Collaborative Heart Attack Management Program (CHAMP): use of prehospital thrombolytics to improve timeliness of STEMI management in British Columbia
Bibliographic record
Abstract
Coronary artery disease is the second leading cause of death in Canada. Time to treatment in ST-elevation myocardial infarction (STEMI) is directly related to morbidity and mortality. Thrombolysis is the primary treatment for STEMI in many regions of Canada because of prolonged transport times to percutaneous coronary intervention-capable centres. To reduce time from first medical contact (FMC) to thrombolysis, some emergency medical services (EMS) systems have implemented prehospital thrombolysis (PHT). PHT is not a novel concept and has a strong evidence base showing reduced mortality.Here, we describe a quality improvement initiative to decrease time from FMC to thrombolysis using PHT and aim to describe our methods and challenges during implementation. We used a quality improvement framework to collaborate with hospitals, EMS, cardiology, emergency medicine and other stakeholders during implementation. We trained advanced care paramedics to administer thrombolysis in STEMI with remote cardiologist support and aimed to achieve a guideline-recommended median FMC to needle time of <30 min in 80% of patients.Overall, we reduced our median FMC to needle time by 70%. Our baseline patients undergoing in-hospital thrombolysis had a median time of 84 min (IQR 62-116 min), while patients after implementation of PHT had a median time of 25 min (IQR 23-39 min). Patients treated within the guideline-recommended time from FMC to needle of <30 min increased from 0% at baseline to 61% with PHT. Return on investment analysis showed $2.80 saved in acute care costs for every $1.00 spent on the intervention.While we did not achieve our goal of 80% compliance with FMC to needle time of <30 min, our results show that the intervention substantially reduced the FMC to needle time and overall cost. We plan to continue with ongoing implementation of PHT through expansion to other communities in our province.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".