Effect of Prehospital Digital Electrocardiogram Transmission on Revascularization Delays and Mortality in ST-Elevation Myocardial Infarction Patients: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background Pre-hospital transmission of the 12-lead electrocardiogram (ECG) to the interventional cardiologist has become the standard of care in many ST-elevation myocardial infarction (STEMI) networks but has not been universally adopted. In this systematic review and meta-analysis, we assess the effect of pre-hospital digital ECG transmission in STEMI patients on door-to-device times, first medical contact-to-device times, and mortality. Methods We performed a systematic review of all English studies in MEDLINE, EMBASE and CENTRAL (from inception to July 24, 2023), comparing the effect of pre-hospital digital ECG transmission to no ECG transmission in STEMI patients. We performed a random-effects meta-analysis. Results We included 17 observational studies totalling 4,306 patients. Door-to-device times were reduced by 33.3 minutes in patients with pre-hospital digital ECG transmission (95% confidence intervals (CI) -50.5, -16.2 minutes; p<0.001; I 2 99%). First medical contact-to-device time was also reduced with pre-hospital digital ECG transmission (mean difference -24.7 minutes; 95% CI -37.1, -12.3 minutes; p<0.001; i 2 96%). Pre-hospital digital ECG transmissions was associated with a 47% reduction in mortality compared to no pre-hospital digital ECG transmission (117/1,322 (8.9%) versus 181/1,322 (13.7%), odds ratio 0.53, 95% CI 0.40, 0.69; p < 0.001; I 2 = 0%). Conclusions Pre-hospital ECG transmission in STEMI patients, coupled with a systems of care reduced door-to-device times, first medical contact-to-device times, and mortality. STEMI networks should consider these findings to advocate for pre-hospital ECG transmission within their systems of care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".