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

Identifying Top Predictors of Change in Noncalcified Coronary Burden in Psoriasis by Machine... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Psoriasis and Psoriatic Arthritis SAGE

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

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References (12)

Publisher
SAGE
Copyright
© The Author(s) 2021
ISSN
2475-5303
eISSN
2475-5311
DOI
10.1177/24755303211000757
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Journal of Psoriasis and Psoriatic Arthritis – SAGE

Published: Apr 1, 2021

Keywords: machine learning; coronary artery disease; inflammation; psoriasis

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