Paramedic Interventions and Adverse Patient Events during Prolonged Interfacility Ground Transport in a “Drip and Ship” Pharmacoinvasive Model of STEMI Care
Bibliographic record
Abstract
OBJECTIVE: Primary percutaneous coronary intervention (PCI) is the preferred reperfusion strategy for patients with ST-segment elevation myocardial infarction (STEMI). However, when primary PCI is not available in a timely fashion, fibrinolysis and early transfer for routine PCI is recommended. Prince Edward Island (PEI) is the only province in Canada without a PCI facility, and distances to the nearest PCI-capable facilities are between 290 and 374 kilometers. This results in prolonged out-of-hospital time for critically ill patients. We sought to characterize and quantify paramedic interventions and adverse patient events during prolonged ground transport to PCI facilities post-fibrinolysis. METHODS: We performed a retrospective chart review of patients presenting to any of four emergency departments (ED) on PEI during the calendar years 2016 and 2017. We identified patients through administrative discharge data and cross referenced with emergent out-of-province ambulance transfers. All included patients were managed as STEMIs in the EDs and subsequently transferred (primary PCI, pharmacoinvasive) directly from the EDs to PCI facilities. We excluded patients having STEMIs on inpatient wards and those transported by other means. We reviewed electronic and paper ED charts plus paper EMS records. We performed summary statistics. RESULTS: We identified 149 patients meeting inclusion criteria. Most patients were males (77.9%), mean age 62.1 (SD 13.8) years. The mean transport interval was 202 (SD 29.0) minutes. Thirty-two adverse events occurred during 24 transports (16.1%). There was one death, and four patients required diversion to non-PCI facilities. Hypotension was the most common adverse event (n = 13, 8.7%), and fluid bolus (n = 11, 7.4%) was the most common intervention. Three (2.0%) patients required electrical therapy. Nitrates (n = 65, 43.6%) and opioid analgesics (n = 51, 34.2%) were the most common drugs administered during transport. CONCLUSION: In a setting where primary PCI is not feasible due to distance, a pharmacoinvasive model of STEMI care is associated with a 16.1% proportion of adverse events. Crew configuration including ALS clinicians is the key in managing these events.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".