Fenotipos de los pacientes respondedores al tratamiento de la apnea obstructiva del sueño mediante los dispositivos de avance mandibular
Notice bibliographique
Résumé
Introduction: Obstructive sleep apnea (OSA) is a common disorder characterized by episodes of upper airway obstruction, leading to oxygen desaturation, disrupted sleep, and systemic complications. Although continuous positive airway pressure (CPAP) is the standard treatment for OSA, mandibular advancement devices (MADs) present an effective alternative for patients who cannot tolerate CPAP or reject surgery. However, the effectiveness of MADs varies, with a success rate of 60-70%. Identifying phenotypic predictors is crucial for optimizing patient selection and improving therapeutic outcomes. This thesis aims to establish predictive factors to guide clinical decision-making in MAD treatment for OSA. Objectives: The goal of this research is to analyze the phenotypic characteristics that differentiate responders to MAD treatment from non-responders in OSA through four studies: 1. Systematic review and meta-analysis of predictors of MAD efficacy. 2. Analysis of polysomnographic phenotypes and their integration into predictive models. 3. Evaluation of anatomical and physiological characteristics of the airway. 4. Classification of OSA subgroups and assessment of the impact of facial morphology on MAD efficacy. Materials and Methods: - Study 1: A systematic review and meta-analysis were conducted, including studies on MAD efficacy. Clinical, anatomical, and polysomnographic predictors were analyzed, and the quality was assessed using Newcastle-Ottawa, Cochrane, and GRADE tools. - Studies 2 and 3: A prospective analysis of patients treated with MAD over six years was carried out. Logistic regression and CHAID analysis were used to define predictive models based on clinical, anatomical, and polysomnographic variables. - Study 4: A retrospective analysis classified OSA subgroups using K-means clustering. Results: - Study 1: Of 99 studies, 60 were included in the meta-analysis. Responders were younger, with lower BMI, cervical circumference, facial height, hyoid-C3 distance, and minimal cross-sectional area of the airway (CSAmin), along with higher minimum oxygen saturation. Responders also required lower CPAP pressures. - Study 2: In 112 patients, the response rate was higher in positional OSA and lower in REM-dependent OSA or OSA with predominant apneas. Key predictors included T90% and positional OSA in the first definition of response, BMI and apnea-predominant phenotype in the second. CHAID analysis established clinically relevant cutoff values. - Study 3: Predictive models were based on airway length, anterior facial height, and basal T90% (first definition of response); Jarabak index, gonion angle, CSAmin, basal BMI, and basal AHI (second definition). - Study 4: Two subgroups were identified. Subgroup 1, with more severe OSA (higher BMI, T90%, and AHI), vertical facial pattern, and narrower airways, exhibited lower therapeutic success. Facial morphology influenced response, with dolichofacial pattern patients showing the lowest success rate. Conclusions: This thesis identified phenotypic factors that influence the efficacy of MAD treatment in OSA. Predictive models could enhance patient selection and optimize therapeutic outcomes. Future research should validate these models in prospective studies.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».