Mortality in Ankylosing Spondylitis According to Treatment: A Nationwide Retrospective Cohort Study of 5,900 Patients From Israel
Bibliographic record
Abstract
OBJECTIVE: In this large population-based study we aimed: 1) to assess mortality in patients with ankylosing spondylitis (AS) compared to the general population, considering demographics, comorbidities, and treatment, and 2) to assess factors associated with mortality within patients with AS. METHODS: This study was designed as a retrospective cohort study using the electronic database of the largest health maintenance organization in Israel. All patients with AS diagnosed between 2002 and 2018 were included. Controls were matched by age, sex, clinic, and enrollment time. Follow-up continued until death or the end of the study. RESULTS: The study comprised 5,930 AS patients and 29,018 matched controls who were followed up for a median period of 7.5 years. There were 667 deaths within the AS cohort and 2,919 deaths within controls; the mean age at death was 76.9 years and 77.1 years, respectively (P = 0.74). A total of 3,249 AS patients (54.8%) were treated only with nonsteroidal antiinflammatory drugs, 1,760 (29.7%) were treated with tumor necrosis factor inhibitors (TNFi), and 1,687 (28.4%) with disease-modifying antirheumatic drugs (DMARDs). Mortality rates were increased among AS patients compared to controls, with an age- and sex-adjusted hazard ratio (HR) of 1.19 (95% confidence interval [95% CI] 1.10-1.30). The association was significant for men (HR 1.15 [95% CI 1.04-1.27]) and women (HR 1.32 [95% CI 1.13-1.54]), and after adjusting for background comorbidities (HR 1.14 [95% CI 1.05-1.24]). AS patients treated with TNFi or with a combination of TNFi and DMARDs did not have significant difference in mortality rates compared to controls (HR 0.67 [95% CI 0.38-1.18] and HR 0.93 [95% CI 0.69-1.25], respectively). Age, male sex, mean C-reactive protein (CRP) levels and general comorbidities were predictors of mortality within the AS cohort. CONCLUSION: AS patients had an increased mortality risk compared to the general population after adjusting for age, sex, and baseline comorbidities. AS patients treated with TNFi did not demonstrate excess mortality compared to matched controls. Within the AS cohort, age, male sex, background comorbidities, and higher CRP levels were identified as risk factors for mortality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".