In Reply: Characteristics of a COVID-19 Cohort With Large Vessel Occlusion: A Multicenter International Study
Bibliographic record
Abstract
To the Editor: We read with prodigious interest and pleasure the letter by Wang.1 We commend the authors on their copious contribution to the field and value their time to read our work2 and share/reflect on it through this letter. It has already been established that age, with its comorbidities, is a risk factor for stroke worldwide.3 Thus, it is normal for the non–COVID-19 cohort to be of old age. Moreover, because the rates of COVID-19 positivity are highest in the younger age group (18-24 years), this created the heterogenic cohort in our study. Based on the prognostic study by Son et al,4 older patients in the non–COVID-19 cohort should have not only benefited from mechanical thrombectomy but also showed higher rates of poor functional outcome. However, this was not the case as our study showed comparable 24 h National Institutes of Health Stroke Scale (NIHSS) scores between both cohorts and a higher rate of poor functional outcome in the younger COVID-19 cohort. Because both increasing age and COVID-19 positivity are independent factors for poor functional outcomes in patients with stroke, this corroborates the association between COVID-19 solely and stroke severity. We acknowledge the work by Casetta et al5 which demonstrated that female patients with stroke with LVOs have better clinical and functional outcomes after mechanical thrombectomies which may have affected our study results given the higher number of female patients in the non–COVID-19 cohort. However, based on univariate and multivariate analyses before and after propensity score analysis, which were performed to control such bias, sex was neither associated with poor functional outcome nor with complete revascularization. For stroke characteristics, the higher NIHSS and Alberta Stroke Program Early CT Score (ASPECTS) scores of patients with COVID-19 on presentation proves that COVID-19 is associated with more severe strokes, especially large vessel occlusions (LVOs). We acknowledge that because these scores were higher in the COVID-19–positive cohort, this may have affected our outcomes and tried to tackle such bias with propensity score analysis. However, larger trials with a more homogenous cohort must be encouraged to assess whether COVID-19 positivity alone affects functional outcome or other factors come in play. The length of hospital stay was defined as the number of days patients stayed in the hospital after mechanical thrombectomy regardless of COVID-19 status because patients who were COVID-19–positive can be discharged to quarantine at home. Length of stay was directly affected by patients' functional independence and their need for hospital care. It goes without mention that the shorter the duration from stroke onset to hospital admission and treatment, the better the outcomes in patients with stroke because time is brain.6,7 In our study, the duration from stroke onset to hospital admission was shorter in the COVID-19 cohort while the duration from door to arterial access was 24 minutes longer.2 The latter was comparable with other studies in the literature where this delay might be due to workflow during the pandemic.8 However, even with shorter duration from stroke onset to hospital admission, patients with COVID-19 had higher rates of poor functional outcomes compared with non–COVID-19 patients in our study.2 Finally, we want to thank Wang1 for their insightful feedback and want to take this opportunity to scrutinize several points. COVID-19 is an independent predictor of poor functional outcome in patients with strokes due to large vessel occlusion. These patients are usually younger, have less comorbidities, and are suffering from high morbidity and mortality rates. A low threshold for COVID-19 diagnosis is essential; especially with recurrent waves, we are witnessing to prevent catastrophic events. We also agree with Wang1 that large clinical trials are essential to further validate our conclusions.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".