Ignite Creation Date:
2024-10-26 @ 3:35 PM
Last Modification Date:
2024-10-26 @ 3:35 PM
Study NCT ID:
NCT06512779
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-07-16
Brief Title:
Developing a Treatment Clustering System for Obstructive Sleep Apnea Using Polysomnographic Physiological Signals
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Developing a Treatment Clustering System for Obstructive Sleep Apnea Using Polysomnographic Physiological Signals
Status:
RECRUITING
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Obstructive sleep apnea syndrome OSA is marked by repeated upper airway obstructions during sleep affecting approximately 14 of men and 5 of women aged 30-70 years
However precise clinical prediction tools for selecting optimal treatment strategies are lacking This study aims to develop an automated treatment clustering system using artificial intelligence to classify patients based on etiology into i anatomical factors ii reduced muscle responsiveness and iii other non-anatomical factors This system will analyze physiological sleep assessments such as electromyography EMG and pneumotachographs from a retrospective polysomnography PSG database Cross-validation will be conducted on new OSA patients undergoing various management strategies including surgical intervention CPAP therapy and oropharyngeal training delivered face-to-face or via telerehabilitation This system aims to enhance clinicians ability to predict treatment success rates and improve patient outcomes
However precise clinical prediction tools for selecting optimal treatment strategies are lacking This study aims to develop an automated treatment clustering system using artificial intelligence to classify patients based on etiology into i anatomical factors ii reduced muscle responsiveness and iii other non-anatomical factors This system will analyze physiological sleep assessments such as electromyography EMG and pneumotachographs from a retrospective polysomnography PSG database Cross-validation will be conducted on new OSA patients undergoing various management strategies including surgical intervention CPAP therapy and oropharyngeal training delivered face-to-face or via telerehabilitation This system aims to enhance clinicians ability to predict treatment success rates and improve patient outcomes
Detailed Description:
Backgrounds
Obstructive sleep apnea syndrome OSA is marked by repeated upper airway obstructions during sleep affecting about 14 of men and 5 of women aged 30-70 years The etiology of OSA is divided into anatomical and non-anatomical factors Anatomical factors include upper airway narrowing or collapse while non-anatomical factors encompass reduced muscle responsiveness low arousal threshold and high loop gain Anatomical issues can be managed using surgical interventions or dental appliances Non-anatomical issues like low arousal threshold and high loop gain may require pharmacological treatment or oxygen therapy The genioglossus GG muscles activity crucial during sleep is insufficient in about 30 of OSA patients Regular oropharyngeal muscle exercises can reduce OSA severity and related symptoms
However precise clinical prediction tools for selecting optimal treatment strategies are lacking and research on telerehabilitation for OSA patients is insufficient This study aims to develop an automated treatment clustering system using artificial intelligence to classify patients based on etiology into i anatomical factors ii reduced muscle responsiveness and iii other non-anatomical factors This system will analyze physiological sleep assessments from a retrospective polysomnography PSG database Cross-validation will be conducted on new OSA patients undergoing various management strategies including surgical intervention CPAP therapy and oropharyngeal training delivered face-to-face or via telerehabilitation
Methods
The automated treatment clustering system employs artificial intelligence to classify patients into etiological groups i anatomical factors like upper airway narrowing or collapse ii non-anatomical factors such as reduced muscle responsiveness and iii other non-anatomical factors The classification relies on analyzing multiple physiological sleep assessments including electromyography EMG and pneumotachographs from a retrospective PSG database The system will undergo cross-validation with novel OSA patients who will be screened based on inclusion and exclusion criteria and provide consent
During the cross-validation phase the OSA patients will undergo various assessments including polysomnography sleep-related questionnaire drug-induced sleep endoscopy DISE computed tomography CT scans functional magnetic resonance imaging fMRI tongue muscle strength and endurance tests and mental state evaluations Pre- and post-treatment measurements will be conducted CT scans and DISE will assess anatomical structures before and after treatment while fMRI will examine brain activation status Muscle strength and endurance tests will evaluate the responsiveness level of tongue muscle before and after intervention
The automated treatment clustering system utilizing machine learning will determine the phenotype of each case based on PSG CT sleep endoscopy fMRI and tongue strength and endurance results These results will aid clinicians in categorizing patients and predicting treatment success rates Treatment decisions will involve collaboration between physicians and patients considering clinical expertise and patient preferences
