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R E S E A R C H A R T I C L E Open Access
Risk assessment and prediction of TD
incidence in psychiatric patients taking
concomitant antipsychotics: a retrospective
data analysis
Oscar Patterson-Lomba
1
, Rajeev Ayyagari
1
and Benjamin Carroll
2*
Abstract
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: Tardive dyskinesia, Risk factors, Psychiatric patients, antipsychotics, Prediction model, Least absolute
shrinkage and selection operator
© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
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(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
* Correspondence: Benjamin.Carroll02@tevapharm.com
2
Teva Pharmaceuticals, 41 Moores Rd, Malvern, PA 19355, USA
Full list of author information is available at the end of the article
Patterson-Lomba et al. BMC Neurology (2019) 19:174
https://doi.org/10.1186/s12883-019-1385-4
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Background
Tardive dyskinesia (TD) is a hyperkinetic, potentially irre-
versible movement disorder that is typically caused by
prolonged exposure to antipsychotic drugs [1–6]. The
clinical manifestations include abnormal movements of
the face, lips, tongue, cheeks, jaws and extremities, and se-
verity of symptoms can range from mild to disabling and
potentially life-threatening [1,2,6–9]. Besides the physical
discomfort experienced due to the disease, the involun-
tary, repetitive and pronounced nature of TD symptoms
can exacerbate the stigmatization often already faced by
patients with mental illness, leading to social alienation,
behavioral disturbances and nonadherence [2,3,5,10].
Motor side effects have been reported in as many as
40% of patients receiving antipsychotics; thus, elucida-
tion of both modifiable and nonmodifiable risk factors
for TD susceptibility remains a research priority [4,11].
An increased risk for developing TD has been associated
with older age, female sex, underlying mental disorders,
history of extrapyramidal symptoms (EPS), diabetes, and
higher antipsychotic dose and longer duration of expos-
ure to antipsychotics [7,12–15]. Several studies also sug-
gest a higher risk for TD incidence with the use of first-
generation (typical) compared with second-generation
(atypical) antipsychotic drugs [4,7,16]. A 2008 review
reported annual TD incidence rates in adults of 3.0%
with second-generation antipsychotics versus 7.7% with
first-generation antipsychotics [17]. In contrast, results
from various groups show similar frequencies of TD
occurrence regardless of the class of antipsychotic
drug treatment, and that movement disorders associ-
ated with newer antipsychotic drugs are not clinically
negligible when taking into consideration methodo-
logical differences (e.g. study population, clinical set-
ting and differential diagnosis) that can potentially
lead to the underestimation of incidence in patients
treated with second-generation antipsychotics [18–20].
Therefore, no consensus currently exists on the exact
epidemiology of TD [17,19,20].
Prevention and treatment of TD continue to pose sig-
nificant challenges to clinicians [2,20]. First, detection
of TD onset can be delayed due to inconsistent clinical
presentations, significant variability in developmental
timelines, masking of symptoms by the very drugs that
cause TD, and misclassification of motor symptoms as
medication-induced side effects [1,3,5,9,19,21]. Even
after a definitive diagnosis is made, the lowering or ces-
sation of treatment with causative drugs may be contra-
indicated due to aggravation of psychosis and other
symptoms of underlying comorbidities in TD patients
[2,6,19]. Furthermore, considering that TD is some-
times irreversible, early diagnosis or withdrawal from
antipsychotic therapy may confer only partially ameliora-
tive benefits [3,21].
Although novel drugs for treatment have been recently
approved for TD [22], the best management strategy
should include better monitoring and implementation of
risk-stratified prophylactic measures, such as the modifi-
cation of treatment plans for patients at risk of develop-
ing TD [2,4,23]. Further investigation for potential risk
factors to identify “true predictors”of disease is war-
ranted to accurately identify high-risk populations [3,
11]. In the current study, we developed and validated a
predictive model assessing the combined effect of clin-
ical characteristics on TD risk, which, to our knowledge,
is the first of its kind for US populations. The resulting
prediction model has the potential to guide decision-
making regarding treatment and follow-up management.
Methods
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 diag-
noses), 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 medi-
cations 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 Classifi-
cation 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 1ICD-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 base-
line 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
Patterson-Lomba et al. BMC Neurology (2019) 19:174 Page 2 of 10
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mg/day chlorpromazine equivalent [24–26]). Patients
from New Jersey who turned 65 after 2012 were dual-eli-
gible for Medicare and Medicaid and thus excluded from
the study to eliminate the possibility of incomplete cap-
ture 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 require-
ment for post-index eligibility.
