Abstract 17432: Specific Care Process Implementation Associated With Improved Reperfusion Times Across Multiple STEMI Networks: Insights From The AHA Mission: Lifeline STEMI Accelerator Program
Bibliographic record
Abstract
Introduction: The STEMI Accelerator Program occurred in 16 U.S. metropolitan regions and resulted in more patients receiving timely reperfusion. We assessed whether implementing key care processes was associated with shorter reperfusion times. Methods: Hospitals (n=167 with 23,498 STEMI patients) were surveyed pre- (02/2012) and post- (08/2014) intervention with a standardized care process questionnaire. Survey data were then merged with patient-level clinical data over the same time period. For reperfusion times, hospitals were stratified by whether they implemented a specific process of care, had a pre-existing process, or never implemented the process. Results: Uptake of several care processes increased following intervention: pre-hospital activation (62% to 91% of hospitals; p<0.001), single call transfer protocol from an outside facility (45% to 70%; p<0.001) and emergency department (ED) bypass for both direct presenters via paramedics (48% to 59%; p=0.002) and transfer patients (56% to 79%; p=0.001). There were significant differences in median first medical contact-to-device (FMC) times (Figure) among patients treated at hospitals that implemented pre-activation compared to non-implementers (88 min for implementers; vs 89 min for pre-existing; vs 98 min for non-implementers; p<0.001 for group comparisons). Similarly, patients treated at hospitals implementing single call transfer protocols had shorter median FMC times (112 min vs 128 min vs 152 min; p<0.001). ED bypass was also associated with shorter FMC times for both direct presenters (84 min vs 88 min vs 94 min; p<0.001) and transfers (123 min vs 127 min vs 167 min; p<0.001). Conclusions: The STEMI Accelerator program increased the uptake of key care processes, which were in turn associated with shorter median reperfusion times. These findings support ongoing efforts to implement regional STEMI networks focused upon pre-hospital activation, single call transfer protocols and ED bypass.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".