CArdiovasculaR Outcomes Based Upon EjectIon Systolic TimE in Patients With ST Elevation Myocardial Infarction (ARISE-STEMI) Study
Bibliographic record
Abstract
Background Despite improvements in revascularization, systems of care, and secondary prevention therapies, 30-day mortality rates in patients presenting with ST-elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI) remains 4% to 6%. This study aims to investigate the utility of the ejection systolic time (EST) and ejection systolic period (ESP) in identifying high-risk STEMI patients. Methods In this retrospective study, consecutive patients with STEMI undergoing PPCI at a tertiary cardiac center between January 2020 and October 2021 were included. EST and ESP were calculated on the MacLab. Univariable and multivariable Cox regression analysis were used to identify risk predictors. The primary outcome was mortality at 30 days. Results Six hundred forty-one STEMI patients (mean age: 64.4 ± 13.2 years; 182/641 [28.4%] female patients) were recruited. Within 30 days of presentation, 32 patients (5.0%) died, and they were more frequently older, female, and had higher rates of previous stroke, chronic kidney disease, and dialysis use. Patients dying within 30 days had lower EST (0.20 ± 0.04 vs 0.24 ± 0.04 seconds/beat; P < 0.0001) and ESP (17.64 ± 2.66 vs 19.29 ± 2.74 seconds/min; P = 0.004). After multivariable modeling, only EST was a significant predictor of early (<30 days) mortality (hazard ratio 4.5, 95% confidence interval 1.7-12.1; P = 0.003), prolonged in-hospital stay (>4 days), diuretic use, new diagnosis of heart failure, need for intubation or ventilation, and inotrope and/or vasopressor use during the index hospital admission. ESP and EST were not associated with the mortality between 30 days and 1 year. Conclusions A lower EST was associated with mortality at 30 days and in-hospital adverse outcomes. EST may serve as a useful hemodynamic marker to risk-stratify STEMI patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".