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Machine learning algorithms to automate differentiating cardiac amyloidosis from hypertrophic cardiomyopathy

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Abstract

Cardiac amyloidosis has a poor prognosis, and high mortality and is often misdiagnosed as hypertrophic cardiomyopathy, leading to delayed diagnosis. Machine learning combined with speckle tracking echocardiography was proposed to automate differentiating two conditions. A total of 74 patients with pathologically confirmed monoclonal immunoglobulin light chain cardiac amyloidosis and 64 patients with hypertrophic cardiomyopathy were enrolled from June 2015 to November 2018. Machine learning models utilizing traditional and advanced algorithms were established and determined the most significant predictors. The performance was evaluated by the receiver operating characteristic curve (ROC) and the area under the curve (AUC). With clinical and echocardiography data, all models showed great discriminative performance (AUC > 0.9). Compared with logistic regression (AUC 0.91), machine learning such as support vector machine (AUC 0.95, p = 0.477), random forest (AUC 0.97, p = 0.301) and gradient boosting machine (AUC 0.98, p = 0.230) demonstrated similar capability to distinguish cardiac amyloidosis and hypertrophic cardiomyopathy. With speckle tracking echocardiography, the predictive performance of the voting model was similar to that of LightGBM (AUC was 0.86 for both), while the AUC of XGBoost was slightly lower (AUC 0.84). In fivefold cross-validation, the voting model was more robust globally and superior to the single model in some test sets. Data-driven machine learning had shown admirable performance in differentiating two conditions and could automatically integrate abundant variables to identify the most discriminating predictors without making preassumptions. In the era of big data, automated machine learning will help to identify patients with cardiac amyloidosis and timely and effectively intervene, thus improving the outcome.

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Acknowledgements

The authors thank Xiao-Jun Chen for providing purely technical help.

Funding

This research did not receive any specific grant from funding: agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Cardiology, The First Affiliated Hospital, School of Medicine, Zhejiang University, No.79 qingchun Road, Hangzhou, 310003, Zhejiang, China

    Zi-Wen Wu, Jin-Lei Zheng, Lin Kuang & Hui Yan

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  1. Zi-Wen Wu
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  2. Jin-Lei Zheng
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  3. Lin Kuang
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Contributions

ZW as the first author collected data, drafted, and revised the manuscript. JZ and LK contributed to the acquisition, analysis, and interpretation of data. HY is the corresponding author, who performed the conception and design of the study, and the final approval of the submitted manuscript.

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Correspondence to Hui Yan.

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The authors declare that they have no competing interests.

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All of the procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study was approved by the Clinical Research Ethics Committee of the First Affiliated Hospital, College of Medicine, Zhejiang University (No. IIT20200654A).

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Wu, ZW., Zheng, JL., Kuang, L. et al. Machine learning algorithms to automate differentiating cardiac amyloidosis from hypertrophic cardiomyopathy. Int J Cardiovasc Imaging 39, 339–348 (2023). https://doi.org/10.1007/s10554-022-02738-1

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  • DOI: https://doi.org/10.1007/s10554-022-02738-1

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