http://jpp.sagepub.com/Journal of Pharmacy Practice

http://jpp.sagepub.com/content/early/2014/08/07/0897190014544793The online version of this article can be found at:

 DOI: 10.1177/0897190014544793

published online 8 August 2014Journal of Pharmacy PracticeScott D. Nelson, Joanne LaFleur, Emily Hunter, Melissa Archer, Carin Steinvoort, CarrieAnn Maden and Gary M. Oderda

Drug Interactions−Identifying and Communicating Clinically Meaningful Drug  

Published by:

http://www.sagepublications.com

On behalf of: 

  New York State Council of Health-system Pharmacists

can be found at:Journal of Pharmacy PracticeAdditional services and information for    

  http://jpp.sagepub.com/cgi/alertsEmail Alerts:

 

http://jpp.sagepub.com/subscriptionsSubscriptions:  

http://www.sagepub.com/journalsReprints.navReprints:  

http://www.sagepub.com/journalsPermissions.navPermissions:  

What is This? 

- Aug 8, 2014OnlineFirst Version of Record >>

by guest on December 3, 2014jpp.sagepub.comDownloaded from by guest on December 3, 2014jpp.sagepub.comDownloaded from

Research Article

Identifying and Communicating ClinicallyMeaningful Drug–Drug Interactions

Scott D. Nelson, PharmD1, Joanne LaFleur, PharmD, MSPH1,Emily Hunter, PharmD, MLS1, Melissa Archer, PharmD2,Carin Steinvoort, PharmD2, CarrieAnn Maden, PharmD, BCPS2,and Gary M. Oderda, PharmD, MPH1

AbstractObjective: Providing care to patients with comorbid medical problems may result in complicated, multiple drug therapy regimens,increasing the risk of clinically meaningful drug–drug interactions (DDIs). The purpose of this article is to describe the prevalenceof DDIs and provide examples on how to identify and intervene on DDIs. Methods: We described DDI data from the Utah DrugRegimen Review Center, where adult Medicaid patients were reviewed by pharmacists from 2005 to 2009. Patients were selectedby the number of prescriptions filled per month (>7) or having a high RxRisk score. Summary: A total of 8860 patients werereviewed, and 16.6% had at least 1 clinically meaningful DDI. Patients with DDIs were slightly younger (mean age 45.2 vs 48.2),more likely to be female (75.0% vs 68.9%), and had more prescriptions per month (13.4 vs 12.5) compared to patients without (P <.001). Pharmacodynamic DDIs were more prevalent (80.2%) than pharmacokinetic. Pharmacodynamic DDIs mainly occurred withdrugs used to treat psychiatric/seizure/sleep disorders (69.4%) and pain/migraine (56.6%). Pharmacokinetic DDIs mainly occurredwith drugs used to treat psychiatric/seizure/sleep disorders (53.2%), cardiovascular diseases (46.3%), and infectious diseases(29.6%). Conclusions: Clinically meaningful DDIs are common in patients with complex medication regimens. A systematicapproach for identifying DDIs, determining clinical significance, formulating patient-specific recommendations, and communicatingrecommendations is important in pharmacy practice.

Keywordscommunity pharmacy services, drug interactions, pharmacodynamics, pharmacokinetics

Background

Practitioners today are responsible for providing care to

patients with many comorbid medical problems. In doing

so, prescribers utilize multiple drug therapy regimens,

complicated recommendations from clinical practice guide-

lines, and consumer-driven health care demands resulting

in complex medication regimens for patients. The complex-

ity of a drug therapy regimen can be defined by the number

of medications being prescribed combined with the different

dosing schedules for each of the medications.1 These com-

plex medication regimens contribute to an estimated

US$177.4 billion (equivalent to approximately US$240 bil-

lion in 2013) per year spent on drug-related morbidity and

mortality resulting from nonadherence, therapeutic failures,

and drug–drug interactions (DDIs).2 Prescribers often rely

on pharmacists to detect DDIs that may be overlooked, yet

identifying and addressing DDIs can be difficult for phar-

macist interns, newly licensed professionals, and even some

experienced pharmacists. Mastery of DDI detection is

important for ensuring optimal drug therapy, especially in

patients with complex drug regimens.

