30-day in-hospital mortality after acute myocardial infarction in Tuscany (Italy): An observational study using hospital discharge data
Bibliographic record
Abstract
BACKGROUND: Coronary heart disease is the leading cause of mortality in the world. One of the outcome indicators recently used to measure hospital performance is 30-day mortality after acute myocardial infarction (AMI). This indicator has proven to be a valid and reproducible indicator of the appropriateness and effectiveness of the diagnostic and therapeutic process for AMI patients after hospital admission. The aim of this study was to examine the determinants of inter-hospital variability on 30-day in-hospital mortality after AMI in Tuscany. This indicator is a proxy of 30-day mortality that includes only deaths occurred during the index or subsequent hospitalizations. METHODS: The study population was identified from hospital discharge records (HDRs) and included all patients with primary or secondary ICD-9-CM codes of AMI (ICD-9 codes 410.xx) that were discharged between January 1, 2009 and November 30, 2009 from any hospital in Tuscany. The outcome of interest was 30-day all-cause in-hospital mortality, defined as a death occurring for any reason in the hospital within 30 days of the admission date. Because of the hierarchical structure of the data, with patients clustered into hospitals, random-effects (multilevel) logistic regression models were used. The models included patient risk factors and random intercepts for each hospital. RESULTS: The study included 5,832 patients, 61.90% male, with a mean age of 72.38 years. During the study period, 7.99% of patients died within 30 days of admission. The 30-day in-hospital mortality rate was significantly higher among patients with ST segment elevation myocardial infarction (STEMI) compared with those with non-ST segment elevation myocardial infarction (NSTEMI). The multilevel analysis which included only the hospital variance showed a significant inter-hospital variation in 30-day in-hospital mortality. When patient characteristics were added to the model, the hospital variance decreased. The multilevel analysis was then carried out separately in the two strata of patients with STEMI and NSTEMI. In the STEMI group, after adjusting for patient characteristics, some residual inter-hospital variation was found, and was related to the presence of a cardiac catheterisation laboratory. CONCLUSION: We have shown that it is possible to use routinely collected administrative data to predict mortality risk and to highlight inter-hospital differences. The distinction between STEMI and NSTEMI proved to be useful to detect organisational characteristics, which affected only the STEMI subgroup.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".