Identifying Top Predictors of Change in Noncalcified Coronary Burden in Psoriasis by Machine Learning Over 1-Year

• Published in: Journal of psoriasis and psoriatic arthritis Vol. 6; no. 2; pp. 113 - 117
• Main Authors: Munger, Eric, Dey, Amit K., Rodante, Justin, Playford, Martin P., Sorokin, Alexander V., Tian, Xin, Wu, Colin O., Hasan, Ahmed, Chen, Marcus Y., Gelfand, Joel M., Jafri, Mohsin Saleet, Mehta, Nehal N.
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01-04-2021
• Subjects: coronary artery disease; machine learning; inflammation; psoriasis
• ISSN: 2475-5303; 2475-5311
• DOI: 10.1177/24755303211000757

Background:
Psoriasis is associated with accelerated non-calcified coronary burden (NCB) by coronary computed tomography angiography (CCTA). Machine learning (ML) algorithms have been shown to effectively identify cardiometabolic variables with NCB in cross-sectional analysis.
Objective:
To use ML methods to characterize important predictors of change in NCB by CCTA in psoriasis over 1-year of observation.
Methods:
The analysis included 182 consecutive patients with 80 available variables from the Psoriasis Atherosclerosis Cardiometabolic Initiative, a prospective, observational cohort study at baseline and 1-year using the random forest regression algorithm. NCB was assessed at baseline and 1-year from CCTA.
Results:
Using ML, we identified variables of high importance in the context of predicting changes in NCB. For the cohort that improved NCB (n = 102), top baseline variables were cholesterol (total and HDL), white blood cell count, psoriasis area severity index score, and diastolic blood pressure. Top predictors of 1-year change were change in visceral adiposity, white blood cell count, total cholesterol, c-reactive protein, and absolute lymphocyte count. For the cohort that worsened NCB (n = 80), the top baseline variables were HDL cholesterol related including apolipoprotein A1, basophil count, and psoriasis area severity index score, and top predictors of 1-year change were change in apoA, apoB, and systolic blood pressure.
Conclusion:
ML methods ranked predictors of progression and regression of NCB in psoriasis over 1 year providing strong evidence to focus on treating LDL, blood pressure, and obesity; as well as the importance of controlling cutaneous disease in psoriasis.