DDIs can be divided into 2 main types: pharmacodynamic

and pharmacokinetic interactions. Pharmacodynamic interac-

tions can occur in 2 ways: (1) a drug antagonizing the effects

of another drug, resulting in a lower than expected therapeutic

effect and (2) 2 drugs exhibiting additive effects. Pharmacoki-

netic interactions relate to the body’s effect on a drug; these

DDIs are due to alterations in absorption, distribution, metabo-

lism, or excretion that result from the combination of 2 drugs.

In any given week, more than 80% of adults in the United

States take some type of medication including prescription

drugs, over-the-counter treatments, herbals, and vitamins.3

Among these medication users, nearly one-third take 5 or more

prescriptions, leading to an increased risk of DDIs. It is

1 L.S. Skaggs Pharmacy Institute, Department of Pharmacotherapy, University

of Utah College of Pharmacy, Salt Lake City, UT, USA2 Utah Medicaid Drug Regimen Review Center, Salt Lake City, UT, USA

Corresponding Author:

Scott D. Nelson, L.S. Skaggs Pharmacy Institute, University of Utah College

of Pharmacy, 30 South 2000 East, 4th Floor, Salt Lake City, UT 84112, USA.

Email: scott.nelson@pharm.utah.edu

Journal of Pharmacy Practice1-6ª The Author(s) 2014Reprints and permission:sagepub.com/journalsPermissions.navDOI: 10.1177/0897190014544793jpp.sagepub.com

by guest on December 3, 2014jpp.sagepub.comDownloaded from

estimated that a DDI occurs in up to 11% of patients.4 Although

the severity of DDIs is widely variable, clinically meaningful

DDIs, such as interactions that are likely to cause patient harm

or be life threatening if not detected, occur in about 0.81% of

visits where the patient was prescribed 2 or more medications.5

Additionally, each additional medication prescribed doubles

the likelihood of a clinically meaningful DDI.5

At the Utah Medicaid Drug Regimen Review Center

(DRRC), pharmacists check patients’ pharmacy records for a

variety of drug-related problems. These problems include phar-

macokinetic and pharmacodynamic DDIs as well as other

issues including polypharmacy, therapeutic duplications, and

dose discrepancies. A program description and definitions for

all drug-related problems have been previously published.6

Much of the available literature on DDIs is limited to inter-

actions between specific drugs,7 drug classes,8 or patient popu-

lations9 rather than discussing DDIs in general. The goal of this

article is to describe some of the most common clinically

meaningful DDIs found by pharmacists and to provide insight

into the identification and potential solutions of these prob-

lems. This article may serve as a reference for pharmacists

dealing with DDIs in complex patients seen in a variety of prac-

tices, and the technique described here can empower pharma-

cists to make appropriate DDI-related interventions.

Methods

Program Description

The analysis set for this study was extracted from data previously

collected by the DRRC as a routine part of their operations.6 The

DRRC was established in 2001 with the primary goal of aiding in

the management of pharmaceutical therapy for medically com-

plex patients to improve the health of Medicaid recipients. Med-

icaid enrollees are chosen for DRRC evaluation based on several

different factors, such as selecting the patients with the top num-

ber of prescriptions they had filled within any given month (>7),

or the patients with the top RxRisk score.10,11 The RxRisk tool

provides an estimated risk assessment based on automated phar-

macy and demographic data.12 Patients were reviewed if they had

not already been reviewed in the previous 12 months.

After patients were listed for review, pharmacists evaluated

their pharmacy claims for potential drug-related problems,

including DDIs. Data available to pharmacists for review

included diagnoses and procedure codes submitted with medi-

cal claims since 2001, all prescription and nonprescriptions

pharmacy claims paid by Medicaid in the month of review and

the prior year, and prior DRRC recommendations if the patient

had previously been reviewed. After review, the pharmacist

would fax a letter to the physician with formal evidence-

based recommendations if a drug-related problem was found.

