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ORIGINAL RESEARCH Open Access
Predictors of persistence and adherence to deutetrabenazine among
patients with Huntington disease or tardive dyskinesia
Daniel O. Claassen, MD
1
; Rajeev Ayyagari, PhD
2
; Viviana García-Horton, PhD
3
;
Su Zhang, PhD
4
; Sam Leo, PharmD
5
How to cite: Claassen DO, Ayyagari R, García-Horton V, Zhang S, Leo S. Predictors of persistence and adherence to deutetrabenazine among patients with Huntington disease
or tardive dyskinesia. Ment Health Clin [Internet]. 2023;13(5):207-16. DOI: 10.9740/mhc.2023.10.207.
Submitted for Publication: September 8, 2022; Accepted for Publication: June 12, 2023
Abstract
Introduction: Deutetrabenazine is approved for treatment of Huntington disease (HD)-related chorea and tardive
dyskinesia (TD) in adults. Factors associated with deutetrabenazine persistence and adherence are not well understood.
Methods: Claims data from the Symphony Health Solutions Integrated Dataverse (2017-2019) were analyzed
to identify real-world predictors of deutetrabenazine persistence and adherence in adults with HD or TD in the
United States. Predictive models for persistence and adherence that considered patient demographics, payer type,
comorbidities, treatment history, and health care resource use were developed.
Results: In HD, use of anticonvulsants (HR ¼2.00 [95% CI ¼1.03, 3.85];P,.05), lipid-lowering agents (2.22
[1.03, 4.76];P,.05), and Medicaid versus Medicare insurance (2.27 [1.03, 5.00];P,.05) predicted persistence, whereas
only comorbid anxiety disorders predicted discontinuation (0.46 [0.23, 0.93];P,.05). Of these patients, 62.5% were
adherent at 6 months. Use of 2treatmentsforchronicdiseases(OR¼0.18 [95% CI ¼0.04, 0.81];P,.05) and
Medicaid versus Medicare insurance (0.27 [0.09, 0.75];P,.05) was associated with lower odds of adherence. In TD,
use of lipid-lowering agents (HR ¼4.76 [95% CI ¼1.02, 20.00];P,.05) predicted persistence, while comorbid
schizoaffective disorder and/or schizophrenia (0.16 [0.14, 0.69];P,.05) and sleep-wake disorders (0.18 [0.04, 0.82];
P,.05) predicted discontinuation. Of these patients, 46.7% were adherent at 6 months. Comorbid schizoaffective
disorder and/or schizophrenia was associated with lower odds of adherence (OR ¼0.26 [0.07, 0.91];P,.05).
Discussion: Identifying factors predictive of discontinuation and/or nonadherence to deutetrabenazine may facilitate
the development of personalized support programs that seek to improve outcomes in patients with HD or TD.
Keywords: deutetrabenazine, adherence, persistence, tardive dyskinesia, chorea, Huntington disease
1
(Corresponding author) Associate Professor of Neurology, Chief, Division
of Behavioral and Cognitive Neurology, Director, Huntington’sDisease
Center of Excellence, Vanderbilt University Medical Center, Nashville,
Tennessee, daniel.claassen@vanderbilt.edu,ORCID:https://orcid.org/0000-
0002-9853-4902;
2
Vice President, Analysis Group, Inc, Boston, Massachusetts,
ORCID: https://orcid.org/0000-0003-0870-2309;
3
Manager, Analysis Group, Inc,
New York, New York, ORCID: https://orcid.org/0000-0003-0835-800X;
4
Manager,
Analysis Group, Inc, Boston, Massachusetts, ORCID: https://orcid.org/
0000-0002-2056-8838;
5
Director, Austedo HEOR Lead, Teva Branded
Pharmaceutical Products R&D, Inc., Global Health Economics and
Outcomes Research, Parsippany, New Jersey, ORCID: https://orcid.org/0000-
0002-0424-9693
Disclosures: Daniel Claassen has received grants from Alterity Therapeutics
and Spark Therapeutics; consulting fees from Annexon, Spark, Alterity, Novartis,
and Teva Pharmaceuticals; and honoraria from Teva Pharmaceuticals. He has
served on data safety monitoring and advisory boards for Photopharmics and in
a leadership and fiduciary role for the Multiple System Atrophy Coalition and
the Huntington Study Group. Dr Claassen’sinstitutionhasreceivedgrants
from AbbVie, Acadia Pharmaceuticals, Annexon, Neurocrine, Genentech/
Roche, Novartis, Prilenia, and uniQure. Rajeev Ayyagari, Viviana Garcia-
Horton, and Su Zhang are employees of Analysis Group, Inc., which has
received payments from Teva Pharmaceuticals in relation to this study. Sam
Leo is an employee and shareholder of Teva Pharmaceuticals. This study was
supported by Teva Branded Pharmaceutical Products R&D, Inc.
Introduction
Chorea associated with Huntington disease (HD) and
tardive dyskinesia (TD) are hyperkinetic movement disorders,
Q2023 AAPP. The Mental Health Clinician is a publication of the American Association of Psychiatric Pharmacists. This is an
open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License, which permits
non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
characterized by excessive abnormal involuntary movements,
that can greatly diminish patient QoL.
1
Patient surveys have
shown that in chorea associated with HD, overall QoL is
known to decline as the severity of chorea increases; chorea has
also been shown to negatively impact daily functioning.
