Periodic Reporting for period 1 - SLEEP REVOLUTION (Revolution of sleep diagnostics and personalized health care based on digital diagnostics and therapeutics with health data integration)
Reporting period: 2021-03-01 to 2022-08-31
SLEEP REVOLUTION aims to fundamentally change clinical sleep medicine with the introduction of a new diagnostic and digital management paradigm. The technology developed in this program combines the highest degree of diagnostic accuracy for sleep-disordered breathing (SDB), the home setting instead of the artificial sleep environment of a hospital setting, advanced telemedicine technology, novel machine learning diagnostic algorithms together with a high degree of participatory patient involvement.
Research participants in the project self-administer the sleep studies, sleeping at home for multiple nights with diagnostic equipment beyond the current clinical state-of-the-art. The latest innovations in telemedicine is employed to provide comprehensive information of wellbeing, symptom burden and treatment adherence by a self-administered sleep diary and a new European Sleep Questionnaire, developed within the project, continuous monitoring of biosignals via wearables and objective daytime testing, all performed at home by the patient. All of the clinically relevant information and sleep study results are available to the patient, emphasizing the important component of participatory health care. All data is transferred to a central data cluster and digital management platform with secure and controlled access to the patient subject, as well as relevant healthcare and research staff. Novel analysis algorithms using machine learning provide sleep parameters with high diagnostic precision and significantly enhanced predictive value for patient-related outcome measures compared to current clinical practice.
Individuals with SDB, from habitual snoring to severe obstructive sleep apnoea (OSA) constitute the main target population for the SLEEP REVOLUTION because the unmet medical need for improved diagnostic and management algorithms is huge in Europe and worldwide. However, the principles the new diagnostic and management approach that we are introducing can be transferred to the clinical management of all other sleep disorders. The predictive capacity of novel diagnostic parameters is now being validated in a Pan-European study setting aiming to generate a new sleep diagnostic standard in Europe – a true SLEEP REVOLUTION
Research participants in the project self-administer the sleep studies, sleeping at home for multiple nights with diagnostic equipment beyond the current clinical state-of-the-art. The latest innovations in telemedicine is employed to provide comprehensive information of wellbeing, symptom burden and treatment adherence by a self-administered sleep diary and a new European Sleep Questionnaire, developed within the project, continuous monitoring of biosignals via wearables and objective daytime testing, all performed at home by the patient. All of the clinically relevant information and sleep study results are available to the patient, emphasizing the important component of participatory health care. All data is transferred to a central data cluster and digital management platform with secure and controlled access to the patient subject, as well as relevant healthcare and research staff. Novel analysis algorithms using machine learning provide sleep parameters with high diagnostic precision and significantly enhanced predictive value for patient-related outcome measures compared to current clinical practice.
Individuals with SDB, from habitual snoring to severe obstructive sleep apnoea (OSA) constitute the main target population for the SLEEP REVOLUTION because the unmet medical need for improved diagnostic and management algorithms is huge in Europe and worldwide. However, the principles the new diagnostic and management approach that we are introducing can be transferred to the clinical management of all other sleep disorders. The predictive capacity of novel diagnostic parameters is now being validated in a Pan-European study setting aiming to generate a new sleep diagnostic standard in Europe – a true SLEEP REVOLUTION
The Sleep Revolution project has achieved its overall aims for the reporting period. A review paper describing the concept and objectives of the project is now published in the 50-year anniversary edition of the Journal of Sleep Research, which was distributed in paper to over 2000 attendees of the Sleep Europe conference in 2022 as well as being open access online. Already, 15 papers have been published and many more are in the pipeline, extending the modest estimate of 5-6 papers per year in the dissemination plan. The project has received attention worldwide as evidenced by the already high number of keynote and invited talks and accepted conference symposiums and talks, with high attendance (again far exceeding the estimated 5 conference presentations per year).
The project is progressing well. This work relies on the use of the large retrospective datasets being collected, with analysis performed on the data cluster. The change from time-consuming manual scoring to machine learning-based scoring is advancing well and will be tested extensively in prospective studies. The final models for a new diagnostic paradigm will be ready before the end of the Sleep Revolution project.
Our ambitious aim to change the way sleep diagnostics are performed for SDB, from a one night hospital recording or a limited home study to a self-applied sleep study at home for three nights at home is progressing well. The new setup has been validated against the gold standard polysomnography recording as well as with extensive testing of the user experience. Furthermore, the clinical use of this system will be compared in the prospective European Sleep Apnea Database (ESADA) study, with a cost and clinical efficacy comparison between the traditional and Sleep Revolution setups. The digital managment platform (DMP) with app and wearables for long-term monitoring of sleep and daytime functioning has been developed and extensively tested and is now ready for use in the prospective studies as planned.
The DMP acts as a bridge between three different end-user groups; the research participant/patient, the researcher and the healthcare provider and the platform allows us to promote participatory healthcare.
The development of two new personalized treatment options for SDB patients, oromyofunctional therapy and lifestyle treatment, is progressing according to plan with an ongoing pilot study for the oromyofunctional therapy and the randomized clinical trial for the lifestyle treatment already underway.