Patients and Data Set

In this descriptive study, we identified all adult Medicaid

patients who were reviewed by DRRC pharmacists from

2005 to 2009. For patients who were reviewed more than once

during the 4-year study period, only data from the first review

were used. A data set was constructed that contained age and

gender, month and date of review, number of prescriptions in

the month of review, all drug-related problems that were iden-

tified by pharmacists, and a pharmacist’s note about each spe-

cific DDI, including drugs of interest. This project was

reviewed by the University of Utah Institutional Review Board

(IRB) and determined to be nonhuman subjects research

because no identifiable, individual, or private information was

used by the researchers.

Outcomes

The outcome of interest was the prevalence of clinically

meaningful DDIs found from the selected cohort. A clinically

meaningful DDI was defined as an interaction that is likely to

cause patient harm or be life threatening if not detected. An

investigator classified the DDIs based on the pharmacists’ text

note as either ‘‘pharmacokinetic’’ or ‘‘pharmacodynamic’’ and

into disease state categories for the interacting drugs including

allergy/cough/cold, asthma/chronic obstructive pulmonary dis-

ease (COPD), cardiovascular, contraception, diabetes/endocrine,

gastrointestinal, genitourinary, gout, immunologic disease, infec-

tious disease, nutritional supplementation, pain/migraine, and

psych/seizure/sleep, and other.

Data Analysis

Descriptive statistics were used to characterize patient demo-

graphics, frequencies, and percentages of DDIs overall by

pharmacokinetic/pharmacodynamic classification and by ther-

apeutic category for the implicated drugs. Simple univariate

comparisons were done to compare patient demographics in

patients with and without DDIs using the Student’s t test, for

continuous variables, and the chi-square test, for categorical

variables. This study used STATA 12 statistical software.13

Results

Patients

As summarized in Figure 1, there were 202 299 patients who

filled at least 2 prescriptions during the same month between

January 2005 and December 2010. Of those, 11 067 patients

were flagged as eligible for review due to the number of fills

or RxRisk score during the month of review. Pharmacists

excluded 2207 of the patients for various reasons including

(1) not enough claims history, (2) no useful intervention could

be made based on the patient’s disease state, or (3) the patient

was incorrectly identified by the system as having >7 medica-

tions but upon pharmacist review had fewer than 7 unique med-

ications due to multiple fills of the same medication in a month.

The remaining 8860 patients comprised the cohort for this

study. The mean (standard deviation) age of the cohort was

47.7 (16.4), and 69.9% were female. The mean (standard devia-

tion) number of prescriptions per patient in the review month

was 12.6 (3.5). Patients’ characteristics are summarized in

2 Journal of Pharmacy Practice

by guest on December 3, 2014jpp.sagepub.comDownloaded from

Table 1 for those with identified clinically meaningful DDIs

versus those without clinically meaningful DDIs.

DDIs

Of 8860 reviewed patients, 1470 (16.6%) had at least 1 clini-

cally meaningful DDI for a total of 1846 clinically meaningful

DDIs. Patients with clinically meaningful DDIs were slightly

younger (mean age 45.2 vs 48.2, P < .001), more likely to be

female (75.0% vs 68.9%, P < .001), and had more prescriptions

for the month in review (13.4 vs 12.5, P < .001) compared to

patients without. Pharmacodynamic DDIs were much more

prevalent (80.2% of all DDIs) than pharmacokinetic DDIs

(27.8%), and some drugs had both pharmacodynamic and phar-

macokinetic interactions. As shown in Table 2, the drugs most

commonly implicated in pharmacodynamic DDIs were those

used to treat psychiatric/seizure/sleep disorders (69.4%) and

pain/migraine (56.6%). (Note: percentages do not add up to

100% since medications could belong to multiple categories).

