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Clinical Utility of Obstructive Sleep Apnoea Physiology in Predicting Response to Treatment

An Observational Study of the Clinical Utility of Obstructive Sleep Apnoea Physiology in Predicting Response to Treatment

Status
Not yet recruiting
Phases
Unknown
Study type
Interventional
Source
ANZCTR
Registry ID
ACTRN12619001211156
Enrollment
300
Registered
2019-08-30
Start date
2020-01-01
Completion date
Unknown
Last updated
2019-09-16

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

None listed

Brief summary

Continuous positive airway pressure (CPAP) is the current gold standard treatment for OSA. While efficacious, CPAP is poorly tolerated. Nightly use may be inadequately brief and 50% of patients do not continue CPAP therapy beyond 3 months. Although alternative treatments such as mouth guard devices and surgery exist for CPAP-intolerant patients, individual responses to these treatments are difficult to predict and as such many clinicians and patients are stuck in a trial and error paradigm for these treatments. Traditionally OSA has been thought to be a disorder of aberrant upper airway anatomy. Recently, it has become clear though that OSA can be caused by both anatomical and non-anatomical pathophysiology. Much of this paradigm change can be attributed to recent developments to the ability to measure this physiology – work that has been carried out by our research team at Monash Health and Monash University. We have developed readily applicable clinical tool that can predict the patient’s response to upper airway surgery and mandibular advancement splint treatments. The tools we have developed are now ready for direct translation to the clinical setting. In this exciting research project we will run an observation study to assess the impact of making physiological information available to clinicians at the time of OSA diagnosis on the treatment referral patterns and treatment outcomes of OSA patients. We hope that this extra physiological data will be able to predict patient response to CPAP-alternative treatments and give clinicians and patients extra treatment options that are tailored to patients personal physiology.

Interventions

This intervention study will investigate the effect that making additional physiological information available to clinicians and patients at the time of obstructive sleep apnoea diagnosis has on treatment referral patterns and patient outcomes. Over the last 5yrs there has been an accumulating body of evidence that understanding an individual patient's physiology can help predict response to treatment for obstructive sleep apnoea. In particular understanding the contribution of loop gain, arou

This intervention study will investigate the effect that making additional physiological information available to clinicians and patients at the time of obstructive sleep apnoea diagnosis has on treatment referral patterns and patient outcomes. Over the last 5yrs there has been an accumulating body of evidence that understanding an individual patient's physiology can help predict response to treatment for obstructive sleep apnoea. In particular understanding the contribution of loop gain, arousal threshold, upper airway dilator muscle activity and upper airway anatomy. These experimental parameters are not available for clinical use. Our group has developed tools that allow for this information to be made readily available to clinicians at the time of OSA diagnosis. The aim of this study will be to make measurements of an individual's loop gain, arousal threshold, upper airway dilator muscle activity and upper airway anatomy at the time of obstructive sleep apnoea diagnosis. Our tool allows for these measurements to be made from routinely collected clinical data (sleep study) and thus does not require any additional testing above and beyond standard clinical practice. The tool that we will employ for the purpose of this study is a bespoke Matlab code that models the respiratory system. We have currently employed this methodology in ober a dozen clinical studies in Australia and the USA. The tool is experimental and not commercially available. We take regular clinical physiological data (sleep study) and feed that data into our MATLAB code which analyzes the signals and outputs the variables mentioned above. This is then output in a report that clinicians can read. The MATLAB code and analysis is made on data collected using standard sleep study setup. No additional instrumentation or equipment is required. The additional physiological information will be made available at the time of diagnosis and will be kept with the patient's sleep study results (medical records) so that it can be referred back to at any time. The additional physiological information will be kept with the patient's medical record in accordance with usual practice for keeping and maintaining medical records see https://www2.health.vic.gov.au/about/legislation/health-records-act The first year of the study (observational arm) will be comppared to the second year of the study (interventional arm) to understand how clinicians treatment patterns have changed with the access to additional physiological parameters (treatment modality recommended, number of treatment trials for a given patient, patient ouutcomes including sleepp study results and QOL measures).

Sponsors

Monash Health
Lead SponsorHospital

Study design

Allocation
Non-randomised trial
Intervention model
Parallel
Primary purpose
Diagnosis
Masking
Blinded (masking used) (Investigator, Outcomes Assessor)

Eligibility

Sex/Gender
All
Age
18 Years to No maximum
Healthy volunteers
No

Inclusion criteria

Patients who are being investigated for suspected obstructive sleep apnoea through the Monash University Healthy Sleep Clinic

Exclusion criteria

• Receiving medication that could affect ventilation (i.e. morphine derivatives, benzodiazepines, theophylline) or muscle control. • Previous surgical treatment for OSA and/or obesity • Women who are pregnant or breastfeeding

Outcome results

None listed

Source: ANZCTR · Data processed: Feb 4, 2026