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Application of Gradient Boosting in Evaluating Surgical Ablation for Atrial Fibrillation

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Abstract

Atrial fibrillation (AF) represents a condition of irregularities of heartbeats. Timely diagnosis and treatment of AF are crucial to avoid serious consequences such as stroke, heart failure and death. AF is detected from patient’s rhythm data as a binary longitudinal sequence. In this article, we introduce an approach for analyzing the binary longitudinal sequence of AF. Our approach is based on gradient boosting, a machine learning approach. We use multivariate tree as a base learner to model complex relationships between multiple covariates and response. In order to model covariate-time interactions, we use B-spline. Application of our approach to a randomized trial data provides the importance of surgical ablation in the treatment of AF among patients with persistent or long-standing persistent AF. Comparison of prediction performance of our approach with other methods using randomized trial data and simulated data shows that our approach has a better prediction performance. Variable selection using variable importance has identified duration of AF as an important covariate that has strong interaction with post-surgery time. This helps to identify patients’ variability and groups of patients who derived the most benefits from the treatment. Our method can be implemented using the R package boostmtree.

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Funding

This work is supported in part by R01 HL103552 and by a cooperative agreement (U01 HL088942) with the National Heart, Lung, and Blood Institute, including funding by the National Institute of Neurological Disorders and Stroke and the Canadian Institutes of Health Research. Clinicaltrials.gov Identifier: NCT00903370.

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Author notes

  1. Hemant Ishwaran, Eugene Blackstone, Jeevanantham Rajeswaran and Marc Gillinov have contributed equally to this work.

Authors and Affiliations

  1. Heart, Vascular and Thoracic Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, 44016, OH, USA

    Amol Pande, Eugene Blackstone & Marc Gillinov

  2. Division of Biostatistics, University of Miami, 1120 NW 14th St, Miami, 33136, FL, USA

    Hemant Ishwaran

  3. Quantitative Health Sciences, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, 44016, OH, USA

    Eugene Blackstone & Jeevanantham Rajeswaran

Authors
  1. Amol Pande
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  2. Hemant Ishwaran
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  3. Eugene Blackstone
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  4. Jeevanantham Rajeswaran
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  5. Marc Gillinov
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Correspondence to Amol Pande.

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Pande, A., Ishwaran, H., Blackstone, E. et al. Application of Gradient Boosting in Evaluating Surgical Ablation for Atrial Fibrillation. SN COMPUT. SCI. 3, 466 (2022). https://doi.org/10.1007/s42979-022-01350-3

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