Patients characteristics and study variables
The following patient information was collected: demo-
graphics (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, includ-
ing psychiatric comorbidities, Charlson Comorbidity
Index (CCI) score (a method of categorizing comorbidi-
ties based on ICD codes, where each comorbidity cat-
egory has a weight associated to its risk of mortality or
resource use, and the sum of the weights results in a sin-
gle 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 syn-
drome, autism, dyslexia and other scholastic disorders;
traumatic brain injury; smoking history and alcohol
abuse; diabetes; and duration of follow-up (see Add-
itional file 1ICD-9-CM and ICD-10 CM Codes for Se-
lected 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 po-
tential risk factors for TD. Means and standard devia-
tions were summarized for continuous variables,
whereas frequencies and percentages were summarized
for categorical variables. Statistical comparisons were
conducted using Wilcoxon rank-sum tests for continu-
ous variables, McNemar’s test for dichotomous variables,
and chi-squared tests for categorical variables. For mutu-
ally 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 character-
istic 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 pre-
diction 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 characteris-
tics 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, pre-
dictors used also included transformed dose and age var-
iables. Covariates in the model (before selection) were:
age at index date; sex; index diagnosis; type of index
antipsychotic; history and number of EPS; dose, trans-
formed dose (as a continuous effect for doses up to 100
mg/day of chlorpromazine equivalents, and as a continu-
ous effect for doses larger than 100 mg/day of chlorpro-
mazine equivalents); CCI; comorbid movement
disorders, including parkinsonism, akathisia, bradykine-
sia, tremors, and myoclonus; comorbid psychiatric disor-
ders, including anxiety disorders, depressive disorders,
bipolar and related disorders; and other factors, includ-
ing brain damage, dementia, diabetes, and alcohol his-
tory. 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 pre-
dicted TD. The model was selected to minimize a cross-
validated prediction error, which helped to avoid overfit-
ting and to enhance the interpretability of the model. A
Cox regression was then performed with only the se-
lected covariates from the LASSO regression to obtain
HR estimates and the corresponding Pvalue 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
Patterson-Lomba et al. BMC Neurology (2019) 19:174 Page 3 of 10
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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.
Results
Baseline characteristics by diagnosis
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 sub-
stance-related and addictive disorders, anxiety disorders,
personality disorders, trauma- and stressor-related disor-
ders, brain damage, and smoking history.
Comparison of baseline characteristics by TD cohort
A sample of 151,280 patients with at least 1 year of con-
tinuous eligibility after the index date was used to iden-
tify 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 base-
line 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.
Identification of TD predictors using univariate Cox
analyses
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 anti-
psychotic medication (Table 3). The results suggest asso-
ciative relationships between TD onset and mostly the
same baseline risk factors identified by the cohort
analysis described above (Table 2). According to the uni-
variate Cox model, a significant increase in risk of TD
was found to be associated with diagnosis of schizophre-
nia (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 (num-
ber 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 mul-
tiple-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 pa-
tients with a history of EPS than in those without, and
longer in patients taking second-generation antipsy-
chotics than in those taking first-generation or multiple
first- and second-generation antipsychotics (data not
shown).
TD prediction models
A multivariate Cox prediction model was estimated
using the predictors selected by the LASSO. The result-
ing prediction model (“re-estimated LASSO model”) had
a clinically meaningful concordance of 70.6% and was
well calibrated (P= 0.32 for Hosmer–Lemeshow good-
ness-of-fit test) (Fig. 2). The multivariate model selected
and estimated by the LASSO had similar predictive per-
formance (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 medi-
cation (up to 100 mg/day of chlorpromazine equiva-
lent, HR = 1.65, P< 0.01), and presence of bipolar and
related disorders (HR = 1.39, P< 0.01) were signifi-
cantly associated with an increased risk of TD. Other
potential predictors of TD diagnosis included history
of EPS, movement disorders (parkinsonism, bradyki-
nesia, tremors, and myoclonus), and diabetes (Table 3).
The use of second-generation antipsychotic medica-
tion was associated with a modest reduction in risk
of TD (HR = 0.85; Table 3).
