Impact of diabetes on bleeding events in ST-elevation myocardial infarction patients after urgent percutaneous coronary intervention
Bibliographic record
Abstract
Patients with diabetes mellitus (DM) have more ischemic events and a decreased survival rate after percutaneous coronary intervention (PCI) than non-DM patients. However, it is unknown whether short-term or long-term bleeding events are associated with DM. We aimed to determine the impact of DM on mortality and bleeding events in ST-elevation myocardial infarction (STEMI) patients after urgent PCI.This retrospective cohort study included 435 STEMI patients who had undergone urgent PCI between 2010 and 2013, comprising 97 DM patients and 338 non-DM patients. The primary outcomes were the 30-day bleeding and 30-day mortality rates. The median follow-up period was 2 years. Data regarding patient demographics, peri-PCI medication, and invasive procedures were compared between DM and non-DM patients. Multivariate logistic regression was applied to estimate the association between DM and bleeding events. Kaplan-Meier curves were calculated to elucidate the survival rate.Compared with non-DM patients, DM patients with STEMI had a higher incidence of left ventricular ejection fraction <40% (17.6% vs 4.2%, P < 0.05), Killip class >II (11.3% vs 3.8%, P < 0.05), and smoking (44.3% vs 63.0%, P < 0.05). Similar peri-PCI medication and invasive procedures were administered in the 2 groups. The incidence of 30-day bleeding events was significantly higher for DM patients than non-DM patients (6.2% vs 0.9%, P < 0.05). A multivariate analysis showed that DM was strongly associated with 30-day bleeding events after adjusting for confounders. DM patients had significant increased mortality rates at both the 30-day and 2-year end points.DM was an independent predictor for an increased risk of 30-day bleeding events and correlated with increased 30-day and 2-year mortality rates in STEMI patients with PCI. Our study has significant clinical implications for risk stratification before the application of urgent PCI.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".