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01-03-2021 | Artificial Intelligence | Original Paper

Machine learning-based risk prediction of intrahospital clinical outcomes in patients undergoing TAVI

Authors: Bruna Gomes, Maximilian Pilz, Christoph Reich, Florian Leuschner, Mathias Konstandin, Hugo A. Katus, Benjamin Meder

Published in: Clinical Research in Cardiology | Issue 3/2021

Abstract

Background

Currently, patient selection in TAVI is based upon a multidisciplinary heart team assessment of patient comorbidities and surgical risk stratification. In an era of increasing need for precision medicine and quickly expanding TAVI indications, machine learning has shown promise in making accurate predictions of clinical outcomes. This study aims to predict different intrahospital clinical outcomes in patients undergoing TAVI using a machine learning-based approach. The main clinical outcomes include all-cause mortality, stroke, major vascular complications, paravalvular leakage, and new pacemaker implantations.

Methods and results

The dataset consists of 451 consecutive patients undergoing elective TAVI between February 2014 and June 2016. The applied machine learning methods were neural networks, support vector machines, and random forests. Their performance was evaluated using five-fold nested cross-validation. Considering all 83 features, the performance of all machine learning models in predicting all-cause intrahospital mortality (AUC 0.94–0.97) was significantly higher than both the STS risk score (AUC 0.64), the STS/ACC TAVR score (AUC 0.65), and all machine learning models using baseline characteristics only (AUC 0.72–0.82). Using an extreme boosting gradient, baseline troponin T was found to be the most important feature among all input variables. Overall, after feature selection, there was a slightly inferior performance. Stroke, major vascular complications, paravalvular leakage, and new pacemaker implantations could not be accurately predicted.

Conclusions

Machine learning has the potential to improve patient selection and risk management of interventional cardiovascular procedures, as it is capable of making superior predictions compared to current logistic risk scores.

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Title: Machine learning-based risk prediction of intrahospital clinical outcomes in patients undergoing TAVI
Authors: Bruna Gomes
Maximilian Pilz
Christoph Reich
Florian Leuschner
Mathias Konstandin
Hugo A. Katus
Benjamin Meder
Publication date: 01-03-2021
Publisher: Springer Berlin Heidelberg
Keywords: Artificial Intelligence
Transcatheter Aortic Valve Implantation
Conventional Pacemaker
Conventional Pacemaker
Pacemaker
Pacemaker
Pacemaker
Published in: Clinical Research in Cardiology / Issue 3/2021
Print ISSN: 1861-0684
Electronic ISSN: 1861-0692
DOI: https://doi.org/10.1007/s00392-020-01691-0

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