Temporal Trends and Short‐ and Long‐Term Mortality of People With Acute Myocardial Infarction and Rheumatoid Arthritis: A Nationwide Cohort Study
Bibliographic record
Abstract
OBJECTIVE: We investigated whether a diagnosis of rheumatoid arthritis (RA) affects the quality of inpatient acute myocardial infarction (AMI) care and long-term mortality post-AMI. METHODS: We analyzed data from 784,091 adults, 6,047 with a diagnosis of RA, from England and Wales hospitalized with AMI between 2005 and 2019 from the Myocardial Ischaemia National Audit Project registry, linked with Office for National Statistics mortality data and hospital episode statistics. Cox regression models were used to compare risk of all-cause mortality at different time points according to the presence of RA. RESULTS: There was no difference in adjusted 30-day mortality between groups (adjusted hazard ratio [aHR] 1.09, 95% confidence interval [CI] 0.99-1.19; P = 0.075). Beyond this, at 1 year (aHR 1.14, 95% CI 1.07-1.21), 5 years, (aHR 1.28, 95% CI 1.23-1.33), and to the end of the study period (aHR 1.31, 95% CI 1.26-1.36), the risk of all-cause mortality was significantly higher in patients with RA (all P < 0.001). Risk of cardiovascular mortality was not significantly different at 30 days or 1 year (aHR 1.08, 95% CI 1.00-1.17; P = 0.058), but it was at 5 years (aHR 1.15, 95% CI 1.08-1.23; P < 0.001) and to the study endpoint post-AMI (aHR 1.18, 95% CI 1.11-1.24; P < 0.001). CONCLUSION: We found no meaningful disparities in inpatient care according to the presence of RA; however, those with RA have elevated long-term all-cause mortality post-AMI. Our findings suggest that the mortality burden of RA post-AMI is not driven by the quality of AMI care during admission and is likely driven by the progressive nature of the comorbidities and the complications of treatments associated with RA.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".