Mechanical thrombectomy for acute ischemic stroke in COVID-19 patients: multicenter experience in 111 cases
Bibliographic record
Abstract
BACKGROUND: Data on the frequency and outcome of mechanical thrombectomy (MT) for large vessel occlusion (LVO) in patients with COVID-19 is limited. Addressing this subject, we report our multicenter experience. METHODS: A retrospective cohort study was performed of consecutive acute stroke patients with COVID-19 infection treated with MT at 26 tertiary care centers between January 2020 and November 2021. Baseline demographics, angiographic outcome and clinical outcome evaluated by the modified Rankin Scale (mRS) at discharge and 90 days were noted. RESULTS: We identified 111 out of 11 365 (1%) patients with acute or subsided COVID-19 infection who underwent MT due to LVO. Cardioembolic events were the most common etiology for LVO (38.7%). Median baseline National Institutes of Health Stroke Scale score and Alberta Stroke Program Early CT Score were 16 (IQR 11.5-20) and 9 (IQR 7-10), respectively. Successful reperfusion (mTICI ≥2b) was achieved in 97/111 (87.4%) patients and 46/111 (41.4%) patients were reperfused completely. The procedure-related complication rate was 12.6% (14/111). Functional independence was achieved in 20/108 (18.5%) patients at discharge and 14/66 (21.2%) at 90 days follow-up. The in-hospital mortality rate was 30.6% (33/108). In the subgroup analysis, patients with severe acute COVID-19 infection requiring intubation had a mortality rate twice as high as patients with mild or moderate acute COVID-19 infection. Acute respiratory failure requiring ventilation and time interval from symptom onset to groin puncture were independent predictors for an unfavorable outcome in a logistic regression analysis. CONCLUSION: Our study showed a poor clinical outcome and high mortality, especially in patients with severe acute COVID-19 infection undergoing MT due to LVO.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 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".