Predicting stroke and mortality in mitral regurgitation: A machine learning approach
Journal article
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 Zhang, Qingpeng 2022. Predicting stroke and mortality in mitral regurgitation: A machine learning approach. Current Problems in Cardiology. 48 (2), p. 101464. https://doi.org/S0146-2806(22)00361-9
| Authors | 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 Zhang, Qingpeng |
|---|---|
| Abstract | 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. [Abstract copyright: Copyright © 2022. Published by Elsevier Inc.] |
| Keywords | Mortality; Mitral regurgitation; Stroke; Machine learning; Gradient boosting |
| Year | 2022 |
| Journal | Current Problems in Cardiology |
| Journal citation | 48 (2), p. 101464 |
| Publisher | Elsevier |
| ISSN | 1535-6280 |
| Digital Object Identifier (DOI) | https://doi.org/S0146-2806(22)00361-9 |
| https://doi.org/10.1016/j.cpcardiol.2022.101464 | |
| Official URL | https://www.sciencedirect.com/science/article/pii/S0146280622003619?via%3Dihub |
| Publication dates | |
| Online | 16 Oct 2022 |
| Feb 2023 | |
| Publication process dates | |
| Accepted | 13 Oct 2022 |
| Deposited | 02 Nov 2022 |
| Output status | Published |
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https://repository.canterbury.ac.uk/item/92zy7/predicting-stroke-and-mortality-in-mitral-regurgitation-a-machine-learning-approach
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