- ICH GCP
- Yhdysvaltain kliinisten tutkimusten rekisteri
- Kliininen tutkimus NCT04112927
Acoustic OSA Prediction During Wakefulness and Monitoring During Sleep
Tutkimuksen yleiskatsaus
Tila
Ehdot
Yksityiskohtainen kuvaus
Sleep apnea, in particular obstructive sleep apnea (OSA), is one of the most common breathing disorders and is associated with major comorbidities such as higher risk perioperative complications. This is particularly concerning given that about 40-80% of people with moderate-severe OSA remain undiagnosed. Due to the resource-intensive assessments required to diagnose OSA and the significantly increased risk of car accidents and perioperative complications associated with undiagnosed OSA, there is a critical need to develop a more effective method to screen for OSA quickly and reliably.
The most widely used clinical OSA screening tool is the STOP-Bang questionnaire, which is a quick and easy-to-implement inquiry form that has a high sensitivity to detect moderate and severe OSA (>93%), but it has a very high rate of false positives (>63%). Thus, a significant number of patients without OSA will continue to be referred for PSG, which contributes to a strain on the healthcare system. Therefore, a quick and reliable screening tool for OSA and its severity during wakefulness is very appealing but challenging, as people with OSA do not show any apparent symptoms during wakefulness.
We have developed a novel screening algorithm for OSA based on the analysis of tracheal breathing sounds recorded from an individual during wakefulness, called AWakeOSA. It can predict OSA with a sensitivity (86%) similar to STOP-Bang, but with a much higher specificity (84%) for detecting individuals without OSA. The AWakeOSA technology still needs significant research and quality improvements to become a reliable home-care device for screening under unsupervised conditions, which is the central purpose of this project. In addition, we are interested to investigate the breathing sound changes from wakefulness to sleep in both groups of healthy and apneic population. For that, we need to record PSG data and breathing sounds during sleep in addition to recording breathing sounds during wakefulness.
We have also designed a specialized hardware device, called ASAD-3, capable of recording breathing sounds with high quality during both wakefulness (short-period recording) and during sleep (long hours recording) that uses two small microphones that are placed in contact with the skin over the trachea and lung, respectively. The hardware device will be utilized to optimize the AWakeOSA algorithm and work towards achieving a reliable home-care device for screening under unsupervised conditions.
The proposed technology will enable a reliable and quick diagnosis of OSA that can be either used in a clinician's office during wakefulness and/or used at home by people to monitor their own OSA. The outcomes of this study will benefit the health care system and society significantly as it will: 1) reduce the financial burden of OSA on the healthcare system by reducing the need for PSG and unnecessary preoperative resources; and 2) provide a quick and reliable personal OSA home-care monitoring system for better OSA treatment management.
Opintotyyppi
Ilmoittautuminen (Odotettu)
Yhteystiedot ja paikat
Opiskeluyhteys
- Nimi: Abnoor Kaur, M.Sc.
- Puhelinnumero: 2044747023
- Sähköposti: abnoorkaur9@gmail.com
Tutki yhteystietojen varmuuskopiointi
- Nimi: Zahra Moussavi, Ph.D.
- Puhelinnumero: 2044747023
- Sähköposti: Zahra.Moussavi@umanitoba.ca
Opiskelupaikat
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Manitoba
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Winnipeg, Manitoba, Kanada, R3t5V6
- Rekrytointi
- University of Manitoba
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Ottaa yhteyttä:
- Abnoor Kaur, M.Sc.
- Puhelinnumero: 2044747023
- Sähköposti: abnoorkaur9@gmail.com
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Ottaa yhteyttä:
- Zahra Moussavi, Ph.D.
- Sähköposti: zahra.moussavi@umanitoba.ca
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Osallistumiskriteerit
Kelpoisuusvaatimukset
Opintokelpoiset iät
Hyväksyy terveitä vapaaehtoisia
Sukupuolet, jotka voivat opiskella
Näytteenottomenetelmä
Tutkimusväestö
Kuvaus
Inclusion Criteria:
- 1) age between 18 to 70 years.
