In-hospital mortality of acute coronary syndrome in elderly patients
Bibliographic record
Abstract
OBJECTIVES: To analyze predictors of death in elderly patients diagnosed with acute coronary syndrome (ACS). Methods: A record-based study carried out between January 2016 and January 2018 at The central province in Saudi Arabia. All elderly patients (greater than 75 years) with definite diagnosis of ACS were retrospectively included. Demographic data, echocardiographic, and angiographic parameters were reported. Results: A total of 179 patients were enrolled, 129 (72%) were male. The mean age was 79±4.7 years. Approximately 102 (57%) patients were diagnosed with ST-segment elevation myocardial infarction (STEMI). Of all 125 (70%) underwent invasive coronary angiography, we found that 43 (24%) had significant single vessel disease (1VD), 29 (16.2%) had 2 vessel disease (2VD), and 41 (22.9%) had 3 vessel disease (3VD) or left main stenosis. During hospitalization 21 (11.7%) patients died, t-test analysis showed patients who died were significantly older (82±6.7 versus [vs.] 79±4.2 years, p=0.003). In addition we found that ejection fraction was lower in death group (30.2%±10.7) vs. (36.5%±1.1) in survivors, p=0.017); STEMI was more common in death group (90.5%) vs. (52.5%) in survivors, p=0.001); similarly, the prevalence of 3VD was higher in death group (38.1%) vs. (20.9%) in survivors, p=0.018). Importantly, PCI was not significantly different between death and survival groups (40% vs. 53.8%, p=0.177). A multivariate regression analysis demonstrated that predictors of death were: age (hazard ratio [HR], 1.214; 95% confidence interval [CI], 1.122-1.384; p less than 0.0001), intubation (HR, 10.106; 95% CI, 9.844-10.792; p less than 0.0001), and raised creatinine kinase-MB (CK-MB) (HR, 1.005; 95% CI, 1.002-1.013; p=0.04) predicted in hospital death. Conclusion: Older age, mechanical ventilation and raised CK-MB can significantly predict death in elderly patients (greater than 75-year-old) diagnosed with ACS; nevertheless, PCI showed no survival benefits.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".