Assessment of Use of Combined Dextromethorphan and Quinidine in Patients With Dementia or Parkinson Disease After US Food and Drug Administration Approval for Pseudobulbar Affect | Amyotrophic Lateral Sclerosis | JAMA Network
[Skip to Content]
Sign In
Individual Sign In
Create an Account
Institutional Sign In
OpenAthens Shibboleth
Purchase Options:
[Skip to Content Landing]
Figure 1.  Changes Over Time in Main Associated Diagnoses Among Study Patients in the Optum Database
Changes Over Time in Main Associated Diagnoses Among Study Patients in the Optum Database

Includes patients with amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), or dementia and/or Parkinson disease (PD) who received a prescription for dextromethorphan hydrobromide and quinidine sulfate from 2011 through 2017. The diagnoses were identified before and within 30 days after a prescription claim for the combination drug dextromethorphan-quinidine in the Optum Clinformatics Data Mart database. The exact periods represented by each year were October 29, 2010, through September 30, 2011 (2011); October 1, 2011, through September 30, 2012 (2012); October 1, 2012, through September 30, 2013 (2013); October 1, 2013, through September 30, 2014 (2014); October 1, 2014, through September 30, 2015 (2015); October 1, 2015, through September 30, 2016 (2016); and October 1, 2016, through March 1, 2017 (2017).

Figure 2.  Changes Over Time in Main Associated Diagnoses Among Study Patients in the Truven Database
Changes Over Time in Main Associated Diagnoses Among Study Patients in the Truven Database

Includes patients with amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), or dementia and/or Parkinson disease (PD) who received a prescription for dextromethorphan hydrobromide and quinidine sulfate from 2011 through 2015. The diagnoses were identified before and within 30 days after a prescription claim for the combination drug dextromethorphan-quinidine in the Truven Health MarketScan database (IBM Corp). The exact periods represented by each year were October 29, 2010, through September 30, 2011 (2011); October 1, 2011, through September 30, 2012 (2012); October 1, 2012, through September 30, 2013 (2013); October 1, 2013, through September 30, 2014 (2014); and October 1, 2014, through December 31, 2015 (2015).

Figure 3.  Estimates on Spending and the Number of Prescriptions Among Medicare Part D Beneficiaries
Estimates on Spending and the Number of Prescriptions Among Medicare Part D Beneficiaries

Includes patients who received a prescription for the combination drug dextromethorphan hydrobromide and quinidine sulfate from 2011 through 2016.

