62 Predictors of Tardive Dyskinesia in Psychiatric Patients Taking Concomitant Antipsychotics. (12th March 2019)

• Record Type: Journal Article
• Authors: Patterson-Lomba, Oscar; Ayyagari, Rajeev; Carroll, Benjamin
• Is Part Of: CNS spectrums. Volume 24:Number 1(2019:Feb)
• Page Start: 207
• Page End: 208
• Publication Date: 2019-03-12
• Subjects: Neuropsychiatry -- Periodicals; Nervous system -- Diseases -- Periodicals; Neurology -- Periodicals; 616.8005
• DOI: 10.1017/S1092852919000488
• Languages: English
• ISSNs: 1092-8529

Abstract

Abstract: Background: Tardive dyskinesia (TD) is typically caused by exposure to antipsychotics, is often irreversible, and can be debilitating. TD symptoms can increase the social stigma of patients with comorbid psychiatric disorders, negatively impact quality of life, and potentially increase medical morbidity and mortality. An increased risk of developing TD has been associated with factors such as older age, female sex, underlying mental illness, and long-term use and higher doses of antipsychotics. The association of TD with the use of typical versus atypical antipsychotics has also been evaluated, with mixed results. To date, predictive models assessing the joint effect of clinical characteristics on TD risk have not been developed and validated in the US population. Study Objective: To develop a prediction model to identify patient and treatment characteristics associated with the occurrence of TD among patients with psychiatric disorders taking antipsychotic medications, using a retrospective database analysis. Methods: Adult patients with schizophrenia, major depressive disorder, or bipolar disorder who were taking oral antipsychotics, and who had 6months of data prior to the index date were identified from Medicaid claims from six US states. The index date was defined as the date of the first claim for an antipsychotic drug after a claim for the underlying disorder but before TD diagnosis. A multivariate Cox prediction model was developed using a cross-validated version of the least absolute shrinkage and selection operator (LASSO) regression method to improve prediction accuracy and interpretability of the model. The predictive performance was assessed in a separate validation set via model discrimination (concordance) and calibration. Results: A total of 189, 415 patients were identified: 66, 723 with bipolar disorder, 68, 573 with depressive disorder, and 54, 119 with schizophrenia. The selected prediction model had a clinically meaningful concordance of 70% and was well calibrated (P=0.46 for Hosmer–Leme show goodness-of-fit test). Patient's age at index date (hazard ratio [HR]: 1.03), diagnosis of schizophrenia (HR: 1.73), dosage of antipsychotic at index date (up to 100mg/day chlorpromazine equivalent; HR: 1.40), and presence of bipolar and related disorders (HR: 1.16) were significantly associated with an increased risk of TD diagnosis. Use of atypical antipsychotics at index date was associated with a modest reduction in the risk of TD (HR=0.94). Conclusions: This study identified a group of factors associated with the development of TD among patients with psychiatric disorders treated with antipsychotics. This may allow physicians to better monitor their patients receiving antipsychotics, allowing for the prompt identification and treatment of TD to help maintain quality of life. Presented at: American Psychiatric Association Annual Meeting; May 5–9, 2018, New York, New York, USA Funding Acknowledgements: This study was supported by Teva Pharmaceuticals, Petach Tikva, Israel.