Predictors of Medication Adherence

Article Type
Changed
Mon, 05/22/2017 - 18:40
Display Headline
Predictors of medication adherence postdischarge: The impact of patient age, insurance status, and prior adherence

In the outpatient setting, medication adherence (defined as percentage of prescribed medication doses taken by a patient during a specific time period) ranges between 40% and 80% for chronic conditions.1 During acute care hospitalization, changes are often made to patients' medication regimens, which can be confusing and contribute to nonadherence, medication errors, and harmful adverse events.2 Indeed, it is estimated that almost half of patients encounter a medication error after discharge, and approximately 12%17% experience an adverse drug event after returning home.36 It is likely that some of these adverse events may be the result of medication nonadherence.7 Improved patientprovider communication, systems to reconcile prehospitalization and posthospitalization medications, as well as development of mechanisms to enhance adherence, may prevent many of these errors and have become new targets for quality improvement.4, 8 Although postdischarge medication adherence is a crucial target for avoiding adverse events and rehospitalization, few studies have focused on understanding its incidence and predictors, in particular, patient demographic factors such as age and insurance status.911

In addition, few studies have looked at general and posthospital adherence in a population where health literacy is measured, an important area because medication changes during hospitalization may be particularly confusing for patients with low health literacy.11, 12 Health literacy is defined as the degree to which an individual has the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions.13 Prior outpatient research shows that low health literacy is associated with poor patient understanding of the medication regimen and instructions for medication use, which may contribute to postdischarge medication nonadherence.14, 15 Understanding the factors associated with postdischarge medication adherence could help refine interventions that are oriented toward improving transitions in care, patient safety, and reducing unnecessary rehospitalization.

We report here on factors associated with postdischarge medication adherence using data from the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) study.16

METHODS

Study and Participants

PILL‐CVD was a federally funded, 2‐site randomized controlled trial using pharmacist‐assisted medication reconciliation, inpatient pharmacist counseling, low‐literacy adherence aids, and telephone follow‐up that aimed to decrease rates of serious medication errors after hospital discharge.16 The study targeted patients with cardiovascular disease (hospitalized on cardiology or general medical or geriatric services for acute coronary syndromes [ACS] or acute decompensated heart failure [ADHF]) at 2 large academic hospitals, Brigham and Women's Hospital (BWH) and Vanderbilt University Hospital (VUH).

Subjects were eligible for enrollment if they met criteria for ACS or ADHF, were likely to be discharged to home as determined by the primary medical team at the time of study enrollment, and took primary responsibility for administering their medications prior to admission (caregivers could be involved in medication management after discharge). Exclusion criteria included severe visual or hearing impairment, inability to communicate in English or Spanish, active psychiatric illness, dementia, delirium, illness too severe to participate, lack of a home phone number, being in police custody, or participation in another intensive medication adherence program (eg, due to renal transplant).

Out of 6416 patients originally screened for possible enrollment, 862 were randomly assigned to receive usual care or usual care plus the intervention, and 851 remained in the study.16 Both the main study and this secondary data analysis were approved by the Institutional Review Boards of each site.

Baseline Measures

Following informed consent and study enrollment, a variety of baseline data were collected on study participants from medical records and patient interview, including primary language, demographic information (age, race, insurance status, income, and education level), cognition (through administration of the 05‐point MiniCog scale),17 and level of health literacy (through use of the 036‐point short form of the Test of Functional Health Literacy in Adults [s‐TOFHLA] scale).18 Baseline information was also collected on medication use, including number of preadmission medications, measurement of self‐reported adherence prior to admission (using the Morisky scale, a validated 04‐point questionnaire shown to correlate with disease control and indicative of general patterns of adherence),19 and a medication understanding score, adapted from other instruments, which quantifies understanding of the indication, dose, and frequency of up to 5 randomly selected preadmission medications on a 03‐point scale.16, 20, 21

Outcome Measures

Outcomes were collected 30 days postdischarge through a structured questionnaire, administered by telephone. Only patients who completed this call are included in the present analysis. Postdischarge medication adherence was assessed by asking patients to report the number of days out of the previous week they had taken each medication from their postdischarge regimen exactly as prescribed.22 A score was calculated for each medication as the proportion of adherent days (eg, if a patient reported missing 2 days of a medication in the previous week, then adherence would be 5/7 or 71%). A global postdischarge adherence score was then derived for each patient by averaging the adherence score for all regularly scheduled medications. This quantitative measure focused on adherence to medications patients knew they should be taking and did not measure medication discrepancies (sometimes termed unintentional nonadherence).

Analysis

Patient characteristics were summarized and reported using simple descriptive statistics. Candidate predictors of postdischarge medication adherence were chosen a priori from patient characteristics assessed during hospital admission. These included patient age, gender, race, ethnicity, marital status, insurance, years of education, presence of primary care physician (PCP), study site, number of preadmission medications, medication understanding, baseline adherence, cognition, and health literacy. Unadjusted results were calculated using univariable linear regression, with each patient's adherence score as the dependent variable and each predictor as the independent variable. Adjusted results were then derived using multivariable linear regression with all the candidate predictors in the model.

Lastly, because of missing data for some predictors, in particular baseline adherence and medication understanding, multiple imputation techniques were used to impute missing data and increase statistical power.23 We used the Markov Chain Monte Carlo (MCMC) method for multiple imputation, which generally assumes that the data came from a normal distribution and that the missing data are missing at random. Because of the essentially normal distribution of the data, and because the amount of missing data was so small (<1% for almost all variables, 5% for baseline adherence, and 8% for medication understanding), we expected little bias and present the complete case analysis, which maximized statistical power.

Two‐sided P values <0.05 were considered significant, and SAS version 9.2 (Cary, NC) was used for all analyses.

RESULTS

Table 1 shows descriptive baseline patient characteristics of study sample (responders) as well as nonresponders at 30 days. For the responders, the mean age of the 646 patients was 61.2 years, 94.7% were insured, and 19.3% had inadequate or marginal health literacy. Patients were prescribed an average of 8 preadmission medications. Most patients (92.3%) had a regular PCP prior to admission. Nonresponders had nonsignificant trends towards having lower health literacy, medication understanding, and baseline medication adherence.

Baseline Characteristics
CharacteristicTotal N, 30‐Day RespondentsValueTotal N, NonrespondentsValue
  • Abbreviations: PCP, primary care physician; SD, standard deviation; s‐TOFHLA, short form of the Test of Functional Health Literacy in Adults. *03, with 3 indicating better understanding. 036, with higher scores indicating higher health literacy. 04, with 4 indicating higher baseline adherence. 05, with higher scores indicating better cognition; a score <3 indicates dementia.

Age, mean in yr (SD)64661.2 (13.5)4555.4 (14.3)
Gender, N (percentage)646 45 
Female272 (42.1)18 (40.0)
Male374 (57.9)27 (60.0)
Race, N (percentage)643 45 
White511 (79.5)32 (71.1)
Black104 (16.2)11 (24.4)
Other28 (4.4)2 (4.4)
Ethnicity, N (percentage)639 45 
Hispanic24 (3.8)1 (2.2)
Not Hispanic615 (96.2)44 (97.8)
Marital status, N (percentage)646 45 
Married/cohabitate382 (59.1)20 (44.4)
Separated/divorced118 (18.3)11 (24.4)
Widowed81 (12.5)5 (11.1)
Never married65 (10.1)9 (2.0)
Insurance type, N (percentage)646 45 
Medicaid53 (8.2)5 (11.1)
Medicare270 (41.8)13 (28.9)
Private289 (44.7)19 (42.2)
Self‐pay34 (5.3)8 (17.8)
Years of education, mean in yr (SD)64314.0 (3.1)4513.3 (2.7)
Presence of PCP prior to admission, N (percentage)646 45 
Yes596 (92.3)38 (84.4)
No50 (7.74)7 (15.6)
Site, N (percentage)646 45 
Site 1358 (55.4)8 (17.8)
Site 2288 (44.6)37 (82.2)
No. of preadmission medications, mean no. (SD)6417.8 (4.8)457.7 (5.4)
Medication understanding score, mean (SD)*5972.4 (0.5)402.2 (0.62)
Health literacy (s‐TOFHLA) score, mean (SD)64229.1 (8.9)4526.0 (12.0)
Baseline adherence (SD)6132.7 (1.1)452.4 (1.2)
MiniCog score, N (percentage)646 45 
Demented63 (9.8)5 (11.1)
Not demented583 (90.2)40 (88.9)

The average postdischarge adherence score was 95% (standard deviation [SD] = 10.2%), and less than 10% of patients had an adherence score of less than 85%; overall the distribution was left‐skewed. Table 2 illustrates crude and adjusted parameter estimates for variables in the model. Table 3 shows significant findings in the fully adjusted model, which used multiple imputation techniques to account for missing data.

