Development and Validation of a Machine Learning Individualized Treatment Rule in First-Episode Schizophrenia

Abstract

Importance: Little guidance exists to date on how to select antipsychotic medications for patients with first-episode schizophrenia. Objective: To develop a preliminary individualized treatment rule (ITR) for patients with first-episode schizophrenia. Design, Setting, and Participants: This prognostic study obtained data from Taiwan's National Health Insurance Research Database on patients with prescribed antipsychotic medications, ambulatory claims, or discharge diagnoses of a schizophrenic disorder between January 1, 2005, and December 31, 2011. An ITR was developed by applying a targeted minimum loss-based ensemble machine learning method to predict treatment success from baseline clinical and demographic data in a 70% training sample. The model was validated in the remaining 30% of the sample. The probability of treatment success was estimated for each medication for each patient under the model. The analysis was conducted between July 16, 2018, and July 15, 2019. Exposures: Fifteen different antipsychotic medications. Main Outcomes and Measures: Treatment success was defined as not switching medication and not being hospitalized for 12 months. Results: Among the 32277 patients in the analysis, the mean (SD) age was 36.7 (14.3) years, and 15 752 (48.8%) were male. In the validation sample, the treatment success rate (SE) was 51.7% (1.0%) under the ITR and was 44.5% (0.5%) in the observed population (Z = 7.1; P