Abstract WP206: Risk of Recurrent Stroke in Patients Diagnosed with Comorbid Depression at the Time of Transient Ischemic Attack
Bibliographic record
Abstract
Introduction: Although rates of depression are increased in patients with transient ischemic attack (TIA), the effect of depression on recurrent cerebrovascular events is uncertain. Therefore, we aimed to calculate the risk of subsequent stroke in patients with comorbid depression at the time of TIA. Methods: We used all-payer claims data on all nonfederal acute care hospitalizations in New York, California, and Florida from 2005-2013. Our cohort comprised all patients hospitalized for TIA ( ICD-9-CM 435.x). The predictor variable was a diagnosis of depression ( ICD-9-CM 296.20-.25, 296.30-.35, 300.4, 311 in any diagnosis code position) during the index hospitalization for TIA. Kaplan-Meier survival statistics were used to calculate cumulative rates of our primary composite outcome of ischemic and hemorrhagic stroke ( ICD-9-CM 431, 433.x1, 434.x1, or 436 without concomitant trauma or rehabilitation codes). Cox proportional hazard analysis was used to examine the association between depression and stroke. Results: We identified a total of 1,817,842 TIA patients, among whom 223,311 (12.3%) were discharged from their index TIA visit with a comorbid depression diagnosis. Over a mean follow-up of 4.2 (+/- 2.1) years, 227,501 patients were diagnosed with subsequent stroke. The 1-year cumulative rate of stroke was 3.5% in both patients with depression (95% CI, 3.3-3.7%) and without depression (95% CI, 3.5-3.6%). The 5-year cumulative rate of stroke was 8.5% (95% CI, 8.2-8.9%) in those with depression compared to 8.9% (95% CI, 8.7-9.0%) in patients without depression at the time of their TIA diagnosis. After adjusting for demographics and vascular risk factors, depression was not associated with subsequent stroke (hazard ratio, 1.0; 95% CI, 1.0-1.1; p =0.7). To reduce any misclassification error, these results were unchanged in a sensitivity analysis which included only those patients who underwent brain magnetic resonance imaging during their index hospitalization for TIA. Conclusions: A comorbid diagnosis of depression at the time of TIA is not associated with an increased risk of subsequent stroke. Future research should evaluate whether incident depression diagnosed after hospital discharge for TIA is associated with heightened stroke risk.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".