Crude and Adjusted Measurements
PredictorCrude Parameter Estimate (Beta) With 95% Confidence IntervalsP ValueAdjusted Parameter Estimate (Beta) With 95% Confidence IntervalsP Value
  • NOTE: For crude estimates, value is category vs absence of parameter in univariable testing. For adjusted estimates of categorical variables, value is each category compared to referent category. Beta‐coefficient represents absolute change in adherence (eg, 0.010 for age means a 1% absolute increase in adherence for every 10 yr increase in patient age). Abbreviations: PCP, primary care physician; Ref, referent; s‐TOFHLA, short form of the Test of Functional Health Literacy in Adults.

Age per 10 yr0.010 (0.007, 0.020)<0.00010.010 (0.002, 0.020)0.018
Male gender0.012 (0.004, 0.028)0.1370.003 (0.014, 0.020)0.727
Race/ethnicity    
White0.011 (0.009, 0.031)0.266RefRef
Black0.017 (0.038, 0.005)0.130.006 (0.017, 0.030)0.598
Other0.010 (0.029, 0.049)0.5990.017 (0.027, 0.062)0.446
Hispanic/Latino0.005 (0.037, 0.047)0.8030.036 (0.013, 0.085)0.149
Marital status    
Married/cohabitate0.006 (0.011, 0.022)0.500RefRef
Separated/divorced0.005 (0.025, 0.016)0.6640.009 (0.014, 0.031)0.446
Widowed0.001 (0.023, 0.025)0.9220.013 (0.039, 0.013)0.338
Never married0.009 (0.035, 0.018)0.5150.004 (0.033, 0.025)0.784
Insurance type    
Private0.008 (0.008, 0.024)0.347RefRef
Medicaid0.046 (0.075, 0.018)0.0020.026 (0.058, 0.007)0.121
Medicare0.012 (0.004, 0.028)0.1380.002 (0.023, 0.018)0.844
Self‐pay0.027 (0.062, 0.008)0.1350.029 (0.073, 0.015)0.202
Years of education0.003 (0.0003, 0.005)0.0280.0001 (0.003, 0.003)0.949
Presence of PCP prior to admission0.007 (0.022, 0.037)0.6300.002 (0.032, 0.036)0.888
Site0.050 (0.065, 0.034)<0.00010.038 (0.056, 0.021)<0.0001
No. of preadmission medications0.0003 (0.002, 0.001)0.6840.0001 (0.002, 0.002)0.918
Medication understanding score per point0.007 (0.009, 0.023)0.3900.006 (0.011, 0.023)0.513
Health literacy (s‐TOFHLA) score per 10 points0.0006 (0.008, 0.01)0.8970.003 (0.008, 0.01)0.644
Baseline adherence per point0.023 (0.016, 0.031)<0.00010.017 (0.009, 0.024)<0.0001
Cognitive function0.004 (0.022, 0.031)0.7570.008 (0.019, 0.036)0.549
Significant Results in Adjusted Analyses With Multiple Imputation
PredictorParameter Estimate (Beta) With 95% Confidence IntervalsP Value
  • NOTE: Total observations, 646; 67 with missing values. All variables adjusted for gender, race, cognitive function, number of preadmission medications, marital status, health literacy score, medication understanding score, presence of primary care physician (PCP), years of school, Hispanic/Latino ethnicity. Abbreviations: Ref, referent.

Age per 10 yr0.010 (0.004, 0.020)0.004
Insurance type  
PrivateRefRef
Medicaid0.045 (0.076, 0.014)0.005
Medicare0.010 (0.030, 0.010)0.333
Self‐pay0.013 (0.050, 0.025)0.512
Site0.036 (0.053, 0.019)<0.0001
Baseline adherence per point0.016 (0.008, 0.024)<0.0001

Intervention arm was of borderline statistical significance in predicting postdischarge adherence (P = 0.052), and so was removed from the final model. Study site, age, insurance, and baseline adherence were the only significant independent predictors of postdischarge adherence in the fully adjusted model (Table 3). For example, for every 10‐year increase in age, patients had, on average, an adjusted 1% absolute increase in their adherence score (95% confidence interval [CI] 0.4% to 2.0%). For every 1‐point increase in baseline medication adherence (based on the Morisky scale), there was a 1.6% absolute increase in medication adherence (95% CI 0.8% to 2.4%). In unadjusted analyses, patients with Medicaid were less adherent with medications after discharge than were patients with private insurance. This difference became nonsignificant in adjusted analyses, but when analyses were repeated using multiple imputation techniques, the results again became statistically significantMedicaid insurance was associated with a 4.5% absolute decrease in postdischarge adherence compared with private insurance (95% CI 7.6% to 1.4%). Study site (specifically, Brigham and Women's Hospital) was also a significant predictor of greater postdischarge medication adherence. Years of education was a significant predictor of adherence in unadjusted analyses, but was not an independent predictor when adjusted for other factors. When baseline adherence was removed from the multiple imputation model, there were no changes in which factors were significant predictors of adherence.

DISCUSSION

In this study, we found that low baseline adherence, younger age, Medicaid insurance, and study site were significant predictors of lower 30‐day medication adherence. Of particular interest is our finding regarding baseline adherence, a simple measure to obtain on hospitalized patients. It is notable that in our study, education was not an independent significant predictor of postdischarge adherence, even when baseline adherence was removed from the model. The same is true for medication understanding, cognitive function, and health literacy.

Older patients appeared more adherent with medications in the month after hospital discharge, perhaps reflecting increased interaction with the healthcare system (appointments, number of physician interactions), a greater belief in the importance of chronic medication management, or a higher level of experience with managing medications. A similar relationship between age and adherence has been shown in outpatient studies of patients with hypertension, diabetes, and other chronic diseases.2427

Medicaid patients may be less likely to remain adherent because of the plan's limited coverage of medications relative to patients' ability to pay. For example, Medicaid in Tennessee covers the first 5 generic medications at no cost to the patient but has co‐payments for additional medications and for brand name drugs. Medicaid in Massachusetts has co‐payments of $1 to $3 for each medication. Alternatively, Medicaid insurance may be a marker for other patient characteristics associated with low adherence for which we were not fully able to adjust.

Site differences were also notable in this study; these differences could have been due to differences in insurance coverage in Tennessee versus Massachusetts (which has near‐universal coverage), differences in types of insurance (eg, fewer patients at Brigham and Women's Hospital had Medicaid than at Vanderbilt), cultural and geographic differences between the 2 locations, or other differences in transitional care between the 2 sites.

This study corroborates previous literature on medication adherence (specifically unintentional nonadherence) in the outpatient setting,4, 811 for example, on the association of younger age with low adherence in certain populations. On the other hand, it may contrast with previous literature which has sometimes shown a relationship between patient education or health literacy and medication adherence.14, 15, 2835 However, previous studies have not focused on the transition from inpatient to outpatient settings. Perhaps intensive medication education in the hospital, even under usual care, mitigates the effects of these factors on postdischarge adherence. Finally, baseline adherence seems to correlate with postdischarge adherence, a finding which makes intuitive sense and has been previously reported for specific medications.36

There are several limitations to this study. Although large, the study was performed at only 2 clinical sites where most patients were white and fairly well‐educated, perhaps because patients admitted to a tertiary care center with ACS or ADHF are more affluent than general medical inpatients as a whole; this may limit generalizability. Postdischarge medication adherence might have been higher than in other patient populations given the nature of the population, possible loss‐to‐follow‐up bias, and the fact that half of the subjects received an intervention designed to improve medication management after discharge; such low rates of nonadherence in our study may have reduced our ability to detect important predictors in our models. In addition, the period of follow‐up was 30 days, thus limiting our findings to short‐term postdischarge medication adherence. Postdischarge medication adherence was based on patient self‐report, which not only assumed that the patient was still managing his/her own medications after discharge, but may also be susceptible to both recall and social acceptability bias, which might overestimate our adherence scores, again limiting our ability to detect important predictors of nonadherence. However, other studies have shown a good correlation between self‐reported medication adherence and other more objective measures,37, 38 and recall was only for 7 days, a measure used previously in the literature39, 40 and one designed to reduce recall bias. Systematic underreporting in certain patient populations is less likely but possible.

In the future, research should focus on targeting patients who have low baseline adherence to evaluate the effects of various interventions on postdischarge medication outcomes. Repeating the study in a population with a high prevalence of low health literacy might be illuminating, given that previous studies have shown that patients with low health literacy have less ability to identify their medications and have less refill adherence.29, 30

In conclusion, in patients hospitalized with cardiovascular disease, predictors of lower postdischarge adherence include younger age, Medicaid insurance, and low baseline adherence. It may be prudent to assess baseline adherence and insurance type in hospitalized patients in order to identify those who may benefit from additional assistance to improve medication adherence and medication safety during transitions in care.