2,3
Similarly, individuals with severe TD have significantly
worse QoL and social withdrawal compared with those with
less severe TD and those without TD.
4
Vesicular monoamine-transporter 2 (VMAT2) inhibitors
are the only class of drugs approved by the United States
(US) FDA for treatment of chorea associated with HD and
TD. Deutetrabenazine, a selective VMAT2 inhibitor, was
approved by the US FDA in 2017
5-7
based on phase 3
clinical trials for treatment of chorea associated with HD
8
and treatment of TD.
9,10
Treatment with deutetrabenazine
significantly reduced abnormal involuntary movements and
improved patient QoL.
11-13
Despite this, in a study of real-world adherence patterns in
patients with TD receiving VMAT2 inhibitors, approximately
50% of patients were found to be nonadherent to treatment.
14
In a second study of adherence and discontinuation rates
among patients with chorea associated with HD, patients
receiving deutetrabenazine had greater adherence and lower
discontinuation rates compared with patients treated with
tetrabenazine.
15
Given the demonstrated positive effects of
deutetrabenazine on QoL,
13
treatment discontinuation and/
or nonadherence may be associated with declines in patient
QoL. An understanding of which characteristics (eg, demo-
graphics, comorbidities, concomitant medication, insurance
type) correlate to treatment patterns and behaviors has the
potential to allow for the identification of patients at high risk
of discontinuation and nonadherence and subsequent inter-
vention. However, data on predictors of real-world adherence
to deutetrabenazine in patients with chorea associated with
HD and TD are limited. This retrospective study was designed
to identify patient and treatment characteristics associated
with deutetrabenazine persistence and adherence among
patients with chorea associated with HD or TD, as well as to
develop and validate prediction models of persistence and
adherence based on the identified characteristics.
Methods
Data Source
Patient data were extracted from May 2017 to May 2019 from
the Symphony Health Solutions (SHS) Integrated Dataverse,
an insurance claims database that captures deidentified
medical, hospital, and prescription (.93% of all prescriptions
dispensed from US pharmacies) claims data in all stages of
processing and from various payment types (eg, cash,
Medicaid, Medicare, commercial insurance payments) for
approximately 317 million people in the United States.
16
Patients
Eligible patients were aged 18 to 65 years at index date (date
of first claim for deutetrabenazine) and had 1 claim with a
diagnosis of HD (ie, International Classification of Diseases,
10th Revision, Clinical Modification
17
[ICD-10-CM]code
G10) or TD (ie, ICD-10-CM code G24.01), 1 prescription
claim for deutetrabenazine, continuous clinical activity (1
medical and 1 pharmacy claim) during the baseline period
(6 months prior to index date), no discontinuation of
index deutetrabenazine within 30 days after index date,
and 1-day supply of deutetrabenazine from 30 days after
index date to the earlier date between an additional 6
months and the data cut-off date.
Patients were grouped by disease into cohort 1 (patients with
HD) or cohort 3 (patients with TD) for persistence analyses
(6-month study period starting from 30 days after index date)
(Figure 1). Patients in cohort 1 and cohort 3 who met the
additional inclusion criterion of 1 pharmacy claim after the
7-month period after index date (1-month stabilization period
plus 6-month study period) were selected for adherence
analyses and placed into cohort 2 (patients with HD) and
cohort 4 (patients with TD) (Figure 1). For each cohort, data
were randomly divided into 2 sets, one for model development
(modeling set, two-thirds of the data) and another for model
validation (validation set, the remaining one-third of the data).
Outcomes
The outcome for persistence analyses was time to discontinuation
of deutetrabenazine, defined as a gap in index treatment use of
.30 days from the end of the last observed deutetrabenazine fill
and the end of data. Outcomes for the adherence analyses
included proportion of days covered (PDC) and adherence rate,
defined as the proportion of patients with PDC .80%. All
outcomes were assessed during the 6-month study period.
Kaplan-Meyer analyses were used to characterize the propor-
tions of patients who discontinued deutetrabenazine. Means,
medians, SDs, and ranges were used to describe the distribution
of PDC within each cohort. For patient characteristics, means
and SDs were calculated for continuous variables, while counts
and proportions were calculated for categorical variables. For
cohorts 2 and 4, patient characteristics were compared between
those who were adherent (PDC .80%) versus nonadherent
(PDC 80%). Wilcoxon rank-sum tests were used to compare
continuous variables and Fisher exact tests were used to compare
categorical variables between the 2 groups.
Analysis
Multivariable models adjusted for baseline patient character-
istics were developed—2 Cox proportional hazards models to
Ment Health Clin [Internet]. 2023;13(5):207-16. DOI: 10.9740/mhc.2023.10.207 208
identify predictors of persistence for each disease cohort
separately (cohort 1 and cohort 3) and 2 logistic regression
models to identify predictors of adherence for each disease
cohort separately (cohort 2 and cohort 4)—using the
modeling set. HRs and ORs and the corresponding 95%
CIs and Pvalues were reported to identify predictors of
persistence and adherence, respectively, based on effect
size and significance. Grønnesby and Borgan tests and
Hosmer—Lemeshow tests were used to evaluate good-
ness of fit for the persistence and adherence models,
respectively.Thein-samplepredictiveperformancesof
thefinalpersistencemodelswereevaluatedusingthe
mean of Chambless and Diao’sestimatesofcumulative
or dynamic AUC generated using 10-fold cross valida-
tion. The in-sample predictive performances of the final
adherence models were evaluated using AUC of receiver
operating characteristics curves generated using 5-fold or
10-fold cross-validation, depending on the size of the
modeling set.
