Key result
A gradient boosting machine model demonstrated superior predictive performance for mortality and cerebrovascular events in mitral regurgitation compared to other machine learning models.
Why the study?
It was hypothesized that an interpretable gradient boosting machine model incorporating comorbidities, P-wave, and echocardiographic measurements could better predict mortality and cerebrovascular events in mitral regurgitation.
Does a gradient boosting machine model better predict mortality and cerebrovascular events in patients with mitral regurgitation compared to other machine learning models?
Observational (n=706)
No
Does a gradient boosting machine model better predict mortality and cerebrovascular events in patients with mitral regurgitation compared to other machine learning models?
A gradient boosting machine model incorporating clinical, ECG, and echocardiographic data improves risk prediction for stroke and mortality in patients with mitral regurgitation.
May aid risk stratification in mitral regurgitation; leaves open prospective validation before clinical use.
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.
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Zhou et al. (2022) conducted an observational in Mitral regurgitation (n=706). Gradient boosting machine (GBM) model vs. Logistic regression, decision tree, random forest, support vector machine, and artificial neural networks was evaluated on TIA/stroke and all-cause mortality. A gradient boosting machine model demonstrated superior predictive performance for mortality and cerebrovascular events in mitral regurgitation compared to other machine learning models.
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