The effect of aspirin, ticagrelor and heparin pre-transfer vs. similar precatherization treatment in STEMI on pre-PCI TIMI flow
Bibliographic record
Abstract
Abstract Aims Whether early combined antithrombotic therapy with aspirin, ticagrelor and unfractionated heparin (UFH) in STEMI patients can improve clinical outcomes is unknown. Data regarding the efficacy of UFH in this clinical setting is lacking. The objective of this novel study was to evaluate the effects of early pretreatment with aspirin, ticagrelor and UFH on pre-PCI TIMI flow in the IRA in acute STEMI patients. Methods and results Between January 2015 and June 2018, we retrospectively compared 488 STEMI patients receiving aspirin, ticagrelor and UFH pretreatment in a spoke peripheral hospital before transfer (PHT) versus 233 prehospital triage setting (PTS) STEMI patients receiving in-ambulance aspirin, followed by ticagrelor and UFH pretreatment in the hub catheterization laboratory before PCI. The primary outcome was the presence of a pre-PCI TIMI flow 2–3 in the IRA. The median times from ticagrelor and UFH administration to angiography in the PHT group and in the PTS group were 80 minutes (95% CI: 68.5–93.9) and 10 minutes (95% CI: 5–15.5) respectively (p<0.0001). Inverse probability of treatment weighting was used to minimize differences between groups. Main results are shown in Table 1. Each 10-minute delay between ticagrelor and heparin administration and angiography was associated with a reduced pre-PCI TIMI flow (OR 0.89, 95% CI: 0.85–0.93). Conclusion STEMI patients receiving aspirin, ticagrelor and UFH before peripheral hospital transfer have a significantly greater pre-PCI TIMI flow 2–3 and a lower rate of definite in-hospital stent thrombosis compared to patients receiving in-ambulance aspirin in the prehospital setting followed by ticagrelor and heparin in the catheterization laboratory without greater bleeding risk. While prehospital triage with rapid primary PCI remains the preferred scenario for STEMI patients, a prompt UFH initiation may play a synergistic role with ticagrelor and aspirin in decreasing pre-PCI thrombus burden in the presence of organizational delays. Figure 1. Study Design and Main Results Funding Acknowledgement Type of funding source: Public hospital(s). Main funding source(s): Université de Sherbrooke's Department of Medicine, Division of Cardiology
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".