Acute coronary syndrome care across Australia and New Zealand
Bibliographic record
Abstract
Improving the uptake of guideline-recommended therapy for suspected acute coronary syndrome (ACS) is a global health priority. Australia and New Zealand (NZ) undertook a snapshot of ACS to compare in-hospital care and prevention measures at discharge to published guidelines. Methods: Demographic and clinical details of individuals hospitalised with suspected ACS between 14-27th May 2012 were collected. Some 525 hospitals (39 in NZ) were identified from public records and peers as accepting ACS cases and considered for participation. Descriptive and logistic regression analysis was performed. The main indicators included: rates of guideline-advocated investigations, therapies, referral to cardiac rehabilitation. Outcomes included, in-hospital case-fatality, new myocardial infarction (MI), stroke, cardiac arrest, worsening heart failure. Results: 478 hospitals (91%) agreed to participate, 285 of which saw ACS patients and contributed data over the 2-week collection (46% large urban public/private hospitals, 26% regional and 28% small rural). The other 193 participating predominantly small rural facilities did not have ACS admissions over the two-week study period. 4,365 patients were enrolled, mean age 67 (SD 14) years, 60% men and median GRACE score of 118 (IQR: 96-143). Although the majority of presentations were to large urban hospitals (74%), the audit also captured information on 1,135 patient presenting to regional or rural hospitals. At discharge, 34% were diagnosed as MI, 21% unstable angina, 26% unlikely ischaemia, and 19% had other diagnoses. For the 1474 with MI; angiography was performed in 70%, angioplasty in 41% and cardiac surgery in 8%. As patient risk increased invasive management was less likely (GRACE score <100: 85.0% vs. 101-150: 79.4% vs. 151-200: 49.0% vs. >200: 36.1%, p<0.0001). Case-fatality was 4.4% and new MI 5.0%. Adjusted for GRACE score, there was significant variation in care, clinical course, and secondary prevention measures at discharge, by hospital type/regionality and state/province. Conclusions: This first comprehensive audit of ACS care in large urban, regional and small rural hospitals in Australia and NZ confirms there are significant variations in the application of the guideline-recommended treatment across both countries. Underutilisation of guideline recommended therapy occurred across all hospital types, in particular for the patients deemed at higher risk by the calculated GRACE score. Focus on quality improvement supported by integrated clinical service delivery is warranted to improve access to, and utilisation of, evidence-based ACS care in both countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".