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Prediction of response to cardiac resynchronization therapy using a multi-feature learning method

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Abstract

We hypothesized that a multiparametric evaluation, based on the combination of electrocardiographic and echocardiographic parameters, could enhance the appraisal of the likelihood of reverse remodeling and prognosis of favorable clinical evolution to improve the response of cardiac resynchronization therapy (CRT). Three hundred and twenty-three heart failure patients were retrospectively included in this multicenter study. 221 patients (68%) were responders, defined by a decrease in left ventricle end-systolic volume ≥15% at the 6-month follow-up. In addition, strain data coming from echocardiography were analyzed with custom-made signal processing methods. Integrals of regional longitudinal strain signals from the beginning of the cardiac cycle to strain peak and to the instant of aortic valve closure were analyzed. QRS duration, septal flash and different other features manually extracted were also included in the analysis. The random forest (RF) method was applied to analyze the relative feature importance, to select the most significant features and to build an ensemble classifier with the objective of predicting response to CRT. The set of most significant features was composed of Septal Flash, E, E/A, E/EA, QRS, left ventricular end-diastolic volume and eight features extracted from strain curves. A Monte Carlo cross-validation method with 100 runs was applied, using, in each run, different random sets of 80% of patients for training and 20% for testing. Results show a mean area under the curve (AUC) of 0.809 with a standard deviation of 0.05. A multiparametric approach using a combination of echo-based parameters of left ventricular dyssynchrony and QRS duration helped to improve the prediction of the response to cardiac resynchronization therapy.

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Acknowledgements

This work was partially supported by the French National Research Agency (ANR), under grant ANR-16-CE19-0008-01 (project MAESTRO). We thank Oslo and Leuven teams for their participation in this study.

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Author notes

  1. Alban Gallard and Arnaud Hubert have contributed equally to the building of the manuscript.

Authors and Affiliations

  1. Univ Rennes, CHU Rennes, Inserm, LTSI UMR 1099, 35000, Rennes, France

    Alban Gallard, Arnaud Hubert, Virginie Le Rolle, Christophe Leclercq, Auriane Bidaut, Elena Galli, Erwan Donal & Alfredo I. Hernandez

  2. Oslo University Hospital, Department of Cardiology, Oslo, Norway

    Otto Smiseth

  3. Department of Cardiovascular Diseases, University Hospitals Leuven, Leuven, Belgium

    Jens-Uwe Voigt

  4. Service de Cardiologie – Hôpital Pontchaillou – CHU Rennes, 35033, Rennes, France

    Erwan Donal

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  1. Alban Gallard
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  2. Arnaud Hubert
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  3. Otto Smiseth
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  4. Jens-Uwe Voigt
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  8. Elena Galli
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  10. Alfredo I. Hernandez
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Correspondence to Erwan Donal.

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Gallard, A., Hubert, A., Smiseth, O. et al. Prediction of response to cardiac resynchronization therapy using a multi-feature learning method. Int J Cardiovasc Imaging 37, 989–998 (2021). https://doi.org/10.1007/s10554-020-02083-1

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  • DOI: https://doi.org/10.1007/s10554-020-02083-1

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