Study objective and data sources

A retrospective cohort study was conducted to identify risk factors and develop a model to predict the incidence of TD among psychiatric patients taking antipsychotic medication. Medicaid claims data from a database that represented a sample of the total Medicaid beneficiaries in the US from six states (Iowa, Kansas, Missouri, New Jersey, Mississippi and Wisconsin) were extracted. The claims data included services provided (for most states) from 1997 through the first quarter of 2016. Complete medical claims (e.g. procedures, paid amounts and diagnoses), pharmaceutical claims, enrollment history, and patient demographics were available for analysis from the Medicaid records. The most recent 6 years of data (varies by state) were used for this analysis.

Patient selection

Patients with schizophrenia, major depressive disorder, or bipolar disorder, who were taking antipsychotic medications and who also satisfied the following eligibility criteria were selected from Medicaid claims database (the most recent 6 years of data of each state): at least two diagnoses for schizophrenia (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] codes: 295.xx; International Classification of Diseases, Tenth Revision, Clinical Modification [ICD-10-CM] codes: F20.x), major depressive disorder (296.2, 296.3; F32.xx, F33.xx), or bipolar disorder (296.0, 296.1, 296.4–296.8; F31.x); at least one oral antipsychotic fill (see Additional file 1 ICD-9-CM and ICD-10-CM Codes for Selected Comorbidities and GPIs for Antipsychotics) after first observed diagnosis for the underlying disorder and before any observed TD diagnosis (333.81, 333.82, 333.85; G24.01, G24.4, G24.5); ≥18 years of age at index date (date of the first observed antipsychotic fill); a baseline period of continuous eligibility for ≥6 months before the index date; and cases with daily doses that are not missing nor daily dose outliers (i.e. daily dose > 1,200 mg/day chlorpromazine equivalent [24,25,26]). Patients from New Jersey who turned 65 after 2012 were dual-eligible for Medicare and Medicaid and thus excluded from the study to eliminate the possibility of incomplete capture of their drug claim information. The study period was defined from the index date to the end of eligibility or end of data. There was no minimum time requirement for post-index eligibility.

Patients characteristics and study variables

The following patient information was collected: demographics (age, gender, state, and health plan); disease duration (from first observed diagnosis of schizophrenia, or depression, or bipolar disorder to index date); index antipsychotic treatment by class (i.e. the treatment the patients were treated with on the index date, which can be a first-generation antipsychotic, a second-generation antipsychotic, both or none); comorbidity profile, including psychiatric comorbidities, Charlson Comorbidity Index (CCI) score (a method of categorizing comorbidities based on ICD codes, where each comorbidity category has a weight associated to its risk of mortality or resource use, and the sum of the weights results in a single score) [27, 28], brain damage, diabetes, dementia, parkinsonism, and other selected comorbidities; EPS other than TD, e.g. akathisia, parkinsonism, dystonia, and tremors; cognitive disabilities such as Down’s syndrome, autism, dyslexia and other scholastic disorders; traumatic brain injury; smoking history and alcohol abuse; diabetes; and duration of follow-up (see Additional file 1 ICD-9-CM and ICD-10 CM Codes for Selected Comorbidities and GPIs for Antipsychotics). The main outcome was time to TD diagnosis after index date.

Risk factor identification

Patients with at least 1 year of continuous eligibility after their index date were divided into two cohorts: those who developed TD within 1 year, and those who did not develop TD within 1 year. Patient characteristics were then compared between the two cohorts to identify potential risk factors for TD. Means and standard deviations were summarized for continuous variables, whereas frequencies and percentages were summarized for categorical variables. Statistical comparisons were conducted using Wilcoxon rank-sum tests for continuous variables, McNemar’s test for dichotomous variables, and chi-squared tests for categorical variables. For mutually exclusive categorical variables with more than two categories, the statistical comparisons were conducted using Bowker’s test for symmetry.

Univariate Cox regression models were also used to assess the association of each patient baseline characteristic with the risk of TD diagnosis among all selected patients. Time to event was estimated as the period from index date to the first TD claim. Patients without the event of interest during the study period were censored at the end of their follow-up period.

Development and validation of predictive model

Data were separated randomly into a modeling set (two-thirds of the data), used to develop and parametrize the prediction model, and a validation set (one-third of the data), used to test out-of-sample performance of the prediction model.

A multivariable Cox proportional hazard model was developed using the modeling set to predict the time to TD diagnosis in patients taking antipsychotics at a given time point after the index date. The variables in the model included the aforementioned patient characteristics as potential predictors based on the univariate Cox models and “TD” versus “no TD” cohort comparisons. Based on the non-linear empirical relationships between the probability of TD diagnosis with age and dose, predictors used also included transformed dose and age variables. Covariates in the model (before selection) were: age at index date; sex; index diagnosis; type of index antipsychotic; history and number of EPS; dose, transformed dose (as a continuous effect for doses up to 100 mg/day of chlorpromazine equivalents, and as a continuous effect for doses larger than 100 mg/day of chlorpromazine equivalents); CCI; comorbid movement disorders, including parkinsonism, akathisia, bradykinesia, tremors, and myoclonus; comorbid psychiatric disorders, including anxiety disorders, depressive disorders, bipolar and related disorders; and other factors, including brain damage, dementia, diabetes, and alcohol history. Interactions between underlying type of mental disorder and treatment patterns, or between sex and age were also included in the model. The least absolute shrinkage and selection operator (LASSO) regression method was used to simultaneously estimate the model and identify the patient characteristics that better predicted TD. The model was selected to minimize a cross-validated prediction error, which helped to avoid overfitting and to enhance the interpretability of the model. A Cox regression was then performed with only the selected covariates from the LASSO regression to obtain HR estimates and the corresponding P value associated with each of the model variables. Risk factors for TD were then characterized based on effect size and significance.

Predictive performance was assessed in the validation set by: 1) model discrimination or concordance, which is the ability of the model to distinguish between low and high-risk patients, quantified by the C statistics (C = 0.5 is random prediction, and C = 1 is perfect prediction); and 2) model calibration, which determines the agreement between the observed and predicted risk of TD at any given time after the index date, quantified by the Hosmer–Lemeshow goodness-of-fit test (P > 0.05 suggests a good fit to the data, i.e. good calibration). The Breslow estimator of the baseline hazard was combined with the HRs to obtain predicted risks of TD for each patient at 2 years after the index date.