- 2) suspected of OSA and referred to full PSG study by a doctor.
Exclusion Criteria:
- 1) being diagnosed with a chronic respiratory disease including pulmonary fibrosis, emphysema, respiratory infectious disease, nocturnal asthma, obstructive pulmonary lung disease, pulmonary hypertension, congestive heart failure, sleep related hypoventilation and neuromuscular disorders.
- 2) having insomnia or restless leg.
- 3) drug addiction.
- 4) under the current, direct supervision of the PI of this study.
Opintosuunnitelma
Miten tutkimus on suunniteltu?
Suunnittelun yksityiskohdat
- Havaintomallit: Case-Control
- Aikanäkymät: Takautuva
Kohortit ja interventiot
Ryhmä/Kohortti |
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OSA Participants
Inclusion Criteria: 1) age between 18 to 70 years; 2) suspected of OSA and referred to full PSG study by a doctor. Exclusion Criteria: 1) being diagnosed with a chronic respiratory disease including pulmonary fibrosis, emphysema, respiratory infectious disease, nocturnal asthma, obstructive pulmonary lung disease, pulmonary hypertension, congestive heart failure, sleep related hypoventilation and neuromuscular disorders; 2) having insomnia or restless leg; 3) drug addiction; and 4) under the current, direct supervision of the PI of this study. |
Healthy Controls
Inclusion Criteria: 1) age between 18 to 70 years; 2) non-snorer, and being free of any sleep disorders. Exclusion Criteria: same as the OSA Participants. All participants must not have any cold or any other respiratory illness at the time of recording. Illiterate participants may still participate in the study; however, there must be a witness who is not involved in the study (i.e. PI, Co-PI, study coordinator, research assistants (RA)/students, etc.) present to witness the consent process between the participant and the individual obtaining consent. |
Mitä tutkimuksessa mitataan?
Ensisijaiset tulostoimenpiteet
Tulosmittaus |
Toimenpiteen kuvaus |
Aikaikkuna |
---|---|---|
OSA Severity
Aikaikkuna: 1 Day
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Data recorded during wakefulness will be analyzed using our published AWakeOSA algorithm to classify the participants into two groups of those who have AHI below and above 15.
We will also run Multivariate statistical analysis to find the main effect of OSA severity (AHI) on the sound features and their interaction with the anthropometric information.
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1 Day
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Toissijaiset tulostoimenpiteet
Tulosmittaus |
Toimenpiteen kuvaus |
Aikaikkuna |
---|---|---|
Acoustic AHI correlation to PSG
Aikaikkuna: 1 Day
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For the sleep data, we will find the correlation of the AHI values estimated by our acoustic device using our previous algorithm compared to those of the Polysomnogram (PSG).
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1 Day
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Yhteistyökumppanit ja tutkijat
Sponsori
Tutkijat
- Päätutkija: Zahra Kazem-Moussavi, Ph.D., University of Manitoba
Opintojen ennätyspäivät
Opi tärkeimmät päivämäärät
Opiskelun aloitus (Odotettu)
Ensisijainen valmistuminen (Odotettu)
Opintojen valmistuminen (Odotettu)
Opintoihin ilmoittautumispäivät
Ensimmäinen lähetetty
Ensimmäinen toimitettu, joka täytti QC-kriteerit
Ensimmäinen Lähetetty (Todellinen)
Tutkimustietojen päivitykset
Viimeisin päivitys julkaistu (Todellinen)
Viimeisin lähetetty päivitys, joka täytti QC-kriteerit
Viimeksi vahvistettu
Lisää tietoa
Tähän tutkimukseen liittyvät termit
Muita asiaankuuluvia MeSH-ehtoja
Muut tutkimustunnusnumerot
- B2018:094
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