Abstract

One of the surgical options available for ischemic mitral regurgitation is mitral valve repair but is limited by recurrent regurgitation as it is experienced by a significant percent of patients and has a negative impact on patient outcomes. Efforts to model and identify predictors of recurrent MR rely on complicated echocardiographic and clinical measurements that are subjective and not routinely collected. Kachroo et. al. approached this problem in a unique way by using the STS database and Machine Learning to develop models that predict recurrent MR or death at one year. The STS database contains many routinely collected demographic and clinical parameters but requires a methodology, such as Machine Learning, that will accommodate collinearity and the unknown significance of many predictors. Kachroo et. al. developed three good Machine Learning models with AUC 0.72-0.75. Data- driven selection of important predictors showed that three revascularization targets, peripheral vascular disease and use of beta blockers are most predictive of recurrent mitral regurgitation. We applaud the authors in pioneering a novel methodology and paving the way for a bright future in Machine Learning which includes integrating medical imaging, waveform, and genomic data to practice personalized medicine for our patients.