Abstract P652: Burden of Vascular Events Amongst TIA Patients - A Nationwide Estimate
Bibliographic record
Abstract
Introduction: Literature reported, 9-17% transient ischemic attack (TIA) patients have a vascular event within 90 days but there is limited data on long term risk involvement of vascular events following TIA. Aim: To identify prevalence and association of cardiovascular diseases (CVD) and cerebrovascular disorders (CeVD) amongst early and late TIA. Methods: We performed a retrospective cross-sectional analysis of Nationwide Inpatient Sample (2016-2017) in adults hospitalizations. Early TIA (primary diagnosis), late TIA (secondary diagnosis-within a year) and vascular events [CVD (AFib, IHD, acute MI angina) and CeVD (AIS, ICeH, SAH)] amongst TIA were identified using ICD 10 CM codes. Prevalence of vascular events were compared amongst patients with TIA and without TIA. Weighted analysis to account for sampling strategy using mix-effect multivariable survey logistic regression was performed to evaluate odds of having vascular events amongst TIA in comparison to non-TIA. Results: Amongst 58,259,589 US hospitalizations, 0.38% and 5.92% patients had early and late TIA, respectively. Patients with late TIA had higher prevalence of acute MI (4.9 vs 0.5 vs 3.4%), IHD (44 vs 28.6 vs 20.6%), angina (0.3 vs 0.2 vs 0.2%), AFib (22 vs 15.3 vs 10.9%), AIS (5.3 vs 0.6 vs 2%), SAH (0.2 vs 0.03 vs 0.1%) and ICeH (0.8 vs 0.04 vs 0.4%) compared to early TIA and no-TIA, respectively. (p<.0001) Patients with late TIA had 23% higher risk of having Afib [aOR 1.23; 95%CI 1.22-1.23] and higher odds of having IHD [1.52; 1.52-1.53], AIS [1.72; 1.70-1.74], and ICeH [1.29; 1.25-1.33]. (Table 1) Conclusion: We found a higher prevalence of late TIA amongst US hospitalizations. Additionally, late TIA patients had a higher risk of vascular events like Afib, IHD, and stroke. Hence, a thorough clinical investigation and long term followup of TIA patients may mitigate the risk of future vascular events and associated health care burden.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".