In Journal of affective disorders ; h5-index 79.0
BACKGROUND : Although research shows that more depressed patients respond to combined antidepressants (ADM) and psychotherapy than either alone, many patients do not respond even to combined treatment. A reliable prediction model for this could help treatment decision-making. We attempted to create such a model using machine learning methods among patients in the US Veterans Health Administration (VHA).
METHODS : A 2018-2020 national sample of VHA patients beginning combined depression treatment completed self-report assessments at baseline and 3 months (n = 658). A learning model was developed using baseline self-report, administrative, and geospatial data to predict 3-month treatment response defined by reductions in the Quick Inventory of Depression Symptomatology Self-Report and/or in the Sheehan Disability Scale. The model was developed in a 70 % training sample and tested in the remaining 30 % test sample.
RESULTS : 30.0 % of patients responded to treatment. The prediction model had a test sample AUC-ROC of 0.657. A strong gradient was found in probability of treatment response from 52.7 % in the highest predicted quintile to 14.4 % in the lowest predicted quintile. The most important predictors were episode characteristics (symptoms, comorbidities, history), personality/psychological resilience, recent stressors, and treatment characteristics.
LIMITATIONS : Restrictions in sample definition, a low recruitment rate, and reliance on patient self-report rather than clinician assessments to determine treatment response limited the generalizability of results.
CONCLUSIONS : A machine learning model could help depressed patients and providers predict likely response to combined ADM-psychotherapy. Parallel information about potential harms and costs of alternative treatments would be needed, though, to inform optimal treatment selection.
Bossarte Robert M, Ross Eric L, Liu Howard, Turner Brett, Bryant Corey, Zainal Nur Hani, Puac-Polanco Victor, Ziobrowski Hannah N, Cui Ruifeng, Cipriani Andrea, Furukawa Toshiaki A, Leung Lucinda B, JoormannN Jutta, Nierenberg Andrew A, Oslin David W, Pigeon Wilfred R, Post Edward P, Zaslavsky Alan M, Zubizarreta Jose R, Luedtke Alex, Kennedy Chris J, Kessler Ronald C
2023-Jan-26
Antidepressant medication, Clinical decision support, Depression, Machine learning, Treatment response, Veterans Health Administration