Acknowledgements

Meeting Presentations: SGIM New England Regional Meeting, oral presentation, Boston, MA, March 4, 2011; and SGIM National Meeting, poster presentation, Phoenix, AZ, May 6, 2011. Dr Schnipper had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Disclosures: Financial support was provided by R01 HL089755 (NHLBI, Kripalani), K23 HL077597 (NHLBI, Kripalani), K08 HL072806 (NHLBI, Schnipper), T32HP10251‐02 (Cohen), and by the Division of General Medicine, Massachusetts General Hospital and the Harvard Medical School Fellowship in General Medicine and Primary Care (Cohen). Dr Kripalani is a consultant to and holds equity in PictureRx, LLC, which makes patient education tools to improve medication management. PictureRx did not provide materials or funding for this study. All other authors disclose no relevant or financial conflicts of interest.

Files
References
  1. Osterberg L,Blaschke T.Adherence to medication.N Engl J Med.2005;353(5):487497.
  2. Coleman EA,Smith JD,Raha D,Min SJ.Posthospital medication discrepancies: prevalence and contributing factors.Arch Intern Med.2005;165(16):18421847.
  3. Cua YM,Kripalani S.Medication use in the transition from hospital to home.Ann Acad Med Singapore.2008;37(2):136141.
  4. Moore C,Wisnivesky J,Williams S,McGinn T.Medical errors related to discontinuity of care from an inpatient to an outpatient setting.J Gen Intern Med.2003;18(8):646651.
  5. Forster AJ,Murff HJ,Peterson JF,Gandhi TK,Bates DW.The incidence and severity of adverse events affecting patients after discharge from the hospital.Ann Intern Med.2003;138(3):161167.
  6. Forster AJ,Murff HJ,Peterson JF,Gandhi TK,Bates DW.Adverse drug events occurring following hospital discharge.J Gen Intern Med.2005;20(4):317323.
  7. Schnipper JL,Kirwin JL,Cotugno MC, et al.Role of pharmacist counseling in preventing adverse drug events after hospitalization.Arch Intern Med.2006;166(5):565571.
  8. Vira T,Colquhoun M,Etchells E.Reconcilable differences: correcting medication errors at hospital admission and discharge.Qual Saf Health Care.2006;15(2):122126.
  9. Hassan M,Lage MJ.Risk of rehospitalization among bipolar disorder patients who are nonadherent to antipsychotic therapy after hospital discharge.Am J Health Syst Pharm.2009;66(4):358365.
  10. Mansur N,Weiss A,Hoffman A,Gruenewald T,Beloosesky Y.Continuity and adherence to long‐term drug treatment by geriatric patients after hospital discharge: a prospective cohort study.Drugs Aging.2008;25(10):861870.
  11. Kripalani S,Henderson LE,Jacobson TA,Vaccarino V.Medication use among inner‐city patients after hospital discharge: patient‐reported barriers and solutions.Mayo Clin Proc.2008;83(5):529535.
  12. Lindquist LA,Go L,Fleisher J,Jain N,Friesema E,Baker DW.Relationship of health literacy to intentional and unintentional non‐adherence of hospital discharge medications.J Gen Intern Med.2012;27(2):173178.
  13. Office of Disease Prevention and Health Promotion, US Department of Health and Human Services.Healthy People 2010. Available at: http://www.healthypeople.gov/Document/pdf/uih/2010uih.pdf. Accessed February 15,2012.
  14. Davis TC,Wolf MS,Bass PF, et al.Literacy and misunderstanding prescription drug labels.Ann Intern Med.2006;145(12):887894.
  15. Kripalani S,Henderson LE,Chiu EY,Robertson R,Kolm P,Jacobson TA.Predictors of medication self‐management skill in a low‐literacy population.J Gen Intern Med.2006;21(8):852856.
  16. Schnipper JL,Roumie CL,Cawthorn C, et al;for the PILL‐CVD Study Group.Rationale and design of the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) study.Circ Cardiovasc Qual Outcomes.2010;3(2):212219.
  17. Borson S,Scanlan JM,Watanabe J,Tu SP,Lessig M.Simplifying detection of cognitive impairment: comparison of the Mini‐Cog and Mini‐Mental State Examination in a multiethnic sample.J Am Geriatr Soc.2005;53(5):871874.
  18. Nurss JR.Short Test of Functional Health Literacy in Adults.Snow Camp, NC:Peppercorn Books and Press;1998.
  19. Morisky DE,Ang A,Krousel‐Wood M,Ward HJ.Predictive validity of a medication adherence measure in an outpatient setting.J Clin Hypertens (Greenwich).2008;10(5):348354.
  20. Marvanova M,Roumie CL,Eden SK,Cawthon C,Schnipper JL,Kripalani S.Health literacy and medication understanding among hospitalized adults.J Hosp Med. In press.
  21. Marvanova M,Roumie CL,Eden SK,Cawthon C,Schnipper JL,Kripalani S.Health literacy and medication understanding among hospitalized adults.J Hosp Med.2011;6(9):488493.
  22. Toobert DJ,Hampson SE,Glasgow RE.The summary of diabetes self‐care activities measure: results from 7 studies and a revised scale.Diabetes Care.2000;23(7):943950.
  23. Rubin DB.Multiple Imputation for Nonresponse in Surveys.New York, NY:John Wiley 1987.
  24. Hinkin CH,Hardy DJ,Mason KI, et al.Medication adherence in HIV‐infected adults: effect of patient age, cognitive status, and substance abuse.AIDS.2004;18(suppl 1):S19S25.
  25. Wong MC,Jiang JY,Griffiths SM.Factors associated with antihypertensive drug compliance in 83,884 Chinese patients: a cohort study.J Epidemiol Community Health.2010;64(10):895901.
  26. Wong MC,Kong AP,So WY,Jiang JY,Chan JC,Griffiths SM.Adherence to oral hypoglycemic agents in 26,782 Chinese patients: a cohort study.J Clin Pharmacol.2011;51(10):14741482.
  27. Gazmararian J,Jacobson KL,Pan Y,Schmotzer B,Kripalani S.Effect of a pharmacy‐based health literacy intervention and patient characteristics on medication refill adherence in an urban health system.Ann Pharmacother.2010;44(1):8087.
  28. Kalichman SC,Ramachandran B,Catz S.Adherence to combination antiretroviral therapies in HIV patients of low health literacy.J Gen Intern Med.1999;14(5):267273.
  29. Gazmararian JA,Kripalani S,Miller MJ,Echt KV,Ren J,Rask K.Factors associated with medication refill adherence in cardiovascular‐related diseases: a focus on health literacy.J Gen Intern Med.2006;21(12):12151221.
  30. Persell SD,Osborn CY,Richard R,Skripkauskas S,Wolf MS.Limited health literacy is a barrier to medication reconciliation in ambulatory care.J Gen Intern Med.2007;22(11):15231526.
  31. Chew LD,Bradley KA,Flum DR,Cornia PB,Koepsell TD.The impact of low health literacy on surgical practice.Am J Surg.2004;188(3):250253.
  32. Gatti ME,Jacobson KL,Gazmararian JA,Schmotzer B,Kripalani S.Relationships between beliefs about medications and adherence.Am J Health Syst Pharm.2009;66(7):657664.
  33. Fang MC,Machtinger EL,Wang F,Schillinger D.Health literacy and anticoagulation‐related outcomes among patients taking warfarin.J Gen Intern Med.2006;21(8):841846.
  34. Paasche‐Orlow MK,Cheng DM,Palepu A,Meli S,Faber V,Samet JH.Health literacy, antiretroviral adherence, and HIV‐RNA suppression: a longitudinal perspective.J Gen Intern Med.2006;21(8):835840.
  35. Platt AB,Localio AR,Brensinger CM, et al.Risk factors for nonadherence to warfarin: results from the IN‐RANGE study.Pharmacoepidemiol Drug Saf.2008;17(9):853860.
  36. Muntner P,Mann DM,Woodward M, et al.Predictors of low clopidogrel adherence following percutaneous coronary intervention.Am J Cardiol.2011;108(6):822827.
  37. Shi L,Liu J,Fonseca V,Walker P,Kalsekar A,Pawaskar M.Correlation between adherence rates measured by MEMS and self‐reported questionnaires: a meta‐analysis.Health Qual Life Outcomes.2010;8:99.
  38. Shi L,Liu J,Koleva Y,Fonseca V,Kalsekar A,Pawaskar M.Concordance of adherence measurement using self‐reported adherence questionnaires and medication monitoring devices.Pharmacoeconomics.2010;28(12):10971107.
  39. Grant RW,Devita NG,Singer DE,Meigs JB.Polypharmacy and medication adherence in patients with type 2 diabetes.Diabetes Care.2003;26(5):14081412.
  40. Grant RW,Devita NG,Singer DE,Meigs JB.Improving adherence and reducing medication discrepancies in patients with diabetes.Ann Pharmacother.2003;37(7–8):962969.
Article PDF
Issue
Journal of Hospital Medicine - 7(6)
Page Number
470-475
Sections
Files
Files
Article PDF
Article PDF