Each of the 4 models was validated using the data in the
corresponding validation sets. The predictive performance
was assessed using AUC for all models; AUC 0.8 was
considered an excellent prediction, AUC 0.70 to ,0.80 a
good prediction, AUC 0.60 to ,0.70 a fair prediction, and
AUC 0.50 to ,0.60 a poor prediction.
18
Results
Patient Population
Of the 635 patients who met the inclusion criteria, 281 patients
were categorized into cohort 1 (HD), and 362 were
categorized into cohort 3 (TD); these 2 cohorts were used
for the analysis of persistence. Of the 635 patients, 8 were
diagnosed with both HD and TD and were included in
cohort 1 as well as cohort 3. For the analysis of adherence, 128
patients in cohort 1 were further categorized into cohort 2,
and 180 patients in cohort 3 were further categorized into
cohort 4 (Figure 1). Of these patients, 5 were diagnosed with
both HD and TD and were included in cohort 2 as well as
cohort 4.
For patients with HD (cohort 1), the majority (89.3%) were
aged 38 to 65 years, and 60.9% were female (Table). The
majority of patients (90.4%) had a diagnosis of HD before
the index date, among whom the mean (SD) observed disease
duration between their first diagnosis and index date was
Patients with at least one claim with a diagnosis of HD or TDaand
at least one prescription claim for deutetrabenazine between May 2017 —May 2019, and
who were between 18 and 65 years old on the date of their first prescription claim of deutetrabenazine
Definition: The date of the first claim for deutetrabenazine was defined as the index date
N=1246
Patients with at least one pharmacy and one medical claim
during the 6 months prior to index date (baseline period), and
at least one pharmacy claim prior to the baseline period
N=878 (70.6%)
Patients who did not discontinue deutetrabenazine within 30 days from index,
whose index date is at least 30 days prior to the data cut -off date, and
who have at least 30 total days of supply of deutetrabenazine
N=635 (72.3%)
Cohort 1: Patients with HD for discontinuation analysis
Patents with ≥1 claim with a diagnosis of HD b
N=281 (44.3%)
Cohort 2: Patients with HD for adherence analysis
Patients with at least one pharmacy claim after the
7-month period after the index date (one-month
stabilization period and a 6-month study period)c
N=128 (45.6%)
Cohort 4: Patients with TD for adherence analysis
Patients with at least one pharmacy claim after the
7-month period after the index date (one-month
stabilization period and a 6-month study period)c
N=180 (49.7%)
Cohort 3: Patients with TD for discontinuation analysis
Patients with ≥1 claim with a diagnosis of TD b
N=362 (57.0%)
FIGURE 1: Sample selection
HD ¼Huntington disease; ICD-10-CM ¼International Classification of Diseases, 10th Revision, Clinical Modification; TD ¼tardive dyskinesia.
a
ICD-10-CM G10 was used to identify HD. ICD-10-CM G24.01 code was used to identify TD.
17
b
There were 8 patients with both HD and TD diagnoses. These patients were included cohort 1 and cohort 3.
c
There were 5 patients with both HD and TD diagnoses. These patients were included in cohort 2 and cohort 4.
Ment Health Clin [Internet]. 2023;13(5):207-16. DOI: 10.9740/mhc.2023.10.207 209
TABLE: Baseline characteristics in patients with HD and TD
HD TD
1 (Persistence) 2 (Adherence) 3 (Persistence) 4 (Adherence)
n¼281 n ¼128 n ¼362 n ¼180
Age Category, y, n (%)
18-27 10 (3.6) 8 (6.3) 6 (1.7) 3 (1.7)
28-37 20 (7.1) 8 (6.3) 16 (4.4) 9 (5.0)
38-47 61 (21.7) 31 (24.2) 52 (14.4) 32 (17.8)
48-57 87 (31.0) 37 (28.9) 129 (35.6) 62 (34.4)
