Using machine-learning to predict sudden gains in treatment for major depressive disorder

Objective: Sudden gains during psychotherapy have been found to consistently predict treatment outcome but evidence on predictors of sudden gains has been equivocal. To address this gap, the present study utilized three machine learning algorithms to predict sudden gains during treatment for major depressive disorder. Method: We examined predictors of sudden gains in two large samples of individuals receiving treatment in a partial hospital setting (n = 726 and n = 788; total N = 1514). Predictors included age, gender, marital status, education, employment status, previous hospitalization, comorbid diagnoses, and pretreatment measures of depressive and generalized anxiety symptoms. We used three machine learning models: a Random Forest model, a Random Forest model with an adaptive boosting meta-algorithm, and a Support Vector Machine model. Results: In both samples, sudden gains were identified and found to significantly predict outcome. However, none of the machine learning algorithms was able to identify robust predictors of sudden gains. Thus, even though some models achieved fair prediction of sudden gains in the training subset, prediction in the test subset was poor. Conclusions: Despite the use of a large sample and three machine-learning models, we were unable to identify robust demographic and pretreatment clinical predictors of sudden gains. Implications for clinical decision making and future studies are discussed.