Predictors of In-Hospital Mortality in Patients Admitted with Acute Myocardial Infarction in a Developing Country
Bibliographic record
Abstract
BACKGROUND: Limited data are available on the predictors of mortality in patients hospitalized with acute myocardial infarction (AMI) in developing countries. In this study, we analyze the predictors for in--hospital mortality in patients hospitalized with AMI (ST segment elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI)) in a large tertiary referral university hospital in Lebanon. METHODS: This was a retrospective study of 503 patients admitted to the American University of Beirut Medical Center with AMI (228 with STEMI and 275 with NSTEMI). RESULTS: The in-hospital mortality rate was 7.8%. The multivariate predictors of mortality in the overall population were similar to what has been reported in large registries in the USA and Europe. They included older age (> 65 years) (OR = 2.99, 95% CI = 1.22 - 7.36, P = 0.02), systolic blood pressure < 100 mm Hg (OR = 2.75, 95% CI = 1.12 - 6.76, P = 0.03), history of stroke (OR = 4.28, 95% CI = 1.29 - 14.17, P = 0.02), history of coronary artery bypass graft (CABG) (OR = 2.68, 95% CI = 1.15 - 6.23, P = 0.02), heart failure (OR = 3.92, CI = 1.62 - 9.49, P = 0.002) and ejection fraction (EF) < 35% (OR = 2.32, 95% CI = 1.05 - 5.14, P = 0.04). In a separate analysis of STEMI and NSTEMI patients, age, heart failure and a low EF continued to be multivariate predictors of mortality in both subgroups. In addition, prior stroke was an added predictor in STEMI patients, and prior CABG was an added predictor in NSTEMI. CONCLUSION: Predictors of in-hospital mortality in patients hospitalized with AMI in a tertiary referral university hospital in the Middle East are similar to what has been reported in large registries in the USA and Europe.
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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 | 0.000 | 0.000 |
| 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".