In Reply: Characteristics of a COVID-19 Cohort With Large Vessel Occlusion: A Multicenter International Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,012 | 0,014 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».