Development of a multi-label deep neural network model for predicting immediate paravalvular leakage and new-onset conduction disturbances after transcatheter aortic valve replacement: A retrospective cohort study

Paravalvular leakage (PVL) and conduction disturbances (CDs) are important complications after transcatheter aortic valve replacement (TAVR). While existing risk prediction models predominantly adopt single-complication modeling strategies, overlooking the interrelatedness.

We aimed to develop a multi-label prediction model based on deep learning to predict immediate PVL and new-onset CDs post-TAVR simultaneously.

The study retrospectively included 966 patients who underwent first-time TAVR for aortic stenosis between April 2012 and July 2023 from the Sichuan University TAVR Registry. A deep learning-based model using the optimization algorithm Muex with 79 features and neural network labels for PVL and new-onset CDs immediately after TAVR was developed. The Muex model was validated using the bootstrap method, evaluated by area under the receiver operating characteristic curve (AUROC) and calibration curves, interpreted with Shapley Additive Explanations, and subsequently compared with a neural network model and two traditional multi-label classification models.

The dataset included 771 training and 195 testing patients, with 6.63% exhibiting more than mild PVL and 39.6% developing new-onset CDs. The Muex model outperformed the neural network, label powerests, and multi-label k-nearest neighbor in both discrimination (micro-average AUROC: 0.739 vs. 0.705 vs. 0.504 vs. 0.514) and calibration (integrated calibration index [ICI]: 0.012 vs. 0.116 vs. 0.046 vs. 0.051), demonstrating strong performance in predicting both complications simultaneously.

The study demonstrated that the Muex model is feasible for simultaneously predicting PVL and CDs post-TAVR, excelling in both performance and interpretability, while identifying high-risk patients and inferring patient-specific risk factors to facilitate informed clinical decision-making.

ClinicalTrials.gov, NCT04415047.