Abstract 60: Utilization, Workflow, and Outcomes of Endovascular Thrombectomy in Patients With versus Without Pre-Morbid Disability in a National Stroke Registry
Bibliographic record
Abstract
Background: Given the paucity of high-quality data on acute stroke therapies in patients with pre-morbid disability, they risk being routinely excluded from such therapies. We examined utilization of endovascular thrombectomy (EVT), workflow, and outcomes among ischemic stroke patients with vs without pre-morbid disability in a national registry. Methods: We used data for the Czech Republic from 1-January-2016 to 31-December-2020. Pre-morbid disability was defined as pre-stroke modified Rankin Scale score (mRS) >2. We compared receipt of EVT, workflow times, ΔmRS (change from pre-stroke to 3-months), intracerebral hemorrhage (ICH), mortality, and discharge NIHSS among patients with vs without pre-morbid disability, adjusting for age, sex, baseline NIHSS, and comorbidities, and verified using propensity score-weighting (PSW) for differences in treatment assignment. Results: Among 22,405 patients, 1,712 (7.6%) had pre-stroke mRS >2. Patients with pre-morbid disability were less likely to receive EVT (10.1% vs 20.7%, aOR:0.30, 95%CI:0.24-0.36) and had longer door-to-puncture times (median:75-minutes, IQR:58-100 vs 54, IQR:27-77, adjusted-difference:12.5, 95%CI:2.68-22.3), worse ΔmRS (adjusted rate-ratio, aIRR on PSW:1.57, 1.43-1.72), rates of 3-month mRS 5-6, discharge NIHSS, and mortality (aOR-PSW[mortality]:2.54, 1.92-3.34); ICH rates did not differ. Among those with pre-morbid disability, 32.1% returned to pre-stroke state; this ranged from 19.6% for those >85-years to 66.0% for <65-years. EVT was associated with better outcomes including lower ΔmRS (aIRR-PSW:0.87, 0.83-0.91) and mortality, with no interaction of treatment effect by pre-morbid disability (e.g. mortality p interaction =0.73). EVT recipients with pre-morbid disability did not differ significantly for several key outcomes including ΔmRS (aIRR:0.99, 0.84-1.17), but were more likely to have mRS 5-6 (70.1% vs 39.5%, aOR:1.85, 1.12-3.04). Conclusions: Patients with pre-morbid disability were less likely to receive EVT and had slower treatment and worse outcomes than those without disability. However, patients fared better with EVT versus medical care, and one-third with pre-stroke disability returned to their pre-stroke state.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".