Application of machine learning to determine top predictors of noncalcified coronary burden in psoriasis: An observational cohort study.
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:...
| Publicado en: | Journal of the American Academy of Dermatology Vol. 83; no. 6; pp. 1647 - 1654 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Elsevier B.V.
Dec2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146910973&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146910973 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01909622 1ZF jtl: Journal of the American Academy of Dermatology issn: 01909622 maglogo: N pubinfo: dt: Dec2020 vid: 83 iid: 6 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 146910973 146910973 NLM31678339 146910973 10.1016/j.jaad.2019.10.060 NLM31678339 146910973 ppf: 1647 ppct: 7 formats: tig: atl: Application of machine learning to determine top predictors of noncalcified coronary burden in psoriasis: An observational cohort study. aug: au: Munger, Eric Choi, Harry Dey, Amit K. Elnabawi, Youssef A. Groenendyk, Jacob W. Rodante, Justin Keel, Andrew Aksentijevich, Milena Reddy, Aarthi S. Khalil, Noor Argueta-Amaya, Jenis Playford, Martin P. Erb-Alvarez, Julie Tian, Xin Wu, Colin Gudjonsson, Johann E. Tsoi, Lam C. Jafri, Mohsin Saleet Sandfort, Veit Chen, Marcus Y. affil: George Mason University, Fairfax, Virginia sug: subj: Coronary Arteriosclerosis Epidemiology Psoriasis Complications Coronary Vessels Hyperlipidemia Immunology Risk Assessment Methods Hyperlipidemia Epidemiology Obesity Epidemiology Risk Factors Adult Psoriasis Epidemiology Obesity Immunology Coronary Arteriosclerosis Immunology Hyperlipidemia Blood Psoriasis Immunology Comorbidity Male Inflammation Blood Middle Age Prospective Studies Inflammation Epidemiology Female Psoriasis Blood Inflammation Immunology Obesity Blood Coronary Arteriosclerosis Diagnosis Coronary Arteriosclerosis Blood Tomography, X-Ray Computed Funding Source Human Adult: 19-44 years Middle Aged: 45-64 years Male Female 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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