Word Count: 1422
Abstract (195 words)
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. Kachrooet. 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. Kachrooet. 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.