Multimodal machine learning-based technical success prediction in patients undergoing transcatheter aortic valve implantation

Abstract Background Procedural success is fundamental to achieving optimal clinical outcome, and prediction of procedural success and failure is of key importance in the selection of the appropriate treatment strategy in patients with severe aortic stenosis requiring aortic valve replacement. Purpose We aimed to develop a prediction model for procedural success in patients undergoing transcatheter aortic valve implantation (TAVI) using multimodal information and employing machine learning algorithms. Method In a prospective TAVI registry, patient baseline demographics, clinical history, medication, laboratory, procedural characteristics, electrocardiogram, angiography, transthoracic and transesophagel echocardiography, and computed tomography information were collected prospectively and extracted as numeric data. Data preprocessing included the imputation of missing values and the elimination of features with high collinearity. We incorporated several feature selection (FS) and classification algorithms for model development. Datasets for each target were randomly split into train/validation sets (70/10%) and hold-out test sets (20%). All machine learning processes, including preprocessing, feature selection, parameter, and hyperparameter optimization, were carried out on training and validation sets in 10-fold cross-validation. Different metrics, including area under the curve (AUC) in receiver operating characteristic curve analyses, sensitivity, and specificity, were calculated for model evaluation on the holdout test set. Procedural success was defined as technical success in the Valve Academic Research Consortium-3 (VARC-3). Technical failure differentiated between vascular and cardiac complications. Results In a total of 2,576 patients undergoing TAVI with a contemporary device between March 2012 and June 2023, 242 (9.4%) had technical failure: 70 (2.7%) had cardiac and 178 (6.9%) had vascular technical failure. The sensitivity and specificity for overall technical failure were 0.71 and 0.77, respectively, with AUC of 0.76 (95% CI: 0.69-0.84) achieved by the recursive feature elimination and AdaBoost algorithm. For cardiac technical failure, the ANOVA FS and Random Forest (RF) model yielded sensitivity and specificity of 0.71 and 0.77, with an AUC of 0.76 (95% CI: 0.65 to 0.87). Vascular technical failure showed AUC of 0.76 (95% CI: 0.69 to 0.84), with sensitivity and specificity of 0.72 and 0.73, respectively, with achieved using the ANOVA FS and RF model. Conclusion The initial results of the novel prediction model for technical success are promising. Further improvements and external validation are warranted to support the use of the proposed model in clinical practice.Central Illustration