The drugs most commonly implicated in pharmacokinetic

DDIs were also those used to treat psychiatric/seizure/sleep

disorders (53.2%) and cardiovascular diseases (46.3%), with

drugs used to treat infectious diseases in third (29.6%).

Discussion

Our data show that clinically meaningful DDIs are present in a

high percentage of the patients reviewed and tended to occur

disproportionately more frequently in women compared to

men. This is consistent with our previous findings that higher

utilization, greater complexity, and more drug-related prob-

lems are more common in women.6 We also found that the

same drug classes were implicated in both pharmacokinetic and

pharmacodynamic DDIs, namely, drugs used to treat psychia-

tric disorders, seizures, and chronic pain. This is the first study

to look broadly at all drug categories in a general population of

Medicaid patients.

Many of the drugs in the therapeutic classes most commonly

implicated in DDIs are used interchangeably at variable doses for

alternative disease states; for example, topiramate can be used for

either seizure disorders or for migraine prophylaxis, and trazodone

can be used either as a sleep aid or as an antidepressant, adding to

the complexity of a patient’s drug regimen. These concepts will be

discussed further in the context of the drugs associated with the

most common DDI disease categories in the primer below.

How to Handle DDIs

Pharmacists can use the following guidance when approaching

DDIs. These steps include (1) identifying the DDI, (2) deter-

mining clinical significance, (3) formulating patient-specific

recommendations, and (4) communicating recommendations.

We describe this stepwise process and include patient cases for

pharmacokinetic and pharmacodynamic DDIs. These patient

cases are presented using an assessment, recommendation, and

rationale approach for communicating recommendations.

Step 1: Identifying the DDI

Pharmacists who are experienced in identifying DDIs may be

able to quickly and accurately recognize significant DDIs in a

Table 1. Patient Demographics.

With DDI(N ¼ 1470),

N (%)

Without DDI(N ¼ 7390),

N (%) Pa

Female 1103 (75.0) 5092 (68.9) <.0001

Age during review monthAge 18-30 262 (17.8) 1263 (17.1) <.0001

Age 31-40 329 (22.4) 1216 (16.5)

Age 41-50 346 (23.6) 1611 (21.8)Age 51-65 440 (29.9) 2420 (32.7)

Age > 65 93 (6.3) 880 (11.9)Prescriptions during review month

2-5 8 (0.5) 298 (4.0) <.00016-10 124 (8.4) 1195 (16.2)

11-15 1051 (71.5) 4721 (63.9)>15 287 (19.6) 1176 (15.9)

Abbreviation: DDI, clinically meaningful drug–drug interaction.a P value was calculated using Pearson’s chi-square.

Medicaid patients over 18 years old that received at least2 perscription within a given month between

January 1, 2005 and December 1, 2010N = 202,299

Patients flagged for review by RxRisk score or Number of

prescriptions filledN = 11,067

Excluded by Pharmacist-Not enough claims history-No useful intervention could be made based on the patient’s disease state-Patient was incorrectly identifed by the system

N = 2,207

Patients reviewedN = 8,860

Figure 1. Attrition summary for study cohort.

Nelson et al 3

by guest on December 3, 2014jpp.sagepub.comDownloaded from

patient receiving few medications. However, when a complex

drug regimen is being assessed, or when drugs the pharmacist

is not familiar with are encountered, the pharmacist should

utilize a drug interaction checking program and review the

results. There are numerous resources available to identify

potential DDIs including Stockley’s Drug Interactions,14 Micro-

medex,15 Lexi-Comp,16 and Hansten & Horn17 along with phar-

macy software programs to signal DDI at an order entry/checking

level. Two programs commonly used by pharmacists in our study

include Micromedex and Facts & Comparisons.15,18 Although

DDI screening software may augment a pharmacist’s ability

to detect clinically meaningful interactions, these systems are

not fail safe. No DDI checking system is perfect, and it is a good

rule of thumb to use a second program, whenever possible, to

identify interactions that may have been omitted by the first.

