Long-term risk of stroke after acute coronary syndrome: the ABC-10* study on heart disease
Bibliographic record
Abstract
BACKGROUND: Previous studies link myocardial infarction to increased stroke risk. This long-term prospective study examines stroke incidence and stroke-related mortality in acute coronary syndrome (ACS) patients, identifying risk factors and geographic disparities. METHODS: We enrolled 535 ACS patients admitted to hospitals across three provinces in the Veneto region of Italy. Patients' residences were classified into three urban and three rural areas in each province. Patients were followed prospectively for 24 years or until death. Survival analysis was conducted using uni- and Multivariable Cox regression models. RESULTS: All patients, except for three, completed the follow-up, totaling 6.151 person-years. During follow-up, 84 patients experienced a stroke, with 85% being ischemic and 15% hemorrhagic, proving fatal in 43 cases. The stroke incidence rate was 14/1.000 person-years. Older age (HR 1.84; 95% CI 1.30-2.60), atrial fibrillation (AF) (HR 2.64; 95% CI 1.49-4.67), and a higher albumin-to-creatinine ratio (ACR) tertile (HR 1.38; 95% CI 1.04-1.83) were independent predictors of overall stroke risk, while higher estimated glomerular filtration rate tertile (eGFR) (HR 0.71; 95% CI 0.53-0.95) was independent predictor a lower risk. A sub-analysis revealed older age (HR 2.67; 95% CI 1.60-4.45) and AF (HR 2.95; 95% CI 1.38-6.32) as independent predictors of fatal stroke. Unexpectedly, we observed a higher fatal stroke risk in urban areas (HR 1.89; 95% CI 1.03-3.48) and southern provinces (HR 1.71; 95% CI 1.15-2.53). CONCLUSION: This long-term cohort study reinforces the role of established clinical predictors (age, AF, renal function) in post-ACS stroke risk and highlights novel geographic disparities in fatal stroke outcomes. These findings support the integration of geographic and clinical risk stratification in long-term secondary prevention strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".