Key result
A gradient boosting machine model incorporating multi-modality clinical data significantly outperformed baseline machine learning models in predicting cerebrovascular events and all-cause mortality in mitral regurgitation.
Why the study?
It was hypothesized that an interpretable gradient boosting machine model considering comorbidities, P-wave, and echocardiographic measurements could better predict mortality and cerebrovascular events in mitral regurgitation.
Does a gradient boosting machine model improve the prediction of mortality and stroke compared to traditional models in patients with mitral regurgitation?
Cohort (n=706)
No
Does a gradient boosting machine model improve the prediction of mortality and stroke compared to traditional models in patients with mitral regurgitation?
An interpretable gradient boosting machine model incorporating multi-modality clinical data significantly improves risk prediction for mortality and stroke in patients with mitral regurgitation.
May enhance cerebrovascular and mortality risk stratification in mitral regurgitation; hypothesis-generating pending prospective validation.
Introduction 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). Methods 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. Results 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. Conclusion Gradient boosting model incorporating clinical data from different investigative modalities significantly improves risk prediction performance and identify key indicators for outcome prediction in MR.
No takes yet. Share an insight, caveat, or question.
Zhou et al. (2021) conducted a cohort 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 All-cause mortality. A gradient boosting machine model incorporating multi-modality clinical data significantly outperformed baseline machine learning models in predicting cerebrovascular events and all-cause mortality in mitral regurgitation.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: