Abstract TP273: Characteristics and Incidence of Stroke and Bleeding in Patients with a First-Ever Transient Ischemic Attack: A US Multi-Database Observational Study
Bibliographic record
Abstract
Introduction: Patients suffering from transient ischemic attack (TIA) are at high risk of ischemic stroke (IS). This study describes clinical characteristics and outcomes in patients with a first non-cardioembolic TIA. Methods: Using two US administrative claims databases (MarketScan and Optum’s de-identified Clinformatics® Data Mart Database [CDM]) converted to the Observational Medical Outcomes Partnership (OMOP) common data model, we conducted an observational, retrospective cohort study of adults with a first diagnosis of non-cardioembolic TIA between 2012 and 2022. Demographic and clinical characteristics were described at baseline, and incidence rates of IS, intracranial bleeding, and bleeding leading to hospitalization with sensitivity analyses at different time points were calculated. Results: Overall, 203,757 patients were included in the study, 97,481 from MarketScan, 106,276 from CDM. Mean age was 62 years in MarketScan and 72 years in CDM. Patients were mostly women (57.6% in MarketScan, 59.3% in CDM). At baseline, prevalence of comorbidities was high (hypertension 66% and 84%, hyperlipidemia 53% and 75%, coronary artery disease 18% and 31%, diabetes 25% and 38% in MarketScan and CDM, respectively). Median follow-up time was 569 days in MarketScan and 716 days in CDM. At 1 year follow-up, incidence rates per 100 person-years of IS, intracranial bleeding, and bleeding leading to hospitalization were 10.9, 0.9, and 4.2, respectively, in MarketScan and 20.2, 1.6, and 7.6 respectively, in CDM. Sensitivity analyses showed that most IS events occurred within 7 days of the index event. Additional event rates and sensitivity analyses are shown in Table 1. Conclusion: Results from two US claims databases show that the annual risk of IS is higher than expected following a first TIA diagnosis, especially when including the first 7 days in the ascertainment. Implementation of guideline directed antiplatelet therapies, or new antithrombotic strategies, is needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| 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) | 0.002 | 0.001 |
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".