Table.  Baseline Characteristics of Patients Prescribed Dextromethorphan-Quinidinea
Baseline Characteristics of Patients Prescribed Dextromethorphan-Quinidinea
1.
Downing  NS, Aminawung  JA, Shah  ND, Krumholz  HM, Ross  JS.  Clinical trial evidence supporting FDA approval of novel therapeutic agents, 2005-2012.  JAMA. 2014;311(4):368-377. doi:10.1001/jama.2013.282034PubMedGoogle ScholarCrossref
2.
Fralick  M, Avorn  J, Franklin  JM, Abdurrob  A, Kesselheim  AS.  Application and impact of run-in studies.  J Gen Intern Med. 2018;33(5):759-763. doi:10.1007/s11606-018-4344-7PubMedGoogle ScholarCrossref
3.
McMurray  JJV, Packer  M, Desai  AS,  et al; PARADIGM-HF Investigators and Committees.  Angiotensin-neprilysin inhibition versus enalapril in heart failure.  N Engl J Med. 2014;371(11):993-1004. doi:10.1056/NEJMoa1409077PubMedGoogle ScholarCrossref
4.
Hurria  A, Naylor  M, Cohen  HJ.  Improving the quality of cancer care in an aging population: recommendations from an IOM report.  JAMA. 2013;310(17):1795-1796. doi:10.1001/jama.2013.280416PubMedGoogle ScholarCrossref
5.
Murthy  VH, Krumholz  HM, Gross  CP.  Participation in cancer clinical trials: race-, sex-, and age-based disparities.  JAMA. 2004;291(22):2720-2726. doi:10.1001/jama.291.22.2720PubMedGoogle ScholarCrossref
6.
Ellis  B, Hicken  M. The little red pill being pushed on the elderly: CNN investigation exposes inappropriate use of drug in nursing homes. https://www-m.cnn.com/2017/10/12/health/nuedexta-nursing-homes-invs/index.html. Updated October 12, 2017. Accessed August 10, 2018.
7.
FDA Center for Drug Evaluation and Research. Medical review application number: 021879Orig1s000. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2010/021879Orig1s000MedR.pdf. April 30, 2010. Accessed August 10, 2018.
8.
US Food and Drug Administration. Highlights of prescribing information: Nuedexta [drug label]. https://www.accessdata.fda.gov/drugsatfda_docs/label/2010/021879s000lbl.pdf. October 29, 2010. Accessed August 10, 2018.
9.
King  RR, Reiss  J.  The epidemiology and pathophysiology of pseudobulbar affect and its association with neurodegeneration.  Degener Neurol Neuromuscul Dis. 2013;3:23-31. doi:10.2147/DNND.S34160Google Scholar
10.
Wortzel  HS, Oster  TJ, Anderson  CA, Arciniegas  DB.  Pathological laughing and crying: epidemiology, pathophysiology and treatment.  CNS Drugs. 2008;22(7):531-545. doi:10.2165/00023210-200822070-00001PubMedGoogle ScholarCrossref
11.
Pioro  EP, Brooks  BR, Cummings  J,  et al; Safety, Tolerability, and Efficacy Results Trial of AVP-923 in PBA Investigators.  Dextromethorphan plus ultra low-dose quinidine reduces pseudobulbar affect.  Ann Neurol. 2010;68(5):693-702. doi:10.1002/ana.22093PubMedGoogle ScholarCrossref
12.
Cummings  JL, Lyketsos  CG, Peskind  ER,  et al.  Effect of dextromethorphan-quinidine on agitation in patients with Alzheimer disease dementia.  JAMA. 2015;314(12):1242-1254. doi:10.1001/jama.2015.10214PubMedGoogle ScholarCrossref
13.
Optum. Real-world data portal. https://www.optum.com/solutions/life-sciences/explore-data/advanced-analytics.html. Accessed November 10, 2018.
14.
IBM Watson Health. Research data and analytic tools: Truven health analytics. https://truvenhealth.com/markets/life-sciences/products/data-tools. Accessed August 10, 2018.
15.
Fralick  M, Kesselheim  AS, Avorn  J, Schneeweiss  S.  Use of health care databases to support supplemental indications of approved medications.  JAMA Intern Med. 2018;178(1):55-63. doi:10.1001/jamainternmed.2017.3919PubMedGoogle ScholarCrossref
16.
Centers for Medicare and Medicaid Services. 2015 Medicare drug spending dashboard. https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/information-on-prescription-drugs/2015medicare.html. Modified May 15, 2018. Accessed August 10, 2018.
17.
Wang  SV, Verpillat  P, Rassen  JA, Patrick  A, Garry  EM, Bartels  DB.  Transparency and reproducibility of observational cohort studies using large healthcare databases.  Clin Pharmacol Ther. 2016;99(3):325-332. doi:10.1002/cpt.329PubMedGoogle ScholarCrossref
18.
Robert  PH, Verhey  FRJ, Byrne  EJ,  et al.  Grouping for behavioral and psychological symptoms in dementia: clinical and biological aspects: consensus paper of the European Alzheimer Disease Consortium.  Eur Psychiatry. 2005;20(7):490-496. doi:10.1016/j.eurpsy.2004.09.031PubMedGoogle ScholarCrossref
19.
Gitlin  LN, Kales  HC, Lyketsos  CG.  Nonpharmacologic management of behavioral symptoms in dementia.  JAMA. 2012;308(19):2020-2029. doi:10.1001/jama.2012.36918PubMedGoogle ScholarCrossref
20.
Sheehan  R, Hassiotis  A, Walters  K, Osborn  D, Strydom  A, Horsfall  L.  Mental illness, challenging behaviour, and psychotropic drug prescribing in people with intellectual disability: UK population based cohort study.  BMJ. 2015;351:h4326. doi:10.1136/bmj.h4326PubMedGoogle ScholarCrossref
21.
de Kuijper  G, Hoekstra  P, Visser  F, Scholte  FA, Penning  C, Evenhuis  H.  Use of antipsychotic drugs in individuals with intellectual disability (ID) in the Netherlands: prevalence and reasons for prescription.  J Intellect Disabil Res. 2010;54(7):659-667. doi:10.1111/j.1365-2788.2010.01275.xPubMedGoogle ScholarCrossref
22.
Lunsky  Y, Khuu  W, Tadrous  M, Vigod  S, Cobigo  V, Gomes  T.  Antipsychotic use with and without comorbid psychiatric diagnosis among adults with intellectual and developmental disabilities.  Can J Psychiatry. 2018;63(6):361-369. doi:10.1177/0706743717727240PubMedGoogle ScholarCrossref
23.
Eguale  T, Buckeridge  DL, Verma  A,  et al.  Association of off-label drug use and adverse drug events in an adult population.  JAMA Intern Med. 2016;176(1):55-63. doi:10.1001/jamainternmed.2015.6058PubMedGoogle ScholarCrossref
24.
Leucht  S, Wahlbeck  K, Hamann  J, Kissling  W.  New generation antipsychotics versus low-potency conventional antipsychotics: a systematic review and meta-analysis.  Lancet. 2003;361(9369):1581-1589. doi:10.1016/S0140-6736(03)13306-5PubMedGoogle ScholarCrossref
25.
Wang  PS, Schneeweiss  S, Avorn  J,  et al.  Risk of death in elderly users of conventional vs atypical antipsychotic medications.  N Engl J Med. 2005;353(22):2335-2341. doi:10.1056/NEJMoa052827PubMedGoogle ScholarCrossref
26.
Maust  DT, Kim  HM, Seyfried  LS,  et al.  Antipsychotics, other psychotropics, and the risk of death in patients with dementia: number needed to harm.  JAMA Psychiatry. 2015;72(5):438-445. doi:10.1001/jamapsychiatry.2014.3018PubMedGoogle ScholarCrossref
27.
Kongkaew  C, Noyce  PR, Ashcroft  DM.  Hospital admissions associated with adverse drug reactions: a systematic review of prospective observational studies.  Ann Pharmacother. 2008;42(7):1017-1025. doi:10.1345/aph.1L037PubMedGoogle ScholarCrossref
28.
Rochon  PA, Stukel  TA, Bronskill  SE,  et al.  Variation in nursing home antipsychotic prescribing rates.  Arch Intern Med. 2007;167(7):676-683. doi:10.1001/archinte.167.7.676PubMedGoogle ScholarCrossref
29.
Newman  JC, Steinman  MA.  Dextromethorphan-quinidine for agitation in Alzheimer disease.  JAMA. 2016;315(11):1166. doi:10.1001/jama.2015.18286PubMedGoogle ScholarCrossref
30.
Sacks  CA, Lee  CC, Kesselheim  AS, Avorn  J.  Medicare spending on brand-name combination medications vs their generic constituents.  JAMA. 2018;320(7):650-656. doi:10.1001/jama.2018.11439PubMedGoogle ScholarCrossref
31.
Starkstein  SE, Migliorelli  R, Tesón  A,  et al.  Prevalence and clinical correlates of pathological affective display in Alzheimer’s disease.  J Neurol Neurosurg Psychiatry. 1995;59(1):55-60. doi:10.1136/jnnp.59.1.55PubMedGoogle ScholarCrossref
32.
Siddiqui  MS, Fernandez  HH, Garvan  CW,  et al.  Inappropriate crying and laughing in Parkinson disease and movement disorders.  World J Biol Psychiatry. 2009;10(3):234-240. doi:10.1080/15622970701639445PubMedGoogle ScholarCrossref
33.
Strowd  RE, Cartwright  MS, Okun  MS, Haq  I, Siddiqui  MS.  Pseudobulbar affect: prevalence and quality of life impact in movement disorders.  J Neurol. 2010;257(8):1382-1387. doi:10.1007/s00415-010-5550-3PubMedGoogle ScholarCrossref
34.
Work  SS, Colamonico  JA, Bradley  WG, Kaye  RE.  Pseudobulbar affect: an under-recognized and under-treated neurological disorder.  Adv Ther. 2011;28(7):586-601. doi:10.1007/s12325-011-0031-3PubMedGoogle ScholarCrossref
35.
Desai  JR, Hyde  CL, Kabadi  S,  et al.  Utilization of positive and negative controls to examine comorbid associations in observational database studies.  Med Care. 2017;55(3):244-251. doi:10.1097/MLR.0000000000000640PubMedGoogle ScholarCrossref
36.
Kim  SC, Solomon  DH, Rogers  JR,  et al.  Cardiovascular safety of tocilizumab versus tumor necrosis factor inhibitors in patients with rheumatoid arthritis: a multi-database cohort study.  Arthritis Rheumatol. 2017;69(6):1154-1164. doi:10.1002/art.40084Google Scholar
37.
Connolly  JG, Wang  SV, Fuller  CC,  et al.  Development and application of two semi-automated tools for targeted medical product surveillance in a distributed data network.  Curr Epidemiol Rep. 2017;4(4):298-306. doi:10.1007/s40471-017-0121-0PubMedGoogle ScholarCrossref
38.
Ball  R, Robb  M, Anderson  SA, Dal Pan  G.  The FDA’s Sentinel Initiative: a comprehensive approach to medical product surveillance.  Clin Pharmacol Ther. 2016;99(3):265-268. doi:10.1002/cpt.320PubMedGoogle ScholarCrossref
Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    1 Comment for this article
    EXPAND ALL
    What is the thrust of this article?
    Robert Wang, Ph.D., M.D. | Primary practice
    The facts reviewed in this article appear accurate within the limitations of the available data. The trends described, the financial considerations, and the potential risks are consistent with the experience of clinicians who practice in this field.

