Mitigating the risk of flow deterioration by deferring stent optimization in STEMI patients with large thrombus burden: Insights from a prospective cohort study
Bibliographic record
Abstract
Abstract Objectives It is uncertain, if omitting post-dilatation and stent oversizing (stent optimization) is safe and may decrease the risk for distal thrombus embolization (DTE) in STEMI patients with large thrombus burden (LTB). Background In patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (pPCI) with stenting, (DTE) and flow deterioration are common and increase infarct size leading to worse outcomes. Methods From a prospective registry, 74 consecutive STEMI patients with LTB undergoing pPCI with stenting and intentionally deferred stent optimization were analyzed. Imaging data and outcomes up to 2 years follow-up were analyzed. Results Overall, 74 patients (18% females) underwent deferred stent optimization. Direct stenting was performed in 13 (18%) patients. No major complications occurred during pPCI. Staged stent optimization was performed after a median of 4 (interquartile range (IQR) 3; 7) days. On optical coherence tomography, under-expansion and residual thrombus were present in 59 (80%) and 27 (36%) cases, respectively. During deferred stent optimization, we encountered no case of flow deterioration (slow or no-reflow) or side branch occlusion. Minimal lumen area (mm 2 ) and stent expansion (%) were corrected from 4.87±1.86mm to 6.82±2.36mm ( p <0.05) and from 69±18% to 91±12% ( p <0.001), respectively. During follow-up, 1 patient (1.4%) required target lesion revascularization and 1 (1.4%) patient succumbed from cardiovascular death. Conclusions Among STEMI patients with LTB, deferring stent optimization in the setting of pPCI appears safe and potentially mitigates the risk of DTE. The impact of this approach on infarct size and clinical outcomes warrants further investigation in a dedicated trial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".