58-65 103 (36.7) 44 (34.4) 159 (43.9) 74 (41.1)
Male, n (%) 110 (39.1) 46 (35.9) 111 (30.7) 64 (35.6)
Payer type,
a
n (%)
Medicare 113 (40.2) 32 (25.0) 144 (39.8) 53 (29.4)
Commercial 53 (18.9) 35 (27.3) 101 (27.9) 65 (36.1)
Medicaid 50 (17.8) 26 (20.3) 60 (16.6) 29 (16.1)
Other
b
19 (6.8) 8 (6.3) 15 (4.1) 10 (5.6)
Unspecified 46 (16.4) 27 (21.1) 42 (11.6) 23 (12.8)
Observed Disease Duration Days, Mean (SD) n ¼254 n ¼109 n ¼287 n ¼133
353.4 (214.3) 244.4 (138.5) 221.9 (190.1) 178.5 (138.9)
Time From Index Date, d, Mean (SD)
To End of Data 229.0 (146.1) 359.2 (79.3) 229.7 (126.0) 336.9 (67.5)
To Last Observed Medical or Pharmacy Activity 209.9 (142.0) 343.3 (79.7) 218.7 (125.4) 327.4 (68.3)
CCI Score, Mean (SD) 0.4 (0.8) 0.4 (0.9) 0.8 (1.4) 0.9 (1.5)
Selected Comorbidities in the CCI, n (%)
Dementia 23 (8.2) 10 (7.8) 13 (3.6) 6 (3.3)
Chronic Pulmonary Disease 19 (6.8) 11 (8.6) 85 (23.5) 41 (22.8)
Diabetes Without Chronic Complication 12 (4.3) 6 (4.7) 53 (14.6) 20 (11.1)
Diabetes With Chronic Complication 1 (0.4) 0 (0.0) 38 (10.5) 18 (10.0)
Cerebrovascular Disease 7 (2.5) 5 (3.9) 23 (6.4) 10 (5.6)
Mild Liver Disease 3 (1.1) 2 (1.6) 20 (5.5) 11 (6.1)
Renal Disease 2 (0.7) 2 (1.6) 18 (5.0) 9 (5.0)
Congestive Heart Failure 3 (1.1) 0 (0.0) 14 (3.9) 10 (5.6)
Peripheral Vascular Disease 4 (1.4) 3 (2.3) 17 (4.7) 9 (5.0)
Psychiatric Comorbidities, n (%)
Depressive Disorders 60 (21.4) 29 (22.7) 99 (27.3) 50 (27.8)
Anxiety Disorders 53 (18.9) 15 (11.7) 113 (31.2) 56 (31.1)
Substance-Related and Addictive Disorders 31 (11.0) 11 (8.6) 78 (21.5) 46 (25.6)
Bipolar and Related Disorders 23 (8.2) 12 (9.4) 125 (34.5) 67 (37.2)
Schizophrenia 2 (0.7) 2 (1.6) 50 (13.8) 21 (11.7)
Schizoaffective Disorder 2 (0.7) 2 (1.6) 48 (13.3) 28 (15.6)
Trauma-Related and Stress-Related Disorders 10 (3.6) 6 (4.7) 37 (10.2) 23 (12.8)
Nonpsychiatric Comorbidities, n (%)
Hypertension 42 (14.9) 14 (10.9) 140 (38.7) 68 (37.8)
Hyperlipidemia 42 (14.9) 15 (11.7) 128 (35.4) 59 (32.8)
Dysphagia 42 (14.9) 21 (16.4) 26 (7.2) 10 (5.6)
Falls 37 (13.2) 16 (12.5) 38 (10.5) 23 (12.8)
Sleep-Wake Disorders 29 (10.3) 14 (10.9) 87 (24.0) 42 (23.3)
Smoking History 19 (6.8) 8 (6.3) 50 (13.8) 29 (16.1)
Osteoarthritis 19 (6.8) 7 (5.5) 45 (12.4) 23 (12.8)
Ment Health Clin [Internet]. 2023;13(5):207-16. DOI: 10.9740/mhc.2023.10.207 210
353.4 (214.3) days (Table). Prior to initiating deutetrabena-
zine (during the 6-month baseline period), most patients
were treated with other agents including antidepressants (65.8%),
anticonvulsants (40.2%), and typical or atypical antipsychotic
agents (37.4%) (Table).
The majority (93.9%) of patients with TD (cohort 3) were
aged 38 to 65 years, and 69.3% were female (Table). Over three-
quarters of patients (79.3%) had a TD diagnosis before the
index date, among whom the mean (SD) observed disease
duration between their first diagnosis and index date was 221.9
(190.1) days (Table). During the baseline period, most patients
were treated with other agents including antidepressants (68.0%),
anticonvulsants (67.4%), and typical or atypical antipsychotic
agents (58.0%) (Table).
Persistence Analyses
The proportions of patients with HD (cohort 1) who
discontinued deutetrabenazine at months 1, 3, and 6 following
the 30-day stabilization period were 3.5%, 14.7%, and 25.4%,
respectively (Figure 2A); the proportions of patients with
TD (cohort 3) were 5.4%, 22.3%, and 36.2%, respectively
(Figure 2B).
The prediction models for persistence were fit on two-thirds
of the total number of patients in cohort 1 (HD) and cohort
3 (TD), corresponding to 187 and 241 patients, respectively.
