Time to Medical Management in Patients Presenting with Non-ST Elevation Myocardial Infarction: A Retrospective Analysis of Two Teaching Hospitals
Bibliographic record
Abstract
Darren R. Kagal1 and Omid Salehian2 1Department of Medicine and 2Division of Cardiology, McMaster University, Hamilton, Canada. Abstract Background: It is clear that early administration of antiplatelet and antithrombotic therapy in patients presenting with Non-ST elevation MI (NSTEMI) is associated with improved outcomes. There are existing guidelines regarding early treatment in this patient population. We investigated how effectively patients are being managed for NSTEMI with respect to time to administration of indicated medical therapies in two teaching institutions. Methods: A retrospective analysis was performed on 100 consecutive patients with no prior history of coronary artery disease who presented to the Emergency Departments of two teaching hospitals affiliated with McMaster University with the diagnosis of NSTEMI (defined as ischemic symptoms in presence of elevated cardiac biomarkers with or without electrocardiographic changes). Times of medication administration were obtained from the nursing notes. Results: The mean age was 63.9 ± 14.1 years. The average time to administration of aspirin was 2.7 ± 4.3 hours, Unfractionated or Low Molecular Weight Heparin 5.4 ± 5.0 hours, and Clopidagrel 10.8 ± 16.5 hours from time of triage. Duration of symptoms prior to presentation were longer in patients with left ventricular (LV) dysfunction (on echocardiography assessed in 73 patients) compared to patients with normal LV, (7.5 h ± 8.3 vs. 3.3 h ± 3.4 p = 0.006). The average age of patients who had ASA, Heparin, and Clopidagrel initiated within 3 hours was lower than those after 6 hours (60.1 ± 12.7 years vs 68.1 ± 14.4 years, p = 0.01). The average age for those who underwent angiography ± percutaneous coronary intervention (PCI) was also lower at 60.2 ± 11.8 versus 76.1 ± 14.4 years (p = 0.0001). Conclusions: There was a lag time in the administration of established medical therapies in patients presenting with first time NSTEMI. An age bias towards time to treat and selection for PCI may have also existed. The fact that increased duration of symptoms was associated with worse LV function may provide impetus for early recognition and management of patients with NSTEMI.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".