Key result
Wearable-enhanced XGBoost achieves 0.82 AUC for predicting postoperative pulmonary complications, outperforming clinical models.
Why the study?
Postoperative pulmonary complications after cardiac valvular surgery carry high morbidity, mortality, and cost, requiring preoperative risk identification to enhance clinical decision-making.
Does the integration of continuous wearable physiological and clinical data improve the preoperative prediction of postoperative pulmonary complications in patients undergoing cardiac valve surgery?
Observational (n=100)
No
Does the integration of continuous wearable physiological and clinical data improve the preoperative prediction of postoperative pulmonary complications in patients undergoing cardiac valve surgery?
Absolute Event Rate: 0.82% vs 0.78%
p-value: p=0.05
Integrating continuous wearable physiological data with clinical data significantly improves the preoperative prediction of postoperative pulmonary complications in patients undergoing cardiac valve surgery.
May enhance preoperative PPC risk stratification in valvular surgery; hypothesis-generating and requires prospective validation before practice change.
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
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Li et al. (2025) conducted an observational in Cardiac valvular surgery (n=100). Combined wearable continuous physiological and clinical data (XGBoost model) vs. Clinical data alone was evaluated on Predictive performance (AUC) for postoperative pulmonary complications (p=0.05). An XGBoost model integrating continuous wearable physiological data and clinical data achieved an AUC of 0.82 for predicting postoperative pulmonary complications, significantly outperforming models using clinical data alone.
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