TY - JOUR
T1 - Application of machine learning to determine top predictors of noncalcified coronary burden in psoriasis
T2 - An observational cohort study
AU - Munger, Eric
AU - Choi, Harry
AU - Dey, Amit K.
AU - Elnabawi, Youssef A.
AU - Groenendyk, Jacob W.
AU - Rodante, Justin
AU - Keel, Andrew
AU - Aksentijevich, Milena
AU - Reddy, Aarthi S.
AU - Khalil, Noor
AU - Argueta-Amaya, Jenis
AU - Playford, Martin P.
AU - Erb-Alvarez, Julie
AU - Tian, Xin
AU - Wu, Colin
AU - Gudjonsson, Johann E.
AU - Tsoi, Lam C.
AU - Jafri, Mohsin Saleet
AU - Sandfort, Veit
AU - Chen, Marcus Y.
AU - Shah, Sanjiv J.
AU - Bluemke, David A.
AU - Lockshin, Benjamin
AU - Hasan, Ahmed
AU - Gelfand, Joel M.
AU - Mehta, Nehal N.
N1 - Funding Information:
Funding sources: Supported by the National Heart, Lung, and Blood Institute Intramural Research Program (HL006193-05). This research was also made possible through the National Institutes of Health (NIH) Medical Research Scholars Program, a public-private partnership supported jointly by the NIH and generous contributions to the Foundation for the NIH from the Doris Duke Charitable Foundation (grant no. 2014194), the American Association for Dental Research, the Colgate-Palmolive Company, Genentech, Elsevier, and other private donors. The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.Disclosure: Dr Gelfand is a copatent holder of resiquimod for treatment of cutaneous T-cell lymphoma and in the past 12 months has served as a consultant for Coherus (data and safety monitoring board), Dermira, Janssen Biologics, Merck (data and safety monitoring board), Novartis, Regeneron, Dr Reddy's Labs, Sanofi, and Pfizer, receiving honoraria; receives research grants (to the Trustees of the University of Pennsylvania) from AbbVie, Janssen, Novartis, Regeneron, Sanofi, Celgene, and Pfizer In; and received payment for continuing medical education work related to psoriasis that was supported indirectly by Lilly and AbbVie. Dr Mehta is a fulltime US government employee and has served as a consultant for Amgen, Eli Lilly, and Leo Pharma, receiving grants/other payments; as a principal investigator and/or investigator for AbbVie, Celgene, Janssen Pharmaceuticals, and Novartis, receiving grants and/or research funding; and as a principal investigator for the National Institutes of Health, receiving grants and/or research funding. Mr Munger; Mr Choi; Drs Dey, Elnabawi, and Groenendyk; Mr Rodante; Mr Keel; Ms Aksentijevich; Ms Reddy; Mr Khalil; Ms Argueta-Amaya; Dr Playford; Ms Erb-Alvarez; and Drs Tian, Wu, Gudjonsson, Tsoi, Jafri, Sandfort, Chen, Shah, Bluemke, Lockshin, and Hasan have no conflicts of interest to declare.
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
KW - atherosclerosis
KW - cardiometabolic disease
KW - coronary artery disease
KW - machine learning
KW - psoriasis
KW - random forest algorithm
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UR - http://www.scopus.com/inward/citedby.url?scp=85081356194&partnerID=8YFLogxK
U2 - 10.1016/j.jaad.2019.10.060
DO - 10.1016/j.jaad.2019.10.060
M3 - Article
C2 - 31678339
AN - SCOPUS:85081356194
VL - 83
SP - 1647
EP - 1653
JO - Journal of the American Academy of Dermatology
JF - Journal of the American Academy of Dermatology
SN - 0190-9622
IS - 6
ER -