Abstract Number ‐ 196: The Impact of Pre‐existing Depression on Functional Outcomes After Endovascular Treatment of Ischemic Stroke
Bibliographic record
Abstract
Introduction Stroke is a leading cause of morbidity and mortality, and many factors predict a poor outcome, including age, NIH Stroke Scale (NIHSS), ambulatory status, and ability to swallow1. Mood disorders have previously been associated with an increased risk of cardiovascular disease2. The association between pre‐existing mood disorders and stroke outcome, however, has not been well studied. The objective of the current study was to explore the impact of pre‐existing depression on functional outcomes post‐endovascular treatment. Methods We reviewed the records of 178 patients who received mechanical thrombectomy (MT) at our institution[BH1][BM2] from 2019–2022. We collected data on baseline characteristics and demographics, including functional outcome at 90 days as measured by modified Rankin Scale (mRS), which was categorized as favorable (mRS 0–2) or unfavorable (mRS 3–6). Results Of patients who received MT, 54 (30.3%) had a prior medical history of depression. On univariate analysis, patients with a history of depression had 2.5 times higher odds of an mRS score of 3–6 (cOR = 2.47, 95% CI = 1.11‐5.48). Multivariate analysis included history of smoking, hypertension, obesity, Alberta Stroke Program Early CT Score (ASPECTS)[YC1][KC2], time to recanalization, and discharge NIHSS score, along with history of depression. We found that the odds of having an unfavorable mRS were 5 times higher in those with a history of depression than those with no history (aOR = 5.15, 95% CI = 1.09‐23.31). Additionally, discharge NIHSS was associated with 1.5 times higher odds of unfavorable mRS for each point increase in NIHSS score (aOR = 1.47, 95%CI = 1.25‐1.74). While pre‐existing depression was associated with poorer functional outcomes, it was not associated with mortality, cOR = 1.12, 95%CI = 0.55‐2.27. Conclusions In this study, we found that a prior medical history of depression is associated with unfavorable functional outcomes at 90 days in patients who received MT. Future studies should investigate the association between the severity of depression and stroke outcomes and explore methods to mitigate the impact of depression on stroke outcomes.
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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 | 0.000 |
| 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) | 0.008 | 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".