    However, the thrust of the article appears to be more concerned about the health of our regulatory process, rather than the care of the patients in this functionally debilitated class.

    Yes, this medication is being prescribed to those with labile behavior associated with brain disease of many kinds. It is being used because it is somewhat clinically effective in
    the experience of clinicians in the field. Yes, it has adverse drug risks, but those risks appear much less severe than behavioral interventions such as neuroleptics, benzodiazepines, and probably anti-epileptics, certainly than most antihistamines. So rather than concentrate on the regulation of prescribing this medication because it is newer, and data bases allow easy data collection, why not insist that efficacy studies be pursued immediately, especially since you quote a price of 69 cents for the generic medication dosing.

    Article such as this should not be directed to suppressing clinical benefit, but to improving the regulatory process and access to low cost generics which add to public health, not subtract from it.
    CONFLICT OF INTEREST: None Reported
    READ MORE
    Original Investigation
    January 7, 2019

    Assessment of Use of Combined Dextromethorphan and Quinidine in Patients With Dementia or Parkinson Disease After US Food and Drug Administration Approval for Pseudobulbar Affect

    Author Affiliations
    • 1Program On Regulation, Therapeutics, And Law (PORTAL), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
    • 2Eliot Phillipson Clinician Scientist Training Program, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
    JAMA Intern Med. 2019;179(2):224-230. doi:10.1001/jamainternmed.2018.6112
    Key Points

    Question  After US Food and Drug Administration approval for treatment of pseudobulbar affect based on clinical trials conducted in patients with multiple sclerosis and amyotrophic lateral sclerosis, what patients received combined dextromethorphan hydrobromide and quinidine sulfate?

    Findings  In this population-based cohort study of 12 858 patients in 2 commercial insurance databases who filled a prescription for the medication, most had a diagnosis of dementia and/or Parkinson disease. Reported Medicare spending on dextromethorphan-quinidine increased from $3.9 million in 2011 to $200.4 million in 2016; the number of patients receiving the medication increased from 3296 to 50 402 in the same period.

    Meaning  Dextromethorphan-quinidine appears to be primarily prescribed for patients with dementia and/or Parkinson disease, although its preapproval studies of efficacy were performed in patients with other diagnoses.

    Abstract

    Importance  In 2010, the US Food and Drug Administration (FDA) approved a combination of dextromethorphan hydrobromide and quinidine sulfate for the treatment of pseudobulbar affect after studies in patients with amyotrophic lateral sclerosis (ALS) or multiple sclerosis (MS). This medication, however, may be commonly prescribed in patients with dementia and/or Parkinson disease (PD).

    Objective  To investigate the prescribing patterns of dextromethorphan-quinidine, including trends in associated costs.

    Design, Setting, and Participants  This population-based cohort study of patients prescribed dextromethorphan-quinidine used data from 2 commercial insurance databases, Optum Clinformatics Data Mart and Truven Health MarketScan. The Medicare Part D Prescription Drug Program data set was used to evaluate numbers of prescriptions and total reported spending by the Centers for Medicare & Medicaid Services. Patients were included if they were prescribed dextromethorphan-quinidine from October 29, 2010, when the drug was approved, through March 1, 2017, for Optum and December 31, 2015, for Truven. Data were analyzed from December 1, 2017, through August 1, 2018.

    Main Outcomes and Measures  The proportion of patients prescribed dextromethorphan-quinidine with a diagnosis of MS, ALS, or dementia and/or PD, as well as the number of patients with a history of heart failure (a contraindication for the drug).

    Results  In the commercial health care databases, 12 858 patients filled a prescription for dextromethorphan-quinidine during the study period. Mean (SD) age was 66.0 (18.5) years, 66.7% were women, and 13.3% had a history of heart failure. Combining results from both databases, few patients had a diagnosis of MS (8.4%) or ALS (6.8%); most (57.0%) had a diagnosis of dementia and/or PD. In the Medicare Part D database, the number of patients prescribed dextromethorphan-quinidine increased 15.3-fold, from 3296 in 2011 to 50 402 in 2016. Reported spending by Centers for Medicare & Medicaid Services on this medication increased from $3.9 million in 2011 to $200.4 million in 2016.

    Conclusions and Relevance  Despite approval by the FDA for pseudobulbar affect based on studies of patients with ALS or MS, dextromethorphan-quinidine appears to be primarily prescribed for patients with dementia and/or PD.

