Gaps in Diagnosis and Treatment of Cardiovascular Risk Factors in Patients with Psoriatic Disease: An International Multicenter Study
Bibliographic record
Abstract
OBJECTIVE: We aimed to estimate the proportion of underdiagnosis and undertreatment of cardiovascular risk factors (CVRF) in an international multicenter cohort of patients with psoriasis and psoriatic arthritis (PsA). METHODS: A cross-sectional analysis was conducted of patients with psoriatic disease from the International Psoriasis and Arthritis Research Team cohort. The presence of modifiable CVRF [diabetes, hypertension (HTN), dyslipidemia, smoking, elevated body mass index, and central obesity] and the use of appropriate therapies for HTN and dyslipidemia were determined. The 10-year CV risk was calculated according to the Framingham Risk Score. Physician adherence with guidelines for the treatment of dyslipidemia and HTN was assessed. Regression analysis was used to assess predictors of undertreatment of HTN and dyslipidemia. RESULTS: A total of 2254 patients (58.9% PsA, 41.1% psoriasis) from 8 centers in Canada, the United States, and Israel were included. Their mean age was 52 ± 13.8 years and 53% were men. Of the patients, 87.6% had at least 1 modifiable CVRF, 45.1% had HTN, 49.4% dyslipidemia, 13.3% diabetes, 75.3% were overweight or obese, 54.3% central obesity, and 17.3% were current smokers. We found 59.2% of patients with HTN and 65.6% of patients with dyslipidemia were undertreated. Undertreatment was associated with younger age (≤ 50 yrs), having psoriasis, and male sex. CONCLUSION: In real-world settings, a large proportion of patients with psoriasis and PsA were underdiagnosed and undertreated for HTN and dyslipidemia. Strategies to improve the management of CVRF in psoriatic patients are warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".