Development and Validation of a Machine Learning Individualized Treatment Rule in First-Episode Schizophrenia
Journal
JAMA network open
Journal Volume
3
Journal Issue
2
Date Issued
2020
Author(s)
Luedtke A.R.
Sadikova E.
Tsai H.-J.
VanderWeele T.J.
Kessler R.C.
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 <.001). The estimated treatment success if all patients were given a prescription for 1 medication was significantly lower for each of the 13 medications than under the ITR (Z = 4.2-16.8; all P <.001). Aripiprazole (3088 [31.9%]) and amisulpride (2920 [30.2%]) were the medications most often recommended by the ITR. Only 1054 patients (10.9%) received ITR-recommended medications. Observed treatment success, although lower than the success under the ITR, was nonetheless significantly higher than if medications had been randomized (44.5% [SE, 0.55%] vs 41.3% [SE, 0.4%]; Z = 6.9; P <.001), although only marginally higher than if medications had been randomized in their observed population proportions (44.5% [SE, 0.5%] vs 43.5% [SE, 0.4%]; Z = 2.2; P =.03]). Conclusions and Relevance: These results suggest that an ITR may be associatded with an increase in the treatment success rate among patients with first-episode schizophrenia, but experimental evaluation is needed to confirm this possibility. If confirmed, model refinement that investigates biomarkers, clinical observations, and patient reports as additional predictors in iterative pragmatic trials would be needed before clinical implementation.. ? 2020 American Medical Association. All rights reserved.
SDGs
Other Subjects
amisulpride; aripiprazole; chlorpromazine; clotiapine; clozapine; flupentixol; haloperidol; neuroleptic agent; olanzapine; paliperidone; quetiapine; risperidone; sulpiride; thioridazine; trifluoperazine; ziprasidone; zotepine; neuroleptic agent; adult; Article; clinical feature; clinical outcome; clinician; demography; emergency ward; female; hospitalization; human; machine learning; major clinical study; male; outpatient care; personalized medicine; prescription; priority journal; prognosis; psychopharmacotherapy; psychosis; schizophrenia; treatment failure; treatment outcome; middle aged; personalized medicine; procedures; retrospective study; schizophrenia; statistical model; young adult; Adult; Antipsychotic Agents; Clinical Decision Rules; Female; Humans; Machine Learning; Male; Middle Aged; Models, Statistical; Precision Medicine; Prognosis; Retrospective Studies; Schizophrenia; Treatment Outcome; Young Adult
Publisher
NLM (Medline)
Type
journal article