Development and validation of an interpretable machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients.

Journal: PloS one
PMID: 39820175

Abstract

PURPOSE: Left atrial thrombus or spontaneous echo contrast (LAT/SEC) are widely recognized as significant contributors to cardiogenic embolism in non-valvular atrial fibrillation (NVAF). This study aimed to construct and validate an interpretable predictive model of LAT/SEC risk in NVAF patients using machine learning (ML) methods.

Authors

  • Chaoqun Huang
    Department of Cardiovascular Medicine, The First Bethune Hospital of Jilin University, Changchun, Jilin Province, China.
  • Shangzhi Shu
    Department of Cardiovascular Medicine, The First Bethune Hospital of Jilin University, Changchun, Jilin Province, China.
  • Miaomiao Zhou
    Department of Cardiovascular Medicine, The First Bethune Hospital of Jilin University, Changchun, Jilin Province, China.
  • Zhenming Sun
    Department of Cardiovascular Medicine, The First Bethune Hospital of Jilin University, Changchun, Jilin Province, China.
  • Shuyan Li

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