A Feasibility Study of an AI-Powered Clinical Decision Aid for Personalized Depression Treatment Selection
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|ClinicalTrials.gov Identifier: NCT04061642|
Recruitment Status : Recruiting
First Posted : August 20, 2019
Last Update Posted : August 26, 2019
|Condition or disease||Intervention/treatment||Phase|
|Depression||Device: Clinical Decision Aid||Not Applicable|
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.
|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|
|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.
- Subjective length of outpatient visits [ Time Frame: Through study completion, 6 months ]
- Objective length of outpatient visits [ Time Frame: Through study completion, 6 months ]
- Physician retention rates [ Time Frame: Through study completion, 6 months ]
- Patient retention rates [ Time Frame: Through study completion, 6 months ]
- 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.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04061642
|Douglas Mental Health University Institute||Recruiting|
|Verdun, Quebec, Canada, H4H 1R3|
|Contact: David Benrimoh, MD, CM, MSc 514-463-7813 firstname.lastname@example.org|
|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|