Psychotherapy remission prediction models show limited cross-clinic generalizability in routine outpatient care

Abstract

Abstract Clinical prediction models for psychotherapy outcomes have the potential to assist therapists in planning a patient’s individual treatment, but their clinical utility depends on maintaining predictive performance across settings. We assessed prediction of post-treatment remission in 2,002 patients with internalizing disorders treated across eight German university outpatient clinics. Penalized logistic-regression models were evaluated within clinics, in pooled clinic data, and using leave-one-clinic-out validation. Within clinics, mean AUC was 0.67, balanced accuracy 0.62, and Brier score 0.21. In leave-one-clinic-out validation, mean AUC and balanced accuracy declined from 0.60 and 0.57 in corresponding held-out test samples to 0.53 and 0.52 in held-out clinics, respectively. The mean Brier score increased from 0.23 to 0.25, but this difference was not statistically significant (p = 0.18). Cross-clinic performance loss was therefore clearest for discrimination and threshold-dependent classification, while overall probabilistic prediction error showed a smaller change. These findings highlight the broader challenge of transporting psychotherapy outcome predictions across heterogeneous clinical settings.

The authors' abstract, as published at the source. Scientific Reports, 2026 · DOI ↗