Li Lixuan, Hu Yuekong, Yang Zhicheng, Luo Zeruxin, Wang Jiachen, Wang Wenqing, Liu Xiaoli, Wang Yuqiang, Fan Yong, Yu Pengming, Zhang Zhengbo
Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, China.
Department of Rehabilitation Medicine, West China Tianfu Hospital, Sichuan University, Chengdu, China.
BMC Med Inform Decis Mak. 2025 Jan 31;25(1):47. doi: 10.1186/s12911-025-02875-2.
Postoperative pulmonary complications (PPCs) following cardiac valvular surgery are characterized by high morbidity, mortality, and economic cost. This study leverages wearable technology and machine learning algorithms to preoperatively identify high-risk individuals, thereby enhancing clinical decision-making for the mitigation of PPCs.
A prospective study was conducted at the Department of Cardiovascular Surgery of West China Hospital, Sichuan University, from August 2021 to December 2022. We examined 100 cardiac valvular surgery patients, where wearable technology was utilized to collect and analyze nocturnal physiological data at the 24-hour admission, in conjunction with clinical data extraction from the Hospital Information System's electronic records. We systematically evaluated three different input types (physiological, clinical, and both) and five classifiers (XGB, LR, RF, SVM, KNN) to identify the combination with strong predictive performance for PPCs. Feature selection was conducted using Recursive Feature Elimination with Cross-Validated (RFECV) for each model, yielding an optimal feature subset for each, followed by a grid search to tune hyperparameters. Stratified 5-fold cross-validation was used to evaluate the generalization performance. The significance of AUC differences between models was tested using the DeLong test to determine the optimal prognostic model comprehensively. Additionally, univariate logistic regression analysis was conducted on the features of the best-performing model to understand the impact of individual feature on PPCs.
In this study, 22 patients (22%) developed PPCs. Across classifiers, models combining both physiological and clinical features performed better than physiological or clinical features alone. Specifically, including physiological data in the classification model improved AUC, ACC, F1, and precision by an average of 8.32%, 1.80%, 3.28% and 6.06% compared to using clinical data only. The XGB classifier, utilizing both dataset, achieved the highest performance with an AUC of 0.82 (± 0.08) and identified eight significant features. The DeLong test indicated that the XGB model utilizing the both dataset significantly outperformed the XGB models trained on the physiological or clinical datasets alone. Univariate logistic regression analysis suggested that surgical methods, age, nni_50, and min_ven_in_mean are significantly associated with the occurrence of PPCs.
The integration of continuous wearable physiological and clinical data significantly improves preoperative risk assessment for PPCs, which helps to optimize surgical management and reduce PPCs morbidity and mortality.
心脏瓣膜手术后的术后肺部并发症（PPCs）具有高发病率、高死亡率和高经济成本的特点。本研究利用可穿戴技术和机器学习算法在术前识别高危个体，从而加强减轻PPCs的临床决策。
2021年8月至2022年12月在四川大学华西医院心血管外科进行了一项前瞻性研究。我们检查了100例心脏瓣膜手术患者，利用可穿戴技术在入院24小时时收集和分析夜间生理数据，并从医院信息系统的电子记录中提取临床数据。我们系统地评估了三种不同的输入类型（生理数据、临床数据以及两者结合）和五种分类器（XGB、LR、RF、SVM、KNN），以确定对PPCs具有强大预测性能的组合。使用带交叉验证的递归特征消除法（RFECV）对每个模型进行特征选择，为每个模型生成一个最优特征子集，随后进行网格搜索以调整超参数。采用分层5折交叉验证来评估泛化性能。使用德龙检验来测试模型之间AUC差异的显著性，以全面确定最优预后模型。此外，对表现最佳模型的特征进行单因素逻辑回归分析，以了解个体特征对PPCs的影响。
在本研究中，22例患者（22%）发生了PPCs。在所有分类器中，结合生理和临床特征的模型比单独使用生理或临床特征的模型表现更好。具体而言，与仅使用临床数据相比，在分类模型中纳入生理数据使AUC、ACC、F1和精确率平均分别提高了8.32%、1.80%、3.28%和6.06%。利用两种数据集的XGB分类器性能最高，AUC为0.82（±0.08），并识别出八个显著特征。德龙检验表明，利用两种数据集的XGB模型明显优于仅基于生理或临床数据集训练的XGB模型。单因素逻辑回归分析表明，手术方法、年龄、nni_50和min_ven_in_mean与PPCs的发生显著相关。
持续可穿戴生理数据和临床数据的整合显著改善了PPCs的术前风险评估，这有助于优化手术管理并降低PPCs的发病率和死亡率。
