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Evaluation of the Potential Impact of Drug-Gene Interaction Risk (DGIR) on
Health Resource Utilization (HRU) in an Elderly Population
A. Bress1, S. Unni1, E. Biltaji1,2, X. Ye1, B. Yu1, T. Mamiya3, R. Sass3, J. Biskupiak1, D. Brixner1,2
1. College of Pharmacy, University of Utah, Salt Lake City, UT 2. Program in Personalized Health Care,
University of Utah, Salt Lake City, UT 3. Genelex Corporation, Seattle, WA
Poster Number: PIH61
Background
Results
• Genetic testing for drug metabolizing enzyme (DME)
coding genes has the potential to optimize
medication prescribing, dosing, monitoring, patient
outcomes, health resource utilization (HRU), and
costs1
• Elderly patients may realize a greater benefit from
genetic testing as they are at higher risk for
polypharmacy and adverse drug events2,3
Objectives
• To assess the relationship between Drug-Gene
Interaction Risk (DGIR) and HRU in elderly patients
to evaluate the potential benefit from
pharmacogenetic testing
Table 1. Baseline Characteristics According to DGIR Score*
Age, years (Mean [SD])
Age by category (n [%])
•
•
•
•
•
•
75 (6)
17,953 (23)
70-74
18,342 (31)
23,300 (28)
9,295 (28)
21,171 (28)
75-80
11,829 (20)
17,105 (21)
7,397 (22)
17,507 (23)
≥80
10,620 (18)
17,827 (22)
8,350 (25)
20,331 (26)
Male (n [%])
25,467 (43)
29,605 (36)
14,391 (43)
33,650 (44)
14,942 (25)
21,396 (26)
8,767 (26)
18,931 (25)
Race (n [%])
Caucasian
African American
Asian or Pacific
Islander
Hispanic/Latino
2,200 (4)
2,950 (4)
1,235 (4)
3,099 (4)
1,530 (3)
2,065 (3)
711 (2)
1,558 (2)
809 (1)
1,563 (2)
682 (2)
961 (1)
Other Races
1,365 (2)
1,763 (2)
614 (2)
1,297 (2)
38,713 (65)
52,487 (64)
21,430 (64)
51,116 (66)
9,204 (15)
14,309 (17)
5,475 (16)
11,197 (15)
48,926 (82)
65,804 (80)
27,150 (81)
63,709 (83)
Medicare
MORE2
Age ≥65 years with continuous enrollment for ≥6
months prior to index date
Index date was claim for ≥1 drug(s) with
pharmacogenetic (PGx) evidence for DGIR from a
predefined list of 55 single ingredient and 6
combination drugs by either:
• Pharmacokinetic in vivo evidence
• Pharmacodynamic evidence
• FDA label/dosing guidance
Taking at least 3 prescription drugs including ≥1
drug(s) with PGx evidence
75 (6)
8,397 (25)
Commercial
Inclusion Criteria
74 (6)
High
N=76,962
23,992 (29)
Medicaid
Other/Unknown
420 (1)
481 (1)
164 (0)
372 (0)
1,009 (2)
1,630 (2)
650 (2)
1,684 (2)
* All groups may not add up to 100% due to rounding
Figure 2. Distribution of DGIR Score Among Analysis Cohort*
35%
33%
31%
30%
Percentage of Patients
•
73 (6)
Medium
N=33,439
18,768 (32)
Study Design, Data Source, and Timeline
Historical cohort study using Inovalon’s
Registry®, a healthcare data warehouse with
national medical/pharmacy administrative claims
Patient identification period: July 1, 2012 through
March 31, 2013
Study outcomes: Counts of HRU during 9 months of
follow-up post index date including all-cause
hospitalization, emergency room visits, and clinic
visits
DGIR represents the probability of at least one
significant drug-gene interaction warning based on
the patient’s medications and U.S. gene
frequencies.
Poisson regression was used to test association
between DGIR and HRU counts
Low
N=82,224
65-69
Unknown
Insurance type (n [%])
Methods
•
Zero
N=59,559
Variable
25%
24%
20%
15%
13%
10%
5%
0%
Zero (Reference)
Low (>0 - <20%)
Medium (≥20 - <40%)
High (≥40%)
* All groups may not add up to 100% due to rounding
Figure 3. Incident Rate Ratios for HRU by DGIR Score Compared
to Zero DGIR*
High DGIR
-5%**
Figure 1. Patient Identification Flow Chart
Unique patients in Inovalon’s MORE2 Registry® (Jan 1, 2012 - Dec 31, 2013)
n = 41,846,662
With continuous enrollment* (Jan 1, 2012 - Dec 31, 2013)
n = 5,772,751 (13.8%)
Age ≥65 years (Jan 1, 2012 - Dec 31, 2013)
n = 1,185,239 (20.5%)
Any claim for ≥ 1 drug(s) from predefined list (Jan1, 2012 – Dec 31, 2013 )
n = 602,336 (51%)
Excluded n = 266,589
• Date trimming
8%**
Medium DGIR
9%**
Low DGIR
-8%
-6%
-4%
-2%
0%
2%
4%
6%
8%
10%
12%
Percent Change in HRU Rate
* Adjusted for age, gender, race, Charlson Comorbidity Index, and
known drug-drug interactions. Overall difference in HRU was 3%
more compared to the zero risk group.
** p<0.00001
• Invalid NDC Value
Any claim for ≥ 1 drug(s) from predefined list (Jul 1, 2012 - Mar 31, 2013)
(Index Date) n = 335,747 (56%)
Excluded n = 83,563
• On <3 prescription drugs at index
date
On ≥3 prescriptions drugs (at least one of which is from predefined list)
n = 252,184 (75%, Analysis Cohort)
Limitations
• Only patients that had NDC codes which could be linked
to a drug name were included, which may introduce
selection bias.
• Genetic risk was estimated by potential drug-gene
interaction.
* Allowing for up to a 30-day gap
References
1.Wu AC, Fuhlbrigge AL. Economic evaluation of
pharmacogenetic tests. Clinical Pharmacology and
Therapeutics 2008;84:272-4
2.Hajjar ER, Hanlon JT, Artz MB, et al. Adverse drug
reaction risk factors in older outpatients. The American
Journal of Geriatric Pharmacotherapy. Dec 2003;1(2):8289
3.Hajjar ER, Cafiero AC, Hanlon JT. Polypharmacy in elderly
patients. The American Journal of Geriatric
Pharmacotherapy. Dec 2007;5(4):345-351
Conclusions
• Elderly patients with DRIG risk had a 3% increase in HRU
compared to patients with zero DRIG risk.
• Low and medium DGIR risk groups were associated with
increased rates of HRU. The high DGIR was associated
with lower HRU rates.
• High risk groups may have more commonly known drug
interactions, which are aggressively managed, whereas in
the low risk groups there may be more ambiguity.
• Further analyses can help define the appropriate target
group for pharmacogenetic testing for variants of drug
metabolism.
Source of funding: Genelex unrestricted research grant
Presented at: ISPOR Annual International Meeting 2015, May 16-20, 2015.
Philadelphia, PA, USA
Pharmacotherapy Outcomes Research Center