Emergency department visits and hospitalizations after a diagnosis of angina with no obstructive coronary artery disease (ANOCA)
Bibliographic record
Abstract
BACKGROUND: Angina with no obstructive coronary artery disease (ANOCA) presents diagnostic and treatment challenges, significantly burdening healthcare resources. This study assessed emergency department (ED) visits and hospitalizations and factors associated with these outcomes following ANOCA and stable angina (SA) with obstructive coronary artery disease (CAD) diagnoses. METHODS: A retrospective cohort of individuals who had their first invasive cardiac catheterization for chest pain in Alberta from 2002 to 2017 was extracted retrospectively from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) database. Incidence rates (IRs) were calculated for ED visits and hospitalizations, while factors associated with these outcomes were analyzed using Cox models. RESULTS: Our analysis included 28,881 individuals (ANOCA, 36%). Two-year postcatheterization IRs of ED visits were 100.3-119.3 per 1,000 person-years for ANOCA and increased over time (unstandardized beta coefficient [b] = 2.19 per biennium [95% CI 0.83-3.55]; P = .008); for SA with obstructive CAD the IRs were 209.3-240.2 per 1,000 person-years and remained stable (b = -1.83 per biennium [95% CI -5.73 to 1.70]; P = .25). IRs of hospitalizations were 12.4-25.8 per 1,000 person-years and stable for ANOCA (b = -0.93 per biennium [95% CI -2.49 to 0.64]; P = .20); for SA with obstructive CAD, they were 106.4-171.4 per 1,000 person-years and decreased over time (b = -9.02 per biennium [95% CI -13.27 to -4.77; P = .002). A previous history of heart failure was most associated with ED visits (HR = 1.74 [95% CI 1.41-2.14]; P < .001) and hospitalizations (HR = 2.40 [95% CI 1.82-3.18]; P < .001) for ANOCA. CONCLUSIONS: ED visits for ANOCA have risen over time while hospitalizations remain stable, indicating a growing burden despite generally lower rates than SA with obstructive CAD. These findings underscore the need for more effective management strategies to address the significant morbidity and resource utilization in ANOCA.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".