Differences in the characteristics and outcomes of STEMI versus NSTEMI cardiogenic shock: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: There is limited evidence exploring the differences in characteristics and outcomes between patients with ST-segment elevation myocardial infarction (STEMI) and with non-ST-segment elevation myocardial infarction (NSTEMI) presenting with cardiogenic shock (CS). METHODS: Medline, Google Scholar, and ScienceDirect databases were searched up to March 2025. Studies reporting data on STEMI-CS and NSTEMI-CS patient characteristics and clinical outcomes were included. Pooled risk ratios (odds ratios [ORs]) and standardized mean differences (SMDs) were calculated using random-effects models, and the I2 statistic measured heterogeneity. The risk of bias was assessed using the Newcastle-Ottawa Scale. RESULTS: The pooled analysis of 12 studies demonstrated that the incidence of in-hospital mortality was comparable between STEMI-CS and NSTEMI-CS patients (pooled OR: 0.82, 95% confidence interval [CI]: 0.52-1.30, I2 = 98.7%, P < .001). However, STEMI-CS patients had a considerably lower age of presentation (pooled SMD: -0.54, 95% CI: -0.67 to -0.42, I2 = 96.9%, P < .001), as well as lower incidence of prior heart failure, prior myocardial infarction, and diabetes, compared to NSTEMI-CS patients (P < .05). Conversely, STEMI-CS was associated with shorter hospital stays (pooled SMD: -0.54, 95% CI: -0.77 to -0.31, I2 = 99.1%, P < .001). CONCLUSION: While the incidence of in-hospital mortality did not significantly differ between STEMI-CS and NSTEMI-CS patients, the study further emphasizes the importance of individualized treatment strategies based on myocardial infarction type and patient characteristics when managing CS 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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".