Mortality and Revascularization among Myocardial Infarction Patients with Schizophrenia: A Population-Based Cohort Study
Bibliographic record
Abstract
Background:Cardiovascular disease is a major source of mortality in schizophrenia, and access to care after acute myocardial infarction (AMI) is poor for these patients.Aims:To understand the relationship between schizophrenia and access to coronary revascularization and the impact of revascularization on mortality among individuals with schizophrenia and AMI.Method:This study used a retrospective cohort of AMI in Ontario between 2008 and 2015. The exposure was a diagnosis of schizophrenia, and patients were followed 1 year after AMI discharge. The primary outcome was all-cause mortality within 1 year. Secondary outcomes were cardiac catheterization and revascularization (percutaneous coronary intervention or coronary artery bypass graft). Cox proportional hazard regression models were used to study the relationship between schizophrenia and mortality, and the time-varying effect of revascularization.Results:A total of 108,610 cases of incident AMI were identified, among whom 1,145 (1.1%) had schizophrenia. Schizophrenia patients had increased mortality, with a hazard ratio (HR) of 1.55 (95% CI, 1.37 to 1.77) when adjusted for age, sex, income, rurality, geographic region, and comorbidity. After adjusting for time-varying revascularization, the HR reduced to 1.38 (95% CI, 1.20 to 1.58). The impact of revascularization on mortality was similar among those with and without schizophrenia (HR: 0.42; 95% CI, 0.41 to 0.44 vs. HR: 0.40; 95% CI, 0.26 to 0.61).Conclusions:In this sample of AMI, mortality in schizophrenia is increased, and treatment with revascularization reduces the HR of schizophrenia. The higher mortality rate yet similar survival benefit of revascularization among individuals with schizophrenia relative to those without suggests that increasing access to revascularization may reduce the elevated mortality observed in individuals with schizophrenia.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".