Patterson-Lomba et al. BMC Neurology (2019) 19:174 Page 4 of 10
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Table 1 Patient demographics and baseline characteristics by diagnosis
Patient characteristics Total
N= 189,415
Bipolar disorder
N= 66,723
Depressive disorder
N= 68,573
Schizophrenia
N= 54,119
Demographics
Age (years) 42.8 + 13.8 39.6 + 13.0 43.9 + 13.8 45.4 ± 13.9
Male, % (n) 38.1% (72,187) 32.6% (21,749) 28.7% (19,662) 56.9% (30,776)
HMO Plan, % (n) 20.3% (38,494) 21.2% (14,133) 20.8% (14,227) 18.7% (10,134)
State
Iowa 8.1% (15,390) 10.8% (7,226) 7.8% (5,366) 5.2% (2,798)
Kansas 8.3% (15,698) 8.8% (5,851) 8.0% (5,469) 8.1% (4,378)
Mississippi 8.9% (16,756) 7.1% (4,741) 9.5% (6,539) 10.1% (5,476)
Missouri 38.7% (73,293) 38.9% (25,924) 43.3% (29,694) 32.7% (17,675)
New Jersey 21.5% (40,667) 19.1% (12,737) 18.5% (12,659) 28.2% (15,271)
Wisconsin 14.6% (27,611) 15.4% (10,244) 12.9% (8,846) 15.7% (8,521)
Observed disease duration (months) 7.8 ± 13.0 7.6 ± 12.8 9.2 ± 13.6 6.2 ± 12.2
Duration of follow-up (months) 38.6 ± 24.6 37.4 ± 23.8 32.6 ± 23.5 47.8 ± 24.2
Index AP use
First generation 10.4% (19,673) 7.4% (4,958) 10.6% (7,239) 13.8% (7,476)
Multiple 2.0% (3,849) 1.2% (818) 0.7% (448) 4.8% (2,583)
Second generation 87.6% (165,893) 91.3% (60,947) 88.8% (60,886) 81.4% (44,060)
Chlorpromazine equivalent daily dose (100 mg/day) 2.2 ± 2.1 2.1 ± 1.9 1.6 ± 1.6 3.1 ± 2.4
Psychiatric comorbidities
Substance-related and addictive disorders 23.8% (45,014) 27.1% (18,090) 26.2% (17,944) 16.6% (8,980)
Anxiety disorders 21.9% (41,438) 23.0% (15,322) 31.5% (21,603) 8.3% (4,513)
Autism 0.6% (1,149) 0.9% (614) 0.4% (258) 0.5% (277)
Bipolar and related disorders 32.6% (61,715) 77.9% (51,959) 9.8% (6,743) 5.6% (3,013)
Depressive disorders 45.2% (85,537) 24.3% (16,195) 90.2% (61,862) 13.8% (7,480)
Personality disorders 4.0% (7,526) 4.6% (3,045) 4.6% (3,168) 2.4% (1,313)
Schizophrenia spectrum disorders (excluding schizophrenia 8.2% (15,550) 6.1% (4,094) 6.6% (4,511) 12.8% (6,945)
Sleep-wake disorders 8.5% (16,024) 8.8% (5,838) 11.4% (7,845) 4.3% (2,341)
Trauma- and stress- or related disorders 10.0% (19,004) 10.8% (7,178) 14.4% (9,883) 3.6% (1,943)
Other comorbidities
CCI 0.6 ± 1.2 0.5 ± 1.1 0.7 ± 1.4 0.4 ± 1.0
Alcohol history 7.7% (14,592) 8.2% (5,461) 8.7% (5,958) 5.9% (3,173)
Brain damage 1.0% (1,790) 0.8% (563) 1.3% (885) 0.6% (342)
Dementia 1.6% (3,080) 0.9% (624) 2.0% (1,392) 2.0% (1,064)
Diabetes 14.0% (26,600) 11.6% (7,767) 16.4% (11,215) 14.1% (7,618)
Down’s Syndrome 0.1% (200) 0.1% (67) 0.1% (84) 0.1% (49)
Dyslexia and other scholastic disorders 0.1% (254) 0.1% (68) 0.2% (123) 0.1% (63)
Smoking history 13.7% (25,927) 16.2% (10,793) 15.1% (10,337) 8.9% (4,797)
Traumatic brain injury 0.4% (673) 0.3% (207) 0.5% (317) 0.3% (149)
Extrapyramidal symptoms
Akathisia 0.1% (248) 0.1% (94) 0.1% (90) 0.1% (64)
Bradykinesia 3.3% (6,251) 3.0% (1,973) 4.2% (2,908) 2.5% (1,370)
Dystonia 0.1% (167) 0.1% (60) 0.1% (34) 0.1% (73)
EPS (unspecified) 0.8% (1,430) 0.8% (559) 0.9% (636) 0.4% (235)
Patterson-Lomba et al. BMC Neurology (2019) 19:174 Page 5 of 10
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Discussion
The variability observed in the onset, developmental pat-
tern, and response to interventional treatment make TD
a difficult condition to diagnose and to treat [3,5,9]. Be-
cause TD is sometimes irreversible, early detection and
prevention of TD in patients with high-risk status is an
important strategy for the clinical management of TD
[3,11,21].