The project is progressing well. This work relies on the use of the large retrospective datasets being collected, with analysis performed on the data cluster. The change from time-consuming manual scoring to machine learning-based scoring is advancing well and will be tested extensively in prospective studies. The final models for a new diagnostic paradigm will be ready before the end of the Sleep Revolution project.
Our ambitious aim to change the way sleep diagnostics are performed for SDB, from a one night hospital recording or a limited home study to a self-applied sleep study at home for three nights at home is progressing well. The new setup has been validated against the gold standard polysomnography recording as well as with extensive testing of the user experience. Furthermore, the clinical use of this system will be compared in the prospective European Sleep Apnea Database (ESADA) study, with a cost and clinical efficacy comparison between the traditional and Sleep Revolution setups. The digital managment platform (DMP) with app and wearables for long-term monitoring of sleep and daytime functioning has been developed and extensively tested and is now ready for use in the prospective studies as planned.
The DMP acts as a bridge between three different end-user groups; the research participant/patient, the researcher and the healthcare provider and the platform allows us to promote participatory healthcare.
The development of two new personalized treatment options for SDB patients, oromyofunctional therapy and lifestyle treatment, is progressing according to plan with an ongoing pilot study for the oromyofunctional therapy and the randomized clinical trial for the lifestyle treatment already underway.
In the SLEEP REVOLUTION project, novel tools, platforms, and computational models to be used for sleep data processing and diagnosing SDB are being generated. The project will have a very high scientific impact on sleep research and medical technology. The expected scientific breakthroughs are:
Technological development:
• Secure data store to integrate various diagnostic data with a digital management platform as the layer above the data store that functions as a bridge between the researchers, patients, and healthcare professionals.
• More reliable diagnosis of sleep disorders via deep learning algorithms. The aim is to generate a machine learning algorithm capable of accurate and fully automatic scoring of sleep recordings.
• The three-dimensional model of severity estimation of SDB.
• Big data analysis and the development of novel physiologically driven approaches for objectively quantifying OSA phenotypes.
Clinical development:
• European Guidelines for the diagnosis of OSA and scoring of sleep recordings
• Recommendations for most relevant and information-rich signals to be measured in sleep studies.
• Availability of pathophysiological phenotyping of OSA in routine sleep diagnostics
• Characterisation of the cardiovascular impact of snoring and other risk in sleep disorders in routine sleep recordings.
• Model for physiology of upper airways during snoring.
• Increase accessibility of sleep recordings as increased number of recordings can be performed at patient’s home (instead of hospitals).
Health care management:
• Real-world estimates of long-term costs of OSA and an interactive tool that allows to project the financial burden of SDB and OSA.
• Increase the quality and effectiveness of clinical work while simultaneously reducing expenses for both diagnosis and providing more personalized treatment.
• Increased information on the most important aspects describing the true severity of OSA and SDB could help clinicians to choose the best treatment for each individual patient.
The work to transform current diagnostic methods for SDB, is progressing well. The new self-applied sleep study setup has been validated against the gold standard sleep recording as well as with extensive testing of the user experience and usability of the system. The digital management platform with app and wearables for long-term monitoring of sleep and daytime functioning has also been developed and extensively tested. The development of two new personalized treatment options for SDB patients, oromyofunctional therapy and lifestyle treatment, is progressing according to plan.
Technological development:
• Secure data store to integrate various diagnostic data with a digital management platform as the layer above the data store that functions as a bridge between the researchers, patients, and healthcare professionals.
• More reliable diagnosis of sleep disorders via deep learning algorithms. The aim is to generate a machine learning algorithm capable of accurate and fully automatic scoring of sleep recordings.
• The three-dimensional model of severity estimation of SDB.
• Big data analysis and the development of novel physiologically driven approaches for objectively quantifying OSA phenotypes.
Clinical development:
• European Guidelines for the diagnosis of OSA and scoring of sleep recordings
• Recommendations for most relevant and information-rich signals to be measured in sleep studies.
• Availability of pathophysiological phenotyping of OSA in routine sleep diagnostics
• Characterisation of the cardiovascular impact of snoring and other risk in sleep disorders in routine sleep recordings.
• Model for physiology of upper airways during snoring.
• Increase accessibility of sleep recordings as increased number of recordings can be performed at patient’s home (instead of hospitals).
Health care management:
• Real-world estimates of long-term costs of OSA and an interactive tool that allows to project the financial burden of SDB and OSA.
• Increase the quality and effectiveness of clinical work while simultaneously reducing expenses for both diagnosis and providing more personalized treatment.
• Increased information on the most important aspects describing the true severity of OSA and SDB could help clinicians to choose the best treatment for each individual patient.
The work to transform current diagnostic methods for SDB, is progressing well. The new self-applied sleep study setup has been validated against the gold standard sleep recording as well as with extensive testing of the user experience and usability of the system. The digital management platform with app and wearables for long-term monitoring of sleep and daytime functioning has also been developed and extensively tested. The development of two new personalized treatment options for SDB patients, oromyofunctional therapy and lifestyle treatment, is progressing according to plan.