Comparison of prediction methods for treatment continuation of antipsychotics in children and adolescents with schizophrenia

Abstract

Objective: There is little evidence for finding optimal antipsychotic treatment for schizophrenia, especially in paediatrics. To evaluate the performance and clinical benefit of several prediction methods for 1-year treatment continuation of antipsychotics.

Design and settings: Population-based prognostic study conducting using the nationwide claims database in Korea.

Participants: 5109 patients aged 2-18 years who initiated antipsychotic treatment with risperidone/aripiprazole for schizophrenia between 2010 and 2017 were identified.

Main outcome measures: We used the conventional logistic regression (LR) and common six machine-learning methods (least absolute shrinkage and selection operator, ridge, elstic net, randomforest, gradient boosting machine, and superlearner) to derive predictive models for treatment continuation of antipsychotics. The performance of models was assessed using the Brier score (BS), area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). The clinical benefit of applying these models was also evaluated by comparing the treatment continuation rate between patients who received the recommended medication by models and patients who did not.

Results: The gradient boosting machine showed the best performance in predicting treatment continuation for risperidone (BS, 0.121; AUROC, 0.686; AUPRC, 0.269). Among aripiprazole models, GBM for BS (0.114), SuperLearner for AUROC (0.688) and random forest for AUPRC (0.317) showed the best performance. Although LR showed lower performance than machine learnings, the difference was negligible. Patients who received recommended medication by these models showed a 1.2-1.5 times higher treatment continuation rate than those who did not.

Conclusions: All prediction models showed similar performance in predicting the treatment continuation of antipsychotics. Application of prediction models might be helpful for evidence-based decision-making in antipsychotic treatment.