A limitation of some DDI checking systems, such as those

integrated into a pharmacy system, is that they may have too

many intrusive alerts, are mentally draining, time consuming,

and result in alert fatigue of ignoring both relevant and irrele-

vant warnings.19 Saverno et al evaluated the performance of

DDI software programs currently used in pharmacy practice

including 24 different software vendors with both commercial

and proprietary systems. Performance of the DDI software sys-

tems varied both within and between vendors. Of the software

vendors utilized by 5 or more pharmacies, none of the results

were consistent across all sites. Studies consistently show that

most systems have a high rate of false positives for identifying

clinically important DDIs, leading to a high risk of alert fatigue

and, ultimately, missed true positives.20-22 Additionally, most

DDI checking programs are best suited for identifying pharma-

cokinetic DDIs.

Pharmacodynamic DDIs are not as reliably identified

through available drug interaction programs, and identifying

these may rely more on a pharmacist’s knowledge base. A

patient receiving a combination of amitriptyline, cyclobenzapr-

ine, diphenhydramine, and oxybutynin may be at risk of serious

anticholinergic toxicities, including cognitive impairment and

cardiac conduction abnormalities due to additive anticholiner-

gic effects from each medication, but neither Micromedex nor

Facts & Comparisons drug interaction checkers identifies this

pharmacodynamic drug interaction. In order to ensure that all

significant pharmacodynamic DDIs are identified, the pharma-

cist must systematically identify significant adverse or thera-

peutic effects associated with each medication in the drug

regimen and determine whether any other medication in the

drug regimen is associated with similar adverse or therapeutic

effects. Combinations that may result in excess therapeutic

effect or intensified adverse effects should then be noted.

Step 2: Determining Clinical Significance

Once a list of potential DDIs is identified, the clinical signifi-

cance of each interaction must be determined. The severity

of pharmacokinetic DDIs is influenced by several factors

including the therapeutic index of the implicated drug, the

severity of the toxicities that may result from excess exposure

to the drug or its metabolites, and the potential therapeutic con-

sequences of underexposure to the drug.

For example, pharmacokinetic DDIs involving warfarin

may result in serious consequences. Since warfarin is a narrow

therapeutic index drug, the amount of drug exposure that pro-

duces excessive anticoagulation and leads to hemorrhage is

not much more than the amount required for the appropriate

therapeutic effect. The consequences of subtherapeutic expo-

sure to warfarin are also serious, since the patient will be at

increased risk of life-threatening thromboembolic events.

Other narrow therapeutic index drugs with potentially serious

Table 2. Frequency and Percentage of DDI Characterized as PK or PD.

Total DDI(N ¼ 1846),

N (%)

PK only,(N ¼ 365),

N (%)

PD only,(N ¼ 1333),

N (%)

Both PD and PK(N ¼ 148),

N (%)

Allergy/cough/cold 34 (1.8) 2 (0.5) 31 (2.3) 1 (0.7)

Asthma/COPD 64 (3.5) 10 (2.7) 54 (4.1) 0 (0)Cardiovascular 433 (23.5) 169 (46.3) 251 (18.8) 13 (8.8)

Contraception 10 (0.5) 9 (2.5) 1 (0.1) 0 (0)

Diabetes/endocrine 32 (1.7) 9 (2.5) 23 (1.7) 0 (0)Gastrointestinal 223 (12.1) 36 (9.9) 186 (13.9) 1 (0.7)

Genitourinary 12 (0.7) 0 (0) 11 (0.8) 1 (0.7)Gout 7 (0.4) 5 (1.4) 2 (0.2) 0 (0)

Immunologic disease 17 (0.9) 13 (3.6) 3 (0.2) 1 (0.7)Infectious disease 188 (10.2) 108 (29.6) 74 (5.6) 6 (4.1)

Nutritional supplementation 6 (0.3) 6 (1.6) 0 (0) 0 (0)Pain/migraine 838 (45.4) 68 (18.6) 755 (56.6) 15 (10.1)

Psych/seizure/sleep 1257 (68.1) 194 (53.2) 925 (69.4) 138 (93.2)Other 9 (0.5) 2 (0.5) 7 (0.5) 0 (0)

Abbreviations: DDI, drug-drug interaction; PK, pharmacokinetic; PD, pharmacodynamic; COPD, chronic obstructive pulmonary disease.a Percentages do not add up to 100% since medications could belong to multiple categories.