    Introduction

    Pivotal clinical trials conducted to earn US Food and Drug Administration (FDA) approval of an investigational drug typically enroll a highly selected patient population.1-5 Stringent selection criteria are necessary to achieve internal validity of trial results, but the trade-off is a lack of external generalizability.1,3 Extrapolating a drug’s efficacy and safety to unstudied patients is commonplace but clinically challenging, especially when the preapproval studies demonstrate limited clinical benefit or important safety concerns. A 2017 report suggests that dextromethorphan hydrobromide and quinidine sulfate (Nuedexta; Avanir Pharmaceuticals, Inc) is an example of a medication studied in a narrow patient population in preapproval trials, which was then used in a broader population after its approval.6

    In 2010, the FDA approved dextromethorphan-quinidine for the treatment of pseudobulbar affect. The pivotal trial at the approved dose was a 12-week placebo-controlled randomized clinical trial of 326 middle-aged patients with amyotrophic lateral sclerosis (ALS) or multiple sclerosis (MS).7,8 Pseudobulbar affect is characterized by sudden, uncontrollable, and often inappropriate episodes of crying or laughing. Pseudobulbar affect is diagnosed clinically, sometimes with the aid of a patient-reported questionnaire.9,10 No specific laboratory markers, imaging findings, or pathologic results are available to confirm the diagnosis. Pseudobulbar affect is diagnosed in approximately 40% of patients with ALS and approximately 10% of patients with MS.9,10 In the main placebo-controlled preapproval clinical trial, patients randomized to dextromethorphan-quinidine had modest reductions in episodes of crying or laughing, the study’s primary end point, and higher rates of dizziness, diarrhea, and urinary tract infection.11 The mean age of patients in this trial was approximately 52 years.11

    The pharmacologic mechanism for why dextromethorphan-quinidine was found to help patients with pseudobulbar affect remains unclear. Dextromethorphan is the active ingredient in some over-the-counter cough syrups, has widely known sedative properties, and is metabolized by the cytochrome P450 2D6 isozyme (CYP2D6). Quinidine, known for its antiarrhythmic properties, is a potent CYP2D6 inhibitor, and its role in the combination is to increase plasma concentrations of dextromethorphan.8 In 2010, the medication was approved with an explicit warning: “Nuedexta has not been shown to be safe or effective in other types of emotional lability that can commonly occur, for example, in Alzheimer’s disease and other dementias.”8

    In 2015, a 10-week phase 2 trial of dextromethorphan-quinidine in 220 patients with Alzheimer disease was published12; it was designed and funded by Avanir Pharmaceuticals. The study found that patients randomized to dextromethorphan-quinidine had a 1- to 2-point greater reduction in agitation scores (scale range, 0 [absence of symptoms] to 12 [daily severe symptoms]) compared with patients receiving placebo.12 Patients randomized to dextromethorphan-quinidine also had higher rates of falls, urinary tract infection, and serious adverse events. In January 2015, the prescribing information was updated to remove the statement about patients with dementia. Because dextromethorphan-quinidine was primarily studied in patients with ALS or MS to earn FDA approval, we examined the prescribing patterns of dextromethorphan-quinidine to characterize the population of patients who received the drug after approval and to determine how its actual use changed over time.

    Methods

    We conducted a population-based cohort study using the following 2 US commercial insurance claims databases: Optum Clinformatics Data Mart13 and Truven Health MarketScan.14 Both databases include patient demographics and deidentified individual-level data on the use of health care services, including pharmacy dispensing of medications.15 Collectively, Optum and Truven provide data for approximately 60 million people. Data were collected for prescriptions from October 29, 2010 (date of drug approval), until December 31, 2015 (the most recent available data for the Truven database), and March 1, 2017 (the most recent available data for the Optum database). Data use agreements for the Truven and Optum databases were in place. The institutional review board of Brigham and Women’s Hospital, Boston, Massachusetts, provided ethics approval and waived the need for informed consent.

    We identified all patients who filled a prescription for dextromethorphan-quinidine. When multiple prescriptions were filled for a single patient over time, we analyzed data from the time of the first prescription (index date). We excluded patients who did not have at least 180 days of baseline enrollment before the index date. We identified diagnoses from the baseline enrollment period using codes from the International Classification of Diseases, Ninth Revision, and International Statistical Classification of Diseases and Health Related Problems, 10th Revision (eTable 1 in the Supplement).

    Although some patients with Medicare Advantage insurance are included (approximately 10% of patients in the Optum and <1% in the Truven databases), commercial insurance claims databases primarily include patients younger than 65 years and therefore likely underestimate the number of patients with dementia, who are often older. We also obtained data from the publicly available Medicare Part D Prescription Drug Program data set.16 This data set, developed by the Centers for Medicare & Medicaid Services (CMS), includes information for a subset of beneficiaries who are enrolled in Medicare Part D, which represents about 70% of Medicare beneficiaries.16 The Part D database differs from commercial claims databases, which typically include limited information on drug pricing and total spending for medications.

    Baseline Covariates

    Baseline demographics (ie, age and sex) and diagnoses related to chronic medical conditions were identified to 365 days before the index date. We determined claims for a prescription medication to 30 days before the index date (eTable 2 in the Supplement). We used information in the Optum database to identify the specialty of the prescribing health care professional; such an identifier was not consistently available in the Truven database.

    Study Outcomes

    The primary outcome was the proportion of patients prescribed dextromethorphan-quinidine with diagnoses of MS, ALS, or dementia and/or PD. We included PD because it is a common cause of dementia. To identify the primary outcome diagnoses, we reviewed available data to 1000 days before or 30 days after the index date. This prolonged duration was used to limit undercounting of patients with the diagnoses. Using data from the Optum database, we assessed the days of medication supplied by the index prescription. We also identified patients with a history of heart failure, which is a contraindication for dextromethorphan-quinidine. Finally, because quinidine is known to prolong the QT interval, we assessed the number of patients with a concomitant prescription for another QT-prolonging medication, most commonly antibiotics or antipsychotics (eTable 2 in the Supplement).

