Prediction of Post-Discharge Bleeding in Elderly Patients with Acute Coronary Syndromes: Insights from the BleeMACS Registry
Bibliographic record
Abstract
Background A poor ability of recommended risk scores for predicting in-hospital bleeding has been reported in elderly patients with acute coronary syndromes (ACS). No study assessed the prediction of post-discharge bleeding in the elderly. The new BleeMACS score (Bleeding complications in a Multicenter registry of patients discharged with diagnosis of Acute Coronary Syndrome), was designed to predict post-discharge bleeding in ACS patients. We aimed to assess the predictive ability of the BleeMACS score in elderly patients. Methods We assessed the incidence and characteristics of severe bleeding after discharge in ACS patients aged ≥ 75 years. Bleeding was defined as any intracranial bleeding or bleeding leading to hospitalization and/or red blood transfusion, occurring within the first year after discharge. We assessed the predictive ability of the BleeMACS score according to age by Fine–Gray proportional hazards regression analysis, calculating receiver-operating characteristic (ROC) curves and the area under the ROC curves (AUC). Results The BleeMACS registry included 15,401 patients of whom 3,376/15,401 (21.9%) were aged ≥ 75 years. Elderly patients were more commonly treated with clopidogrel and less often treated with ticagrelor or prasugrel. Of 3,376 elderly patients, 190 (5.6%) experienced post-discharge bleeding. The incidence of bleeding was moderately higher in elderly patients (hazard ratio [HR], 2.31, 95% confidence interval [CI], 1.92–2.77). The predictive ability of the BleeMACS score was moderately lower in elderly patients (AUC, 0.652 vs. 0.691, p = 0.001). Conclusion Elderly patients with ACS had a significantly higher incidence of post-discharge bleeding. Despite a lower predictive ability in older patients, the BleeMACS score exhibited an acceptable performance in these patients.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".