Psychiatric Times

May 25, 2023
Leah Kuntz

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Presenters at the 2023 APA Annual Meeting compared definitions of treatment-resistant depression.


“In 1964, Supreme Court Justice Potter Stewart was asked for his test for obscenity and his response famously was, ‘I know when I see it.’ In the real world and clinical practice, I think that is essentially how we wind up defining treatment-resistant depression,” said Steven P. Levine, MD.

Presenters of the session, “Treatment Resistant Depression From Multiple Perspectives: Does It Exist?”—Levine; Carl Marci, MD; Joseph Zabinski, PhD; and Lisa Harding, MD—brought attention to 3 varying definitions of treatment-resistant depression (TRD), emphasizing the amorphous nature of this term. Specifically, they provoked discussion by questioning the existence of TRD—if no one can provide a set definition, then how does it exist?

Researchers compared the definitions in a comparison study that looked at the application of 3 different definitions of treatment-resistant depression in real world data. The population from which the 7 years of data was pulled is derived from close to a half a million adult patients who have been diagnosed with major depressive disorder, who have electronic health records.

The 3 definitions constituted:

1. The regulatory definition: having 2 failed sequences of antidepressant treatment of adequate dose and duration within an episode of major depressive disorder. As simple as this definition sounds, as Marci shared, “It turns out to be a very hard definition to apply in the real world, because the devil gets in the details,” and it is unclear how to define ‘adequate treatment’ or to accurately measure the dose and duration.

2. The data-driven definition: having failed 3 different antidepressants, and an antidepressant plus an antipsychotic, within a year.

3. The artificial intelligence (AI) definition: using machine learning and AI methods in real-world data, AI can pick out patterns, phenotypic profiles, and a ‘fingerprint’ with which to determine whether a patient is treatment resistant. “AI is good at saying, ‘We have some intuition about these different factors that are contributing, and it is very hard to nail that balance.’ AI can help us do that,” said Zabinski.

“Treatment resistant depression is a debilitating and costly illness and exciting new treatments are coming to market. But as the results of this research show, there are both challenges and promises of identifying patients with treatment-resistant depression in the real-world,” Marci shared exclusively with Psychiatric Times. “The challenge is determining whether the lack of overlap in these populations represents three distinct subtypes of treatment resistant depression or a fundamental flaw in the definition. The promise is understanding that AI combined thoughtfully with real-world data can help answer that question.”

Among the 73,415 unique patients identified, only 1% met all 3 definitions of TRD.

“We talk about models, we talk about the definitions, but we cannot lose the humaneness of what we are trying to do,” Harding emphasized. “It comes back to the definition of how we see our patients. What is the definition of wellness? Beyond what is treatment-resistant depression, what are you calling wellness to them then look at the patient and see the resistance?”