    We determined the estimated yearly aggregate payment for Part D claims for dextromethorphan-quinidine from 2011 (first available data) until 2016 (end of available data). The aggregated payment includes ingredient cost, dispensing fee, sales tax, and any applicable administration fees. The total drug cost was based on the amounts paid by the Part D plan, Medicare beneficiary, government subsidies, and any other third-party payers; the total cost did not include rebates or discounts from the drug’s manufacturer. Rebate amounts are usually proprietary and thus not publicly available.

    Statistical Analysis

    Data were analyzed from December 1, 2017, through August 1, 2018. We used descriptive statistics to characterize patient- and physician-level characteristics. Analyses were conducted using the Aetion platform,17 SAS statistical software (version 9.3; SAS Institute Inc), and R software (version 3.4.2; R Core Team, 2017).

    Results

    We identified 7923 individuals in the Optum database and 6633 in the Truven database who were prescribed dextromethorphan-quinidine. After excluding 1698 individuals who did not have 180 days of preceding baseline enrollment, the study cohort included 12 858 patients across the 2 databases. Mean (SD) age in the cohort was 66.0 (18.5) years; 66.7% were female and 33.3% were male.

    In the Optum database, 365 patients (5.5%) had a diagnosis of ALS, 427 (6.4%) had a diagnosis of MS, and 4345 (65.3%) had a diagnosis of dementia and/or PD (Table). Similar findings were observed before the drug label update in 2015 (Figure 1). For example, in 2013 and 2014, 633 of 973 patients prescribed dextromethorphan-quinidine (65.1%) had a diagnosis of dementia and/or PD. The percentage of patients with a diagnosis of ALS or MS generally decreased over time, and the percentage of patients with dementia and/or PD generally increased over time (Figure 1).

    In the Truven database, 514 patients (8.3%) had a diagnosis of ALS, 650 (10.5%) had a diagnosis of MS, and 2978 (48.0%) had a diagnosis of dementia and/or PD (Table). Similar findings were observed before the drug label update in 2015; over time the percentage of patients with a diagnosis of ALS or MS decreased and the percentage of patients with a diagnosis of dementia and/or PD generally increased (Figure 2). When we combined results from both databases, few patients prescribed dextromethorphan-quinidine had diagnoses of MS (1077 [8.4%]) or ALS (879 [6.8%]). Most patients (7323 [57.0%]) had a diagnosis of dementia and/or PD. In addition, 1713 patients in our study (13.3%) had a diagnosis of heart failure and 4928 (38.3%) filled a prescription for a QT-prolonging medication within 30 days of receiving dextromethorphan-quinidine (Table).

    Using the Optum database, we identified 3864 distinct prescribers. The most common prescribers included general internists (982 [25.4%]), neurologists (880 [22.8%]), family practitioners (676 [17.5%]), psychiatrists (380 [9.8%]), geriatricians (195 [5.1%]), nurse practitioners (187 [4.8%]), and physician assistants (97 [2.5%]). The mean (SD) supply of the prescriptions was for 22.0 (16.4) days (range, 1-180 days).

    Using the Medicare Part D database, the number of prescriptions for dextromethorphan-quinidine increased 51.2-fold, from 9346 in 2011 to 478 481 in 2016; the number of patients receiving these prescriptions increased 15.3-fold, from 3296 in 2011 to 50 402 in 2016. Estimated Part D spending on dextromethorphan-quinidine in the database increased from $3.9 million in 2011 to $200.4 million in 2016 (Figure 3). Of the 2015 spending, $102.2 million (74.3%) was for patients 65 years and older (2016 data for patients 65 years and older were not available).

    Discussion

    In our study, fewer than 20% of patients prescribed dextromethorphan-quinidine had a diagnosis of ALS or MS. This finding was consistent across 2 national commercial claims databases; it was more pronounced in the Optum database, which has a greater proportion of patients with Medicare Advantage coverage, than in the Truven database. Despite FDA approval for treatment of pseudobulbar affect based on studies of patients with ALS or MS, our findings suggest that dextromethorphan-quinidine was primarily prescribed for patients with dementia and/or PD.

    Pseudobulbar affect is a clinical diagnosis, which might partly explain why the drug was used in a broader population after approval than in the preapproval trials. Although behavioral symptoms are common in patients with dementia, the cause is often not pseudobulbar affect.18,19 Current therapies to treat behavioral symptoms of dementia are largely ineffective, and thus clinicians may want to prescribe dextromethorphan-quinidine to see if it helps their patients, despite the dearth of trial evidence on its efficacy in this context.18,19 Yet the absence of data showing efficacy, coupled with the demonstrated risks of falls and possible cardiac effects, calls this strategy into question.

    The use of dextromethorphan-quinidine in broader populations than those included in the pivotal studies leading to FDA approval has some parallels with the prescribing of antipsychotics.20,21 A population-based cohort study of 51 881 adults in Ontario, Canada, with intellectual and developmental disabilities found that 39% were prescribed an antipsychotic.22 Of these patients, only 40% had a documented major mental illness (ie, psychotic disorder, bipolar disorder, major depressive disorder). Although broader use of medications in understudied patient populations may be clinically justifiable, drugs may also have previously unrecognized adverse effects when used in broader populations.23

    Clinical trials of antipsychotics typically enrolled young to middle-aged patients with major mental illnesses, but with few medical comorbidities.24 After FDA approval, these medications were prescribed to patients who were older, had a greater burden of chronic medical conditions, and often did not have a major mental illness.25 Multiple studies have since demonstrated that antipsychotics increase the risk of mortality in elderly patients.25,26 As a result, these medications now carry a boxed warning about this risk, the most prominent safety warning the FDA can issue. In the case of dextromethorphan-quinidine, we found that it is being used in a population that is approximately 15 years older than those included in the main preapproval clinical trial (mean ages, 66.0 years compared with 52.0 years). Age is one of the strongest risk factors across all drugs for adverse drug events.27

