Secondary Prevention in STEMI Patients: Insights from a Regional Virtual STEMI Clinic
Bibliographic record
Abstract
Background: In many Canadian regions, ST-elevation myocardial infarction (STEMI) patients are managed in a "hub and spoke" model with early repatriation to referring hospitals and rapid discharge pathways that may lead to suboptimal secondary prevention during the critical postdischarge period. We implemented a regional virtual STEMI clinic (VSC) to identify and address gaps in postdischarge care. Methods: We performed standardized virtual follow-ups for STEMI patients who presented at one tertiary hospital (hub) between November 2023 and November 2024. Standardized VSC follow-up data were used to describe baseline characteristics and secondary prevention interventions. Poisson regression was used to identify baseline characteristics associated with the likelihood of requiring secondary prevention interventions to achieve guideline-directed post-STEMI care. Results: A total of 586 patients were seen in the VSC within a median of 3.9 weeks (interquartile range 2.6) post-STEMI, representing 74.6% of all STEMI patients treated at our centre. Notably, 62.2% of diabetic patients had inadequate glucose control, 19.9% of all patients had a suboptimal lipid status, and 6.6% were newly identified as prediabetic. A total of 73.1% of patients received at least one intervention, including guideline-recommended medication adjustment (35.7%), bloodwork recommendation (31.0%), and referral to cardiac rehabilitation (30.6%). Diabetes was associated with an increase in the rate of new interventions, and every 10-year increase in age was associated with a decrease. The discharging hospital was not a significant predictor of new interventions. Conclusions: A structured VSC enabled timely post-STEMI follow-up, identifying and addressing key gaps in secondary prevention postdischarge, including lifestyle modification, guideline-recommended medication optimization, and appropriate follow-up care.
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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 | 0.002 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".