Machine learning as a new frontier in mitral valve surgical strategy

BACKGROUND: One of the surgical options available for ischemic mitral regurgitation (MR) is mitral valve repair but is limited by recurrent regurgitation as it is experienced by a significant percentage 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. AIMS: Kachroo et al. approached this problem in a unique way by using the STS database and machine learning (ML) to develop models that predict recurrent MR or death at 1 year. DISCUSSION: The STS database contains many routinely collected demographic and clinical parameters but requires a methodology, such as ML, that will accommodate collinearity and the unknown significance of many predictors. Kachroo et al. developed three good ML models with the area under curve 0.72-0.75. Data-driven selection of important predictors showed that three revascularization targets, peripheral vascular disease, and use of β-blockers are most predictive of recurrent MR. CONCLUSION: We applaud the authors for pioneering a novel methodology and paving the way for a bright future in ML which includes integrating medical imaging, waveform, and genomic data to practice personalized medicine for our patients.