Despite recent advances, identification of TD predic-
tors remains challenging for researchers and clinicians
[1]. There are common methodological confounding fac-
tors and considerable study limitations, including that
TD can mimic signs of the underlying comorbidity or
that it can be masked by antipsychotics [1,3,4,19,21].
In the current study, the use of large claims data pro-
vided real-world evidence for the incidence of TD due
to antipsychotic medication use among patients with
schizophrenia, major depressive disorder, or bipolar dis-
order. Furthermore, the analytical approach was de-
signed to help identify risk factors for TD by examining
their associations with TD diagnosis both in isolation
and in combination with a large set of factors via multi-
variate modeling, which, to our knowledge, had not pre-
viously been developed or validated in US populations.
Consistent with prior studies [7,12–15], of the baseline
and index-date characteristics under consideration,
Table 1 Patient demographics and baseline characteristics by diagnosis (Continued)
Patient characteristics Total
N= 189,415
Bipolar disorder
N= 66,723
Depressive disorder
N= 68,573
Schizophrenia
N= 54,119
Myoclonus 0.1% (120) 0.1% (37) 0.1% (64) 0.04% (19)
Malignant neuroleptic syndrome 0.02% (46) 0.02% (13) 0.01% (4) 0.1% (29)
Parkinsonism 0.1% (141) 0.1% (34) 0.1% (31) 0.1% (76)
Drug-induced tics 0.0% (7) 0.0% (3) 0.01% (4) 0.0% (0)
Tremors 0.3% (490) 0.3% (183) 0.3% (222) 0.2% (85)
History of EPS 4.3% (8,063) 4.0% (2,663) 5.4% (3,668) 3.2% (1,732)
Number of EPS 0.1 ± 0.2 0.04 ± 0.23 0.1 ± 0.3 0.04 ± 0.21
AP antipsychotic, CCI Charlson Comorbidity Index, EPS extrapyramidal symptoms, HMO health maintenance organization, TD tardive dyskinesia
Table 2 Patient demographics and baseline characteristics by TD cohort
Patient characteristics TD
N= 381
No TD
N= 150,899
Pvalue
Age (years) 51.4 ± 13.2 43.3 ± 13.6 < 0.001
Index Diagnosis, %(n)
Bipolar disorder 24.2% (92) 35.2% (53,169)
Depressive disorder 21.8% (83) 33.3% (50,213) < 0.001
Schizophrenia 54.1% (206) 31.5% (47,517)
Generation of index AP
First generation 16.8% (64) 10.5% (15,850)
Multiple 3.4% (13) 2.3% (3,424) < 0.001
Second generation 79.8% (304) 87.2% (131,625)
Chlorpromazine equivalent daily dose (100 mg/day) 2.78 ± 2.29 2.29 ± 2.11 < 0.001
CCI 0.64 ± 1.18 0.53 ± 1.16 0.05
Diabetes 23.9% (91) 14.4% (21,674) < 0.001
Bipolar and related disorders 26.0% (99) 31.9% (48,075) < 0.05
Depressive disorders 32.6% (124) 42.6% (64,301) < 0.001
Bradykinesia 9.7% (37) 3.3% (4,917) < 0.001
Dystonia 0.8% (3) 0.1% (131) < 0.001
Myoclonus 1.1% (4) 0.1% (91) < 0.001
Parkinsonism 0.8% (3) 0.1% (119) < 0.001
History of EPS 12.3% (47) 4.2% (6,388) < 0.001
Number of EPS 0.14 ± 0.41 0.05 ± 0.23 < 0.001
AP antipsychotic, CCI Charlson Comorbidity Index, EPS extrapyramidal symptoms, TD tardive dyskinesia
Patterson-Lomba et al. BMC Neurology (2019) 19:174 Page 6 of 10
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Table 3 Hazard ratio for risk factors using variables selected by the LASSO method
Patient characteristics Univariate Cox analyses Variables selected by the LASSO method
