Predictors of Outcome After Mechanical Thrombectomy in Stroke Patients Aged ≥85 Years
Bibliographic record
Abstract
BACKGROUND: The effectiveness of mechanical thrombectomy (MT) in elderly stroke patients remains debated. We aimed to describe outcomes and their predictors in a cohort of patients aged ≥ 85 years treated with MT. METHODS: Data from consecutive patients aged ≥ 85 years undergoing MT at two stroke centers between January 2016 and November 2019 were reviewed. Admission National Institutes of Health Stroke Scale (NIHSS), pre-stroke, and 3-month modified Rankin scale (mRS) were collected. Successful recanalization was defined as modified thrombolysis in cerebral ischemia score ≥ 2b. Good outcome was defined as mRS 0-3 or equal to pre-stroke mRS at 3 months. RESULTS: Of 151 included patients, successful recanalization was achieved in 74.2%. At 3 months, 44.7% of patients had a good outcome and 39% had died. Any intracranial hemorrhage (ICH) and symptomatic ICH occurred in 20.3% and 3.6%, respectively. Logistic regression analysis identified lower pre-stroke mRS score (adjusted odds ratio [aOR], 0.52; 95% CI, 0.36-0.76), lower admission NIHSS score (aOR, 0.90; 95% CI, 0.83-0.97), successful recanalization (aOR, 3.65; 95% CI, 1.32-10.09), and absence of ICH on follow-up imaging (aOR, 0.42; 95% CI, 0.08-0.75), to be independent predictors of good outcome. Patients with successful recanalization had a higher proportion of good outcome (45.3% vs 34.3%, p = 0.013) and lower mortality at 3 months (35.8% vs 48.6%, p = 0.006) compared to patients with unsuccessful recanalization. CONCLUSIONS: Among patients aged ≥ 85 years, successful recanalization with MT is relatively common and associated with better 3-month outcome and lower mortality than failed recanalization. Attempting to achieve recanalization in elderly patients using MT appears reasonable.
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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) | 0.001 | 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".