    Other important commonalities between past experiences with antipsychotics and prescribing of dextromethorphan-quinidine may exist. For example, for patients with intellectual and developmental disabilities, prescribing of antipsychotics was more common for patients in group homes (56.4%) compared with 39.2% not in group homes.22 Prior studies have also shown that the use of antipsychotics is more common for patients with dementia living in nursing homes than those living in their own home.28 What proportion of patients in our study were institutionalized is unknown, because insurance claims data rarely include institutionalized patients. However, according to the 2017 CNN report, which used data from Quintiles and IMS Health (currently IQVIA),6 approximately half of all prescriptions for dextromethorphan-quinidine from 2012 to 2016 were for patients in long-term care facilities. Because patients in long-term care are generally not included in insurance claims databases, this finding may explain why we identified more patients prescribed dextromethorphan-quinidine using data from CMS compared with patients in the Optum and Truven databases. According to the report,6 nearly half of the CMS claims for this medication were from physicians who had received money, gifts, or meals from Avanir Pharmaceuticals or its parent company, Otsuka Pharmaceutical. Because the Optum and Truven claims databases use encrypted physician identifiers, we were unable to link these physician identifiers with the federal Open Payments database that records payments from pharmaceutical companies to physicians.

    The phase 2 trial comparing dextromethorphan-quinidine with placebo for the treatment of agitation in patients with Alzheimer disease12 used a patented sequential parallel comparison design method. This design, according to the study report, enhanced “the ability to detect a treatment signal even in the context of a robust placebo response.”12(p1243) Although the study concluded that these “preliminary findings require confirmation in additional clinical trials with longer treatment duration,”12(p1252) a letter to the editor noted that the proprietary study design functionally precluded replication without obtaining a license from the patent holders.29

    The adverse effects of dextromethorphan-quinidine, as reported in the trial in patients with Alzheimer disease,12 also warrant attention. Two of the more common adverse effects were falls and urinary tract infection, both likely the result of the sedating effects of dextromethorphan. Other concerns include QT prolongation and a risk of precipitating heart failure.8 In our study, 13.3% of patients prescribed dextromethorphan-quinidine had a history of heart failure, a contraindication, and 38.3% were recently prescribed another medication known to prolong the QT interval. We were not able to calculate the risk of urinary tract infection or fall because our study lacked an active comparator.

    Our study found that CMS reported spending approximately $520 million on dextromethorphan-quinidine from 2011 to 2016 (Figure 3). A recent study30 comparing the cost of combination medications with the cost of their individual generic constituents found that CMS reported spending $12.30 per dose of dextromethorphan-quinidine, whereas the cost of the generic constituents was $0.69 total. Notably, to determine the cost of the generic constituents, the study30 used the price of a 200-mg dose of quinidine rather than the 10-mg dose used in Nuedexta because quinidine is not available in the United States at that lower dose as an individual product. If the lower dose of quinidine was available at a similar cost, that study estimated that the potential reduction in reported spending by the Medicare drug benefit program in 2016 could have been $189 million had the generic products been prescribed instead. The same study30 also found that the cost of dextromethorphan-quinidine increased by 42% from 2011 to 2016.

    Limitations

    Our study has several limitations. First, we were unable to contact physicians and other health care professionals and determine why dextromethorphan-quinidine was prescribed and thus inferred the reason based on patient diagnoses from the approximate time of the initial prescription. For patients without recent claims connected to ALS or MS, we were unable to determine whether the dextromethorphan-quinidine was for the treatment of agitation or whether those patients displayed signs of pseudobulbar affect, which has been reported to occur in about 18% of patients with dementia and about 7% of patients with PD.31-33 The exact prevalence of pseudobulbar affect for patients with dementia or PD, however, is hard to estimate. A prior study by the manufacturer of dextromethorphan-quinidine suggests that prevalence may range from 9% to 39% for patients with dementia and from 4% to 24% for patients with PD.34 One might argue that extrapolation of available data and prescription of dextromethorphan-quinidine to patients similar to those identified in our data would be reasonable; however, until January 2015, the drug’s labeling explicitly stated that the medication was not shown to be safe or effective in patients with Alzheimer disease and other dementias who do not have pseudobulbar affect.8 Second, some patients had a diagnosis of dementia and/or PD with ALS (1.0%) or MS (1.8%), but these percentages are small, and such dual diagnoses do not meaningfully affect our overall findings. Also, some overlap occurred between patients captured in the Truven and Optum databases, and we may have double-counted some patients. This degree of overlap is small (likely <15%) and is unlikely to change our findings.35,36 Third, because older patients and patients living in long-term care facilities are underrepresented in commercial insurance claims databases, we may have underestimated the extent of prescribing to patients with dementia or PD. Fourth, the cost data from CMS does not include rebates or discounts; thus, the actual figures for CMS spending on dextromethorphan-quinidine are likely somewhat smaller than the published data indicate.

    Conclusions

    In response to findings such as ours, further attention should be paid to educating prescribers about the actual benefits and risks of this costly drug combination. In addition, the FDA could consider more closely monitoring the characteristics of the populations of patients using medication after their approval, as well as their adverse drug-related clinical outcomes. Through the Sentinel Initiative, the FDA already collects near real-time anonymized prescribing data for more than 100 million people.37,38 The agency launched the Sentinel Initiative in 2008 with the goal of monitoring the use and safety of drugs and other medical products. Data from the Sentinel Initiative could be used to identify and quantify how medications are being prescribed following their approval. When medications are identified as being prescribed widely among patient populations that have not been studied in clinical trials, further investigation would be warranted to understand why this is occurring and to conduct studies directly assessing the safety and effectiveness of the medications in these patients.

    In the case of dextromethorphan-quinidine, our findings show that this medication was quickly used after approval primarily in elderly patients with dementia and/or PD. Further studies should be required to evaluate the safety and effectiveness of this medication as it is currently being used.

    Back to top
    Article Information

    Accepted for Publication: September 12, 2018.

    Corresponding Author: Aaron Kesselheim, MD, JD, MPH, Program On Regulation, Therapeutics, And Law (PORTAL), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, 1620 Tremont St, Ste 3030, Boston, MA 02120 (akesselheim@bwh.harvard.edu).

    Published Online: January 7, 2019. doi:10.1001/jamainternmed.2018.6112

    Author Contributions: Dr Fralick had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: All authors.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Fralick.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Fralick.

