AUTHOR=Gong Ziwei , Hella Slamuguli , Shi Jing , Liu Xinya 

TITLE=Construction of a risk prediction model for postoperative atrial fibrillation in lung cancer patients based on multi-dimensional feature fusion and ensemble learning

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1679973

DOI=10.3389/fcvm.2025.1679973

ISSN=2297-055X

ABSTRACT=IntroductionSurgery remains a cornerstone in lung cancer treatment, yet a subset of patients face high risks of recurrence or mortality postoperatively. Poor prognosis significantly shortens survival time, underscoring an urgent clinical need to accurately identify high-risk individuals. To address this, numerous studies have focused on constructing risk prediction models that integrate multi-dimensional data (clinical, pathological, and emerging biomarkers) to quantify postoperative adverse event probabilities, guiding personalized adjuvant therapy and enhancing follow-up management. To investigate risk factors for postoperative atrial fibrillation (POAF) in lung cancer patients and develop/validate a predictive model based on multi-dimensional feature fusion and ensemble learning.MethodsThis retrospective cohort study analyzed 369 lung cancer patients undergoing surgical resection at Xinjiang Medical University Affiliated Tumor Hospital (2019–2024). Univariate analysis screened potential risk factors, followed by multivariable logistic regression to confirm independent predictors. Nine machine learning algorithms were employed to build predictive models, among which the top three performers were selected for ensemble modeling via weighted averaging, resulting in the final risk prediction model.ResultsMultivariate analysis revealed three independent predictors of POAF: cardiac insufficiency (OR = 64.55, 95% CI: 2.41–1727.70), ventricular rate (OR = 1.17, 95% CI: 1.1–1.25), and elevated N-terminal pro-B-type natriuretic peptide (NT-proBNP, OR = 1.005, 95% CI: 1–1.009). The Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machine (GBM) demonstrated the highest accuracy (ACC = 0.9041, 0.9178, and 0.9178, respectively). The ensemble model srg-LCPOAF further improved ACC to 0.9452, significantly outperforming individual algorithms.DiscussionThis study is the first to integrate cardiopulmonary function, biomarkers, and surgical parameters into an ensemble model (srg-LCPOAF), providing evidence-based support for early intervention in high-risk POAF patients.