Derivation and Internal Validation of a <scp>Disease‐Specific</scp> Cardiovascular Risk Prediction Model for Patients With Psoriatic Arthritis and Psoriasis
Bibliographic record
Abstract
OBJECTIVE: To address suboptimal cardiovascular risk prediction in patients with psoriatic disease (PsD), we developed and internally validated a five-year disease-specific cardiovascular risk prediction model. METHODS: We analyzed data from a prospective cohort of participants with PsD without a history of cardiovascular events. Traditional cardiovascular risk factors and PsD-related measures of disease activity were considered as potential predictors. The study outcome included nonfatal and fatal cardiovascular events. A base prediction model included 10 traditional cardiovascular risk factors. Eight PsD-related factors were assessed by adding them to the base model to create expanded models, which were controlled for PsD therapies. Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) penalized regression with 10-fold cross-validation. Model performance was assessed using measures of discrimination and calibration and measures of sensitivity and specificity. RESULTS: Between 1992 and 2020, 85 of 1,336 participants developed cardiovascular events. Discrimination of the base model (with traditional cardiovascular risk factors alone) was excellent, with an area under the receiver operator characteristic curve (AUC) of 85.5 (95% confidence interval [CI] 81.9-89.1). Optimal models did not select any of the tested disease-specific factors. In a sensitivity analysis, which excluded lipid lowering and antihypertensive treatments, the number of damaged joints was selected in the expanded model. However, this model did not improve risk discrimination compared to the base model (AUC 85.5, 95% CI 82.0-89.1). CONCLUSION: Traditional cardiovascular risk factors alone are effective in predicting cardiovascular risk in patients with PsD. A risk score based on these factors performed well, indicating excellent discrimination and calibration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".