    Obtained funding: Kesselheim.

    Conflict of Interest Disclosures: Dr Kesselheim reported receiving grants from the US Food and Drug Administration Office of Generic Drugs and Division of Health Communication (2013-2016) unrelated to the topic of this article. No other disclosures were reported.

    Funding/Support: This study was supported by the Laura and John Arnold Foundation, the Harvard Program in Therapeutic Science, the Engelberg Foundation, and the University of Toronto Clinician Scientist Training Program (Dr Fralick).

    Role of the Funder/Sponsor: The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Additional Contributions: Jun Liu, MD, MPH, Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, Boston, Massachusetts, helped with programming and analyzing the Optum cohort; she received no compensation for her work outside of her usual salary as an employee of Brigham and Women’s Hospital. Jerry Avorn, MD, Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, provided edits and helpful comments to our manuscript, for which he was not compensated.

    References
    1.
    Downing  NS, Aminawung  JA, Shah  ND, Krumholz  HM, Ross  JS.  Clinical trial evidence supporting FDA approval of novel therapeutic agents, 2005-2012.  JAMA. 2014;311(4):368-377. doi:10.1001/jama.2013.282034PubMedGoogle ScholarCrossref
    2.
    Fralick  M, Avorn  J, Franklin  JM, Abdurrob  A, Kesselheim  AS.  Application and impact of run-in studies.  J Gen Intern Med. 2018;33(5):759-763. doi:10.1007/s11606-018-4344-7PubMedGoogle ScholarCrossref
    3.
    McMurray  JJV, Packer  M, Desai  AS,  et al; PARADIGM-HF Investigators and Committees.  Angiotensin-neprilysin inhibition versus enalapril in heart failure.  N Engl J Med. 2014;371(11):993-1004. doi:10.1056/NEJMoa1409077PubMedGoogle ScholarCrossref
    4.
    Hurria  A, Naylor  M, Cohen  HJ.  Improving the quality of cancer care in an aging population: recommendations from an IOM report.  JAMA. 2013;310(17):1795-1796. doi:10.1001/jama.2013.280416PubMedGoogle ScholarCrossref
    5.
    Murthy  VH, Krumholz  HM, Gross  CP.  Participation in cancer clinical trials: race-, sex-, and age-based disparities.  JAMA. 2004;291(22):2720-2726. doi:10.1001/jama.291.22.2720PubMedGoogle ScholarCrossref
    6.
    Ellis  B, Hicken  M. The little red pill being pushed on the elderly: CNN investigation exposes inappropriate use of drug in nursing homes. https://www-m.cnn.com/2017/10/12/health/nuedexta-nursing-homes-invs/index.html. Updated October 12, 2017. Accessed August 10, 2018.
    7.
    FDA Center for Drug Evaluation and Research. Medical review application number: 021879Orig1s000. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2010/021879Orig1s000MedR.pdf. April 30, 2010. Accessed August 10, 2018.
    8.
    US Food and Drug Administration. Highlights of prescribing information: Nuedexta [drug label]. https://www.accessdata.fda.gov/drugsatfda_docs/label/2010/021879s000lbl.pdf. October 29, 2010. Accessed August 10, 2018.
    9.
    King  RR, Reiss  J.  The epidemiology and pathophysiology of pseudobulbar affect and its association with neurodegeneration.  Degener Neurol Neuromuscul Dis. 2013;3:23-31. doi:10.2147/DNND.S34160Google Scholar
    10.
    Wortzel  HS, Oster  TJ, Anderson  CA, Arciniegas  DB.  Pathological laughing and crying: epidemiology, pathophysiology and treatment.  CNS Drugs. 2008;22(7):531-545. doi:10.2165/00023210-200822070-00001PubMedGoogle ScholarCrossref
    11.
    Pioro  EP, Brooks  BR, Cummings  J,  et al; Safety, Tolerability, and Efficacy Results Trial of AVP-923 in PBA Investigators.  Dextromethorphan plus ultra low-dose quinidine reduces pseudobulbar affect.  Ann Neurol. 2010;68(5):693-702. doi:10.1002/ana.22093PubMedGoogle ScholarCrossref
    12.
    Cummings  JL, Lyketsos  CG, Peskind  ER,  et al.  Effect of dextromethorphan-quinidine on agitation in patients with Alzheimer disease dementia.  JAMA. 2015;314(12):1242-1254. doi:10.1001/jama.2015.10214PubMedGoogle ScholarCrossref
    13.
    Optum. Real-world data portal. https://www.optum.com/solutions/life-sciences/explore-data/advanced-analytics.html. Accessed November 10, 2018.
    14.
    IBM Watson Health. Research data and analytic tools: Truven health analytics. https://truvenhealth.com/markets/life-sciences/products/data-tools. Accessed August 10, 2018.
    15.
    Fralick  M, Kesselheim  AS, Avorn  J, Schneeweiss  S.  Use of health care databases to support supplemental indications of approved medications.  JAMA Intern Med. 2018;178(1):55-63. doi:10.1001/jamainternmed.2017.3919PubMedGoogle ScholarCrossref
    16.
    Centers for Medicare and Medicaid Services. 2015 Medicare drug spending dashboard. https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/information-on-prescription-drugs/2015medicare.html. Modified May 15, 2018. Accessed August 10, 2018.
    17.
    Wang  SV, Verpillat  P, Rassen  JA, Patrick  A, Garry  EM, Bartels  DB.  Transparency and reproducibility of observational cohort studies using large healthcare databases.  Clin Pharmacol Ther. 2016;99(3):325-332. doi:10.1002/cpt.329PubMedGoogle ScholarCrossref
    18.
