Fenotipos de los pacientes respondedores al tratamiento de la apnea obstructiva del sueño mediante los dispositivos de avance mandibular
Bibliographic record
Abstract
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.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".