Four characteristics were identified as significant predictors
of persistence in patients with HD (cohort 1). Patients who
used Medicaid for their deutetrabenazine claim had a
significantly higher likelihood of persistence compared
TABLE: Baseline characteristics in patients with HD and TD (continued)
HD TD
1 (Persistence) 2 (Adherence) 3 (Persistence) 4 (Adherence)
n¼281 n ¼128 n ¼362 n ¼180
EPS (Excluding TD) 11 (3.9) 6 (4.7) 42 (11.6) 19 (10.6)
Dystonia 10 (3.6) 6 (4.7) 250 (69.1) 125 (69.4)
Obesity 8 (2.8) 5 (3.9) 61 (16.9) 30 (16.7)
Treatment History, n (%)
Antidepressants 185 (65.8) 80 (62.5) 246 (68.0) 125 (69.4)
Anticonvulsants 113 (40.2) 52 (40.6) 244 (67.4) 121 (67.2)
Typical or Atypical APs 105 (37.4) 49 (38.3) 210 (58.0) 110 (61.1)
Typical APs 23 (8.2) 12 (9.4) 38 (10.5) 21 (11.7)
Atypical APs 85 (30.2) 39 (30.5) 195 (53.9) 101 (56.1)
Antianxiety Medications 64 (22.8) 30 (23.4) 143 (39.5) 82 (45.6)
Lipid-Lowering Agents 53 (18.9) 25 (19.5) 156 (43.1) 75 (41.7)
VMAT2 Inhibitors 46 (16.4) 23 (18.0) 60 (16.6) 32 (17.8)
Anticholinergics 44 (15.7) 22 (17.2) 141 (39.0) 81 (45.0)
Antihypertensives 39 (13.9) 16 (12.5) 129 (35.6) 64 (35.6)
Antidiabetic Drugs 15 (5.3) 5 (3.9) 95 (26.2) 38 (21.1)
Sedatives and Hypnotics 14 (5.0) 8 (6.3) 61 (16.9) 32 (17.8)
Lithium 2 (0.7) 1 (0.8) 34 (9.4) 19 (10.6)
All-Cause HCRU,
c
Mean (SD)
No. Inpatient Admissions 0.1 (0.6) 0.1 (0.4) 0.4 (1.1) 0.3 (0.8)
Total Hospitalization Days 0.4 (1.6) 0.4 (1.3) 1.0 (3.1) 0.8 (2.4)
No. Outpatient Visits 4.0 (5.2) 4.5 (5.6) 9.4 (11.0) 9.7 (11.2)
No. ED Visits 0.3 (0.8) 0.3 (0.9) 0.7 (1.8) 0.7 (1.8)
No. Other Visits 4.0 (16.2) 3.4 (12.7) 4.6 (15.9) 6.2 (19.6)
No. Unknown Visits 0.0 (0.5) 0.1 (0.7) 0.2 (1.2) 0.1 (0.6)
AP ¼antipsychotic agent; CCI ¼Charlson Comorbidity Index; EPS ¼extrapyramidal symptoms; HCRU ¼health care resource use; HD ¼Huntington
disease; No ¼number; TD ¼tardive dyskinesia; VMAT2 ¼vesicular monoamine transporter 2.
a
Health plan type was associated with a patient’s index claim.
b
Included cash, employer group, third-party administrator, processors, and workers’compensation.
c
Outpatient visits include medical office, hospital outpatient, and clinic visits. Other visits include home health, hospital outpatient pharmacy, intermediate
care facility, laboratory, long-term care facility, and other facilities. Total hospitalization days were the sum of the lengths of stay for admissions that began
during the baseline period.
Ment Health Clin [Internet]. 2023;13(5):207-16. DOI: 10.9740/mhc.2023.10.207 211
with those using Medicare (HR [95% CI],2.27[1.030,
5.00];P,.05). Patients with anxiety disorders at baseline
were at a significantly higher risk of discontinuation
compared with those without (0.46 [0.23, 0.93];P,.05),
whereas patients taking anticonvulsants (2.00 [1.03,
3.85];P,.05), or lipid-lowering agents (2.22 [1.03,
4.76];P,.05)duringthebaselineperiodhada
significantly greater likelihood of persistence compared
with those without these treatments (Figure 3). Dyspha-
gia or falls at baseline showed a trend toward increased
persistence (2.56 [0.93, 7.14]and 2.78 [0.71, 11.11],
respectively), whereas substance abuse disorders (0.54
[0.28, 1.06])showedatrendtowarddiscontinuation.The
model demonstrated strong predictive performances for
the modeling set (AUC ¼0.7969) and the validation set
(AUC ¼0.8347). The goodness of fit test indicated no
lack of fit of the model (P¼.1021).
Three baseline characteristics were identified as significant
predictors of persistence in patients with TD (cohort 3).
Patients with schizoaffective disorder or schizophrenia (HR
[95% CI], 0.16 [0.04, 0.69];P,.05) or sleep-wake disorders
Time to Discontinuation
Proportion Who Discontinued (%)
100
75
50
25
0
Patients at risk, n
Discontinuation rate, %
Time (months)
0123456
– 3.5 10.4 14.7 20.1 22.7 25.4
A Patients With HD (Cohort 1)
281 250 178 158 129 114 105
Time to Discontinuation
Proportion Who Discontinued (%)
100
75
50
25
0
Time (months)
0123456
– 5.4 14.1 22.3 26.8 32.7 36.2
B Patients With TD (Cohort 2)
Patients at risk, n
Discontinuation rate, %
362 320 258 211 174 146 123
FIGURE 2: Time to discontinuation of deutetrabenazine. (A) shows patients with HD (cohort 1); (B) shows patients with TD
(cohort 3)
HD ¼Huntington disease; TD ¼tardive dyskinesia.
Ment Health Clin [Internet]. 2023;13(5):207-16. DOI: 10.9740/mhc.2023.10.207 212
(0.18 [0.14, 0.82];P,.05) at baseline were at a significantly
higher risk for discontinuation of deutetrabenazine
compared with those without these comorbidities. In
contrast, patients taking lipid-lowering agents (4.76
[1.02, 20.00];P,.05) during baseline had a significantly
higher likelihood of persistence compared with those
without this treatment (Figure 4). Comorbid bipolar
disorder showed a trend toward increased persistence
(4.55 [1.00, 20.00]), whereas obesity (0.21 [0.04, 1.16]),
age (0.64 [0.34, 1.20]), and male gender (0.36 [0.10, 1.25]
showed a trend toward discontinuation. The model
demonstrated strong predictive performance for the
modeling set (AUC ¼0.7919) and the validation set
(AUC ¼0.7715). The goodness of fit test indicated no
lack of fit of the model (P¼.7022).
Adherence Analyses
For patients with HD (cohort 2), the adherence rate with
deutetrabenazine was 62.5%, and the mean PDC during the
6-month study period was 76.7% (SD, 28.2%; median, 92.8%);
for patients with TD (cohort 4), the adherence rate was 46.7%,
and the mean PDC was 65.7% (SD, 30.2%; median, 72.8%).
