk factors, and prediction models of pulmonary infection after high-risk surgery for lung cancer based on machine learning algorithms.
This study included individuals who underwent lung cancer high-risk surgery at Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine from January 2021 to December 2023. Five machine learning algorithms including least absolute shrinkage and selection operator (LASSO)-assisted logistic regression (LR), artificial neural network (ANN), support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGB) were adopted to explore risk factors and prediction models of pulmonary infection after high-risk surgery for lung cancer.
A cohort of 2,650 patients were eligible for the study after application of the exclusion criteria, with an overall incidence of postoperative pulmonary infection at 9.66% (256/2,650). LASSO regression screened out eight characteristic variables including daily smoking, history of diabetes, diffusing capacity of the lung for carbon monoxide percentage of predicted (DLCO%Pred), airway resistance percentage of predicted (Raw%Pred), maximum tumor diameter, perioperative oral nutritional supplements (ONS) supplement, postoperative urinary catheter, and pleural adhesion degree. The risk prediction model of postoperative pulmonary infection was constructed using these eight clinical features. The area under the curve (AUC) range of the five models was 0.893-0.936. The XGB model outperformed the others, with an AUC of 0.936 [95% confidence interval (CI): 0.923-0.949]. The LR model had an AUC of 0.927 (95% CI: 0.921-0.939), second only to the XGB model, which was converted into a nomogram for model visualization.
The establishment of a risk prediction model based on machine learning can help clinical nursing staff identify high-risk patients for pulmonary infection after lung cancer high-risk surgery. The nomogram is expected to be an effective tool for nursing staff to manage the risk of pulmonary infection after lung cancer high-risk surgery.
分级手术管理系统是核心医疗系统之一。基于手术风险程度、难度、资源消耗及伦理风险进行分级管理有助于确保手术质量与安全。随着医学技术进步及医疗标准不断发展，接受高风险手术的肺癌患者比例迅速增加。本研究旨在基于机器学习算法探索肺癌高风险手术后肺部感染的发生率、危险因素及预测模型。
本研究纳入了2021年1月至2023年12月在上海交通大学医学院附属上海胸科医院接受肺癌高风险手术的患者。采用包括最小绝对收缩和选择算子（LASSO）辅助逻辑回归（LR）、人工神经网络（ANN）、支持向量机（SVM）、随机森林（RF）和极端梯度提升（XGB）在内的五种机器学习算法，探索肺癌高风险手术后肺部感染的危险因素及预测模型。
应用排除标准后，2650例患者符合研究条件，术后肺部感染的总体发生率为9.66%（256/2650）。LASSO回归筛选出八个特征变量，包括每日吸烟情况、糖尿病史、一氧化碳弥散量占预计值百分比（DLCO%Pred）、气道阻力占预计值百分比（Raw%Pred）、最大肿瘤直径、围手术期口服营养补充剂（ONS）补充情况、术后留置尿管情况及胸膜粘连程度。利用这八个临床特征构建了术后肺部感染的风险预测模型。五个模型的曲线下面积（AUC）范围为0.893 - 0.936。XGB模型表现优于其他模型，AUC为0.936 [95%置信区间（CI）：0.923 - 0.949]。LR模型的AUC为0.927（95% CI：0.921 - 0.939），仅次于XGB模型，将其转换为列线图用于模型可视化。
基于机器学习建立的风险预测模型有助于临床护理人员识别肺癌高风险手术后肺部感染的高危患者。该列线图有望成为护理人员管理肺癌高风险手术后肺部感染风险的有效工具。
