Obstructive Sleep ApneaNCT06825923
Precision OSA Therapy Based on Phenotypes and Endotypes
An observational study of Obstructive Sleep Apnea, run by Nanjing Medical University at 1 site in China.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Obstructive Sleep Apnea
- What is being tested?
- Clinical and Endotypic Assessmen
- How many people
- 200 participants
- Where
- 1 siteChina
- Study dates
- 1 Jul 2025to 31 Dec 2029
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
The First Affiliated Hospital of Nanjing Medical University
Nanjing, Jiangsu, 210029
About this study
Official title: Precise Intervention of Obstructive Sleep Apnea Based on Phenotypic Characteristics and Endotypic Mechanisms
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.
Read the 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.
- Conditions
- Obstructive Sleep Apnea
- Being tested
- Clinical and Endotypic Assessmen
- Keywords
- Obstructive Sleep Apnea, PALM, Phenotype, Endotype
- Medical subject headings
- Sleep Apnea, Obstructive
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Clinical and Endotypic AssessmenOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: Clinical and Endotypic Assessmen |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: End of the study, expected 24 months after enrollment.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Feb 2025
- Study start1 Jul 2025 (estimated)
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Dec 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for Obstructive Sleep ApneaStudies of the same condition, open and under way.
- More trials run by Nanjing Medical UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Nanjing Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ning Ding, Nanjing Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06825923
- Sponsor's protocol ID
- 2025-SRFA-006
- Status last verified
- Feb 2025
- First submitted
- 4 Feb 2025
- Last updated
- 13 Feb 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- 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.
Publications
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.
PubMed 31539538Zinchuk 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.
PubMed 27815038Aishah 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.
PubMed 31503212Eckert 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.
PubMed 23721582Eckert 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.
PubMed 28110857Van 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.
PubMed 34418668
Study at a glance
From the registry record, last updated by the sponsor on 13 Feb 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Precise Intervention of Obstructive Sleep Apnea Based on Phenotypic Characteristics and Endotypic Mechanisms
- ClinicalTrials.gov ID
- NCT06825923
- Acronym / sponsor ID
- 2025-SRFA-006
- Conditions
- Obstructive Sleep Apnea
- Keywords
- Obstructive Sleep Apnea, PALM, Phenotype, Endotype
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Nanjing Medical UniversityUniversity, hospital or other
- Study start
- 1 Jul 2025 (estimated)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Dec 2029 (estimated)
- First posted
- 13 Feb 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| OSA Comprehensive Assessment Group | – | Other: Clinical and Endotypic AssessmenThis 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. |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (5 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Clinical and Endotypic AssessmenThis 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. | Other | OSA Comprehensive Assessment Group |
Primary outcome
Phenotype and Endotype Classification
Time frame: 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
Time frame: 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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Feb 2025.
- Background: PubMed 31539538Zinchuk 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.
- Background: PubMed 27815038Zinchuk 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.
- Background: PubMed 31503212Aishah 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.
- Background: PubMed 23721582Eckert 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.
- Background: PubMed 28110857Eckert 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.
- Background: PubMed 34418668Van 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.
- Individual participant dataSharing planned: 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.
Related trials for Obstructive Sleep Apnea
Active, not recruitingPhase 3
A Multicenter, Randomized, Double-blind, Placebo-controlled Phase III Clinical Study to Evaluate the Efficacy and Safety of HRS9531 Injection in Subjects With Obstructive Sleep Apnea (OSA) and Obesity Who Are on Positive Airway Pressure Ventilation (PAP) Therapy
Subjects With Moderate-to-severe OSA and Obesity Who Are on PAP Therapy
RecruitingPhase 3
A Study of Eloralintide (LY3841136) in Participants With Obstructive Sleep Apnea and Obesity or Overweight
Sleep Apnea, Obstructive, Obesity
RecruitingObservational study
Prediction of Tongue Base Obstruction During Sleep
Obstructive Sleep Disordered Breathing
RecruitingObservational study
Characterization of the Tongue in Children With Obstructive Sleep-disordered Breathing
Obstructive Sleep Disordered Breathing
RecruitingObservational study
Development of a Multimodal Deep Learning Model for Pediatric Patients
Pediatric Obstructive Sleep Apnea, Polysomnography
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Obstructive Sleep Apnea, run by Nanjing Medical University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 13 Feb 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Ding Ning, doctor (86-25-68136723, dr.ningding@live.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Phenotype and Endotype Classification (time frame: 12 months post-enrollment.). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06825923, which also lists 6 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Feb 2025.