The prediction models for adherence were fit on two-thirds
of the total number of patients in cohort 2 (HD) and cohort
4 (TD), corresponding to 85 and 120 patients, respectively.
Two predictors were found to be significantly associated
with adherence in cohort 2 (HD). Patients with 2
treatments for chronic diseases (OR [95% CI],0.18[0.04,
0.81];P,.05) and those with Medicaid versus Medicare
insurance (0.27 [0.09, 0.75];P,.05) had lower odds of
Number of emer gency visit s
Number of outpatientᵈ visits
Total hospit alization days
Number of inpatien t admi ssion s
Number of baseline tr eatment sᶜ ≤ 2
Antihypertensives
Antic holiner gics
VMAT2 inhibitors
Lipid-l owerin g agents
Anti-anxiety medications
Anticonvulsants
Antidepr essants
Typical or atypical ant ipsychot ics
Sleep-awak e disorders
Falls
Dysphagia
Substan ce-rel ated and addic tive disorder s
Anxiety disor ders
Depressive disorders
Last daily dose of DTBZ during titration ≤18 mg
CCI scoreduringbaseline
Otherᵇ or unspecified vs Medicare
Medicaid vs Medicare
Commercial vs Medicare
Male vs female
Age
1.23 (0.75–2.04)
0.95 (0.90–1.00)
0.97 (0.68–1.39)
1.22 (0.65–2.33)
1.41 (0.58–3.33)
1.67 (0.80–3.45)
0.61 (0.31–1.19)
0.81 (0.45–1.43)
2.22 (1.03–4.76)
1.20 (0.58–2.50)
2.00 (1.03–3.85)
1.28 (0.74–2.22)
0.75 (0.44–1.28)
1.08 (0.52–2.22)
2.78 (0.71–11.11)
2.56 (0.93–7.14)
0.54 (0.28–1.06)
0.46 (0.23–0.93)
1.22 (0.60–2.44)
0.72 (0.46–1.15)
0.88 (0.67–1.16)
1.67 (0.83–3.33)
2.27 (1.03–5.00)
2.22 (0.81–6.25)
1.01 (0.61–1.64)
1.05 (0.83–1.33)
0.1 1 10
Pred ic tor s of Pe rsi stence
HRa(95% CI)
0.88 (0.42–1.82)
1.04 (0.96–1.12)
NA
NA
0.18 (0.04–0.81)
0.75 (0.23–2.40)
0.31 (0.09–1.07)
1.86 (0.76–4.56)
NA
0.73 (0.27–2.02)
0.65 (0.22–1.91)
0.98 (0.44–2.20)
0.40 (0.15–1.11)
1.07 (0.44–2.60)
NA
2.12 (0.86–5.27)
NA
NA
0.70 (0.31–1.55)
0.88 (0.45–1.74)
1.16 (0.79–1.69)
0.42 (0.14–1.21)
0.27 (0.09–0.75)
0.64 (0.14–3.02)
1.39 (0.72–2.71)
1.15 (0.85–1.54)
0.01 0.1 1 10 100
Predict ors of Adher ence
ORe(95% CI)
FIGURE 3: Model predictors for deutetrabenazine persistence (cohort 1, n ¼187) and adherence (cohort 2, n ¼85) among
patients with HD
CCI ¼Charlson Comorbidity Index; DTBZ ¼deutetrabenazine; HD ¼Huntington disease; NA ¼not applicable; VMAT2 ¼vesicular monoamine
transporter 2. Predictors shown in bold were found to be statistically significant (P,.05).
a
Health plan type is associated with a patient’s index claim. Other health plan types include cash, employer group, third party administrator, processors,
and workers’compensation.
b
Number of baseline treatments was calculated as the sum of binary indicators for any baseline use of antidepressants, anticonvulsants, antipsychotic
agents, antianxiety medications, anticholinergics, lipid-lowering agents, antihypertensives, antidiabetic drugs, sedatives and hypnotics, lithium, and
stimulants or attention deficit or hyperactivity disorder medications.
c
Outpatient visits include medical office, hospital outpatient, and clinic visits. Total hospitalization days were the sum of the lengths of stay for
admissions that began during the baseline period.
d
Cox proportional hazards regression without regularization was fit on the modeling set (2/3 of the total number of patients in cohort 1, n ¼187 ),
using the above characteristics as predictors. Individual comorbidities and treatments with less than 10% prevalence during the baseline period were
excluded and considered as the base-case. In order to avoid potential multicollinearity, hypertension and hyperlipidemia were excluded from the list
of predictors, and their related treatments (ie, use of antihypertensives and lipid-lowering agents) were kept instead.
e
Logistic regression without regularization was fit on the modeling set (2/3 of the total number of patients in cohort 2, n ¼85), using the listed
characteristics as predictors.
Ment Health Clin [Internet]. 2023;13(5):207-16. DOI: 10.9740/mhc.2023.10.207 213
adherence (Figure 3). Despite no lack of fit (P¼.9075),
the model had limited predictive performance; AUC
was 0.6103 and 0.5625 for the modeling and validation
sets, respectively.
