Screening for sleep apnoea in patients with spinal cord injury during rehabilitation
Notice bibliographique
Résumé
Introduction Patients with spinal cord injury (SCI) have an increased risk of sleep apnea. A systematic review on sleep apnea in patients with tetraplegia reported a prevalence between 46 and 97% (1), but smaller studies also suggest a problem among patients with paraplegia (2). Therefore, more studies with focus on the whole population is needed. Assessment of sleep apnea can be done with polysomnography or cardiorespiratory monitoring (CRM), but these examinations take time and require a team of professionals with expertise in sleep and are therefore not feasible to perform in all hospitalized patients. A recent study examining questionnaires for screening of sleep apnea showed the Stop Bang Questionnaire (SBQ) to be the most sensitive for detecting patients with obstructive sleep apnea (OSA) (3). However, the SBQ may not capture all types of sleep apnea and more knowledge is needed on the specificity in people with SCI. The purpose of the current study is to examine the prevalence of sleep apnea in first-time hospitalized patients with SCI and whether the SBQ can detect patients at risk for sleep apnea. In this abstract, preliminary data on the prevalence and agreement between the SBQ and the CRM are presented. Method The project was carried out at the Spinal Cord Injury Center of Western Denmark. We screened a cohort of first-time hospitalized patients with SCI from September 2022 to February 2023. All patients received verbal and written information about the project and those who gave consent were screened for sleep apnea. Screening consisted of SBQ and a regular clinical sleep questionnaire. Next, everyone was examined with CRM and assessed for sleep apnea by a doctor specialized in sleep disorders. Results During 5 months, 35 inpatients with first-time SCI were identified. Twenty-six agreed to be part of the project. The participants had an average age of 53 years, 17 men/ 9 women, three with severity of AIS C T1–S3 and 23 with AIS D. None being ventilator dependent. Twenty of 26 (prevalence of 77%) was diagnosed as having sleep apnea based on assessment and CRM. The SBQ classified 3/7/10 respectively being in low/moderate/severe risk of OSA of the 20 diagnosed with sleep apnea. In the six patients not diagnosed with sleep apnea the SBQ showed 3/3 being low/moderate at risk of OSA. Conclusion The preliminary results of the study showed a high prevalence of sleep apnea in first time SCI hospitalized patients. Moreover, the results point towards that the SBQ cannot stand alone identifying sleep apnea in patients with SCI. Further analysis of data is needed to find possible discriminators and guide the relevance of the current setup. (1) Prevalence of sleep-disordered breathing in people with tetraplegia—a systematic review and meta-analysis. Marnie Graco et al. 2021 (2) Sleep disordered breathing in spinal cord injury: A systematic review. Chiodo AE et al. 2016 (3) Screening Questionnaires for ObstructiveSleep Apnea: An Updated SystematicReview. Amra B et al. 2018
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».