First impressions count: Therapists’ impression on patients’ motivation and helping alliance predicts psychotherapy dropout

<b>Objective:</b> With meta-analytically estimated rates of about 25%, dropout in psychotherapies is a major concern for individuals, clinicians, and the healthcare system at large. To be able to counteract dropout in psychotherapy, accurate insights about its predictors are needed. <b>Method</b>: We compared logistic regression models with two machine learning algorithms (elastic net regressions and gradient boosting machines) in the prediction of therapy dropout in two large inpatient samples (<i>N</i> = 1,691 and <i>N</i> = 12,473) using baseline and initial process variables reported by patients and therapists. <b>Results</b>: Predictive accuracies of the two machine learning algorithms were similar and higher than for logistic regressions: Therapy dropout could be predicted with an AUC of .73 and .83 for Sample 1 and 2, respectively. The initial evaluation of patients' motivation and the therapeutic alliance rated by the respective therapist were the most important predictors of dropout. <b>Conclusions</b>: Therapy dropout in naturalistic inpatient settings can be predicted to a considerable degree by using baseline indicators and therapists' first impressions. Feature selection via regularization leads to higher predictive performances whereas non-linear or interaction effects are dispensable. The most promising point of intervention to reduce therapy dropouts seems to be patients' motivation and the therapeutic alliance.