Hazard Ratio 95% CI Pvalue Hazard ratio 95% CI Pvalue
Age (years) 1.04 (1.03, 1.04) < 0.001 1.04 (1.03, 1.04) < 0.001
Square root of age 1.72 (1.60, 1.84) < 0.001
Age less than 65 indicator 0.33 (0.27, 0.41) < 0.001
Index Diagnosis vs. Bipolar Disorder
Depressive Disorder 0.95 (0.78, 1.14) 0.57 1.02 (0.79, 1.32) 0.88
Schizophrenia 1.96 (1.67, 2.29) < 0.001 1.99 (1.57, 2.53) < 0.001
Generation of Index AP vs. First generation
Multiple 0.88 (0.58, 1.33) 0.54 0.80 (0.52, 1.21) 0.29
Second generation 0.72 (0.59, 0.87) < 0.001 0.85 (0.70, 1.03) 0.09
Dose (continuous effect for dose≤100 mg/day of chlorpromazine) 1.91 (1.38, 2.66) < 0.001 1.65 (1.17, 2.31) < 0.01
Dose (continuous effect for dose> 100 mg/day of chlorpromazine) 1.05 (1.02, 1.08) < 0.01
CCI 1.06 (1.00, 1.12) < 0.05
Depressive disorders 0.78 (0.68, 0.89) < 0.001
Bipolar and related disorders 0.84 (0.72, 0.97) < 0.05 1.39 (1.11, 1.75) < 0.01
Dementia 2.04 (1.38, 3.01) < 0.001
Diabetes 1.52 (1.29, 1.79) < 0.001 1.13 (0.96, 1.34) 0.14
Number of EPS 1.91 (1.60, 2.29) < 0.001
History of EPS 2.37 (1.88, 2.99) < 0.001 1.35 (0.74, 2.47) 0.33
Parkinsonism 4.29 (1.38, 13.33) < 0.05 1.43 (0.44, 4.72) 0.55
Bradykinesia 2.48 (1.92, 3.21) < 0.001 1.44 (0.77, 2.68) 0.25
Tremors 3.93 (1.96, 7.89) < 0.001 2.12 (0.97, 4.60) 0.06
Myoclonus 4.27 (1.07, 17.11) < 0.05 2.33 (0.56, 9.7) 0.25
AP antipsychotic, CCI Charlson Comorbidity Index, CI confidence interval, EPS extrapyramidal symptoms, LASSO least absolute shrinkage and selection operator
Fig. 1 Kaplan-Meier curves of time to TD diagnosis. Estimated TD incidence rate within 7 years after antipsychotic drug initiation were stratified
by index psychiatric disorder diagnosis. TD, tardive dyskinesia
Patterson-Lomba et al. BMC Neurology (2019) 19:174 Page 7 of 10
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patient age, diagnosis of schizophrenia, dosage of anti-
psychotic medication (up to 100 mg/day of chlorpro-
mazine equivalent), and presence of bipolar and
related disorders were associated with greater risk of
TD in patients taking antipsychotics. Interestingly, the
presence of bipolar and related disorders was found
to be associated with a significant decrease in the risk
of TD in the univariate analyses, but this association
was reversed in the multivariate model, indicating the
importance of examining these associations while ac-
counting for other factors. Also, female sex, a variable
previously observed to be associated with an in-
creased risk of TD [11,29], was not among the best
predictors of TD in this study.
Although the relationship we found between predic-
tors included in this study and TD diagnosis was asso-
ciative rather than causal in nature, these observations
are clinically relevant findings that can aid in risk-miti-
gation planning and implementation. The resulting pre-
diction model can provide the risk or probability that
TD will occur within any time period after the index
date (e.g. 1 or 2 years) for each patient based on their
baseline or index-date prognostic factors, which can
guide decision-making regarding treatment and follow-
up management from the time of the diagnosis of the
psychiatric disorder.
