Machine learning-based predictive model for atrial arrhythmia following transcatheter atrial septal defect closure
Bibliographic record
Abstract
Atrial septal defects (ASDs) are frequently closed percutaneously. Despite successful closure, many patients still develop atrial arrhythmias. There is inconsistent data on the risk factors associated with these atrial arrhythmias. As such, we aimed to develop a machine learning (ML) model predicting atrial arrhythmias following transcatheter ASD closure in adolescents and adults. Patients with secundum-type ASDs undergoing transcatheter closure between 2008-2024 at a single center were retrospectively analyzed. Patients with prior atrial arrhythmias were excluded. A deep neural network, adapted via transfer learning from a large external ECG dataset, was used to extract predictive features from preprocedural 12-lead ECGs. These features were combined with clinical, demographic, biochemical and hemodynamic variables in ensemble survival models. Model performance was assessed using the integrated Brier scores and the area under the receiver operating curve (AUC). A total of 148 adult patients (median 44.4 years [30.6–57.8], 105 females [70.9%]) were eligible for included. There were a total of 1055 person-years of follow-up (median follow-up 7.3 [3.1–11.3]), during which 28 patients (18.9%) developed atrial arrhythmias. The final ensemble ML model incorporating ECG-derived features demonstrated strong predictive performance (integrated Brier score 0.044, mean AUC 0.823). Subgroup and sensitivity analyses confirmed the robustness of the model across various patient profiles. We developed a novel ML-based risk model using a transfer learning approach to predict atrial arrhythmias after transcatheter ASD closure. Further research and external validation are needed to refine the proposed risk stratification prior to clinical implementation. • Following closure, a significant proportion of patients with atrial septal defects (ASDs) still develop late-onset atrial arrhythmias. • Existing prediction models for late-onset atrial arrhythmias after ASD closure have limited accuracy and clinical utility. • Our study introduces a novel, high-performing ML model, leveraging transfer learning on ECG data, to accurately predict new-onset atrial arrhythmias following transcatheter ASD closure. • ECG-derived features outperform traditional clinical and hemodynamic predictors in risk stratification. • The model shows strong and consistent predictive performance across diverse patient subgroups, supporting the model’s potential generalizability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".