Risk assessment and prediction of TD incidence in psychiatric patients taking concomitant antipsychotics: a retrospective data analysis

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.

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 [24‐26]). 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.

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.

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.

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 total of 189,415 patients met the inclusion criteria in the Medicaid claims database used (see Additional file 2 Sample Selection Flow Chart). Patient characteristics and treatment history are summarized for all patients and by initial psychiatric diagnosis in Table 1. Briefly, the mean age of all patients was 42.8 years (bipolar, 39.6 years; depressive disorder, 43.9 years; schizophrenia, 45.4 years); 38.1% of all patients were men (bipolar, 32.6%; depressive disorder, 28.7%; schizophrenia, 56.9%); the overall average daily dose of antipsychotic medication was 220 mg chlorpromazine equivalent (bipolar, 211 mg; depressive disorder, 157 mg; schizophrenia, 309 mg). The vast majority of all patients (86.7%) were prescribed a second-generation antipsychotic at index date (bipolar, 91.3%; depressive disorder, 88.8%; schizophrenia, 81.4%). The comorbidity profiles were different among the three diagnostic groups; patients with schizophrenia showed the lowest CCI scores, as well as lower rates of substance-related and addictive disorders, anxiety disorders, personality disorders, trauma- and stressor-related disorders, brain damage, and smoking history.

A sample of 151,280 patients with at least 1 year of continuous eligibility after the index date was used to identify potential risk factors of TD. A total of 381 patients developed TD within 1 year and were classified as ‘TD,’ and the remaining 150,899 patients who did not develop TD within 1 year were labeled as ‘No TD.’ Age, diagnosis of schizophrenia, use of first-generation antipsychotics, antipsychotic dose, CCI, diabetes, and incidence of EPS-related comorbidities were significantly higher at baseline in the ‘TD’ cohort than in the ‘No TD’ cohort. The characteristics that were significantly different between the two cohorts are shown in Table 2.

Univariate Cox analysis was conducted in the full sample of 189,415 patients to identify potential risk factors for TD in psychiatric patients taking concurrent antipsychotic medication (Table 3). The results suggest associative relationships between TD onset and mostly the same baseline risk factors identified by the cohort analysis described above (Table 2). According to the univariate Cox model, a significant increase in risk of TD was found to be associated with diagnosis of schizophrenia (HR = 1.96 compared with bipolar), antipsychotic dose (up to 100 mg/day of chlorpromazine, HR = 1.91), dementia (HR = 2.04), EPS-related comorbidities (number of EPS, HR = 1.91; history of EPS, HR = 2.37) and diabetes (HR = 1.52). A small but significant association was determined for CCI (HR = 1.06) and age (HR = 1.04). Compared with first-generation antipsychotics, use of second-generation antipsychotics was associated with a lower risk of TD (HR = 0.72), and so was use of multiple-generation antipsychotics (HR = 0.88). Furthermore, depressive (HR = 0.78) and bipolar-related disorders (HR = 0.84) were associated with a significant decrease in the risk of TD. Finally, other movement disorders (parkinsonism, HR = 4.29; myoclonus, HR = 4.27; tremors, HR = 3.93; and bradykinesia, HR = 2.48) were associated with significantly higher risk of TD (Table 3).

Kaplan–Meier (KM) curves of time to TD diagnosis stratified by various risk factors were also generated. Consistent with the univariate Cox results, the time to TD diagnosis was shorter in patients with schizophrenia than in those with bipolar or depressive disorder (Fig. 1). In addition, the time to TD diagnosis was shorter in patients with a history of EPS than in those without, and longer in patients taking second-generation antipsychotics than in those taking first-generation or multiple first- and second-generation antipsychotics (data not shown).

A multivariate Cox prediction model was estimated using the predictors selected by the LASSO. The resulting prediction model (“re-estimated LASSO model”) had a clinically meaningful concordance of 70.6% and was well calibrated (P = 0.32 for Hosmer–Lemeshow goodness-of-fit test) (Fig. 2). The multivariate model selected and estimated by the LASSO had similar predictive performance (concordance = 70.5%, P = 0.46 for Hosmer–Lemeshow goodness-of-fit test) and covariate estimates. In the re-estimated LASSO model, age (HR = 1.04, P < 0.001), diagnosis of schizophrenia (HR = 1.99, P < 0.001, compared with bipolar), dosage of antipsychotic medication (up to 100 mg/day of chlorpromazine equivalent, HR = 1.65, P < 0.01), and presence of bipolar and related disorders (HR = 1.39, P < 0.01) were significantly associated with an increased risk of TD. Other potential predictors of TD diagnosis included history of EPS, movement disorders (parkinsonism, bradykinesia, tremors, and myoclonus), and diabetes (Table 3). The use of second-generation antipsychotic medication was associated with a modest reduction in risk of TD (HR = 0.85; Table 3).

Although the study yielded a well-calibrated prediction model with a clinically meaningful concordance of 71%, it was subject to limitations that are inherent to using a claims database. The study population was limited to patients within the Medicaid database and represented only six US states, and therefore its findings may not be generalizable to other patient populations. TD was relatively rare in this study population (the KM-estimated proportion of patients with TD at 7 years after antipsychotic drug initiation was less than 2%), which is partly due to a relatively short follow-up period for the condition under study. As a result, the prediction performance of the model, in terms of its discrimination power (concordance), was acceptable rather than excellent. This issue was mitigated by using the LASSO methodology, which can provide better predictions than standard regression by avoiding overfitting in data sets with few events. In addition, comorbidities may have been underestimated because they were identified using diagnosis codes, which are typically used for administrative purposes. Although the data set used in this analysis provides a large and representative real-world evidence of patients in the US, it spans a limited follow-up time (up to 7 years), which is an important limitation given that the development of TD is associated with long-term use of antipsychotics. Thus, the rate of TD claims in these data was low compared with the prevalence of TD, which is 20–50% among all patients treated with antipsychotics [9]. Another likely limitation of the study is that, due to its observational design, results may have been confounded due to unobserved factors that cannot be accounted for in multivariable regression analyses. For example, this study did not examine the role of race/ethnicity in the risk of TD, which was previously identified as a potential risk factor [13]. Also, given the large number of antipsychotic medications and the relatively low number of TD events observed in these data, the risk of TD was analyzed by class of antipsychotics and not by each antipsychotic separately. Thus, the risk of TD associated with each specific antipsychotic was not ascertained in this analysis.

The paucity of claims for TD in the database, in comparison with the reported prevalence for motor disorder of up to 40% reported previously [4], may affect the prognostic implications of the findings reported herein. One possibility is that the constraints of a retrospective study design may lead to underestimation of TD prevalence [33]. However, the discrepancy between observed and anticipated TD prevalence rates in the study may underscore a more-systemic problem regarding the epidemiology of TD, namely the potential underreporting due to a lack of clinical awareness or standardization of diagnostic criteria [34].