Predictors of stroke within 30 days in patients with non-ST-segment elevation acute coronary syndromes
Bibliographic record
Abstract
AIMS: Stroke is an uncommon but serious complication after non-ST-segment elevation acute coronary syndrome (NSTE-ACS). We aimed to identify predictors of stroke within 30 days in patients who suffered NSTE-ACS. METHODS AND RESULTS: We pooled data from six trials (n=31 402) that randomized NSTE-ACS patients either to platelet glycoprotein (GP) IIb/IIIa receptor blockers or to placebo/control therapy. Potential predictors of stroke included treatment, demographic, and clinical characteristics. We identified predictors using univariable and multivariable logistic models, and their performance was evaluated with calibration (Hosmer-Lemeshow test) and discrimination (c-statistic). We found 228 (0.7%) all-cause strokes: 155 (0.5%) non-haemorrhagic, 20 (0.06%) haemorrhagic, and 53 without computed tomography (CT) confirmation. Patients with any type of stroke had a 30-day mortality of 25%. Randomization to GP IIb/IIIa receptor blockers was not significantly associated with all-cause stroke [OR (95% CI) 1.08 (0.83-1.41)]. Older age [OR per 10-year increase 1.5 (1.3-1.7)], prior stroke [2.1 (1.4-3.1)], and elevated heart rate [per 10-beat increase 1.1 (1.0-1.2)] were the strongest predictors of 30-day all-cause stroke. Similar predictors were found for non-haemorrhagic and haemorrhagic strokes. Smoking, previous myocardial infarction, diabetes, and hypertension were not independent predictors of all-cause stroke. The multivariable model to predict all-cause stroke was well calibrated, but its discrimination was only moderate [c-statistic 0.69 (0.65-0.72)]. CONCLUSION: Stroke is a rare complication occurring early after NSTE-ACS, but is associated with high mortality. We found no evidence that GP IIb/IIIa receptor blockers increase stroke risks. A few clinical characteristics predicted higher stroke risks. Thus, incident strokes in NSTE-ACS patients remain largely unexplained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".