Parsimonious Machine Learning of Preprocedural Cardiac CT Predicts All-Cause Mortality After Transcatheter Aortic Valve Implantation
TL;DR
A machine learning (ML) model using two preprocedural cardiac CT-derived metrics, automatically extractable with open-source tools, predicts 1-year mortality after TAVI.
Abstract
To develop a machine learning (ML) model using preprocedural cardiac CT data for predicting 1-year mortality after transcatheter aortic valve implantation (TAVI) for severe aortic stenosis (AS), deployable as a web-based solution. A retrospective single-center study was conducted on consecutive participants undergoing TAVI from October 2020 to March 2023 (training) and April to November 2023 (internal testing), with CT performed on a dual-source scanner SOMATOM Definition Flash or Drive. Two logistic regression models were developed: full-variable (clinical, echocardiographic, and cardiac CT features) and CT-only (left ventricular ejection fraction and indexed right atrial end-diastolic volume). ROC analysis and risk stratification by tertiles were used for performance evaluation. The CT-only model was further tested with features automatically extracted using TotalSegmentator. The training set included 495 participants (260[53%] women; median age 82 years [IQR,78–85]); the internal test set included 143 (72[50%] women; median age 82 years [IQR,77–85]). One-year mortality occurred in 56/495 (11%) and 14/143 (10%), respectively. The CT-only model achieved an AUC of 0.67 [95%CI,0.59-0.75], comparable to the full-variable model (AUC: 0.70 [95%CI,0.62-0.78]). Internal testing confirmed performance (AUC: 0.68 [95%CI, 0.54-0.81]) and risk stratification into groups of low- (1.7%), intermediate- (13.5%), and high-risk (17.0%) of 1-year mortality. Automatically extracting CT metrics with TotalSegmentator preserved discrimination (AUC: 0.67 [95%CI, 0.54-0.79]), with outcome probability of 4.3% (low-risk), 11.1% (intermediate-risk), and 14.3% (high-risk). The model was successfully deployed via a web-application (https://uc3-model.srace.ai-hub.it/). An ML model using two preprocedural cardiac CT-derived metrics, automatically extractable with open-source tools, predicts 1-year mortality after TAVI. A web-based solution has been made publicly available to facilitate external validation, deployment for prognostication independent of commercial software or multi-source data integration.