In the outpatient setting, medication adherence (defined as percentage of prescribed medication doses taken by a patient during a specific time period) ranges between 40% and 80% for chronic conditions.1 During acute care hospitalization, changes are often made to patients' medication regimens, which can be confusing and contribute to nonadherence, medication errors, and harmful adverse events.2 Indeed, it is estimated that almost half of patients encounter a medication error after discharge, and approximately 12%17% experience an adverse drug event after returning home.36 It is likely that some of these adverse events may be the result of medication nonadherence.7 Improved patientprovider communication, systems to reconcile prehospitalization and posthospitalization medications, as well as development of mechanisms to enhance adherence, may prevent many of these errors and have become new targets for quality improvement.4, 8 Although postdischarge medication adherence is a crucial target for avoiding adverse events and rehospitalization, few studies have focused on understanding its incidence and predictors, in particular, patient demographic factors such as age and insurance status.911

In addition, few studies have looked at general and posthospital adherence in a population where health literacy is measured, an important area because medication changes during hospitalization may be particularly confusing for patients with low health literacy.11, 12 Health literacy is defined as the degree to which an individual has the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions.13 Prior outpatient research shows that low health literacy is associated with poor patient understanding of the medication regimen and instructions for medication use, which may contribute to postdischarge medication nonadherence.14, 15 Understanding the factors associated with postdischarge medication adherence could help refine interventions that are oriented toward improving transitions in care, patient safety, and reducing unnecessary rehospitalization.

We report here on factors associated with postdischarge medication adherence using data from the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) study.16

METHODS

Study and Participants

PILL‐CVD was a federally funded, 2‐site randomized controlled trial using pharmacist‐assisted medication reconciliation, inpatient pharmacist counseling, low‐literacy adherence aids, and telephone follow‐up that aimed to decrease rates of serious medication errors after hospital discharge.16 The study targeted patients with cardiovascular disease (hospitalized on cardiology or general medical or geriatric services for acute coronary syndromes [ACS] or acute decompensated heart failure [ADHF]) at 2 large academic hospitals, Brigham and Women's Hospital (BWH) and Vanderbilt University Hospital (VUH).

Subjects were eligible for enrollment if they met criteria for ACS or ADHF, were likely to be discharged to home as determined by the primary medical team at the time of study enrollment, and took primary responsibility for administering their medications prior to admission (caregivers could be involved in medication management after discharge). Exclusion criteria included severe visual or hearing impairment, inability to communicate in English or Spanish, active psychiatric illness, dementia, delirium, illness too severe to participate, lack of a home phone number, being in police custody, or participation in another intensive medication adherence program (eg, due to renal transplant).

Out of 6416 patients originally screened for possible enrollment, 862 were randomly assigned to receive usual care or usual care plus the intervention, and 851 remained in the study.16 Both the main study and this secondary data analysis were approved by the Institutional Review Boards of each site.

Baseline Measures

Following informed consent and study enrollment, a variety of baseline data were collected on study participants from medical records and patient interview, including primary language, demographic information (age, race, insurance status, income, and education level), cognition (through administration of the 05‐point MiniCog scale),17 and level of health literacy (through use of the 036‐point short form of the Test of Functional Health Literacy in Adults [s‐TOFHLA] scale).18 Baseline information was also collected on medication use, including number of preadmission medications, measurement of self‐reported adherence prior to admission (using the Morisky scale, a validated 04‐point questionnaire shown to correlate with disease control and indicative of general patterns of adherence),19 and a medication understanding score, adapted from other instruments, which quantifies understanding of the indication, dose, and frequency of up to 5 randomly selected preadmission medications on a 03‐point scale.16, 20, 21

Outcome Measures

Outcomes were collected 30 days postdischarge through a structured questionnaire, administered by telephone. Only patients who completed this call are included in the present analysis. Postdischarge medication adherence was assessed by asking patients to report the number of days out of the previous week they had taken each medication from their postdischarge regimen exactly as prescribed.22 A score was calculated for each medication as the proportion of adherent days (eg, if a patient reported missing 2 days of a medication in the previous week, then adherence would be 5/7 or 71%). A global postdischarge adherence score was then derived for each patient by averaging the adherence score for all regularly scheduled medications. This quantitative measure focused on adherence to medications patients knew they should be taking and did not measure medication discrepancies (sometimes termed unintentional nonadherence).

Analysis

Patient characteristics were summarized and reported using simple descriptive statistics. Candidate predictors of postdischarge medication adherence were chosen a priori from patient characteristics assessed during hospital admission. These included patient age, gender, race, ethnicity, marital status, insurance, years of education, presence of primary care physician (PCP), study site, number of preadmission medications, medication understanding, baseline adherence, cognition, and health literacy. Unadjusted results were calculated using univariable linear regression, with each patient's adherence score as the dependent variable and each predictor as the independent variable. Adjusted results were then derived using multivariable linear regression with all the candidate predictors in the model.

Lastly, because of missing data for some predictors, in particular baseline adherence and medication understanding, multiple imputation techniques were used to impute missing data and increase statistical power.23 We used the Markov Chain Monte Carlo (MCMC) method for multiple imputation, which generally assumes that the data came from a normal distribution and that the missing data are missing at random. Because of the essentially normal distribution of the data, and because the amount of missing data was so small (<1% for almost all variables, 5% for baseline adherence, and 8% for medication understanding), we expected little bias and present the complete case analysis, which maximized statistical power.

Two‐sided P values <0.05 were considered significant, and SAS version 9.2 (Cary, NC) was used for all analyses.

RESULTS

Table 1 shows descriptive baseline patient characteristics of study sample (responders) as well as nonresponders at 30 days. For the responders, the mean age of the 646 patients was 61.2 years, 94.7% were insured, and 19.3% had inadequate or marginal health literacy. Patients were prescribed an average of 8 preadmission medications. Most patients (92.3%) had a regular PCP prior to admission. Nonresponders had nonsignificant trends towards having lower health literacy, medication understanding, and baseline medication adherence.

Baseline Characteristics
CharacteristicTotal N, 30‐Day RespondentsValueTotal N, NonrespondentsValue
  • Abbreviations: PCP, primary care physician; SD, standard deviation; s‐TOFHLA, short form of the Test of Functional Health Literacy in Adults. *03, with 3 indicating better understanding. 036, with higher scores indicating higher health literacy. 04, with 4 indicating higher baseline adherence. 05, with higher scores indicating better cognition; a score <3 indicates dementia.

Age, mean in yr (SD)64661.2 (13.5)4555.4 (14.3)
Gender, N (percentage)646 45 
Female272 (42.1)18 (40.0)
Male374 (57.9)27 (60.0)
Race, N (percentage)643 45 
White511 (79.5)32 (71.1)
Black104 (16.2)11 (24.4)
Other28 (4.4)2 (4.4)
Ethnicity, N (percentage)639 45 
Hispanic24 (3.8)1 (2.2)
Not Hispanic615 (96.2)44 (97.8)
Marital status, N (percentage)646 45 
Married/cohabitate382 (59.1)20 (44.4)
Separated/divorced118 (18.3)11 (24.4)
Widowed81 (12.5)5 (11.1)
Never married65 (10.1)9 (2.0)
Insurance type, N (percentage)646 45 
Medicaid53 (8.2)5 (11.1)
Medicare270 (41.8)13 (28.9)
Private289 (44.7)19 (42.2)
Self‐pay34 (5.3)8 (17.8)
Years of education, mean in yr (SD)64314.0 (3.1)4513.3 (2.7)
Presence of PCP prior to admission, N (percentage)646 45 
Yes596 (92.3)38 (84.4)
No50 (7.74)7 (15.6)
Site, N (percentage)646 45 
Site 1358 (55.4)8 (17.8)
Site 2288 (44.6)37 (82.2)
No. of preadmission medications, mean no. (SD)6417.8 (4.8)457.7 (5.4)
Medication understanding score, mean (SD)*5972.4 (0.5)402.2 (0.62)
Health literacy (s‐TOFHLA) score, mean (SD)64229.1 (8.9)4526.0 (12.0)
Baseline adherence (SD)6132.7 (1.1)452.4 (1.2)
MiniCog score, N (percentage)646 45 
Demented63 (9.8)5 (11.1)
Not demented583 (90.2)40 (88.9)

The average postdischarge adherence score was 95% (standard deviation [SD] = 10.2%), and less than 10% of patients had an adherence score of less than 85%; overall the distribution was left‐skewed. Table 2 illustrates crude and adjusted parameter estimates for variables in the model. Table 3 shows significant findings in the fully adjusted model, which used multiple imputation techniques to account for missing data.

