Application of Machine Learning to Determine Top Predictors of Non-calcified Coronary Burden in Psoriasis: an Observational Cohort Study

Abstract Background Psoriasis is associated with elevated risk of heart attack as well as increased accumulation of subclinical non-calcified coronary burden by coronary computed tomography angiography (CCTA). Machine learning algorithms have been shown to effectively analyze well characterized datasets. Objective In this study, we used machine learning algorithms to determine top predictors of non-calcified coronary burden by CCTA in psoriasis. Methods The analysis included 263 consecutive patients with 62 available variables from the Psoriasis Atherosclerosis Cardiometabolic Initiative. The random forest algorithm was utilized to determine top predictors of non-calcified coronary burden by CCTA. We evaluated our results using linear regression models. Results Using the random forest algorithm, the top 10 predictors of non-calcified coronary burden were: body mass index, visceral adiposity, total adiposity, apolipoprotein A1, high-density lipoprotein, erythrocyte sedimentation rate, subcutaneous adiposity, small low-density lipoprotein particle and cholesterol efflux capacity. Linear regression of non-calcified coronary burden yielded results consistent with our machine learning output. Limitation We were unable to provide external validation and did not study cardiovascular events. Conclusion Machine learning methods identified top predictors of non-calcified coronary burden in psoriasis. These factors were related to obesity, dyslipidemia, and inflammation demonstrating that these are important targets to treat comorbidities in psoriasis.