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A Feasibility Study of an AI-Powered Clinical Decision Aid for Personalized Depression Treatment Selection

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04061642
Recruitment Status : Recruiting
First Posted : August 20, 2019
Last Update Posted : August 26, 2019
Sponsor:
Information provided by (Responsible Party):
Aifred Health Inc.

Brief Summary:
The Clinical Decision Aid (CDA) is a predictive model that takes as input individual patient characteristics, called 'features', which are inputted by the physician or by patient self-report, and outputs a list of possible treatments, with each treatment associated with a predicted efficacy (likelihood to achieve response and likelihood to achieve remission, each expressed as a percentage). The treatments, which may include any approved treatment for depression, will be presented to the physician who will then make a treatment choice.

Condition or disease Intervention/treatment Phase
Depression Device: Clinical Decision Aid Not Applicable

Detailed Description:

Hypothesis 1. There will not be a significant difference in measured non-initial intake appointment lengths between the baseline period and the appointment length measured at two and four months after introduction of the study software and CDA.

Hypothesis 2. Physicians will not subjectively report that using the CDA and study software increased the length of their appointments.

Hypothesis 3. At least 66% of patients and 66% of physicians will rate the trustworthiness of the CDA as a 4 or 5 on a 5 point Likert scale (with higher ratings indicating greater trust).

Hypothesis 4. At least 66% of patients and 66% of physicians will rate the overall usability of the CDA as a 4 or 5 on a 5 point Likert scale (with higher ratings indicating greater usability).

Hypothesis 5. At least 70% of physicians and 65% of patients will still be using the application regularly by the end of the study. For physicians, regularly will be defined as the application being used in every study-related visit. For patients regularly will be defined as completing at least one PHQ-9 and GAD-7 questionnaire on the application per week.

Exploratory hypothesis: Based on our machine learning results to date, we expect between 40-50% of patients starting a new treatment for depression and whose treatment follows the highest probability treatment output by the CDA to remit within 14 weeks. This is exploratory, and the study is not necessarily powered to demonstrate this.


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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 10 participants
Intervention Model: Single Group Assignment
Masking: None (Open Label)
Primary Purpose: Device Feasibility
Official Title: A Feasibility Study of a Hybrid-Classic/Deep-Learning Enabled Clinical Decision Aid for Personalized and Individualized Pharmacological Depression Treatment Selection
Estimated Study Start Date : October 1, 2019
Estimated Primary Completion Date : March 31, 2020
Estimated Study Completion Date : March 31, 2020

Resource links provided by the National Library of Medicine


Arm Intervention/treatment
Experimental: Clinical Decision Aid Device: Clinical Decision Aid
The Clinical Decision Aid is a predictive model that takes as input individual patient characteristics, called 'features', which are inputted by the physician or by patient self-report, and outputs a list of all possible treatments, with each treatment associated with a predicted efficacy (likelihood to achieve response and likelihood to achieve remission, each expressed as a percentage). The treatments, which may include any approved treatment for depression, will be ordered by efficacy and presented to the physician. Lifestyle interventions, such as exercise or mindfulness, which have an evidence base, but do not require formal regulatory approval, will also be outputted. The system will additionally produce a side effect profile for each pharmacological treatment recommended, including known side effects, modified by a prediction about which side effects may be more likely for a given individual based on their individual characteristics.




Primary Outcome Measures :
  1. Subjective length of outpatient visits [ Time Frame: Through study completion, 6 months ]
  2. Objective length of outpatient visits [ Time Frame: Through study completion, 6 months ]
  3. Physician retention rates [ Time Frame: Through study completion, 6 months ]
  4. Patient retention rates [ Time Frame: Through study completion, 6 months ]
  5. Patient self-rated experience using the study software [ Time Frame: Through study completion, 6 months ]
    We will be using our Clinical Decision Aid Feasibility Questionnaire (Version 1), a descriptive questionnaire with 5-point Likert scales (with higher values representing better outcomes) and narrative questions about experience using the tool.



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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria:

  • All patients of the physicians in the study are diagnosed with major depressive disorder by a physician using DSM-V criteria.
  • All participants must be able to provide informed consent.
  • Contraception will be used as per established clinical guidelines and usual clinical practice for medications known to cause birth defects. The medications prescribed and the use of and type of contraception will be determined by the physicians in the study in consultation with their patients as would usually occur in clinical practice.

Exclusion Criteria:

  • Bipolar disorder type 1 or 2, as the data we have used to train the model does not allow for generalization to bipolar disorder (either pre-existing or as diagnosed according to DSM-5 criteria).
  • Inability or unwillingness of individual to give informed consent.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04061642


Locations
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Canada, Quebec
Douglas Mental Health University Institute Recruiting
Verdun, Quebec, Canada, H4H 1R3
Contact: David Benrimoh, MD, CM, MSc    514-463-7813    david.benrimoh@mail.mcgill.ca   
Principal Investigator: Howard Margolese, MD, CM, MSc         
Sub-Investigator: David Benrimoh, MD, CM, MSc         
Sub-Investigator: Myriam Tanguay-Sela, BA&Sc         
Sub-Investigator: Colleen Rollins, BSc         
Sub-Investigator: Sonia Israel, BSc         
Sub-Investigator: Kelly Perlman, BSc         
Sub-Investigator: Robert Fratila, BSc         
Sub-Investigator: Caitrin Armstrong, MSc         
Sub-Investigator: Joseph Mehltretter, BSc         
Sub-Investigator: Liliana Gomez Cardona, PhD         
Sponsors and Collaborators
Aifred Health Inc.

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Responsible Party: Aifred Health Inc.
ClinicalTrials.gov Identifier: NCT04061642     History of Changes
Other Study ID Numbers: IUSMD-19-08
First Posted: August 20, 2019    Key Record Dates
Last Update Posted: August 26, 2019
Last Verified: August 2019
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Yes

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Aifred Health Inc.:
Artificial intelligence
Deep learning
Clinical decision aid
Additional relevant MeSH terms:
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Depression
Depressive Disorder
Behavioral Symptoms
Mood Disorders
Mental Disorders