实用临床医药杂志2026，Vol.30Issue(6)：48-54,83,8.DOI:10.7619/jcmp.20256842
基于机器学习变量筛选的老年胸腔镜肺癌手术患者术后谵妄预测模型的构建与验证
Construction and validation of a predictive model for postoperative delirium in elderly patients with thoracoscopic lung cancer surgery based on machine learning variable screening
摘要
Abstract
Objective To explore the risk factors for postoperative delirium(POD)in elderly patients with thoracoscopic radical resection for lung cancer and construct a nomogram model.Meth-ods A retrospective study was conducted,data from 597 elderly patients(≥65 years old)who un-derwent thoracoscopic radical resection for lung cancer at Handan Central Hospital from January 2022 to January 2025 were collected.Patients were divided into modeling set(n=417)and validation set(n=180)in a 7∶3 ratio using a random number table method.Patients were categorized into POD group and non-POD group based on the Confusion Assessment Method(CAM)scale.A predictive model was established by combining machine learning variable screening methods(LASSO regression)with traditional Logistic regression to construct a nomogram model.The discrimination and calibration of the model were evaluated using the receiver operating characteristic(ROC)curve and calibration curve,respectively.The goodness-of-fit was assessed via the Hosmer-Lemeshow test,and the clini-cal net benefit was quantified using decision curve analysis(DC A).Results LASSO regression a-nalysis identified eight predictive variables with non-zero coefficients.Multivariate Logistic regres-sion analysis determined five independent risk factors:age(OR=1.30,95％CI,1.070 to 1.193),education level(OR=0.581,95％CI,0.344 to 0.982),preoperative cognitive function[Montre-al Cognitive Assessment Scale(MoCA)](OR=0.821,95％CI,0.745 to 0.904),history of cere-brovascular disease(OR=2.667,95％CI,1.325 to 5.367),and operative time(OR=1.023,95％CI,1.010 to 1.036).A nomogram prediction tool was constructed by integrating these five core indicators spanning physiological-cognitive-social dimensions.The area under the curve(AUC)of the model was 0.804(95％CI,0.757 to0.850)in the modeling set and 0.793(95％CI,0.723 to 0.863)in the validation set,with no significant difference(P=0.804).The Hosmer-Lemeshow test indicated good calibration in both the modeling set(x2=10.508,P=0.231)and the validation set(x2=8.641,P=0.373).DC A demonstrated a significant clinical net benefit of using the pre-dictive model across a wide range of threshold probabilities(0.01 to 0.56 in the modeling set and 0.01 to 0.50 in the validation set).Conclusion The nomogram model constructed in this study for elderly patients(≥65 years old)with thoracoscopic radical resection for lung cancer provides a quantitative tool for early identification of high-risk patients and implementation of targeted inter-ventions.关键词
谵妄/肺癌/胸腔镜/风险因素/列线图/机器学习/蒙特利尔认知评估量表/决策曲线分析Key words
delirium/lung cancer/thoracoscopy/risk factors/nomogram/machine learning/Montreal Cognitive Assessment Scale/decision curve analysis分类
医药卫生引用本文复制引用
陈士欢,陈永学,侯俊德,程少飞,李立英..基于机器学习变量筛选的老年胸腔镜肺癌手术患者术后谵妄预测模型的构建与验证[J].实用临床医药杂志,2026,30(6):48-54,83,8.基金项目
河北省重点研发计划(182777195) （182777195）