In cohort 4 (TD), only patients with schizoaffective
disorder or schizophrenia were significantly less likely to
be adherent to deutetrabenazine than patients without these
comorbidities (OR [95% CI], 0.26 [0.07, 0.91];P,.05)
(Figure 4). Despite no lack of fit (P¼.6412), the adherence
model had limited predictive performance; AUC was
0.5769 and 0.7011 for the modeling and validation sets,
respectively.
Discussion
This retrospective study used claims data to characterize and
identify predictors of real-world persistence and adherence
patterns with deutetrabenazine among patients with HD or
TD in the United States. In patients with HD (cohort 1), four
predictors of persistence were found to be statistically significant
(P,.05). Patients using Medicaid were more likely to be
persistent with deutetrabenazine compared with those using
Medicare; similarly, patients using anticonvulsants or lipid-
lowering agents were more likely to be persistent with
deutetrabenazine compared with patients not on those
treatments. In contrast, patients with an anxiety disorder were
more likely to discontinue deutetrabenazine than those without
Number of emergenc y vis its
Number of outpatien tᵈ visits
Total hospi talizati on days
Number of inpatien t admi ssions
Number of baseline treat ments ᶜ ≤2
VMAT2 inhibitors
Sedatives and Hypnot ics
Anti- diabetic dr ugs
Antihypertensives
Antich oliner gics
Anti-anxiety medications
Lipid-lowering agents
Anticonvulsants
Antidepr essants
Typical or atypical an tipsych otics
Falls
EPS (excluding TD)
Osteoart hrit is
Smokin g histor y
Obesity
Sleep-awake disorders
Dystonia
Trauma- and str ess- or r elated di sorders
Schizoaf fect ive disorder or schizophr enia
Substan ce-rel ated and addictive disorders
Depressive disorders
Anxiety disor ders
Bipolar an d related di sorders
Last daily dose of DTBZ during titration ≤18 mg
CCI score during baseline
Otherᵇ or un specif ied vs Medicar e
Medicaid vs Medicare
Commercial vs Medicare
Male vs female
Age
0.81 (0.58–1.14)
1.05 (0.96–1.15)
0.93 (0.67–1.27)
1.27 (0.50–3.13)
1.92 (0.30–12.50)
1.39 (0.25–7.69)
0.35 (0.07–1.75)
2.50 (0.41–16.67)
4.00 (0.81–20.00)
0.67 (0.19–2.33)
2.38 (0.64–9.09)
4.76 (1.02–20.00)
0.67 (0.17–2.70)
0.93 (0.22–3.85)
1.16 (0.29–4.55)
2.86 (0.24–33.33)
0.88 (0.14–5.56)
0.58 (0.05–6.25)
0.44 (0.04–4.55)
0.21 (0.04–1.16)
0.18 (0.04–0.82)
0.79 (0.21–3.03)
1.56 (0.14–16.67)
0.16 (0.04–0.69)
0.29 (0.03–2.78)
0.57 (0.13–2.63)
1.35 (0.33–5.56)
4.55 (1.00–20.00)
1.19 (0.36–4.00)
1.04 (0.70–1.56)
2.17 (0.25–20.00)
0.66 (0.11–4.00)
2.44 (0.16–33.33)
0.36 (0.10–1.25)
0.64 (0.34–1.20)
0.01 0.1 1 10 100
Pred ic tor s of Per siste nce
HRa (95% CI)
0.65 (0.21–2.00)
3.13 (0.39–25.36)
NA
2.16 (0.58–8.02)
0.46 (0.11–1.88)
1.18 (0.32–4.40)
0.91 (0.27–3.08)
1.84 (0.52–6.49)
0.76 (0.27–2.18)
0.92 (0.35–2.43)
0.59 (0.21–1.67)
1.37 (0.48–3.86)
0.86 (0.33–2.28)
1.48 (0.48–4.52)
0.56 (0.19–1.63)
1.12 (0.27–4.69)
NA
NA
0.40 (0.06–2.58)
0.28 (0.06–1.45)
0.49 (0.14–1.71)
0.99 (0.37–2.65)
NA
0.26 (0.07–0.91)
2.95 (0.57–15.15)
1.00 (0.31–3.28)
1.72 (0.63–4.72)
2.33 (0.83–6.51)
0.59 (0.23–1.50)
1.12 (0.81–1.54)
NA
NA
NA
0.91 (0.34–2.47)
0.99 (0.58–1.67)
0.01 0.1 1 10 100
Predictors of Adherence
ORe (95% CI)
FIGURE 4: Model predictors for deutetrabenazine persistence (cohort 3, n ¼241) and adherence (cohort 4, n ¼120) among patients
with TD
CCI ¼Charlson Comorbidity Index; DTBZ ¼deutetrabenazine; EPS ¼extrapyramidal symptoms; NA ¼not applicable; TD ¼tardive dyskinesia;
VMAT2 ¼vesicular monoamine transporter 2. Predictors shown in bold were found to be statistically significant (P,.05 ).
a
Health plan type is associated with a patient’s index claim. Other health plan types include cash, employer group, third party administrator,
processors, and workers’compensation.
b
Number of baseline treatments was calculated as the sum of binary indicators for any baseline use of antidepressants, anticonvulsants, antipsychotic
agents, antianxiety medications, anticholinergics, lipid-lowering agents, antihypertensives, antidiabetic drugs, sedatives and hypnotics, lithium, and
stimulants or attention deficit or hyperactivity disorder medications.
c
Outpatient visits include medical office, hospital outpatient, and clinic visits. Total hospitalization days were the sum of the lengths of stay for
admissions that began during the baseline period.
d
Cox proportional hazards regression without regularization was fit on the modeling set (2/3 of the total number of patients in cohort 3, n ¼241),
using the above characteristics as predictors. Individual comorbidities and treatments with less than 10% prevalence during the baseline period were
excluded and considered as the base-case. In order to avoid potential multicollinearity, hypertension and hyperlipidemia were excluded from the list
of predictors, and their related treatments (ie, use of antihypertensives and lipid-lowering agents) were kept instead.
e
Logistic regression without regularization was fit on the modeling set (2/3 of the total number of patients in cohort 4, n ¼120), using the listed
characteristics as predictors.
