Study results in context 
A criticism of ML models in the past has been their lack of interpretability as the predictors that drive model output are unknown. Kachroo et. al. have addressed this limitation by providing analysis of the important model predictors, thus providing novel mechanistic and clinical insights. In this study, the authors’ feature importance analysis reinforces the notion that each predictor on its own may not contain much predictive power, but, the combination of predictors, and the use of a powerful tool, like ML, can generate predictive power. When each predictor in this study is placed in order of importance, up to 20 predictors have up to 20% relative importance to the most important predictor and include features like hypertension and diabetes that when evaluated alone contain minimal predictive power.
Despite the lack of echocardiographic measurements of cardiomyopathy in the STS dataset, there were some novel findings. In this paper, of the five most important predictors of recurrent MR, three of them (bypass to the ramus, obtuse marginal II and diagonal II) represent revascularization targets. Perhaps these represent the geometric changes that take place due to ischemia and ventricular remodelling, that result in leaflet tethering and mitral annular dilation and circularization [1]. These measures are consistent with previously identified markers of recurrent MR such as basal inferior aneurysms or dyskinesia. This suggests that certain patterns of myocardial ischemia may be more likely to suffer from recurrent MR post repair. Additionally, these insights can be used to deliver surgical strategies that reduce risk of recurrent MR. For example, as a bypass to the ramus is an important predictor of recurrent MR, surgeons would be more vigilant about revascularization of the anterior wall. The use of beta blockers also was one of the five most important predictors and is likely collinear with NYHA III/IV symptoms and represents symptomatic reduced ejection fraction heart failure. In this setting, it is likely that patients that were not in beta blockers were at higher risk for recurrent MR. This shines light on the role of ventricular reverse remodelling and the role of ongoing optimal guideline directed medical therapy in order for valve repair to be durable.
In summary, the great benefit of the STS database is that these predictors are easy to obtain and do not rely on patient reported outcomes or subjective and expensive echocardiographic assessment of the heart. In this way, Kachroo et. al . have succeeded in using universally accessible, objective measures that are routinely collected at a national level to identify patients that receive durable mitral valve repairs. This tool can be used by all surgeons for all patients using only clinical demographics and a revascularization plan. As the amount of data collected in medicine continues to grow and become more digitized and multidimensional, ML will be the tool that can amalgamate, analyze and interpret this high dimensionality data. ML and its counterpart, Artificial Intelligence, will allow for the integration of datasets, like the STS, with raw imaging data, such as echocardiograms and cardiac MRI, with waveform data, such as electrocardiograms, with genomic data for highly personalized care (Figure).  This study by Kachroo et. al. is the first building block in this pursuit, showing that high prediction accuracy can be achieved from routinely collected STS variables, and can only be improved as datasets continue to grow in dimensionality. We can look forward to a rich future in data and data analytics and using ML with the STS database can prove to be beneficial for clinicians, scientists and patients.