Participants classified as upper airway narrowing or collapse due to anatomical factors by the phenotyping system will be recommended for surgical management For patients with reduced muscle responsiveness a 12-week program of oropharyngeal muscle training is recommended This training will be administered in two modes face-to-face sessions and telerehabilitation Each session will last 45-60 minutes with participants attending face-to-face sessions in the lab or online classes telerehabilitation 1-3 days per week Both groups will be instructed to perform additional oropharyngeal exercises at home Patients not fitting these groups will use CPAP therapy the gold standard for OSA management During the treatment period participants from all groups will have regular follow-ups to assess potential risks Each group is expected to include 50 cases After six months of treatment the apnea-hypopnea index will be collected based on polysomnography to evaluate the success rates comparing them to the predicted value analyzed using the phenotyping system
Obstructive sleep apnea syndrome OSA is marked by repeated upper airway obstructions during sleep affecting about 14 of men and 5 of women aged 30-70 years The etiology of OSA is divided into anatomical and non-anatomical factors Anatomical factors include upper airway narrowing or collapse while non-anatomical factors encompass reduced muscle responsiveness low arousal threshold and high loop gain Anatomical issues can be managed using surgical interventions or dental appliances Non-anatomical issues like low arousal threshold and high loop gain may require pharmacological treatment or oxygen therapy The genioglossus GG muscles activity crucial during sleep is insufficient in about 30 of OSA patients Regular oropharyngeal muscle exercises can reduce OSA severity and related symptoms
However precise clinical prediction tools for selecting optimal treatment strategies are lacking and research on telerehabilitation for OSA patients is insufficient This study aims to develop an automated treatment clustering system using artificial intelligence to classify patients based on etiology into i anatomical factors ii reduced muscle responsiveness and iii other non-anatomical factors This system will analyze physiological sleep assessments from a retrospective polysomnography PSG database Cross-validation will be conducted on new OSA patients undergoing various management strategies including surgical intervention CPAP therapy and oropharyngeal training delivered face-to-face or via telerehabilitation
Methods
The automated treatment clustering system employs artificial intelligence to classify patients into etiological groups i anatomical factors like upper airway narrowing or collapse ii non-anatomical factors such as reduced muscle responsiveness and iii other non-anatomical factors The classification relies on analyzing multiple physiological sleep assessments including electromyography EMG and pneumotachographs from a retrospective PSG database The system will undergo cross-validation with novel OSA patients who will be screened based on inclusion and exclusion criteria and provide consent
During the cross-validation phase the OSA patients will undergo various assessments including polysomnography sleep-related questionnaire drug-induced sleep endoscopy DISE computed tomography CT scans functional magnetic resonance imaging fMRI tongue muscle strength and endurance tests and mental state evaluations Pre- and post-treatment measurements will be conducted CT scans and DISE will assess anatomical structures before and after treatment while fMRI will examine brain activation status Muscle strength and endurance tests will evaluate the responsiveness level of tongue muscle before and after intervention
The automated treatment clustering system utilizing machine learning will determine the phenotype of each case based on PSG CT sleep endoscopy fMRI and tongue strength and endurance results These results will aid clinicians in categorizing patients and predicting treatment success rates Treatment decisions will involve collaboration between physicians and patients considering clinical expertise and patient preferences
Participants classified as upper airway narrowing or collapse due to anatomical factors by the phenotyping system will be recommended for surgical management For patients with reduced muscle responsiveness a 12-week program of oropharyngeal muscle training is recommended This training will be administered in two modes face-to-face sessions and telerehabilitation Each session will last 45-60 minutes with participants attending face-to-face sessions in the lab or online classes telerehabilitation 1-3 days per week Both groups will be instructed to perform additional oropharyngeal exercises at home Patients not fitting these groups will use CPAP therapy the gold standard for OSA management During the treatment period participants from all groups will have regular follow-ups to assess potential risks Each group is expected to include 50 cases After six months of treatment the apnea-hypopnea index will be collected based on polysomnography to evaluate the success rates comparing them to the predicted value analyzed using the phenotyping system
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None