Precision OSA Therapy Based on Phenotypes and Endotypes

NCT06825923 | Not Yet Recruiting DMC

• 1 other identifier: 2025-SRFA-006
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

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.

Conditions

Obstructive Sleep Apnea

Keywords

Obstructive Sleep Apnea; PALM; Phenotype; Endotype

Outcome Measures

Primary Outcomes (2)

• 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.

  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.

  End of the study, expected 24 months after enrollment.

Study Arms (1)

OSA Comprehensive Assessment Group

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

Interventions

Clinical and Endotypic Assessmen OTHER

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.

OSA Comprehensive Assessment Group

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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.

You may qualify if:

• Patients aged between 18 and 80 years.
• Diagnosed with Obstructive Sleep Apnea (OSA)(apnea-hypopnea index≥5/h).
• First-time diagnosis, with no previous surgical interventions or CPAP treatment for OSA.
• Ability and willingness to provide informed consent for participation in the study.

You may not qualify if:

• History of severe stroke or cerebral hemorrhage, or presence of neurological or psychiatric conditions that could affect study results.
• Presence of active malignancies or other severe underlying diseases, such as severe liver or kidney dysfunction. Diagnosed with diabetes or other significant vascular diseases.
• Presence of severe chronic obstructive pulmonary disease (COPD), severe asthma, severe pulmonary hypertension, or heart failure caused by any condition.
• Pregnancy or having other conditions that make participation in this study unsuitable.
• Extremely debilitated patients or those with severe underlying conditions.

Sponsors & Collaborators

• Nanjing Medical University: lead

Study Sites (1)

The First Affiliated Hospital of Nanjing Medical University

Nanjing, Jiangsu, 210029, China

Location

Related Publications (6)

• Zinchuk A, Yaggi HK. Phenotypic Subtypes of OSA: A Challenge and Opportunity for Precision Medicine. Chest. 2020 Feb;157(2):403-420. doi: 10.1016/j.chest.2019.09.002. Epub 2019 Sep 17.

  PMID: 31539538 BACKGROUND

• Zinchuk AV, Gentry MJ, Concato J, Yaggi HK. Phenotypes in obstructive sleep apnea: A definition, examples and evolution of approaches. Sleep Med Rev. 2017 Oct;35:113-123. doi: 10.1016/j.smrv.2016.10.002. Epub 2016 Oct 12.

  PMID: 27815038 BACKGROUND

• Aishah A, Eckert DJ. Phenotypic approach to pharmacotherapy in the management of obstructive sleep apnoea. Curr Opin Pulm Med. 2019 Nov;25(6):594-601. doi: 10.1097/MCP.0000000000000628.

  PMID: 31503212 BACKGROUND

• Eckert DJ, White DP, Jordan AS, Malhotra A, Wellman A. Defining phenotypic causes of obstructive sleep apnea. Identification of novel therapeutic targets. Am J Respir Crit Care Med. 2013 Oct 15;188(8):996-1004. doi: 10.1164/rccm.201303-0448OC.

  PMID: 23721582 BACKGROUND

• Eckert DJ. Phenotypic approaches to obstructive sleep apnoea - New pathways for targeted therapy. Sleep Med Rev. 2018 Feb;37:45-59. doi: 10.1016/j.smrv.2016.12.003. Epub 2016 Dec 18.

  PMID: 28110857 BACKGROUND

• Van den Bossche K, Van de Perck E, Kazemeini E, Willemen M, Van de Heyning PH, Verbraecken J, Op de Beeck S, Vanderveken OM. Natural sleep endoscopy in obstructive sleep apnea: A systematic review. Sleep Med Rev. 2021 Dec;60:101534. doi: 10.1016/j.smrv.2021.101534. Epub 2021 Aug 3.

  PMID: 34418668 BACKGROUND

MeSH Terms

Conditions

Sleep Apnea, Obstructive

Condition Hierarchy (Ancestors)

Sleep Apnea Syndromes; Apnea; Respiration Disorders; Respiratory Tract Diseases; Sleep Disorders, Intrinsic; Dyssomnias; Sleep Wake Disorders; Nervous System Diseases

Central Study Contacts

Ding Ning, doctor

CONTACT

86-25-68136723; dr.ningding@live.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: February 4, 2025
• First Posted: February 13, 2025
• Study Start: July 1, 2025
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2029
• Last Updated: February 13, 2025

Record last verified: 2025-02

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)