Development of a model to predict antidepressant treatment response for depression among Veterans

Puac-Polanco V, Ziobrowski H, Ross E, Liu H, Turner B, Cui R, Leung LB, Bossarte RM, Bryant C, Joormann J, Nierenberg AA, Oslin DW, Pigeon WR, Post EP, Zainal NH, Zaslavsky AM, Zubizarreta JR, Luedtke A, Kennedy CJ, Cipriani A, Furukawa TA, Kessler RC. Development of a model to predict antidepressant treatment response for depression among Veterans. Psychological medicine. 2022 Jul 15; 1-11.

Related HSR&D Project(s)

• CDA 19-108 – Improving Depression Management in Primary Care

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Abstract:

Background Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA). Methods A 2018-2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample. Results In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (S.E.) of 0.66 (0.04) in the test sample. A strong gradient in probability (S.E.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors. Conclusions Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.