1. Application of machine learning to determine top predictors of noncalcified coronary burden in psoriasis: An observational cohort study
- Author
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Eric Munger, Jenis Argueta-Amaya, Colin Wu, Milena Aksentijevich, Noor Khalil, Julie Erb-Alvarez, Aarthi S Reddy, Justin A. Rodante, Nehal N. Mehta, Sanjiv J. Shah, David A. Bluemke, Veit Sandfort, Andrew Keel, Jacob Groenendyk, Amit K. Dey, Harry Choi, Benjamin Lockshin, Lam C. Tsoi, Johann E. Gudjonsson, Marcus Y. Chen, Youssef A. Elnabawi, Joel M. Gelfand, Mohsin S. Jafri, Xin Tian, Martin P. Playford, and Ahmed A. K. Hasan
- Subjects
Adult ,Male ,Comorbidity ,Coronary Artery Disease ,Dermatology ,Machine learning ,computer.software_genre ,Risk Assessment ,Lipoprotein particle ,Article ,Machine Learning ,Coronary artery disease ,030207 dermatology & venereal diseases ,03 medical and health sciences ,0302 clinical medicine ,Risk Factors ,Psoriasis ,Humans ,Medicine ,Obesity ,Prospective Studies ,Dyslipidemias ,Inflammation ,biology ,business.industry ,C-reactive protein ,Middle Aged ,medicine.disease ,Coronary Vessels ,030220 oncology & carcinogenesis ,biology.protein ,Female ,Apolipoprotein A1 ,Artificial intelligence ,Tomography, X-Ray Computed ,business ,computer ,Body mass index ,Dyslipidemia ,Cohort study - Abstract
Background Psoriasis is associated with elevated risk of heart attack and increased accumulation of subclinical noncalcified coronary burden by coronary computed tomography angiography (CCTA). Machine learning algorithms have been shown to effectively analyze well-characterized data sets. Objective In this study, we used machine learning algorithms to determine the top predictors of noncalcified coronary burden by CCTA in psoriasis. Methods The analysis included 263 consecutive patients with 63 available variables from the Psoriasis Atherosclerosis Cardiometabolic Initiative. The random forest algorithm was used to determine the top predictors of noncalcified coronary burden by CCTA. We evaluated our results using linear regression models. Results Using the random forest algorithm, we found that the top 10 predictors of noncalcified 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, cholesterol efflux capacity and the absolute granulocyte count. Linear regression of noncalcified 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 the top predictors of noncalcified coronary burden in psoriasis. These factors were related to obesity, dyslipidemia, and inflammation, showing that these are important targets when treating comorbidities in psoriasis.
- Published
- 2020