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

NCT04061642 | Completed

• 1 other identifier: IUSMD-19-08
• Study Type: interventional
• Target: 17
• Locations: 1 country
• Sites: 1

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.

Conditions

Depression

Keywords

Artificial intelligence; Deep learning; Clinical decision aid

Outcome Measures

Primary Outcomes (5)

• Subjective length of outpatient visits

  Through study completion, 6 months

• Objective length of outpatient visits

  Through study completion, 6 months

• Physician retention rates

  Through study completion, 6 months

• Patient retention rates

  Through study completion, 6 months

• Patient self-rated experience using the study software

  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

Study Arms (1)

Clinical Decision Aid

EXPERIMENTAL

Device: Clinical Decision Aid

Interventions

Clinical Decision Aid DEVICE

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.

Clinical Decision Aid

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Aifred Health Inc.: lead

Study Sites (1)

Douglas Mental Health University Institute

Verdun, Quebec, H4H 1R3, Canada

Location

Related Publications (1)

• Popescu C, Golden G, Benrimoh D, Tanguay-Sela M, Slowey D, Lundrigan E, Williams J, Desormeau B, Kardani D, Perez T, Rollins C, Israel S, Perlman K, Armstrong C, Baxter J, Whitmore K, Fradette MJ, Felcarek-Hope K, Soufi G, Fratila R, Mehltretter J, Looper K, Steiner W, Rej S, Karp JF, Heller K, Parikh SV, McGuire-Snieckus R, Ferrari M, Margolese H, Turecki G. Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered, Web-Based Clinical Decision Support System for the Treatment of Depression in Adults: Longitudinal Feasibility Study. JMIR Form Res. 2021 Oct 25;5(10):e31862. doi: 10.2196/31862.

  PMID: 34694234 DERIVED

MeSH Terms

Conditions

Depression

Condition Hierarchy (Ancestors)

Behavioral Symptoms; Behavior

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DEVICE FEASIBILITY
• Intervention Model: SINGLE GROUP
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 5, 2019
• First Posted: August 20, 2019
• Study Start: December 16, 2019
• Primary Completion: December 31, 2020
• Study Completion: December 31, 2020
• Last Updated: March 17, 2021

Record last verified: 2021-03

Data Sharing

• IPD Sharing: Will share

Locations

CA (1)