Crude and Adjusted Measurements
PredictorCrude Parameter Estimate (Beta) With 95% Confidence IntervalsP ValueAdjusted Parameter Estimate (Beta) With 95% Confidence IntervalsP Value
  • NOTE: For crude estimates, value is category vs absence of parameter in univariable testing. For adjusted estimates of categorical variables, value is each category compared to referent category. Beta‐coefficient represents absolute change in adherence (eg, 0.010 for age means a 1% absolute increase in adherence for every 10 yr increase in patient age). Abbreviations: PCP, primary care physician; Ref, referent; s‐TOFHLA, short form of the Test of Functional Health Literacy in Adults.

Age per 10 yr0.010 (0.007, 0.020)<0.00010.010 (0.002, 0.020)0.018
Male gender0.012 (0.004, 0.028)0.1370.003 (0.014, 0.020)0.727
Race/ethnicity    
White0.011 (0.009, 0.031)0.266RefRef
Black0.017 (0.038, 0.005)0.130.006 (0.017, 0.030)0.598
Other0.010 (0.029, 0.049)0.5990.017 (0.027, 0.062)0.446
Hispanic/Latino0.005 (0.037, 0.047)0.8030.036 (0.013, 0.085)0.149
Marital status    
Married/cohabitate0.006 (0.011, 0.022)0.500RefRef
Separated/divorced0.005 (0.025, 0.016)0.6640.009 (0.014, 0.031)0.446
Widowed0.001 (0.023, 0.025)0.9220.013 (0.039, 0.013)0.338
Never married0.009 (0.035, 0.018)0.5150.004 (0.033, 0.025)0.784
Insurance type    
Private0.008 (0.008, 0.024)0.347RefRef
Medicaid0.046 (0.075, 0.018)0.0020.026 (0.058, 0.007)0.121
Medicare0.012 (0.004, 0.028)0.1380.002 (0.023, 0.018)0.844
Self‐pay0.027 (0.062, 0.008)0.1350.029 (0.073, 0.015)0.202
Years of education0.003 (0.0003, 0.005)0.0280.0001 (0.003, 0.003)0.949
Presence of PCP prior to admission0.007 (0.022, 0.037)0.6300.002 (0.032, 0.036)0.888
Site0.050 (0.065, 0.034)<0.00010.038 (0.056, 0.021)<0.0001
No. of preadmission medications0.0003 (0.002, 0.001)0.6840.0001 (0.002, 0.002)0.918
Medication understanding score per point0.007 (0.009, 0.023)0.3900.006 (0.011, 0.023)0.513
Health literacy (s‐TOFHLA) score per 10 points0.0006 (0.008, 0.01)0.8970.003 (0.008, 0.01)0.644
Baseline adherence per point0.023 (0.016, 0.031)<0.00010.017 (0.009, 0.024)<0.0001
Cognitive function0.004 (0.022, 0.031)0.7570.008 (0.019, 0.036)0.549
Significant Results in Adjusted Analyses With Multiple Imputation
PredictorParameter Estimate (Beta) With 95% Confidence IntervalsP Value
  • NOTE: Total observations, 646; 67 with missing values. All variables adjusted for gender, race, cognitive function, number of preadmission medications, marital status, health literacy score, medication understanding score, presence of primary care physician (PCP), years of school, Hispanic/Latino ethnicity. Abbreviations: Ref, referent.

Age per 10 yr0.010 (0.004, 0.020)0.004
Insurance type  
PrivateRefRef
Medicaid0.045 (0.076, 0.014)0.005
Medicare0.010 (0.030, 0.010)0.333
Self‐pay0.013 (0.050, 0.025)0.512
Site0.036 (0.053, 0.019)<0.0001
Baseline adherence per point0.016 (0.008, 0.024)<0.0001

Intervention arm was of borderline statistical significance in predicting postdischarge adherence (P = 0.052), and so was removed from the final model. Study site, age, insurance, and baseline adherence were the only significant independent predictors of postdischarge adherence in the fully adjusted model (Table 3). For example, for every 10‐year increase in age, patients had, on average, an adjusted 1% absolute increase in their adherence score (95% confidence interval [CI] 0.4% to 2.0%). For every 1‐point increase in baseline medication adherence (based on the Morisky scale), there was a 1.6% absolute increase in medication adherence (95% CI 0.8% to 2.4%). In unadjusted analyses, patients with Medicaid were less adherent with medications after discharge than were patients with private insurance. This difference became nonsignificant in adjusted analyses, but when analyses were repeated using multiple imputation techniques, the results again became statistically significantMedicaid insurance was associated with a 4.5% absolute decrease in postdischarge adherence compared with private insurance (95% CI 7.6% to 1.4%). Study site (specifically, Brigham and Women's Hospital) was also a significant predictor of greater postdischarge medication adherence. Years of education was a significant predictor of adherence in unadjusted analyses, but was not an independent predictor when adjusted for other factors. When baseline adherence was removed from the multiple imputation model, there were no changes in which factors were significant predictors of adherence.

DISCUSSION

In this study, we found that low baseline adherence, younger age, Medicaid insurance, and study site were significant predictors of lower 30‐day medication adherence. Of particular interest is our finding regarding baseline adherence, a simple measure to obtain on hospitalized patients. It is notable that in our study, education was not an independent significant predictor of postdischarge adherence, even when baseline adherence was removed from the model. The same is true for medication understanding, cognitive function, and health literacy.

Older patients appeared more adherent with medications in the month after hospital discharge, perhaps reflecting increased interaction with the healthcare system (appointments, number of physician interactions), a greater belief in the importance of chronic medication management, or a higher level of experience with managing medications. A similar relationship between age and adherence has been shown in outpatient studies of patients with hypertension, diabetes, and other chronic diseases.2427

Medicaid patients may be less likely to remain adherent because of the plan's limited coverage of medications relative to patients' ability to pay. For example, Medicaid in Tennessee covers the first 5 generic medications at no cost to the patient but has co‐payments for additional medications and for brand name drugs. Medicaid in Massachusetts has co‐payments of $1 to $3 for each medication. Alternatively, Medicaid insurance may be a marker for other patient characteristics associated with low adherence for which we were not fully able to adjust.

Site differences were also notable in this study; these differences could have been due to differences in insurance coverage in Tennessee versus Massachusetts (which has near‐universal coverage), differences in types of insurance (eg, fewer patients at Brigham and Women's Hospital had Medicaid than at Vanderbilt), cultural and geographic differences between the 2 locations, or other differences in transitional care between the 2 sites.

This study corroborates previous literature on medication adherence (specifically unintentional nonadherence) in the outpatient setting,4, 811 for example, on the association of younger age with low adherence in certain populations. On the other hand, it may contrast with previous literature which has sometimes shown a relationship between patient education or health literacy and medication adherence.14, 15, 2835 However, previous studies have not focused on the transition from inpatient to outpatient settings. Perhaps intensive medication education in the hospital, even under usual care, mitigates the effects of these factors on postdischarge adherence. Finally, baseline adherence seems to correlate with postdischarge adherence, a finding which makes intuitive sense and has been previously reported for specific medications.36

There are several limitations to this study. Although large, the study was performed at only 2 clinical sites where most patients were white and fairly well‐educated, perhaps because patients admitted to a tertiary care center with ACS or ADHF are more affluent than general medical inpatients as a whole; this may limit generalizability. Postdischarge medication adherence might have been higher than in other patient populations given the nature of the population, possible loss‐to‐follow‐up bias, and the fact that half of the subjects received an intervention designed to improve medication management after discharge; such low rates of nonadherence in our study may have reduced our ability to detect important predictors in our models. In addition, the period of follow‐up was 30 days, thus limiting our findings to short‐term postdischarge medication adherence. Postdischarge medication adherence was based on patient self‐report, which not only assumed that the patient was still managing his/her own medications after discharge, but may also be susceptible to both recall and social acceptability bias, which might overestimate our adherence scores, again limiting our ability to detect important predictors of nonadherence. However, other studies have shown a good correlation between self‐reported medication adherence and other more objective measures,37, 38 and recall was only for 7 days, a measure used previously in the literature39, 40 and one designed to reduce recall bias. Systematic underreporting in certain patient populations is less likely but possible.

In the future, research should focus on targeting patients who have low baseline adherence to evaluate the effects of various interventions on postdischarge medication outcomes. Repeating the study in a population with a high prevalence of low health literacy might be illuminating, given that previous studies have shown that patients with low health literacy have less ability to identify their medications and have less refill adherence.29, 30

In conclusion, in patients hospitalized with cardiovascular disease, predictors of lower postdischarge adherence include younger age, Medicaid insurance, and low baseline adherence. It may be prudent to assess baseline adherence and insurance type in hospitalized patients in order to identify those who may benefit from additional assistance to improve medication adherence and medication safety during transitions in care.

