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

March 16, 2021 updated by: Aifred Health Inc.

A Feasibility Study of a Hybrid-Classic/Deep-Learning Enabled Clinical Decision Aid for Personalized and Individualized Pharmacological Depression Treatment Selection

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.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

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.

Study Type

Interventional

Enrollment (Actual)

17

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Canada
    • Quebec
      • Verdun, Quebec, Canada, H4H 1R3
        • Douglas Mental Health University Institute

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Device Feasibility
  • Allocation: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Subjective length of outpatient visits
Time Frame: Through study completion, 6 months
Through study completion, 6 months
Objective length of outpatient visits
Time Frame: Through study completion, 6 months
Through study completion, 6 months
Physician retention rates
Time Frame: Through study completion, 6 months
Through study completion, 6 months
Patient retention rates
Time Frame: Through study completion, 6 months
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.
Through study completion, 6 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Aifred Health Inc.

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

December 16, 2019

Primary Completion (Actual)

December 31, 2020

Study Completion (Actual)

December 31, 2020

Study Registration Dates

First Submitted

August 5, 2019

First Submitted That Met QC Criteria

August 19, 2019

First Posted (Actual)

August 20, 2019

Study Record Updates

Last Update Posted (Actual)

March 17, 2021

Last Update Submitted That Met QC Criteria

March 16, 2021

Last Verified

March 1, 2021

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • IUSMD-19-08

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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