Background: Tardive dyskinesia (TD) is a serious, often irreversible movement disorder caused by prolonged exposure to antipsychotics; identifying patients at risk for TD is critical to preventing it. Predictive models for the occurrence of TD can improve patient monitoring and inform implementation of counteractive interventions. This study aims to identify risk factors associated with TD and to develop a model using a retrospective data analysis to predict the incidence of TD among patients taking antipsychotic medications.
Methods: Adult patients with schizophrenia, major depressive disorder, or bipolar disorder taking oral antipsychotics were identified in a Medicaid claims database (covering six US states from 1997 to 2016) and divided into cohorts based on whether they developed TD within 1 year after the first observed claim for antipsychotics. Patient characteristics between cohorts were compared, and univariate Cox analyses were used to identify potential TD risk factors. A cross-validated version of the least absolute shrinkage and selection operator regression method was used to develop a parsimonious multivariable Cox proportional hazards model to predict diagnosis of TD.
Results: A total of 189,415 eligible patients were identified. Potential TD risk factors were identified based on the cohort analysis within a sample of 151,280 patients with at least 1 year of continuous eligibility. The prediction model had a clinically meaningful concordance of 70% and was well calibrated (P = 0.32 for Hosmer-Lemeshow goodness-of-fit test). Age (hazard ratio [HR] = 1.04, P < 0.001), diagnosis of schizophrenia (HR = 1.99, P < 0.001), antipsychotic dosage (up to 100 mg/day chlorpromazine equivalent; HR = 1.65, P < 0.01), and comorbid bipolar and related disorders (HR = 1.39, P < 0.01) were significantly associated with an increased risk of TD. Other potential risk factors included history of extrapyramidal symptoms (HR = 1.35), other movement disorders (parkinsonism, HR = 1.43; bradykinesia, HR = 1.44; tremors, HR = 2.12, and myoclonus, HR = 2.33), and diabetes (HR = 1.13). A modest reduction in the risk of TD was associated with the use of second-generation antipsychotics (HR = 0.85) versus first-generation drugs.
Conclusions: This study identified factors associated with development of TD among patients taking antipsychotics. The prediction model described herein can enable physicians to better monitor patients at high risk for TD and recommend appropriate treatment plans to help maintain quality of life.
Keywords: Least absolute shrinkage and selection operator; Prediction model; Psychiatric patients, antipsychotics; Risk factors; Tardive dyskinesia.