Obstructive Sleep Apnea
Conditions
Keywords
Obstructive Sleep Apnea, PALM, Phenotype, Endotype
Brief summary
Analyzing the phenotypic and endotypic characteristics of Sleep Apnea, along with DISE obstruction situations, is crucial for precise diagnosis and treatment. In this study, we aim to construct and apply a multidimensional predictive model based on four aspects: basic physiological characteristics of OSA, clinical phenotypes, mechanistic endotypes, and DISE obstruction levels. The study will begin by categorizing the clinical phenotypes; subsequently, it will quantify endotypic indicators based on PSG signal information and construct the PALM scale for Chinese individuals. Following this, a comprehensive clinical profile and a treatment efficacy prediction model for OSA patients will be built based on the results from the aforementioned multidimensional data.
Detailed description
Obstructive Sleep Apnea (OSA) is characterized by repeated episodes of upper airway obstruction and apneas during sleep, resulting in chronic intermittent hypoxemia, autonomic fluctuations, and sleep fragmentation. OSA is a heterogeneous disease influenced by multifactorial elements. The effectiveness of treatments and prognoses may vary due to differences in etiological factors, pathophysiological mechanisms, and clinical subtypes. Zinchuk et al. identified four clinical symptom and comorbidity-based subtypes and two subtypes based on polysomnography (PSG) indicators, which are useful for guiding treatment. However, relying solely on external phenotypes does not allow for analysis of intrinsic mechanisms, often leading to large treatment outcome disparities within the same phenotype due to different underlying mechanisms. Thus, the concept of OSA endotypes, which can elucidate pathophysiological mechanisms, has been introduced. OSA phenotypes are broadly defined as a classification of OSA patients related to clinically significant attributes such as symptoms, treatment response, underlying diseases, and quality of life; whereas endotypes refer to disease subtypes with distinct functional or pathophysiological mechanisms. There are at least four key pathophysiological endotypes in OSA, including 1) high upper airway closing pressure (Pcrit), 2) low arousal threshold (ArThr), 3) high loop gain (LG), and 4) impaired pharyngeal dilator muscle responsiveness. Each endotype represents a target or treatable trait from a mechanistic perspective. The advantages of OSA endotype quantification based on PSG signal information are evident. Eckert et al. proposed a potential classification of OSA patients into three subgroups based on the impairment of upper airway anatomy and the non-anatomical phenotypes (loop gain, arousal threshold, and muscle responsiveness) - the PALM scale. This phenotyping introduces different possible therapeutic strategies. The same PSG outcomes may be caused by different endotypic mechanisms, and different endotypic mechanisms may lead to varying PSG outcomes, resulting in inconsistent treatment effects. To accurately align endotypes with PSG outcomes, a standard for obstruction anchoring is essential. Drug-induced sleep endoscopy (DISE) offers a bridge between the two by providing an assessment of the severity and plane of upper airway obstruction, which is related to both the severity of apneas and the upper airway closing pressure in the PALM model. In our preliminary research, the measurement of upper airway closing pressure and muscle responsiveness was achievable through DISE-PAP. Given the importance of distinguishing OSA patient phenotypic characteristics, quantifying endotypes, developing new indices, and assessing DISE obstruction planes, this study aims to construct and apply a multidimensional predictive model that integrates basic physiological characteristics of OSA, clinical phenotypes, mechanistic endotypes, and DISE obstruction planes. The study will start with the classification of clinical phenotypes, followed by the quantification of endotypic indicators based on PSG signal information and the construction of a PALM scale suitable for Chinese individuals. Subsequently, based on the results from the aforementioned multidimensional data, a comprehensive clinical portrait and predictive model of treatment outcomes for OSA patients will be built.
Interventions
OTHERClinical and Endotypic Assessmen
This observational study involves a detailed clinical and endotypic assessment of patients diagnosed with Obstructive Sleep Apnea (OSA). Assessments include polysomnography (PSG) to measure sleep patterns and disturbances, drug-induced sleep endoscopy (DISE) to evaluate upper airway obstruction, and various biomarker analyses to characterize endotypic traits. The study aims to collect comprehensive phenotypic and endotypic data to develop predictive models for OSA patient characterization and management.
Sponsors
Nanjing Medical University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
No
Inclusion criteria
1. Patients aged between 18 and 80 years. 2. Diagnosed with Obstructive Sleep Apnea (OSA)(apnea-hypopnea index≥5/h). 3. First-time diagnosis, with no previous surgical interventions or CPAP treatment for OSA. 4. Ability and willingness to provide informed consent for participation in the study.
Exclusion criteria
1. History of severe stroke or cerebral hemorrhage, or presence of neurological or psychiatric conditions that could affect study results. 2. Presence of active malignancies or other severe underlying diseases, such as severe liver or kidney dysfunction. Diagnosed with diabetes or other significant vascular diseases. 3. Presence of severe chronic obstructive pulmonary disease (COPD), severe asthma, severe pulmonary hypertension, or heart failure caused by any condition. 4. Pregnancy or having other conditions that make participation in this study unsuitable. 5. Extremely debilitated patients or those with severe underlying conditions.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Phenotype and Endotype Classification | 12 months post-enrollment. | This outcome measure will evaluate the new phenotypic and endotypic features in OSA patients by integrating various clinical symptoms, traditional and novel PSG metrics, upper airway imaging indicators, diaphragm morphology and function parameters, and DISE results. A comprehensive classification system will be developed by combining these data points to classify OSA patients into distinct clinical phenotypes and endotypes. This classification aims to provide insights into the underlying pathophysiology of OSA and to better understand patient-specific characteristics for personalized treatment plans. |
| Multidimensional Predictive Model | End of the study, expected 24 months after enrollment. | This outcome measure will assess the effectiveness of a multidimensional predictive model constructed using clinical phenotype, endotype, novel biomarkers, and DISE results. We used six commonly employed supervised machine learning algorithms: Random Forest, XGBoost, Support Vector Classifier (SVC), Logistic Regression, Multi-layer Perceptron (MLP), and Stacking Regression to classify OSA patients based on their survival status. The Stacking Regression model was designed by combining the outputs of Random Forest, XGBoost, and Support Vector Regression. The best-performing model will be selected to compute the final prediction, providing a powerful tool to predict the treatment response and clinical outcomes for OSA patients. |
Countries
China
Contacts
Primary ContactDing Ning, doctor
Outcome results
None listed