Endovascular Treatment in Stroke Patients of Working Age: A Multicenter Observational Study of Real-World Outcomes
Bibliographic record
Abstract
Purpose: The prevalence of acute ischemic stroke (AIS) is increasing among people of working age, posing socioeconomic and healthcare challenges. Inability to return to work can have significant negative consequences and contribute to the economic burden of stroke. Endovascular treatment (EVT) has been established as the standard of care for large vessel occlusion AIS patients. In this study, we aimed to identify factors predicting favorable outcome among working age AIS patients undergoing EVT. Patients and Methods: We analyzed data from 309 patients from five comprehensive stroke centers between 2019 and 2023. All patients were working age (18<59) with symptoms onset of within 24 hours. Modified thrombolysis in cerebral infarction (mTICI) score of 2b-3 was considered as successful recanalization. We used 3-months mRS post-EVT to evaluate the outcome; mRS of 0-2 was defined as favorable outcome, whereas mRS of 3-6 was considered unfavorable outcome. Results: The unfavorable outcome group consisted of 150 patients, and 159 patients were in the favorable outcome group. More people in the unfavorable outcome group had diabetes (29.33% vs 15.72%, p=0.004) and hypertension (61.33% vs 40.88, p<0.001). A multivariable regression analysis demonstrated that several factors, including successful recanalization (odds ratio (OR) 5.298, 95% confidence interval (CI) 1.735-16.174, p=0.003), pre-EVT NIHSS (OR 0.892, 95% CI 0.852-0.934, p=0.000), baseline Alberta stroke program early CT score (ASPECTS) (OR 1.609, 95% CI 1.274-2.032, p=0.000), hypertension (OR 0.477, 95% CI 0.270-0.845, p=0.011), diabetes mellitus (OR 0.413, 95% CI 0.208-0.820, p=0.011), and symptomatic intracerebral hemorrhage (sICH) (OR 0.045, 95% CI 0.006-0.359, p=0.003) can predict the outcome of patients undergoing EVT. Conclusion: Favorable outcome of working age patients with AIS undergoing EVT can be predicted using multiple factors, including hypertension, diabetes mellitus, successful recanalization, pre-EVT NIHSS, baseline ASPECTS, and sICH.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".