Machine learning insight into the role of imaging and clinical variables for the prediction of obstructive coronary artery disease and revascularization: An exploratory analysis of the CONSERVE study

Machine learning (ML) is able to extract patterns and develop algorithms to construct data-driven models.

The Authors used ML models to gain insight into the relative importance of variables to predict obstructive coronary artery disease (CAD) using the Coronary Computed Tomographic Angiography for Selective Cardiac Catheterization (CONSERVE) study, as well as to compare prediction of obstructive CAD to the CAD consortium clinical score (CAD2). They further perform ML analysis to gain insight into the role of imaging and clinical variables for revascularization.

For obstructive CAD, the ML model outperformed CAD2. BMI is an important variable, although currently not included in most scores.

Reference

1. Baskaran L, Ying X, Xu Z, Al'Aref SJ, Lee BC, Lee SE, Danad I, Park HB, Bathina R, Baggiano A, Beltrama V, Cerci R, Choi EY, Choi JH, Choi SY, Cole J, Doh JH, Ha SJ, Her AY, Kepka C, Kim JY, Kim JW, Kim SW, Kim W, Lu Y, Kumar A, Heo R, Lee JH, Sung JM, Valeti U, Andreini D, Pontone G, Han D, Villines TC, Lin F, Chang HJ, Min JK, Shaw LJ. Machine learning insight into the role of imaging and clinical variables for the prediction of obstructive coronary artery disease and revascularization: An exploratory analysis of the CONSERVE study. PLoS One 2020 Jun 25;15(6):e0233791.doi: 10.1371/journal.pone.0233791 Go to PubMed