Endovascular Treatment in Stroke Patients of Working Age: A Multicenter Observational Study of Real-World Outcomes
Bibliographic record
Abstract
Yongting Zhou,1 Mohammad Mofatteh,2 Zijie Zheng,3 Feng Liu,4 Sijie Zhou,5 Jicai Ma,6 Zile Yan,7 Yuzheng Lai,8 Weijiang Li,1 Weiying Chen,1 Mohamad Abdalkader,9 Robert W Regenhardt,10 Xiangmin Liu1 1Department of Neurology, Huadu District People’s Hospital of Guangzhou, Guangzhou, People’s Republic of China; 2School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast, UK; 3School of Automation Science and Engineering, South China University of Technology, Guangzhou, People’s Republic of China; 4Department of Internal Medicine, Huadu District People’s Hospital of Guangzhou, Guangzhou, People’s Republic of China; 5Department of Surgery of Cerebrovascular Diseases, First People’s Hospital of Foshan, Foshan, Guangdong Province, People’s Republic of China; 6Department of Neurology, The Affiliated Yuebei People’s Hospital of Shantou University Medical College, Shaoguan, People’s Republic of China; 7Department of Neurology and Advanced National Stroke Center, Foshan Sanshui District People’s Hospital, Foshan, Guangdong Province, People’s Republic of China; 8Department of Neurology, Guangdong Provincial Hospital of Integrated Traditional Chinese and Western Medicine (Nanhai District Hospital of Traditional Chinese Medicine of Foshan City), Foshan, Guangdong Province, People’s Republic of China; 9Department of Radiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA; 10Department of Neurology, Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USACorrespondence: Xiangmin Liu, Department of Neurology, Huadu District People’s Hospital of Guangzhou, Guangzhou, People’s Republic of China, Email 13560494238@163.comPurpose: 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.Keywords: acute ischemic stroke, endovascular treatment, thrombectomy, China, working age, patient outcome
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".