Ment Health Clin [Internet]. 2023;13(5):207-16. DOI: 10.9740/mhc.2023.10.207 214
an anxiety disorder diagnosis. For adherence (cohort 2), patients
with HD using Medicaid were less likely to be adherent to
deutetrabenazine compared with those using Medicare, and
patients using 2 treatments for chronic diseases also had
lower odds of adherence. These results are consistent with the
observation that patients with multimorbidity may require
more frequent visits with physicians and have better access to
health care resources and services, which might be associated
with better persistence and adherence to treatment in general.
19
In TD (cohort 3), 3 predictors of persistence were found to
be statistically significant (P,.05). Patients treated with
lipid-lowering agents were more likely to be persistent with
deutetrabenazine therapy, whereas patients with schizoaf-
fective disorder, schizophrenia, or a sleep-wake disorder were
more likely to discontinue deutetrabenazine. In cohort 4, patients
with schizoaffective disorder or schizophrenia diagnosis were
significantly less likely to be adherent to deutetrabenazine.
Nonadherence to antipsychotic agents is a common problem
in schizophrenia management.
20,21
Lack of patient insight,
which manifests as lack of awareness of their own illness and
need for treatment, is one factor associated with intentional
nonadherence
22
and might precipitate nonadherence to
deutetrabenazine.
Whereas both models of persistence demonstrated strong
predictive performance, both models of adherence had limited
predictive performance. Poor predictive performance could
have been driven by the limited sample size of the adherence
modeling sets (HD, n ¼85; TD, n ¼120).
Statistically significant predictors of deutetrabenazine per-
sistence, such as the use of lipid-lowering agents and the
presence of comorbid conditions, may reflect more frequent
physician visits and/or better access to health care services.
Further studies are needed to better understand the reasons
for associations between certain variables and treatment
persistence or adherence and to investigate the potential
association of other variables, such as social determinants of
health (eg, educational level, financial status) and health care
provider characteristics, with treatment behaviors.
There are a few limitations to this study. Presence of TD,
HD, and comorbidities present at baseline were identified
by ICD-10-CM codes used for administrative billing purposes
and may be underestimated because of lack of coding
completeness. This analysis did not capture comorbidities
present after treatment initiation, so no conclusions can
be drawn regarding treatment side effects. In addition, the
SHS Integrated Dataverse does not capture reasons for
treatment discontinuation. Continuous health plan enroll-
ment was inferred using medical and pharmacy claims
activity, as the SHS Integrated Dataverse database does not
include eligibility records. Because the SHS Integrated
Dataverse database is based on a large convenience sample,
results of this observational study may be confounded by
unmeasured characteristics. Additionally, claims that took place
outside of the SHS Integrated Dataverse were not captured.
Importantly, patient adherence may have been overestimated, as
claims for prescription fills may not capture actual use.
Moreover, patient sampling did not exclude those who switched
from valbenazine or tetrabenazine to deutetrabenazine, which
could introduce confounding effects. As deutetrabenazine was
only approved by the FDA for use in chorea associated with
HD and TD in 2017, sample sizes for patients taking
deutetrabenazine were limited. Future studies may benefit from
larger sample sizes or a time frame beyond 6 months, which
could elucidate additional predictors of persistence and
adherence among patients with HD and TD. In addition,
future research investigating real-world reasons for treatment
discontinuation and side effects is warranted.
In conclusion, these results suggest that underlying psychiatric
comorbidities may negatively affect treatment persistence in
some patients. Pharmacists and health care providers can
leverage these findings to better understand and aid patient
populations at the greatest risk for negative treatment use
outcomes, as well as implement targeted interventions to
maximize adherence to treatment. As deutetrabenazine is
often dispensed in a specialty pharmacy setting, there is
potential for pharmacists to serve as key personnel in the
identification of patients at risk for discontinuation and/or
nonadherence, perhaps facilitated by the development of
software that flags patients with a risk factor for adverse
treatment behavior. Such patients can then be redirected to
health care providers, who can provide personalized support
designed to improve treatment persistence and adherence.
Acknowledgments
Medical writing support for the development of this manuscript,
under the direction of the authors, was provided by Amanda
Cox, PhD, Holly Engelman, PhD, and Jennifer C. Jaworski, MS,
BCMAS, CMPP, and editing support by Dena McWain, BA, all
of Ashfield MedComms, an Inizio Company, and were funded
by Teva Branded Pharmaceutical Products R&D, Inc. Authors
contributed as follows: conceptualization: all authors; study
design: Rajeev Ayyagari, Viviana Garcia-Horton, Su Zhang, Sam
Leo; data analysis: Rajeev Ayyagari, Viviana Garcia-Horton, and
Su Zhang; data interpretation: all authors. All authors commented
on previous versions of the manuscript and approved the final
manuscript.
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