There has been considerable debate regarding the attri-
tion in TD incidence since widespread adoption of second-
generation antipsychotics. One study previously reported a
point prevalence of 13% with second-generation antipsy-
chotics versus 32% with first-generation, whereas other
studies have reported no differences [19,30–32]. In
addition, multiple studies have challenged the notion that
second-generation antipsychotics are relatively free of the
risk of TD [19,20]. The current study utilized univariate
and multivariate Cox models to re-assess the comparative
risk of TD associated with both drug classes. Compared
with first-generation antipsychotics, the use of second-gen-
eration antipsychotics was associated with a statistically sig-
nificant reduction in the risk of TD when analyzed using a
univariate Cox model. However, this reduction was more
modest and no longer significant in the final LASSO pre-
diction model.
Limitations
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 pa-
tients within the Medicaid database and represented
only six US states, and therefore its findings may not be
generalizable to other patient populations. TD was rela-
tively rare in this study population (the KM-estimated
proportion of patients with TD at 7 years after anti-
psychotic 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 per-
formance of the model, in terms of its discrimination
power (concordance), was acceptable rather than excel-
lent. 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 administra-
tive 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 anti-
psychotics [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
Fig. 2 Calibration plot for the re-estimated LASSO prediction model.
A least absolute shrinkage and selection operator (LASSO) prediction
model was used to identify risk factors for TD. The model was
developed with data in the modeling set and validated and
re-estimated with the validation data set. The risk of TD at 2 years
after the index date as predicted by the model was compared with
actual TD observed, within the validation set (one-third of the data
set). Concordance was 70.6%, Hosmer–Lemeshow goodness-of-fit
test, P= 0.32. TD, tardive dyskinesia
Patterson-Lomba et al. BMC Neurology (2019) 19:174 Page 8 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
large number of antipsychotic medications and the rela-
tively 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 com-
parison 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 preva-
lence [33]. However, the discrepancy between observed
and anticipated TD prevalence rates in the study may
underscore a more-systemic problem regarding the epi-
demiology of TD, namely the potential underreporting
due to a lack of clinical awareness or standardization of
diagnostic criteria [34].
Conclusions
This study identified a group of factors associated with
the development of TD among patients who had psychi-
atric disorders treated with antipsychotics. The predic-
tion model developed and validated herein can help
physicians identify patients at high risk for TD in order
to develop treatment and monitoring plans that help pa-
tients maintain their quality of life.
Additional files
Additional file 1: ICD-9-CM and ICD-10 CM codes for selected
comorbidities and GPIs for antipsychotics. (DOCX 37 kb)
Additional file 2: Sample selection flow chart. (DOCX 635 kb)
Abbreviations
CCI: Charlson Comorbidity Index; EPS: Extrapyramidal symptoms; GPI: Generic
Product Indicator; HR: Hazard ratio; ICD-10-CM: International Classification of
Diseases, Tenth Revision, Clinical Modification; ICD-9-CM: International
Classification of Diseases, Ninth Revision, Clinical Modification; KM: Kaplan–
Meier; LASSO: Least absolute shrinkage and selection operator; TD: Tardive
dyskinesia
Acknowledgments
We thank Arpita Biswas, PhD (Chameleon Communications International
with funding from Teva Pharmaceuticals) for editorial assistance in the
preparation of this report. We also thank Sophie Schonfeld and Monica
Macheca (Analysis Group, Inc.) for helpful discussions and support with the
statistical analyses.
Authors’contributions
All authors were involved in the design of the study, interpretation of the
data, writing of the manuscript, and the decision to submit the manuscript
for publication. All authors read and approved the final manuscript.
Funding
This study was funded by Teva Pharmaceuticals, Petach Tikva, Israel. Analysis
Group, Inc. received payment from Teva Pharmaceuticals to conduct
research.
Availability of data and materials
The data that support the findings of this study are available from Analysis
Group, but restrictions apply to the availability of these data, which were
used under license for the current study, and so are not publicly available.
Data are, however, available from the authors upon reasonable request and
with permission of Analysis Group.
Ethics approval and consent to participate
The New England Independent Review Board approved this project as an
exempted retrospective study and determined that informed consent was
not required.
Consent for publication
Not applicable.
Competing interests
OPL is an employee of Analysis Group, Inc. RA is an employee of Analysis
Group, Inc. BC is an employee of Teva Pharmaceuticals.
Author details
1
Analysis Group, Inc., 111 Huntington Avenue, Boston, MA 02199, USA.
2
Teva
Pharmaceuticals, 41 Moores Rd, Malvern, PA 19355, USA.
Received: 22 February 2019 Accepted: 1 July 2019
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