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

Abstract

ObjectiveWith 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. MethodWe 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 (N = 1,691 and N = 12,473) using patient- and therapist-reported variables collected at the time of admission to the clinic.ResultsPredictive 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. ConclusionsTherapy dropout in naturalistic inpatient settings can be predicted to a considerable degree by using baseline indicators. 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.

Predicting treatment response using machine learning: A registered report

“…One limiting factor that our study shares with similar research is a specific, fixed set of available predictor variables. In any case, there are further variables (e.g., patients' motivation to participate in the therapy at baseline; Jankowsky et al, 2023) that might incrementally explain treatment response. The present results have to be seen against this background: For example, we found that demographics had a negligible role in our models.…”