Acknowledgements

Meeting Presentations: SGIM New England Regional Meeting, oral presentation, Boston, MA, March 4, 2011; and SGIM National Meeting, poster presentation, Phoenix, AZ, May 6, 2011. Dr Schnipper had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Disclosures: Financial support was provided by R01 HL089755 (NHLBI, Kripalani), K23 HL077597 (NHLBI, Kripalani), K08 HL072806 (NHLBI, Schnipper), T32HP10251‐02 (Cohen), and by the Division of General Medicine, Massachusetts General Hospital and the Harvard Medical School Fellowship in General Medicine and Primary Care (Cohen). Dr Kripalani is a consultant to and holds equity in PictureRx, LLC, which makes patient education tools to improve medication management. PictureRx did not provide materials or funding for this study. All other authors disclose no relevant or financial conflicts of interest.

In the outpatient setting, medication adherence (defined as percentage of prescribed medication doses taken by a patient during a specific time period) ranges between 40% and 80% for chronic conditions.1 During acute care hospitalization, changes are often made to patients' medication regimens, which can be confusing and contribute to nonadherence, medication errors, and harmful adverse events.2 Indeed, it is estimated that almost half of patients encounter a medication error after discharge, and approximately 12%17% experience an adverse drug event after returning home.36 It is likely that some of these adverse events may be the result of medication nonadherence.7 Improved patientprovider communication, systems to reconcile prehospitalization and posthospitalization medications, as well as development of mechanisms to enhance adherence, may prevent many of these errors and have become new targets for quality improvement.4, 8 Although postdischarge medication adherence is a crucial target for avoiding adverse events and rehospitalization, few studies have focused on understanding its incidence and predictors, in particular, patient demographic factors such as age and insurance status.911

In addition, few studies have looked at general and posthospital adherence in a population where health literacy is measured, an important area because medication changes during hospitalization may be particularly confusing for patients with low health literacy.11, 12 Health literacy is defined as the degree to which an individual has the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions.13 Prior outpatient research shows that low health literacy is associated with poor patient understanding of the medication regimen and instructions for medication use, which may contribute to postdischarge medication nonadherence.14, 15 Understanding the factors associated with postdischarge medication adherence could help refine interventions that are oriented toward improving transitions in care, patient safety, and reducing unnecessary rehospitalization.

We report here on factors associated with postdischarge medication adherence using data from the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) study.16

METHODS

Study and Participants

PILL‐CVD was a federally funded, 2‐site randomized controlled trial using pharmacist‐assisted medication reconciliation, inpatient pharmacist counseling, low‐literacy adherence aids, and telephone follow‐up that aimed to decrease rates of serious medication errors after hospital discharge.16 The study targeted patients with cardiovascular disease (hospitalized on cardiology or general medical or geriatric services for acute coronary syndromes [ACS] or acute decompensated heart failure [ADHF]) at 2 large academic hospitals, Brigham and Women's Hospital (BWH) and Vanderbilt University Hospital (VUH).

Subjects were eligible for enrollment if they met criteria for ACS or ADHF, were likely to be discharged to home as determined by the primary medical team at the time of study enrollment, and took primary responsibility for administering their medications prior to admission (caregivers could be involved in medication management after discharge). Exclusion criteria included severe visual or hearing impairment, inability to communicate in English or Spanish, active psychiatric illness, dementia, delirium, illness too severe to participate, lack of a home phone number, being in police custody, or participation in another intensive medication adherence program (eg, due to renal transplant).

Out of 6416 patients originally screened for possible enrollment, 862 were randomly assigned to receive usual care or usual care plus the intervention, and 851 remained in the study.16 Both the main study and this secondary data analysis were approved by the Institutional Review Boards of each site.

Baseline Measures

Following informed consent and study enrollment, a variety of baseline data were collected on study participants from medical records and patient interview, including primary language, demographic information (age, race, insurance status, income, and education level), cognition (through administration of the 05‐point MiniCog scale),17 and level of health literacy (through use of the 036‐point short form of the Test of Functional Health Literacy in Adults [s‐TOFHLA] scale).18 Baseline information was also collected on medication use, including number of preadmission medications, measurement of self‐reported adherence prior to admission (using the Morisky scale, a validated 04‐point questionnaire shown to correlate with disease control and indicative of general patterns of adherence),19 and a medication understanding score, adapted from other instruments, which quantifies understanding of the indication, dose, and frequency of up to 5 randomly selected preadmission medications on a 03‐point scale.16, 20, 21

Outcome Measures

Outcomes were collected 30 days postdischarge through a structured questionnaire, administered by telephone. Only patients who completed this call are included in the present analysis. Postdischarge medication adherence was assessed by asking patients to report the number of days out of the previous week they had taken each medication from their postdischarge regimen exactly as prescribed.22 A score was calculated for each medication as the proportion of adherent days (eg, if a patient reported missing 2 days of a medication in the previous week, then adherence would be 5/7 or 71%). A global postdischarge adherence score was then derived for each patient by averaging the adherence score for all regularly scheduled medications. This quantitative measure focused on adherence to medications patients knew they should be taking and did not measure medication discrepancies (sometimes termed unintentional nonadherence).

Analysis

Patient characteristics were summarized and reported using simple descriptive statistics. Candidate predictors of postdischarge medication adherence were chosen a priori from patient characteristics assessed during hospital admission. These included patient age, gender, race, ethnicity, marital status, insurance, years of education, presence of primary care physician (PCP), study site, number of preadmission medications, medication understanding, baseline adherence, cognition, and health literacy. Unadjusted results were calculated using univariable linear regression, with each patient's adherence score as the dependent variable and each predictor as the independent variable. Adjusted results were then derived using multivariable linear regression with all the candidate predictors in the model.

Lastly, because of missing data for some predictors, in particular baseline adherence and medication understanding, multiple imputation techniques were used to impute missing data and increase statistical power.23 We used the Markov Chain Monte Carlo (MCMC) method for multiple imputation, which generally assumes that the data came from a normal distribution and that the missing data are missing at random. Because of the essentially normal distribution of the data, and because the amount of missing data was so small (<1% for almost all variables, 5% for baseline adherence, and 8% for medication understanding), we expected little bias and present the complete case analysis, which maximized statistical power.

Two‐sided P values <0.05 were considered significant, and SAS version 9.2 (Cary, NC) was used for all analyses.

RESULTS

Table 1 shows descriptive baseline patient characteristics of study sample (responders) as well as nonresponders at 30 days. For the responders, the mean age of the 646 patients was 61.2 years, 94.7% were insured, and 19.3% had inadequate or marginal health literacy. Patients were prescribed an average of 8 preadmission medications. Most patients (92.3%) had a regular PCP prior to admission. Nonresponders had nonsignificant trends towards having lower health literacy, medication understanding, and baseline medication adherence.

Baseline Characteristics
CharacteristicTotal N, 30‐Day RespondentsValueTotal N, NonrespondentsValue
  • Abbreviations: PCP, primary care physician; SD, standard deviation; s‐TOFHLA, short form of the Test of Functional Health Literacy in Adults. *03, with 3 indicating better understanding. 036, with higher scores indicating higher health literacy. 04, with 4 indicating higher baseline adherence. 05, with higher scores indicating better cognition; a score <3 indicates dementia.

Age, mean in yr (SD)64661.2 (13.5)4555.4 (14.3)
Gender, N (percentage)646 45 
Female272 (42.1)18 (40.0)
Male374 (57.9)27 (60.0)
Race, N (percentage)643 45 
White511 (79.5)32 (71.1)
Black104 (16.2)11 (24.4)
Other28 (4.4)2 (4.4)
Ethnicity, N (percentage)639 45 
Hispanic24 (3.8)1 (2.2)
Not Hispanic615 (96.2)44 (97.8)
Marital status, N (percentage)646 45 
Married/cohabitate382 (59.1)20 (44.4)
Separated/divorced118 (18.3)11 (24.4)
Widowed81 (12.5)5 (11.1)
Never married65 (10.1)9 (2.0)
Insurance type, N (percentage)646 45 
Medicaid53 (8.2)5 (11.1)
Medicare270 (41.8)13 (28.9)
Private289 (44.7)19 (42.2)
Self‐pay34 (5.3)8 (17.8)
Years of education, mean in yr (SD)64314.0 (3.1)4513.3 (2.7)
Presence of PCP prior to admission, N (percentage)646 45 
Yes596 (92.3)38 (84.4)
No50 (7.74)7 (15.6)
Site, N (percentage)646 45 
Site 1358 (55.4)8 (17.8)
Site 2288 (44.6)37 (82.2)
No. of preadmission medications, mean no. (SD)6417.8 (4.8)457.7 (5.4)
Medication understanding score, mean (SD)*5972.4 (0.5)402.2 (0.62)
Health literacy (s‐TOFHLA) score, mean (SD)64229.1 (8.9)4526.0 (12.0)
Baseline adherence (SD)6132.7 (1.1)452.4 (1.2)
MiniCog score, N (percentage)646 45 
Demented63 (9.8)5 (11.1)
Not demented583 (90.2)40 (88.9)

The average postdischarge adherence score was 95% (standard deviation [SD] = 10.2%), and less than 10% of patients had an adherence score of less than 85%; overall the distribution was left‐skewed. Table 2 illustrates crude and adjusted parameter estimates for variables in the model. Table 3 shows significant findings in the fully adjusted model, which used multiple imputation techniques to account for missing data.

