Observational studyJournal of the American Academy of Dermatology2020
Application of machine learning to determine top predictors of noncalcified coronary burden in psoriasis: An observational cohort study.
Observational study in Journal of the American Academy of Dermatology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 31 citations in OpenAlex.
- The construction of HMME-PDT efficacy prediction model for port-wine stain based on machine learning algorithms.Scientific reports · 2025Article
- Artificial Intelligence: A Snapshot of Its Application in Chronic Inflammatory and Autoimmune Skin Diseases.Life (Basel, Switzerland) · 2024Review
- Article
- Machine Learning Approaches for Predicting Psoriatic Arthritis Risk Using Electronic Medical Records: Population-Based Study.Journal of medical Internet research · 2023Article
- Heart failure-type symptom scores in chronic kidney disease: The importance of body mass index.International journal of obesity (2005) · 2022Article
- Novel Biomarkers of Atherosclerotic Vascular Disease-Latest Insights in the Research Field.International journal of molecular sciences · 2022Review
- Epidemiology of Psoriasis and Comorbid Diseases: A Narrative Review.Frontiers in immunology · 2022Review
- Application of machine learning in understanding atherosclerosis: Emerging insights.APL bioengineering · 2021Review
- Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques.Scientific reports · 2021Article
- Machine Learning Applications in the Evaluation and Management of Psoriasis: A Systematic Review.Journal of psoriasis and psoriatic arthritis · 2020Article
- Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations.Dermatology and therapy · 2020Review
Corrections and comments
- Erratum issuedCorrection.2021
Authors and funding
26 authors at 7 institutions in 1 country.
Funding
Abstract
backgroundPsoriasis 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.
objectiveIn this study, we used machine learning algorithms to determine the top predictors of noncalcified coronary burden by CCTA in psoriasis.
methodsThe 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.
resultsUsing 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.
conclusionMachine 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.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.