Description
Treatment-resistant depression (TRD) represents a major challenge in the clinical management of depression. Patients with TRD experience more severe depression symptoms and lower quality of life, social functioning, and work productivity than patients with non-TRD (1). Yet, research aimed at improving outcomes for patients with TRD is complicated by the lack of a consensus definition for TRD and the use of different definitions in different contexts, such as those that inform clinical care, reimbursement decisions, and regulatory decisions. In the context of major depressive disorder, the most commonly used definition requires ‘a minimum of two prior treatment failures and confirmation of prior adequate dose and duration’ (2). However, it is unclear how to define adequate treatment or measure accurately the dose and duration. Questions also exist about whether patients with TRD received treatment under the framework of measurement-based care, which has been shown to improve outcomes in patients with depression compared to treatment as usual (3). Our session will introduce participants to the commonly used definitions of TRD and discuss the strengths and limitations of these definitions in both the research and clinical practice contexts. We will then consider innovative approaches to identifying TRD using machine learning and artificial intelligence methods in real-world data, with a focus on clinical explainability and the practical application of these approaches. Finally, we will consider whether improvements in the definition of TRD would be useful to support development of new therapies, identify areas for further research, and improve patient management and ultimately patient outcomes. We will encourage robust discussion across presenters and attendees about how to define TRD in research and in clinical practice and how to identify and effectively manage these patients.
Format
Recorded webinar.
This content was recorded during the APA 2023 Annual Meeting.
Learning Objectives
- Compare and contrast widely-used definitions of treatment-resistant depression, with a focus on the implications of different definitions on research and clinical practice.
- Assess the potential value and limitations of machine learning and artificial intelligence for identifying patients with TRD.
- Apply definitions of treatment-resistant depression (TRD) in practice to identify patients with TRD and manage those patients in accordance with evidence-based care
Target Audience
Residents/Fellows, Psychiatrists, Psychologists
Estimate Time to Complete
Estimated Duration: 1.5 hours
Program Start Date: May 1, 2025
Program End Date: May 1, 2028
How to Earn Credit
Participants who wish to earn AMA PRA Category 1 Credit™ may do so after completing all sections of the course including the evaluation. After evaluating the program, course participants will be provided with an opportunity to claim hours of participation and print an official CME certificate (physicians) or certificate of participation (non-physicians) showing the completion date and hours earned.
Continuing Education Credit
In support of improving patient care, the American Psychiatric Association is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.
The APA designates this live activity for a maximum of 1.5 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Faculty and Planner Disclosures
The American Psychiatric Association adheres to the ACCME’s Standards for Integrity and Independence in Accredited Continuing Medical Education. Any individuals in a position to control the content of a CME activity — including faculty, planners, reviewers or others — are required to disclose all relevant financial relationships with ineligible entities (commercial interests). All relevant conflicts of interest have been mitigated prior to the commencement of the activity.
Instructors
- Carl Marci, MD
- Joseph Zabinski, PhD, MEM
- Steven Levine, MD
- Lisa Harding, MD
Planners
- Vishal Madaan, MD, Chief of Education and Deputy Medical Director at the American Psychiatric Association.
Accessibility for Participants with Disabilities
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Technical Requirements
This internet-based CME activity is best experienced using any of the following:
- The latest and 2nd latest public versions of Google Chrome, Mozilla Firefox, or Safari
- Internet Explorer 11+
This Web site requires that JavaScript and session cookies be enabled. Certain activities may require additional software to view multimedia, presentation, or printable versions of the content. These activities will be marked as such and will provide links to the required software. That software may be: Adobe Acrobat Reader, Microsoft PowerPoint, and Windows Media Player.
Optimal System Configuration:
- Browser: Google Chrome (latest and 2nd latest version), Safari (latest and 2nd latest version), Internet Explorer 11.0+, Firefox (latest and 2nd latest version), or Microsoft Edge (latest and 2nd latest version)
- Operating System: Windows versions 8.1+, Mac OS X 10.5 (Leopard) +, Android (latest and 2nd latest version), or iOS/iPad OS (latest and 2nd latest version)
- Internet Connection: 1 Mbps or higher
Minimum Requirements:
- Windows PC: Windows 8.1 or higher; 1 GB (for 32-bit)/2 GB (for 64-bit) or higher RAM; Microsoft DirectX 9 graphics device with WDDM driver; audio playback with speakers for programs with video content
- Macintosh: Mac OS X 10.5 or higher with latest updates installed; Intel, PowerPC G5, or PowerPC G4 (867MHz or faster) processor; 512 MB or higher RAM; audio playback with speakers for programs with video content
For assistance: Contact educme@psych.org for questions about this activity | Contact learningcenter@psych.org for technical assistance