Chronic kidney disease in patients with psoriatic arthritis: a cohort study
Bibliographic record
Abstract
Objectives Chronic kidney disease (CKD) is a comorbidity in psoriatic arthritis (PsA). We aimed to define the prevalence of CKD in patients with PsA, describe their long-term renal outcomes and identify risk factors for CKD development. Methods We included patients with PsA followed by our prospective observational cohort. We defined CKD as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m 2 for at least 3 months. We characterised long-term renal outcomes of CKD cases identified following clinic entry. We used time-dependent Cox regression models to identify factors associated with CKD development. Results Of 1336 patients included in the study, 123 (9.2%) had CKD. Of these, 25 (20.3%) were observed to have CKD at clinic entry and 98 (79.7%) developed CKD during follow-up at a median (IQR) of 8.2 (2.8–14.0) years from baseline. Doubling of baseline creatinine was observed in 18 of 98 (18.3%) new patients with CKD. 49 (50%) patients developed a sustained ≥40% reduction in baseline eGFR. Two patients developed eGFR <15 mL/min/1.73 m 2 . In the multivariate Cox regression model adjusted for age at study entry, sex and baseline eGFR, factors independently associated with the development of CKD included diabetes mellitus (HR 2.58, p<0.001), kidney stones (HR 2.14, p=0.01), radiographic damaged joint count (HR 1.02, p=0.02), uric acid (HR 1.21, p<0.001; 50-unit increase), daily use of non-steroidal anti-inflammatory drugs (NSAIDs) (HR 1.77, p=0.02) and methotrexate use (HR 0.51, p=0.01). Conclusion CKD is not infrequent in PsA. Its development is associated with related comorbidities, joint damage and NSAID use. Methotrexate seems to be protective.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".