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The objective of this study is to collect data to finalize the development of MEB-001 software as a medical device. The data collected in this study will be used to develop MEB-001 machine learning algorithms by training the algorithms to match the patient's demographic and clinical information, and the objective physiological signals (i.e., electroencephalogram (EEG) and electrocardiogram (ECG)) recorded during PSG with the diagnosis of cMDE performed through the MINI neuropsychiatric evaluation.
This is a two (2)-phase, single-arm, prospective, non-significant risk, multi-center trial where each enrolled subject's data will be used for the development of MEB-001.
Study Population:
Subjects who are at least 22 years old but not older than 75 years old, who have been referred to a sleep clinic for sleep disturbances and sign an informed consent form (ICF) will be evaluated for participation in this study.
The selection of the population involved in the study will consider the geographic diversity needed to obtain a representative sample of the intended use population. Centers will be selected to secure geographic and clinical diversity across the USA to obtain a representative distribution of the intended patient population.
All patients undergoing PSG due to primary or secondary sleep disorders, which include, but are not limited to, Sleep-Related Movement Disorders, Sleep-Related Breathing Disorders, Intrinsic and Extrinsic Circadian Rhythm Sleep-Wake Disorders, Hypersomnia, Parasomnia, and Insomnia, will be consecutively recruited, according to the inclusion/exclusion criteria.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Self Reported Assessment | This group of patients will self report their answers to the MINI assessment. |
| |
| Clinician Interview Assessment | This group of patients will complete the MINI assessment via an interactive interview conducted by a qualified clinician. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| MEB-001 | Device | The study is for the development of a software medical device. The study participant will complete study specific questionnaires and undergo a routine sleep study. The medical device will not provide any treatment or interventions. |
| Measure | Description | Time Frame |
|---|---|---|
| Development of software as a medical device for the assessment of current major depressive episodes. | Evaluation of patient objective and subjective measures to develop algorithm to determine whether the patient is experiencing a current major depressive episode. | 24 months |
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Inclusion Criteria:
Subjects must meet ALL the following conditions to be eligible to participate in the study:
Exclusion Criteria:
Subjects will not be eligible, and they will not be recruited to participate in the study if any of the following conditions are present:
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Subjects who are at least 22 years old but not older than 75 years old, who have been referred to a sleep clinic for sleep disturbances and sign an informed consent form (ICF) will be evaluated for participation in this study.
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| Name | Affiliation | Role |
|---|---|---|
| Archie Defillo, MD | Medibio Limited | Study Chair |
| Melissa E Bruner, MS | Medibio Limited | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Medbridge Healthcare | Bradenton | Florida | 34205 | United States | ||
| Lakeland Sleep Store |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 35489559 | Result | Caldirola D, Dacco S, Cuniberti F, Grassi M, Alciati A, Torti T, Perna G. First-onset major depression during the COVID-19 pandemic: A predictive machine learning model. J Affect Disord. 2022 Aug 1;310:75-86. doi: 10.1016/j.jad.2022.04.145. Epub 2022 Apr 27. | |
| 35306830 | Result | Caldirola D, Cuniberti F, Dacco S, Grassi M, Torti T, Perna G. Predicting New-Onset Psychiatric Disorders Throughout the COVID-19 Pandemic: A Machine Learning Approach. J Neuropsychiatry Clin Neurosci. 2022 Summer;34(3):233-246. doi: 10.1176/appi.neuropsych.21060148. Epub 2022 Mar 21. |
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| Blaine |
| Minnesota |
| 55449 |
| United States |
| Lakeland Sleep | Plymouth | Minnesota | 55441 | United States |
| Medbridge Healthcare | Clayton | North Carolina | 27520 | United States |
| Medbridge Healthcare | Raleigh | North Carolina | 27612 | United States |
| Medbridge Healthcare | Wilson | North Carolina | 27822 | United States |
| Ohio Sleep Solutions | Columbus | Ohio | 43004 | United States |
| Ohio Sleep Solutions | Grove City | Ohio | 43123 | United States |
| Medbridge Healthcare | North Charleston | South Carolina | 29406 | United States |
| Medbridge Healthcare | Sumter | South Carolina | 29150 | United States |
| Comprehensive Sleep Medicine Associates | Austin | Texas | 78757 | United States |
| Comprehensive Sleep Medicine Associates | Sugar Land | Texas | 77478 | United States |
| Comprehensive Sleep Medicine Associates | The Woodlands | Texas | 77381 | United States |
| 33338557 | Result | Perna G, Dacco S, Alciati A, Cuniberti F, De Berardis D, Caldirola D. Childhood maltreatment history for guiding personalized antidepressant choice in major depressive disorder: Preliminary results from a systematic review. Prog Neuropsychopharmacol Biol Psychiatry. 2021 Apr 20;107:110208. doi: 10.1016/j.pnpbp.2020.110208. Epub 2020 Dec 15. |
| 36644846 | Result | Dacco S, Caldirola D, Grassi M, Alciati A, Perna G, Defillo A. High prevalence of major depression in US sleep clinics: the need for routine depression screening in sleep services. J Clin Sleep Med. 2023 Apr 1;19(4):835-836. doi: 10.5664/jcsm.10398. |
| 32160691 | Result | Perna G, Alciati A, Dacco S, Grassi M, Caldirola D. Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables. Psychiatry Investig. 2020 Mar;17(3):193-206. doi: 10.30773/pi.2019.0289. Epub 2020 Mar 12. |
| 33065816 | Result | Perna G, Cuniberti F, Dacco S, Nobile M, Caldirola D. Impact of respiratory protective devices on respiration: Implications for panic vulnerability during the COVID-19 pandemic. J Affect Disord. 2020 Dec 1;277:772-778. doi: 10.1016/j.jad.2020.09.015. Epub 2020 Sep 7. |
| ID | Term |
|---|---|
| D003863 | Depression |
| D003866 | Depressive Disorder |
| D003865 | Depressive Disorder, Major |
| ID | Term |
|---|---|
| D001526 | Behavioral Symptoms |
| D001519 | Behavior |
| D019964 | Mood Disorders |
| D001523 | Mental Disorders |
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