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

OBJECTIVE

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.

METHOD

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 ( = 1,691 and  = 12,473) using baseline and initial process variables reported by patients and therapists.

RESULTS

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.

CONCLUSIONS

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.