Abstract WMP57: Brain Frailty Mediates The Relationship Between Age And 90-day Functional Outcome After Endovascular Therapy: Analysis Of The Escape-na1 Trial
Bibliographic record
Abstract
Introduction: Age is a predictor of functional outcome after acute ischemic stroke (AIS). Frailty increases with age and comorbidities, and imaging markers of brain-frailty (e.g. atrophy, small-vessel-disease) are associated with outcomes. However, the extent to which the association of age and 90-day outcome is mediated by brain-frailty is unknown. We explored this mediation in AIS patients receiving endovascular therapy (EVT), with a particular interest in neuroimaging. Methods: In this post-hoc analysis of the ESCAPE-NA1 trial, in which all patients underwent EVT, we assessed brain atrophy (subcortical/cortical), white-matter disease (periventricular/deep) and the number of lacunes and chronic infarctions. Structural equation modelling (SEM) was used to create 3 latent variables: “imaging-frailty” (above-mentioned markers), “clinical-frailty” (e.g. pre-stroke mRS, cardiovascular risk factors, cancer) and “total-frailty” (imaging + clinical markers). We created 3 models ( figure1) including each latent variable as a potential mediator of the association of age and 90-day outcome, adjusting for baseline ASPECTS, NIHSS, onset-to-puncture-time, nerinetide and alteplase. Results: Among 1,092 patients, the indirect effect of age on 90-day outcome, mediated by imaging-frailty, contributed 96% of the total effect(β=0.047;p=0.02), while the indirect effect through clinical-frailty accounted for only 21%(β=0.01;p=0.001) of the total effect. When including both frailty constructs, the indirect pathway accounted for 86% of the total effect(β=0.06;p<0.01). These proportions were similar when imaging features were assessed on MRI. Conclusions: Brian frailty mediates the association of age and 90-day outcome after EVT, with most of the effect mediated by imaging as opposed to clinical markers of frailty. This work underscores the importance of considering brain-frailty, as opposed to chronological age alone, in predicting post-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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".