An observational study in Obstructive Sleep Apnea, sponsored by Nanjing Medical University. Not yet recruiting at 1 site in China. Open to participants aged 18 Years to 80 Years. Per ClinicalTrials.gov, last updated 2025-02-13.
Sponsored by Nanjing Medical University · Observational
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.
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.
1,422 studies on the registry are indexed under Apnea; 158 are open to participants now.
This study's planned enrollment of 200 is above the median of 106 across 386 observational studies indexed under Apnea.
Browse Apnea studies →Nanjing Medical University is the lead sponsor of 169 studies on the registry; 51 are open to participants now.
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This study will involve participants diagnosed with OSA who are not currently undergoing any active treatment for the condition. The study aims to explore the detailed phenotypic and endotypic characteristics of these patients to better understand OSA dynamics and develop predictive models for individualized treatment approaches.
Exclusion Criteria:
This group comprises patients diagnosed with Obstructive Sleep Apnea (OSA). Participants will undergo a comprehensive assessment that includes baseline demographic data collection, clinical symptom evaluation, polysomnography (PSG), Drug-Induced Sleep Endoscopy (DISE), and various physiological measurements to identify specific phenotypic and endotypic traits associated with OSA. This holistic evaluation aims to facilitate detailed phenotyping and generate predictive models for personalized treatment approaches.
Other: Clinical 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.
Phenotype and Endotype Classification
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.
Time frame: 12 months post-enrollment.
Multidimensional Predictive Model
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.
Time frame: End of the study, expected 24 months after enrollment.
Plan to share: No — The individual participant data will not be shared. The informed consent will be ansigned before enrolled in the study and ensured to keep personal information confidential.
This study is not yet recruiting, as verified in Feb 2025. You cannot join it, but the record below documents what was studied.
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Nanjing Medical University