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20-05-2019 | Original Article | Uitgave 9/2019 Open Access

Netherlands Heart Journal 9/2019

Value of machine learning in predicting TAVI outcomes

Tijdschrift:
Netherlands Heart Journal > Uitgave 9/2019
Auteurs:
R. R. Lopes, M. S. van Mourik, E. V. Schaft, L. A. Ramos, J. Baan Jr., J. Vendrik, B. A. J. M. de Mol, M. M. Vis, H. A. Marquering
Belangrijke opmerkingen

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1007/​s12471-019-1285-7) contains supplementary material, which is available to authorized users.

Abstract

Background

Transcatheter aortic valve implantation (TAVI) has become a commonly applied procedure for high-risk aortic valve stenosis patients. However, for some patients, this procedure does not result in the expected benefits. Previous studies indicated that it is difficult to predict the beneficial effects for specific patients. We aim to study the accuracy of various traditional machine learning (ML) algorithms in the prediction of TAVI outcomes.

Methods and results

Clinical and laboratory data from 1,478 TAVI patients from a single centre were collected. The outcome measures were improvement of dyspnoea and mortality. Three experiments were performed using (1) screening data, (2) laboratory data, and (3) the combination of both. Five well-established ML techniques were implemented, and the models were evaluated based on the area under the curve (AUC). Random forest classifier achieved the highest AUC (0.70) for predicting mortality. Logistic regression had the highest AUC (0.56) in predicting improvement of dyspnoea.

Conclusions

In our single-centre TAVI population, the tree-based models were slightly more accurate than others in predicting mortality. However, ML models performed poorly in predicting improvement of dyspnoea.

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Netherlands Heart Journal

Het Netherlands Heart Journal wordt uitgegeven in samenwerking met de Nederlandse Vereniging voor Cardiologie en de Nederlandse Hartstichting. Het tijdschrift is Engelstalig en wordt gratis beschikbaa ...

Extra materiaal
Summarized patient characteristics and hyperparameters used for optimisation.
12471_2019_1285_MOESM1_ESM.docx
Literatuur
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