The impact of rheumatoid arthritis on the risk of adverse events following joint replacement: a real-world cohort study
Bibliographic record
Abstract
PURPOSE: To assess whether rheumatoid arthritis (RA) is associated with a greater risk of adverse events following total knee replacement (TKR) and total hip replacement (THR) than osteoarthritis (OA). PATIENTS AND METHODS: Individuals with a diagnosis of RA or OA were identified using primary care records. TKR and THR following diagnosis were identified using linked hospital records. Myocardial infarction (MI), prosthetic joint infection (PJI), venous thromboembolism (VTE), and death were identified within 90 days following surgery, and revision procedures over 10 years following surgery. The impact of RA compared to OA on the risk for these adverse events was assessed using Cox proportional hazard models. Univariable models, with diagnosis as the only explanatory variable, and multivariable models, with age, gender, and year of surgery first added and then a measure of other comorbidities also included, were estimated. RESULTS: In all 20,763 individuals, with 10,260 TKR and 10,961 THR, were included in the analysis. Compared to those with OA, individuals with a diagnosis of RA had a greater incidence of MI over 90 days following TKR (OA: 0.28%, RA: 0.75%) and revision over 10 years following THR (OA: 5.55%, RA: 8.68%). Both of these differences were statistically significant with, for example, hazard ratios of 3.54 (1.44 to 8.73) for MI and 1.61 (1.06 to 2.46) for revision after controlling for age, gender, year of surgery, and other comorbidities. CONCLUSION: These findings suggest that, compared to individuals with OA, those with RA have an increased short-term risk of MI following TKR. While risk of MI remains below 1%, this does underline the importance of the management of cardiovascular risk factors for those with RA. RA was also associated with an increased long-term risk of revision following THR, which strengthens the argument for investing in therapies which may prevent the need for joint replacement.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".