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

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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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Authors and Affiliations

  1. Department of Internal Medicine III, University of Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany

    Bruna Gomes, Christoph Reich, Florian Leuschner, Mathias Konstandin, Hugo A. Katus & Benjamin Meder

  2. Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany

    Maximilian Pilz

  3. Informatics for Life, Heidelberg, Germany

    Benjamin Meder

  4. German Center for Cardiovascular Research (DZHK), Heidelberg, Germany

    Benjamin Meder

  5. Department of Genetics, Genome Technology Center, Stanford University, Stanford, CA, USA

    Benjamin Meder

Authors
  1. Bruna Gomes
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  2. Maximilian Pilz
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  3. Christoph Reich
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  4. Florian Leuschner
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  5. Mathias Konstandin
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  6. Hugo A. Katus
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  7. Benjamin Meder
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Corresponding author

Correspondence to Benjamin Meder.

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Conflict of interest

Prof. Meder is part of the Scientific Advisory Board of Fleischhacker GmbH. All other authors have no conflicts of interest to declare.

Ethics approval and consent to participate

All patients were informed about specific risks and alternatives of TAVI and gave informed written consent to TAVI and pre- and post-interventional monitoring (data collection). The study protocol was approved by the local ethics committee (S-299/2015).

Availability of data and material

An anonymized spreadsheet regarding the data population is annexed to the supplementary data.

Code availability

Preprocessing code, machine learning implementations, and trained clinical outcomes classifications models are all open source and available at: “https://github.com/AIforTAVI/OutcomesTAVIwithML”.

Additional information

All authors take responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation.

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Gomes, B., Pilz, M., Reich, C. et al. Machine learning-based risk prediction of intrahospital clinical outcomes in patients undergoing TAVI. Clin Res Cardiol 110, 343–356 (2021). https://doi.org/10.1007/s00392-020-01691-0

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