Crude and Adjusted Measurements
PredictorCrude Parameter Estimate (Beta) With 95% Confidence IntervalsP ValueAdjusted Parameter Estimate (Beta) With 95% Confidence IntervalsP Value
  • NOTE: For crude estimates, value is category vs absence of parameter in univariable testing. For adjusted estimates of categorical variables, value is each category compared to referent category. Beta‐coefficient represents absolute change in adherence (eg, 0.010 for age means a 1% absolute increase in adherence for every 10 yr increase in patient age). Abbreviations: PCP, primary care physician; Ref, referent; s‐TOFHLA, short form of the Test of Functional Health Literacy in Adults.

Age per 10 yr0.010 (0.007, 0.020)<0.00010.010 (0.002, 0.020)0.018
Male gender0.012 (0.004, 0.028)0.1370.003 (0.014, 0.020)0.727
Race/ethnicity    
White0.011 (0.009, 0.031)0.266RefRef
Black0.017 (0.038, 0.005)0.130.006 (0.017, 0.030)0.598
Other0.010 (0.029, 0.049)0.5990.017 (0.027, 0.062)0.446
Hispanic/Latino0.005 (0.037, 0.047)0.8030.036 (0.013, 0.085)0.149
Marital status    
Married/cohabitate0.006 (0.011, 0.022)0.500RefRef
Separated/divorced0.005 (0.025, 0.016)0.6640.009 (0.014, 0.031)0.446
Widowed0.001 (0.023, 0.025)0.9220.013 (0.039, 0.013)0.338
Never married0.009 (0.035, 0.018)0.5150.004 (0.033, 0.025)0.784
Insurance type    
Private0.008 (0.008, 0.024)0.347RefRef
Medicaid0.046 (0.075, 0.018)0.0020.026 (0.058, 0.007)0.121
Medicare0.012 (0.004, 0.028)0.1380.002 (0.023, 0.018)0.844
Self‐pay0.027 (0.062, 0.008)0.1350.029 (0.073, 0.015)0.202
Years of education0.003 (0.0003, 0.005)0.0280.0001 (0.003, 0.003)0.949
Presence of PCP prior to admission0.007 (0.022, 0.037)0.6300.002 (0.032, 0.036)0.888
Site0.050 (0.065, 0.034)<0.00010.038 (0.056, 0.021)<0.0001
No. of preadmission medications0.0003 (0.002, 0.001)0.6840.0001 (0.002, 0.002)0.918
Medication understanding score per point0.007 (0.009, 0.023)0.3900.006 (0.011, 0.023)0.513
Health literacy (s‐TOFHLA) score per 10 points0.0006 (0.008, 0.01)0.8970.003 (0.008, 0.01)0.644
Baseline adherence per point0.023 (0.016, 0.031)<0.00010.017 (0.009, 0.024)<0.0001
Cognitive function0.004 (0.022, 0.031)0.7570.008 (0.019, 0.036)0.549
Significant Results in Adjusted Analyses With Multiple Imputation
PredictorParameter Estimate (Beta) With 95% Confidence IntervalsP Value
  • NOTE: Total observations, 646; 67 with missing values. All variables adjusted for gender, race, cognitive function, number of preadmission medications, marital status, health literacy score, medication understanding score, presence of primary care physician (PCP), years of school, Hispanic/Latino ethnicity. Abbreviations: Ref, referent.

Age per 10 yr0.010 (0.004, 0.020)0.004
Insurance type  
PrivateRefRef
Medicaid0.045 (0.076, 0.014)0.005
Medicare0.010 (0.030, 0.010)0.333
Self‐pay0.013 (0.050, 0.025)0.512
Site0.036 (0.053, 0.019)<0.0001
Baseline adherence per point0.016 (0.008, 0.024)<0.0001

Intervention arm was of borderline statistical significance in predicting postdischarge adherence (P = 0.052), and so was removed from the final model. Study site, age, insurance, and baseline adherence were the only significant independent predictors of postdischarge adherence in the fully adjusted model (Table 3). For example, for every 10‐year increase in age, patients had, on average, an adjusted 1% absolute increase in their adherence score (95% confidence interval [CI] 0.4% to 2.0%). For every 1‐point increase in baseline medication adherence (based on the Morisky scale), there was a 1.6% absolute increase in medication adherence (95% CI 0.8% to 2.4%). In unadjusted analyses, patients with Medicaid were less adherent with medications after discharge than were patients with private insurance. This difference became nonsignificant in adjusted analyses, but when analyses were repeated using multiple imputation techniques, the results again became statistically significantMedicaid insurance was associated with a 4.5% absolute decrease in postdischarge adherence compared with private insurance (95% CI 7.6% to 1.4%). Study site (specifically, Brigham and Women's Hospital) was also a significant predictor of greater postdischarge medication adherence. Years of education was a significant predictor of adherence in unadjusted analyses, but was not an independent predictor when adjusted for other factors. When baseline adherence was removed from the multiple imputation model, there were no changes in which factors were significant predictors of adherence.

DISCUSSION

In this study, we found that low baseline adherence, younger age, Medicaid insurance, and study site were significant predictors of lower 30‐day medication adherence. Of particular interest is our finding regarding baseline adherence, a simple measure to obtain on hospitalized patients. It is notable that in our study, education was not an independent significant predictor of postdischarge adherence, even when baseline adherence was removed from the model. The same is true for medication understanding, cognitive function, and health literacy.

Older patients appeared more adherent with medications in the month after hospital discharge, perhaps reflecting increased interaction with the healthcare system (appointments, number of physician interactions), a greater belief in the importance of chronic medication management, or a higher level of experience with managing medications. A similar relationship between age and adherence has been shown in outpatient studies of patients with hypertension, diabetes, and other chronic diseases.2427

Medicaid patients may be less likely to remain adherent because of the plan's limited coverage of medications relative to patients' ability to pay. For example, Medicaid in Tennessee covers the first 5 generic medications at no cost to the patient but has co‐payments for additional medications and for brand name drugs. Medicaid in Massachusetts has co‐payments of $1 to $3 for each medication. Alternatively, Medicaid insurance may be a marker for other patient characteristics associated with low adherence for which we were not fully able to adjust.

Site differences were also notable in this study; these differences could have been due to differences in insurance coverage in Tennessee versus Massachusetts (which has near‐universal coverage), differences in types of insurance (eg, fewer patients at Brigham and Women's Hospital had Medicaid than at Vanderbilt), cultural and geographic differences between the 2 locations, or other differences in transitional care between the 2 sites.

This study corroborates previous literature on medication adherence (specifically unintentional nonadherence) in the outpatient setting,4, 811 for example, on the association of younger age with low adherence in certain populations. On the other hand, it may contrast with previous literature which has sometimes shown a relationship between patient education or health literacy and medication adherence.14, 15, 2835 However, previous studies have not focused on the transition from inpatient to outpatient settings. Perhaps intensive medication education in the hospital, even under usual care, mitigates the effects of these factors on postdischarge adherence. Finally, baseline adherence seems to correlate with postdischarge adherence, a finding which makes intuitive sense and has been previously reported for specific medications.36

There are several limitations to this study. Although large, the study was performed at only 2 clinical sites where most patients were white and fairly well‐educated, perhaps because patients admitted to a tertiary care center with ACS or ADHF are more affluent than general medical inpatients as a whole; this may limit generalizability. Postdischarge medication adherence might have been higher than in other patient populations given the nature of the population, possible loss‐to‐follow‐up bias, and the fact that half of the subjects received an intervention designed to improve medication management after discharge; such low rates of nonadherence in our study may have reduced our ability to detect important predictors in our models. In addition, the period of follow‐up was 30 days, thus limiting our findings to short‐term postdischarge medication adherence. Postdischarge medication adherence was based on patient self‐report, which not only assumed that the patient was still managing his/her own medications after discharge, but may also be susceptible to both recall and social acceptability bias, which might overestimate our adherence scores, again limiting our ability to detect important predictors of nonadherence. However, other studies have shown a good correlation between self‐reported medication adherence and other more objective measures,37, 38 and recall was only for 7 days, a measure used previously in the literature39, 40 and one designed to reduce recall bias. Systematic underreporting in certain patient populations is less likely but possible.