    Robert  PH, Verhey  FRJ, Byrne  EJ,  et al.  Grouping for behavioral and psychological symptoms in dementia: clinical and biological aspects: consensus paper of the European Alzheimer Disease Consortium.  Eur Psychiatry. 2005;20(7):490-496. doi:10.1016/j.eurpsy.2004.09.031PubMedGoogle ScholarCrossref
    19.
    Gitlin  LN, Kales  HC, Lyketsos  CG.  Nonpharmacologic management of behavioral symptoms in dementia.  JAMA. 2012;308(19):2020-2029. doi:10.1001/jama.2012.36918PubMedGoogle ScholarCrossref
    20.
    Sheehan  R, Hassiotis  A, Walters  K, Osborn  D, Strydom  A, Horsfall  L.  Mental illness, challenging behaviour, and psychotropic drug prescribing in people with intellectual disability: UK population based cohort study.  BMJ. 2015;351:h4326. doi:10.1136/bmj.h4326PubMedGoogle ScholarCrossref
    21.
    de Kuijper  G, Hoekstra  P, Visser  F, Scholte  FA, Penning  C, Evenhuis  H.  Use of antipsychotic drugs in individuals with intellectual disability (ID) in the Netherlands: prevalence and reasons for prescription.  J Intellect Disabil Res. 2010;54(7):659-667. doi:10.1111/j.1365-2788.2010.01275.xPubMedGoogle ScholarCrossref
    22.
    Lunsky  Y, Khuu  W, Tadrous  M, Vigod  S, Cobigo  V, Gomes  T.  Antipsychotic use with and without comorbid psychiatric diagnosis among adults with intellectual and developmental disabilities.  Can J Psychiatry. 2018;63(6):361-369. doi:10.1177/0706743717727240PubMedGoogle ScholarCrossref
    23.
    Eguale  T, Buckeridge  DL, Verma  A,  et al.  Association of off-label drug use and adverse drug events in an adult population.  JAMA Intern Med. 2016;176(1):55-63. doi:10.1001/jamainternmed.2015.6058PubMedGoogle ScholarCrossref
    24.
    Leucht  S, Wahlbeck  K, Hamann  J, Kissling  W.  New generation antipsychotics versus low-potency conventional antipsychotics: a systematic review and meta-analysis.  Lancet. 2003;361(9369):1581-1589. doi:10.1016/S0140-6736(03)13306-5PubMedGoogle ScholarCrossref
    25.
    Wang  PS, Schneeweiss  S, Avorn  J,  et al.  Risk of death in elderly users of conventional vs atypical antipsychotic medications.  N Engl J Med. 2005;353(22):2335-2341. doi:10.1056/NEJMoa052827PubMedGoogle ScholarCrossref
    26.
    Maust  DT, Kim  HM, Seyfried  LS,  et al.  Antipsychotics, other psychotropics, and the risk of death in patients with dementia: number needed to harm.  JAMA Psychiatry. 2015;72(5):438-445. doi:10.1001/jamapsychiatry.2014.3018PubMedGoogle ScholarCrossref
    27.
    Kongkaew  C, Noyce  PR, Ashcroft  DM.  Hospital admissions associated with adverse drug reactions: a systematic review of prospective observational studies.  Ann Pharmacother. 2008;42(7):1017-1025. doi:10.1345/aph.1L037PubMedGoogle ScholarCrossref
    28.
    Rochon  PA, Stukel  TA, Bronskill  SE,  et al.  Variation in nursing home antipsychotic prescribing rates.  Arch Intern Med. 2007;167(7):676-683. doi:10.1001/archinte.167.7.676PubMedGoogle ScholarCrossref
    29.
    Newman  JC, Steinman  MA.  Dextromethorphan-quinidine for agitation in Alzheimer disease.  JAMA. 2016;315(11):1166. doi:10.1001/jama.2015.18286PubMedGoogle ScholarCrossref
    30.
    Sacks  CA, Lee  CC, Kesselheim  AS, Avorn  J.  Medicare spending on brand-name combination medications vs their generic constituents.  JAMA. 2018;320(7):650-656. doi:10.1001/jama.2018.11439PubMedGoogle ScholarCrossref
    31.
    Starkstein  SE, Migliorelli  R, Tesón  A,  et al.  Prevalence and clinical correlates of pathological affective display in Alzheimer’s disease.  J Neurol Neurosurg Psychiatry. 1995;59(1):55-60. doi:10.1136/jnnp.59.1.55PubMedGoogle ScholarCrossref
    32.
    Siddiqui  MS, Fernandez  HH, Garvan  CW,  et al.  Inappropriate crying and laughing in Parkinson disease and movement disorders.  World J Biol Psychiatry. 2009;10(3):234-240. doi:10.1080/15622970701639445PubMedGoogle ScholarCrossref
    33.
    Strowd  RE, Cartwright  MS, Okun  MS, Haq  I, Siddiqui  MS.  Pseudobulbar affect: prevalence and quality of life impact in movement disorders.  J Neurol. 2010;257(8):1382-1387. doi:10.1007/s00415-010-5550-3PubMedGoogle ScholarCrossref
    34.
    Work  SS, Colamonico  JA, Bradley  WG, Kaye  RE.  Pseudobulbar affect: an under-recognized and under-treated neurological disorder.  Adv Ther. 2011;28(7):586-601. doi:10.1007/s12325-011-0031-3PubMedGoogle ScholarCrossref
    35.
    Desai  JR, Hyde  CL, Kabadi  S,  et al.  Utilization of positive and negative controls to examine comorbid associations in observational database studies.  Med Care. 2017;55(3):244-251. doi:10.1097/MLR.0000000000000640PubMedGoogle ScholarCrossref
    36.
    Kim  SC, Solomon  DH, Rogers  JR,  et al.  Cardiovascular safety of tocilizumab versus tumor necrosis factor inhibitors in patients with rheumatoid arthritis: a multi-database cohort study.  Arthritis Rheumatol. 2017;69(6):1154-1164. doi:10.1002/art.40084Google Scholar
    37.
    Connolly  JG, Wang  SV, Fuller  CC,  et al.  Development and application of two semi-automated tools for targeted medical product surveillance in a distributed data network.  Curr Epidemiol Rep. 2017;4(4):298-306. doi:10.1007/s40471-017-0121-0PubMedGoogle ScholarCrossref
    38.
    Ball  R, Robb  M, Anderson  SA, Dal Pan  G.  The FDA’s Sentinel Initiative: a comprehensive approach to medical product surveillance.  Clin Pharmacol Ther. 2016;99(3):265-268. doi:10.1002/cpt.320PubMedGoogle ScholarCrossref
    ×