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Predicting stroke and mortality in mitral regurgitation: A machine learning approach

Zhou, Jiandong, Lee, Sharen, Liu, Yingzhi, Chan, Jeffrey Shi Kai, Li, Guoliang, Wong, Wing Tak, Jeevaratnam, Kamalan, Cheng, Shuk Han, Liu, Tong, Tse, Gary, and others. (2022) Predicting stroke and mortality in mitral regurgitation: A machine learning approach. Current Problems in Cardiology, 48 (2). Article Number 101464. ISSN 0146-2806. (doi:10.1016/j.cpcardiol.2022.101464) (KAR id:99771)


We hypothesized that an interpretable gradient boosting machine (GBM) model considering comorbidities, P-wave and echocardiographic measurements, can better predict mortality and cerebrovascular events in mitral regurgitation (MR). Patients from a tertiary center were analyzed. The GBM model was used as an interpretable statistical approach to identify the leading indicators of high-risk patients with either outcome of CVAs and all-cause mortality. A total of 706 patients were included. GBM analysis showed that age, systolic blood pressure, diastolic blood pressure, plasma albumin levels, mean P-wave duration (PWD), MR regurgitant volume, left ventricular ejection fraction (LVEF), left atrial dimension at end-systole (LADs), velocity-time integral (VTI) and effective regurgitant orifice were significant predictors of TIA/stroke. Age, sodium, urea and albumin levels, platelet count, mean PWD, LVEF, LADs, left ventricular dimension at end systole (LVDs) and VTI were significant predictors of all-cause mortality. The GBM demonstrates the best predictive performance in terms of precision, sensitivity c-statistic and F1-score compared to logistic regression, decision tree, random forest, support vector machine, and artificial neural networks. Gradient boosting model incorporating clinical data from different investigative modalities significantly improves risk prediction performance and identify key indicators for outcome prediction in MR.

Item Type: Article
DOI/Identification number: 10.1016/j.cpcardiol.2022.101464
Subjects: R Medicine
Divisions: Divisions > Division of Natural Sciences > Kent and Medway Medical School
Funders: University of Kent (
Depositing User: Gary Tse
Date Deposited: 30 Jan 2023 12:31 UTC
Last Modified: 01 May 2024 13:59 UTC
Resource URI: (The current URI for this page, for reference purposes)

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