4 Journal of Pharmacy Practice

by guest on December 3, 2014jpp.sagepub.comDownloaded from

toxicities include lithium, digoxin, carbamazepine, cyclospor-

ine, insulin, and phenytoin.

The severity of pharmacodynamic DDIs is also influenced

by the severity of the toxicities that are involved or the conse-

quences of diminished therapeutic activity of at least one of

the involved drugs. Multiple sources may provide information

that may help determine the significance of an interaction.

Clinical trial data and kinetics studies may help the pharma-

cist understand the individual drugs involved in the interac-

tion including the dosage ranges and drug serum levels

associated with therapeutic, subtherapeutic, or toxic effects

of a drug, and the incidence and severity of adverse effects.

This information combined with kinetics studies and case

reports involving the DDI are very helpful in determining the

significance of an interaction.

Clinical references, including DDI programs, may or may

not adequately summarize this information and the pharmacist

may need to refer to multiple secondary references and/or the

primary literature to properly assess the interaction. Some drug

interaction warnings are based on a single case report or are

simply theoretical. When information is lacking, the pharma-

cist must rely on clinical judgment and, if available, expert con-

sensus to determine the significance of the DDI.

Step 3: Formulating Patient-Specific Recommendations

Once the general significance of each DDI is determined, the

pharmacist can begin to formulate patient-specific recommen-

dations. Most recommendations involve one of the following:

changing the doses, changing the drug, discontinuing a drug,

or monitoring the patient without changing the drug regimen.

Factors that will determine the appropriate action in a given

patient include the doses of the drugs involved, the patient’s

age, any underlying disease states, the efficacy of the current

drug regimen, and whether the patient is experiencing adverse

consequences from the interaction.

Step 4: Communicating Recommendations

Whether written or spoken, the pharmacist should communi-

cate recommendations to the prescriber in an organized

manner. A brief summary of important observations, a clear

recommendation, and a rationale to support the recommen-

dation should be given. The following case examples illus-

trate these principles.

Pharmacokinetics Case

A pharmacist is asked to review the drug regimen of a 57-year-

old woman and provide written recommendations to the

patient’s prescriber. This patient has a history of hypertension,

hyperlipidemia, migraine, and knee pain due to arthritis. Her

drug regimen includes simvastatin 40 mg daily, verapamil SR

240 mg daily (for both migraine prophylaxis and hypertension),

acetaminophen 650 mg 3 times a day daily for arthritis pain, and

sumatriptan 50 mg daily for acute migraine. After identifying

all potential DDIs, determining their clinical significance, and

reviewing the patient’s chart to formulate patient-specific rec-

ommendations, the pharmacist prepares the following recom-

mendations for the prescriber:

Simvastatin/verapamilAssessment. This patient has diagnoses codes for hyperten-

sion, migraines, hyperlipidemia, and has been filling prescrip-

tions for simvastatin 40 mg daily and verapamil SR 240 mg

daily, a combination that is contraindicated due to a significant

DDI.

Recommendation. Consider changing the simvastatin to a sta-

tin that does not interact with verapamil. Rosuvastatin 5 mg

daily and pravastatin 80 mg daily have similar LDL-lowering

effects.

