Development of a Cardiovascular Risk Prediction Model for Individuals with Psoriasis and Psoriatic Arthritis
Bibliographic record
Abstract
Aim: Psoriasis and psoriatic arthritis (PsA), collectively known as psoriatic disease (PsD), are characterized by excess cardiovascular (CV) morbidity and mortality compared to the general population. The aim of this thesis is to identify novel biomarkers and disease-specific variables that predict CV risk beyond traditional CV risk factors in patients with PsD. Methods: Data from prospective cohorts of patients with psoriasis and PsA were analyzed. The association of serum metabolites and CV events was investigated. The association between cardiac troponin I (cTnI) and N-terminal pro-brain-type natriuretic peptide (NT-proBNP) and carotid atherosclerosis presence and progression was also tested. Variable selection was used to construct multiple CV risk prediction models – these included traditional CV risk factors, cardio-metabolic biomarkers and disease-specific variables. Metrics of risk prediction were used to determine whether the addition of biomarkers and disease-specific variables to each model improved their predictive performance beyond traditional CV risk factors and the Framingham Risk Score (FRS). Results: The analysis revealed several metabolites associated with CV risk. A model with 13 metabolites significantly improved prediction of CV events beyond a model with age and sex alone. A FRS-adjusted model with 11 metabolites did not improve CV risk discrimination. Investigation of cardiac biomarkers demonstrated that cTnI was independently associated with the burden of carotid atherosclerosis. Elevated cTnI and NT-proBNP were associated with a higher risk of developing CV events independent of traditional CV risk factors. Addition of cTnI or NT-proBNP did not improve the performance of the FRS for predicting CV events. Considering disease-specific variables, a CV risk prediction model for patients with PsD was constructed – this model included traditional CV risk factors and it had excellent performance in predicting CV events within a 5-year period. Disease-specific risk factors did not improve predictive performance beyond traditional CV risk factors alone. Conclusions: Serum metabolite and cardiac biomarkers are associated with the development of incident CV events in patients with PsD, but do not improve predictive performance compared to the FRS. Psoriatic disease related risk factors were not superior to traditional CV risk factors, which performed very well in predicting CV events in patients with PsD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".