Abstract
Cardiac tamponade is a rare yet catastrophic complication during atrial fibrillation (AF) catheter ablation. Influenced by multiple procedural and patient-related factors, its prediction remains highly challenging. This study aimed to develop and interpret a machine learning-based predictive model for cardiac tamponade during AF catheter ablation. Data were retrospectively collected from 1481 patients who underwent AF catheter ablation at a tertiary hospital in Nanjing, China, between October 2014 and December 2024. After identifying key predictors of intraoperative cardiac tamponade via least absolute shrinkage and selection operator (LASSO) regression, eight machine learning algorithms were trained using Python libraries. Model performance was evaluated through cross-validation, and SHapley Additive exPlanations (SHAP) analysis was performed to interpret the best-performing model. The XGBoost model exhibited the optimal overall performance, with an area under the curve (AUC) of 0.972 in the training set and 0.908 in internal validation, demonstrating excellent calibration and the highest clinical net benefit. SHAP analysis identified five major predictors: operator experience, D-dimer level, total heparin dose, AF type, and left atrial diameter. These predictors represent multidimensional determinants associated with procedural technique, coagulation status, and cardiac anatomy. The XGBoost-based predictive model showed strong discriminative ability and interpretability for predicting cardiac tamponade during AF catheter ablation, which supports accurate preoperative risk stratification and guides intraoperative management to enhance procedural safety and precision. External validation across multiple centers is required to confirm the generalizability of the model.
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Data availability
The data used in this study contain sensitive patient information and therefore cannot be made publicly available. However, the corresponding author (BAO Zhipeng) can provide access to the data upon reasonable request.
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Acknowledgements
We sincerely thank all the medical staff and participants from the First Affiliated Hospital with Nanjing Medical University for their valuable contributions.
Funding
This study was supported by the National Natural Science Foundation of China (No. 72074124), the Jiangsu Youth Science and Technology Talent Promotion Project (JSTJ-2025-386), the Institute for Hospital Reform and Development of Nanjing University (NDYG052), and the Youth Foundation Cultivation Program of the National Natural Science Foundation at Jiangsu Provincial People’s Hospital (PY2022002).
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Z.B. and L.Z. served as the primary coordinators of this project, overseeing the study design and overall project management. L.Z. and H.G. were responsible for drafting the manuscript. Y.Z. and W.S. contributed to data collection and stratification supervision. Y.L. and J.W. assisted with data compilation and statistical analysis. Z.B. and G.S. were involved in manuscript revision and securing research funding. All authors made substantial contributions to the study and approved the final submitted manuscript.
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This study was approved by the Ethics Committee of the First Affiliated Hospital with Nanjing Medical University (2024-SRFA-210). As this was a retrospective observational study, the requirement for informed consent was waived, and all patient identifiers were rigorously anonymized. All procedures in this study adhered to the ethical standards of the institutional committee and to the 1964 Declaration of Helsinki and its subsequent amendments. The prediction model was developed in line with the latest TRIPOD + AI statement (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis + Artificial Intelligence)14.
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Zhou, L., Zhao, Y., Song, W. et al. Explainable machine learning for risk prediction of acute cardiac tamponade during atrial fibrillation ablation. Sci Rep (2026). https://doi.org/10.1038/s41598-026-40302-2
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DOI: https://doi.org/10.1038/s41598-026-40302-2