Rationale. Dosages of simvastatin exceeding 10 mg daily are

contraindicated in patients receiving verapamil. Verapamil has

been shown to increase peak simvastatin serum concentrations

by 2.6-fold, increasing the risk of myopathy and rhabdomyoly-

sis. Changing the statin rather than changing verapamil is likely

the best option because verapamil is treating 2 disease states,

hypertension and migraines.

References. Yeo KR, Yeo WW. Inhibitory effects of verapa-

mil and diltiazem on simvastatin metabolism in human liver

microsomes. Br J Clin Pharmacol. 2001;51(5):461-470.

Jacobson TA. Comparative pharmacokinetic interaction pro-

files of pravastatin, simvastatin, and atorvastatin when coad-

ministered with cytochrome P450 inhibitors. Am J Cardiol.

2004;94(9):1140-1146.

Pharmacodynamics Case

A pharmacist is asked to review the drug regimen of a 42-

year-old man and provide written recommendations to the

patient’s prescriber. This patient has a history of seasonal

allergies, anxiety, postherpetic neuralgia, and fatigue. His

drug regimen includes lorazepam 1 mg twice daily for anxi-

ety, amitriptyline 50 mg daily for postherpetic neuralgia, and

cetirizine 10 mg daily for allergies. After identifying all

potential DDIs, determining their clinical significance and

reviewing the patient’s chart to formulate patient-specific rec-

ommendations, the pharmacist prepares the following recom-

mendation for the prescriber:

Concomitant sedating medicationsAssessment. This patient has a diagnosis of chronic fatigue

and is receiving multiple medications that may cause sedation,

including lorazepam, amitriptyline, and cetirizine.

Recommendation. Consider a trial of less-sedating alterna-

tives to each of his medications. Possible alternatives include

(1) a nonsedating serotonin reuptake inhibitor, such as sertra-

line or citalopram, or you could use a nonbenzodiazepine

anxiolytic such as buspirone in place of lorazepam for anxiety,

(2) a topical treatment, such as capsaicin, or a less-sedating

Nelson et al 5

by guest on December 3, 2014jpp.sagepub.comDownloaded from

tricyclic antidepressant, such as nortriptyline, in place of ami-

triptyline for postherpetic neuralgia, and (3) a nasal steroid,

such as fluticasone, or a less-sedating, second-generation anti-

histamine, such as loratadine, in place of cetirizine to treat

nasal allergy symptoms.

Rationale. This recommendation will help reduce and/or

resolve this patient’s fatigue or rule out additive sedative

effects of this patient’s medications as a cause of fatigue.

Limitations

This is a generalized article to help pharmacists recognize and

communicate potentially clinically meaningful DDIs in their

patients. Not all patients will have many chronic medications

or are only taking an interacting medication acutely, which may

limit the external validity of the patient characteristics; however,

the tools provided can be used in all patients. In the selection

process, patients were selected for review by the number of med-

ications filled or the RxRisk score; these differences in the selec-

tion processes may introduce small differences in patient

demographics of the study; they do not affect the overall results.

Additionally, there were some significant changes in the DRRC

during this time frame. In January 2006, Medicare Part D went

into effect, and in November 2008 the number of patients

selected for review was cut from 300 to 150 per month.

Conclusion

Clinically meaningful DDIs are somewhat common, especially

in patients with complex medication regimens. Using a sys-

tematic approach for identifying and intervening on DDI is

important, such as (1) identifying the DDI, (2) determining

clinical significance, (3) formulating patient-specific recom-

mendations, and (4) communicating recommendations.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to

the research, authorship, and/or publication of this article.

Funding

The author(s) received the following financial support for the research,

authorship, and/or publication of this article: Dr. Oderda received fund-

ing from the Utah Department of Health to support the Drug Regimen

Review Center.

References

1. Muir AJ, Sanders LL, Wilkinson WE, Schmader K. Reducing

medication regimen complexity: a controlled trial. J Gen Intern

Med. 2001;16(2):77-82.

2. Ernst FR, Grizzle AJ. Drug-related morbidity and mortality:

updating the cost-of-illness model. J Am Pharm Assoc (Wash).

