Machine learning prediction for prognosis and long-term effectiveness for transcatheter ventricular septal defect closure: a 5-year single center experience

Abstract Background Transcatheter therapy, with minimal invasiveness and rapid recovery advantages, has emerged as the primary choice for treating perimembranous ventricular septal defect. However, patients receiving transcatheter occlusion may confront conduction abnormalities due to the occluder's proximity to the cardiac conduction bundle during implantation. Few have assessed the risks of postoperative conduction block (CB) via robust machine learning models. Purpose The purpose of this study was to evaluate our long-term follow up results of pmVSD occlusion and establish a prediction model to identify risk predictors of conduction abnormalities via robust machine learning methods. Methods Five methods including logistic regression with a stepwise selection of variables, lasso regularization; random forest; gradient descent boosting (GBM); and support vector machine, were used to train models for predicting risks of late-onset and persistent conduction block through 5 years of follow-up and were validated using 5-fold cross-validation. Receiver-operating characteristic curves and Brier scores were utilized to evaluate model discrimination and calibration, respectively. The top prediction variables were identified by using the best performing models, using the relative influence score of each variable in 5-fold cross-validation. Results The GBM model performed the best with an AUC of 0.82 (95% confidence interval [CI]: 0.75 to 0.89) for predicting late-onset CB (Brier score: 0.0204), and 0.70 (95% CI: 0.62 to 0.78) for persistent CB (Brier score: 0.0003) over entire study period. Conduction block on admission, advanced age and aortic regurgitation before discharge were strongly associated with late-onset CB, whereas decreased left ventricular ejection fraction, enlarged left ventricle end diastolic diameter at 1-month follow-up were the most significant predictors of persistent CB. Conclusions These models predict the risks of temporary and persistent postoperative conduction block in patients with pmVSD occlusion and shed light on preventing long-term complications.Study FlowchartCentral Illustration