Reply to: “Periodic Limb Movements During Sleep and White Matter MRI Hyperintensity in Minor Stroke or TIA”
Notice bibliographique
Résumé
We thank Manconi et al.1 for their interest in our publication.2 We think that several methodological differences explain the discrepancy between the results of Manconi et al.1 and our work.2 First, the white matter hyperintensity (WMH) volumes reported by Manconi et al. (in Tables 1 and 2) were unexpectedly low (median of 1.3–1.8 cm3), which is substantially lower than that which has been previously reported in similarly-aged normal controls3 (mean of 3.64 cm3) and in two cohorts of patients with minor stroke/transient ischemic attack (TIA) (median of 4.65 cm3 in one study4 and a median of 5.08 cm3 in another cohort with transient symptoms5). This may have been because they only scored WMH volumes over the non-affected hemisphere, thereby providing an incomplete picture of the total WMH volume in the patients they examined. Regardless, the inclusion of such low WMH volumes likely gave rise to a floor effect and made it difficult for the authors to demonstrate any relationship with the periodic limb movement (PLM) index. In comparison, we used the Age Related White Matter Change (ARWMC) Scale, a well-validated rating scale,6 which is strongly correlated to WMH volume and furthermore to cognitive function.3 Second, details surrounding the scoring of PLMs are not provided by Manconi et al.1 or in the manuscript that details the methodology of the SAS CARE study.7 For example, it is unclear whether a nasal pressure transducer was used to eliminate respiratory events related to upper airway resistance. In the work reported by Manconi et al.,1 the baseline PLM index was significantly related to the apnea-hypopnea index (AHI) and arousal index, suggesting that underlying sleep-disordered breathing may have played an important role in the scored PLMs and thereby could have confounded any potential relationship with WHM volume. Next, important differences are present between the characteristics of the cohort we studied2 and that reported by Manconi et al.1 The median age of the patients with a PLM index ≥ 5/h (59.7 years) was 10 years younger in the report by Manconi et al.1 compared to the median age observed in our patients with a PLM index ≥ 5/h (70.0 years)2; younger patients are known to have less WMH burden and are likely more resilient to the fluctuations in blood pressure and sympathetic tone that are thought to occur with PLMs. Furthermore, the baseline median AHI in the cohort reported by Manconi et al.1 was 15.4, which was substantially greater than the median AHI of 2.5 observed in our study.2 Other important characteristics of the cohort examined by Manconi et al.1 are not provided. For example, gender, the number of patients that presented with TIA (vs. minor stroke), associated medical co-morbidities, and presence of pre-stroke/TIA PLM triggers (such as Restless Legs Syndrome) are not reported.1 Overall, the two studied cohorts are very different in their underlying characteristics. Given the methodological issues noted above and the major differences between the two cohorts studied, we disagree that the study by Manconi et al.1 is a true replication of our work. As recently reported by our research group,8 the current evidence is limited but does suggest that PLMs are a prognostic factor for incident vascular events and mortality. Other studies also support a close association between PLMs and stroke,9,10 as does our present study.2 On the other hand, the work of Koo et al.11 found a relationship between PLMs and all-cause cardiovascular disease including stroke but could not find a relationship between PLMs and stroke when stroke was studied as an isolated entity, a result more in alignment with that reported by Manconi et al.1 Further work is needed to more fully understand the relationship between PLMs and stroke and to resolve the current contradictions in the literature. Future randomized controlled trials to study the impact of treating PLMs on vascular outcomes could be one such approach. During this study, Dr. Boulos was supported by a Focus on Stroke 2010 Research Fellowship, which was funded by the Heart and Stroke Foundation of Canada, the Canadian Stroke Network and the Canadian Institutes of Health Research; he was also supported by fellowship funding from the Canadian Partnership for Stroke Recovery. Dr. Swartz is supported by the Heart and Stroke Foundation of Canada New Investigator Award and Barnett Award, and this work was supported by operating grant funding from the Heart and Stroke Foundation and the Canadian Institute of Health Research. Dr. Lim has engaged in consulting activities for UCB S.A. and Merck & Co. Inc. Dr. Walters has served as a consultant on RLS to UCB Pharma and MundiPharma, and has also received grant funding for investigator-initiated projects from both companies. Dr. Walters has also participated in a study initiated by UCB. All other authors report no conflicts of interest. Disclosure of any off-label or investigational use: None.
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 | 0,002 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,062 | 0,032 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,007 |
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 ».