2001;41(2):192-199.

3. Preventing Medication Errors: Quality Chasm Series. Washington,

DC: The National Academies Press; 2007.

4. Jankel CA, Speedie SM. Detecting drug interactions: a review of

the literature. DICP. 1990;24(10):982-989.

5. Aparasu R, Baer R, Aparasu A. Clinically important potential

drug-drug interactions in outpatient settings. Res Social Adm

Pharm. 2007;3(4):426-437.

6. LaFleur J, McBeth C, Gunning K, Oderda L, Steinvoort C, Oderda

GM. Prevalence of drug-related problems and cost-savings opportu-

nities in medicaid high utilizers identified by a pharmacist-run drug

regimen review center. J Manag Care Pharm. 2006;12(8):677-685.

7. Phelan KM, Mosholder AD, Lu S. Lithium interaction with the

cyclooxygenase 2 inhibitors rofecoxib and celecoxib and other

nonsteroidal anti-inflammatory drugs. J Clin Psychiatry. 2003;

64(11):1328-1334.

8. Langballe EM, Engdahl B, Selbaek G, Nordeng H. Concomitant

use of anti-dementia drugs with psychotropic drugs in Norway–

a population-based study. Pharmacoepidemiol Drug Saf. 2011;

20(12):1319-1326.

9. Hines LE, Murphy JE. Potentially harmful drug-drug interactions

in the elderly: a review. Am J Geriatr Pharmacother. 2011;9(6):

364-377.

10. Farley JF, Harley CR, Devine JW. A comparison of comorbidity

measurements to predict healthcare expenditures. Am J Manag

Care. 2006;12(2):110-119.

11. Sloan KL, Sales AE, Liu CF, et al. Construction and characteris-

tics of the RxRisk-V: a VA-adapted pharmacy-based case-mix

instrument. Med Care. 2003;41(6):761-774.

12. Fishman PA, Goodman MJ, Hornbrook MC, Meenan RT,

Bachman DJ, O’Keeffe Rosetti MC. Risk adjustment using

automated ambulatory pharmacy data: the RxRisk model. Med

Care. 2003;41(1):84-99.

13. StataCorp. 2011. Stata Statistical Software: Release 12. College

Station, TX: StataCorp LP).

14. Stockley IH, Baxter K. Stockley’s Drug Interactions: A Source Book

of Interactions, Their Mechanisms, Clinical Importance and Man-

agement. 7th ed. London; Chicago: Pharmaceutical Press; 2006.

15. Micromedex Healthcare Series. Greenwood Village, CO: Thomson

Reuters (Healthcare) Inc; Updated periodically.

16. Lexi-Comp Online. Hudson, OH: Lexi-Comp, Inc.; Updated

periodically.

17. Hansten PD, Horn JR. The Top 100 Drug Interactions 2012: A

Guide to Patient Management. Clearwater, FL: H & H Publica-

tions; 2012.

18. Drug Facts & Comparisons. Facts & Comparisons1 eAnswers

[online]. Baltimore, MD: Wolters Kluwer Health, Inc.; Updated

periodically.

19. Cash JJ. Alert fatigue. Am J Health Syst Pharm. 2009;66(23):

2098-2101.

20. Saverno KR, Hines LE, Warholak TL, et al. Ability of pharmacy

clinical decision-support software to alert users about clinically

important drug-drug interactions. J Am Med Inform Assoc.

2011;18(1):32-37.

21. Abarca J, Colon LR, Wang VS, et al. Evaluation of the performance

of drug-drug interaction screening software in community and hos-

pital pharmacies. J Manag Care Pharm. 2006;12(5):383-389.

22. Hazlet TK, Lee TA, Hansten PD, et al. Performance of commu-

nity pharmacy drug interaction software. J Am Pharm Assoc

(Wash). 2001;41(2):200-204.

6 Journal of Pharmacy Practice

by guest on December 3, 2014jpp.sagepub.comDownloaded from