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Machine learning and prediction of left atrial appendage thrombosis in nonvalvular atrial fibrillation

  • Author Footnotes
    1 The first two authors equally contribute to this paper.
    Yue Zhao
    Footnotes
    1 The first two authors equally contribute to this paper.
    Affiliations
    Department of Pharmacy, Southwest Hospital, Third Military Medical University, Chongqing 400038, PR China
    Search for articles by this author
  • Author Footnotes
    1 The first two authors equally contribute to this paper.
    Li-Ya Cao
    Footnotes
    1 The first two authors equally contribute to this paper.
    Affiliations
    Department of Pharmacy, Southwest Hospital, Third Military Medical University, Chongqing 400038, PR China
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  • Ying-Xin Zhao
    Affiliations
    Department of Pharmacy, Army Medical Center, Third Military Medical University, Chongqing 400042, PR China
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  • Fei Wang
    Affiliations
    Medical Big Data and Artificial Intelligence Center, Southwest Hospital, Third Military Medical University, Chongqing 400038, PR China
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  • Hai-Yan Xing
    Correspondence
    Corresponding author.
    Affiliations
    Department of Pharmacy, Army Medical Center, Third Military Medical University, Chongqing 400042, PR China
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  • Qian Wang
    Correspondence
    Correspondence to: Q. Wang, Department of Pharmacy, Southwest Hospital, Third Military Medical University, Chongqing 400038, PR China.
    Affiliations
    Department of Pharmacy, Southwest Hospital, Third Military Medical University, Chongqing 400038, PR China
    Search for articles by this author
  • Author Footnotes
    1 The first two authors equally contribute to this paper.

      Abstract

      Background

      As a major complication of non-valvular atrial fibrillation (NVAF), left atrial appendage (LAA) thrombosis is associated with cerebral ischemic strokes, as well as high morbidity. Due to insufficient incorporation of risk factors, most current scoring methods are limited to the analysis of relationships between clinical characteristics and LAA thrombosis rather than detecting potential risk. Therefore, this study proposes a clinical data-driven machine learning method to predict LAA thrombosis of NVAF.

      Methods

      Patients with NVAF from January 2014 to June 2022 were enrolled from 40 Southwest Hospital. We selected 40 variables for analysis, including demographic data, medical history records, laboratory results, and the structure of LAA. Three machine learning algorithms were adopted to construct classifiers for the prediction of LAA thrombosis risk. The most important variables related to LAA thrombosis and their influences were recognized by SHapley Addictive exPlanations method. In addition, we compared our model with CHADS2 and CHADS2-VASc scoring methods.

      Results

      A total of 713 participants were recruited, including 127 patients with LAA thrombosis and 586 patients with no obvious thrombosis. The consensus models based on Random Forest and eXtreme Gradient Boosting LAA thrombosis prediction (RXTP) achieved the best accuracy of 0.865, significantly outperforming CHADS2 score and CHA2DS2-VASc score (0.754 and 0.754, respectively). The SHAP results showed that B-type natriuretic peptide, left atrial appendage width, C-reactive protein, Fibrinogen and estimated glomerular filtration rate are closely related to the risk of LAA thrombosis in nonvalvular atrial fibrillation.

      Conclusions

      The RXTP-NVAF model is the most effective model with the greatest ROC value and recall rate. The summarized risk factors obtained from SHAP enable the optimization of the treatment strategy, thereby preventing thromboembolism events and the occurrence of cardiogenic ischemic stroke.

      Keywords

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