Multi-modal artificial intelligence-based prediction for cardiovascular mortality after transcatheter aortic valve implantation: a time-to-event survival prediction

Abstract

Abstract Background Predicting long-term outcomes following Transcatheter Aortic Valve Implantation (TAVI) presents a significant challenge. Objective We aimed to predict post-TAVI long-term cardiovascular mortality using multimodal data and employing time-to-event artificial intelligence (AI) algorithms. Methods This study enrolled 3,973 consecutive patients from the prospective TAVI registry (between August 2007 and June 2023). Multimodal information was collected and extracted as numerical data for each patient, encompassing clinical history, medication, laboratory, procedural characteristics, ECG, angiography, echocardiography (TTE and TEE), and CT. Clinical outcomes were adjudicated by an independent clinical event committee. From the multimodal information, 147 features were extracted at baseline before the TAVI procedure. The datasets were divided into a training/validation set (80%) and a hold-out test set (20%) for model development and evaluation, respectively. Cox-LASSO feature selection was employed to identify the most relevant features, followed by the implementation of Random Survival Forest (RSF), Gradient Boosting Survival Analysis (GBSA), and Survival Support Vector Machine (SSVM) to predict the timing and occurrence of cardiac death events in patients. Feature selection, model parameters, and hyperparameters selection were performed using 10-fold cross-validation on the training set. Model performance was evaluated using different metrics, such as the concordance index (c-index) and cumulative Area Under the Curve (C-AUC) on a 20% untouched test set. Results In the patient cohort, 28.4% experienced cardiovascular death. Features were selected from various modalities, including 22 from clinical history, two from medication, eight from laboratory tests, one from ECG, eight from CT scans, and five from echocardiography and angiography. In the univariate Cox-PH model, all features exhibited a C-index ranging from 0.50 to 0.57. In the test set, the conventional Multivariate Cox-PH model achieves a c-index of 0.59 (C-AUC: 0.56). RSF, GBSA, and SSVM achieve a C-index of 0.70 (C-AUC: 0.69), 0.66 (C-AUC: 0.64), and 0.69 (C-AUC: 0.68), respectively, in the holdout test set. Conclusion Integrating multimodal information, encompassing imaging, signal, laboratory data, and clinical history, with AI algorithms has markedly enhanced the prediction of long-term cardiac outcomes following the TAVI procedure. The initially developed model can predict the probability of cardiac death up to 10 years. Furthermore, the RFS model significantly outperformed the conventional Cox model in predicting time-to-event outcomes.