In the future, research should focus on targeting patients who have low baseline adherence to evaluate the effects of various interventions on postdischarge medication outcomes. Repeating the study in a population with a high prevalence of low health literacy might be illuminating, given that previous studies have shown that patients with low health literacy have less ability to identify their medications and have less refill adherence.29, 30

In conclusion, in patients hospitalized with cardiovascular disease, predictors of lower postdischarge adherence include younger age, Medicaid insurance, and low baseline adherence. It may be prudent to assess baseline adherence and insurance type in hospitalized patients in order to identify those who may benefit from additional assistance to improve medication adherence and medication safety during transitions in care.

Acknowledgements

Meeting Presentations: SGIM New England Regional Meeting, oral presentation, Boston, MA, March 4, 2011; and SGIM National Meeting, poster presentation, Phoenix, AZ, May 6, 2011. Dr Schnipper had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Disclosures: Financial support was provided by R01 HL089755 (NHLBI, Kripalani), K23 HL077597 (NHLBI, Kripalani), K08 HL072806 (NHLBI, Schnipper), T32HP10251‐02 (Cohen), and by the Division of General Medicine, Massachusetts General Hospital and the Harvard Medical School Fellowship in General Medicine and Primary Care (Cohen). Dr Kripalani is a consultant to and holds equity in PictureRx, LLC, which makes patient education tools to improve medication management. PictureRx did not provide materials or funding for this study. All other authors disclose no relevant or financial conflicts of interest.

References
  1. Osterberg L,Blaschke T.Adherence to medication.N Engl J Med.2005;353(5):487497.
  2. Coleman EA,Smith JD,Raha D,Min SJ.Posthospital medication discrepancies: prevalence and contributing factors.Arch Intern Med.2005;165(16):18421847.
  3. Cua YM,Kripalani S.Medication use in the transition from hospital to home.Ann Acad Med Singapore.2008;37(2):136141.
  4. Moore C,Wisnivesky J,Williams S,McGinn T.Medical errors related to discontinuity of care from an inpatient to an outpatient setting.J Gen Intern Med.2003;18(8):646651.
  5. Forster AJ,Murff HJ,Peterson JF,Gandhi TK,Bates DW.The incidence and severity of adverse events affecting patients after discharge from the hospital.Ann Intern Med.2003;138(3):161167.
  6. Forster AJ,Murff HJ,Peterson JF,Gandhi TK,Bates DW.Adverse drug events occurring following hospital discharge.J Gen Intern Med.2005;20(4):317323.
  7. Schnipper JL,Kirwin JL,Cotugno MC, et al.Role of pharmacist counseling in preventing adverse drug events after hospitalization.Arch Intern Med.2006;166(5):565571.
  8. Vira T,Colquhoun M,Etchells E.Reconcilable differences: correcting medication errors at hospital admission and discharge.Qual Saf Health Care.2006;15(2):122126.
  9. Hassan M,Lage MJ.Risk of rehospitalization among bipolar disorder patients who are nonadherent to antipsychotic therapy after hospital discharge.Am J Health Syst Pharm.2009;66(4):358365.
  10. Mansur N,Weiss A,Hoffman A,Gruenewald T,Beloosesky Y.Continuity and adherence to long‐term drug treatment by geriatric patients after hospital discharge: a prospective cohort study.Drugs Aging.2008;25(10):861870.
  11. Kripalani S,Henderson LE,Jacobson TA,Vaccarino V.Medication use among inner‐city patients after hospital discharge: patient‐reported barriers and solutions.Mayo Clin Proc.2008;83(5):529535.
  12. Lindquist LA,Go L,Fleisher J,Jain N,Friesema E,Baker DW.Relationship of health literacy to intentional and unintentional non‐adherence of hospital discharge medications.J Gen Intern Med.2012;27(2):173178.
  13. Office of Disease Prevention and Health Promotion, US Department of Health and Human Services.Healthy People 2010. Available at: http://www.healthypeople.gov/Document/pdf/uih/2010uih.pdf. Accessed February 15,2012.
  14. Davis TC,Wolf MS,Bass PF, et al.Literacy and misunderstanding prescription drug labels.Ann Intern Med.2006;145(12):887894.
  15. Kripalani S,Henderson LE,Chiu EY,Robertson R,Kolm P,Jacobson TA.Predictors of medication self‐management skill in a low‐literacy population.J Gen Intern Med.2006;21(8):852856.
  16. Schnipper JL,Roumie CL,Cawthorn C, et al;for the PILL‐CVD Study Group.Rationale and design of the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) study.Circ Cardiovasc Qual Outcomes.2010;3(2):212219.
  17. Borson S,Scanlan JM,Watanabe J,Tu SP,Lessig M.Simplifying detection of cognitive impairment: comparison of the Mini‐Cog and Mini‐Mental State Examination in a multiethnic sample.J Am Geriatr Soc.2005;53(5):871874.
  18. Nurss JR.Short Test of Functional Health Literacy in Adults.Snow Camp, NC:Peppercorn Books and Press;1998.
  19. Morisky DE,Ang A,Krousel‐Wood M,Ward HJ.Predictive validity of a medication adherence measure in an outpatient setting.J Clin Hypertens (Greenwich).2008;10(5):348354.
  20. Marvanova M,Roumie CL,Eden SK,Cawthon C,Schnipper JL,Kripalani S.Health literacy and medication understanding among hospitalized adults.J Hosp Med. In press.
  21. Marvanova M,Roumie CL,Eden SK,Cawthon C,Schnipper JL,Kripalani S.Health literacy and medication understanding among hospitalized adults.J Hosp Med.2011;6(9):488493.
  22. Toobert DJ,Hampson SE,Glasgow RE.The summary of diabetes self‐care activities measure: results from 7 studies and a revised scale.Diabetes Care.2000;23(7):943950.
  23. Rubin DB.Multiple Imputation for Nonresponse in Surveys.New York, NY:John Wiley 1987.
  24. Hinkin CH,Hardy DJ,Mason KI, et al.Medication adherence in HIV‐infected adults: effect of patient age, cognitive status, and substance abuse.AIDS.2004;18(suppl 1):S19S25.
  25. Wong MC,Jiang JY,Griffiths SM.Factors associated with antihypertensive drug compliance in 83,884 Chinese patients: a cohort study.J Epidemiol Community Health.2010;64(10):895901.
  26. Wong MC,Kong AP,So WY,Jiang JY,Chan JC,Griffiths SM.Adherence to oral hypoglycemic agents in 26,782 Chinese patients: a cohort study.J Clin Pharmacol.2011;51(10):14741482.
  27. Gazmararian J,Jacobson KL,Pan Y,Schmotzer B,Kripalani S.Effect of a pharmacy‐based health literacy intervention and patient characteristics on medication refill adherence in an urban health system.Ann Pharmacother.2010;44(1):8087.
  28. Kalichman SC,Ramachandran B,Catz S.Adherence to combination antiretroviral therapies in HIV patients of low health literacy.J Gen Intern Med.1999;14(5):267273.
  29. Gazmararian JA,Kripalani S,Miller MJ,Echt KV,Ren J,Rask K.Factors associated with medication refill adherence in cardiovascular‐related diseases: a focus on health literacy.J Gen Intern Med.2006;21(12):12151221.
  30. Persell SD,Osborn CY,Richard R,Skripkauskas S,Wolf MS.Limited health literacy is a barrier to medication reconciliation in ambulatory care.J Gen Intern Med.2007;22(11):15231526.
  31. Chew LD,Bradley KA,Flum DR,Cornia PB,Koepsell TD.The impact of low health literacy on surgical practice.Am J Surg.2004;188(3):250253.
  32. Gatti ME,Jacobson KL,Gazmararian JA,Schmotzer B,Kripalani S.Relationships between beliefs about medications and adherence.Am J Health Syst Pharm.2009;66(7):657664.
  33. Fang MC,Machtinger EL,Wang F,Schillinger D.Health literacy and anticoagulation‐related outcomes among patients taking warfarin.J Gen Intern Med.2006;21(8):841846.
  34. Paasche‐Orlow MK,Cheng DM,Palepu A,Meli S,Faber V,Samet JH.Health literacy, antiretroviral adherence, and HIV‐RNA suppression: a longitudinal perspective.J Gen Intern Med.2006;21(8):835840.
  35. Platt AB,Localio AR,Brensinger CM, et al.Risk factors for nonadherence to warfarin: results from the IN‐RANGE study.Pharmacoepidemiol Drug Saf.2008;17(9):853860.
  36. Muntner P,Mann DM,Woodward M, et al.Predictors of low clopidogrel adherence following percutaneous coronary intervention.Am J Cardiol.2011;108(6):822827.
  37. Shi L,Liu J,Fonseca V,Walker P,Kalsekar A,Pawaskar M.Correlation between adherence rates measured by MEMS and self‐reported questionnaires: a meta‐analysis.Health Qual Life Outcomes.2010;8:99.
  38. Shi L,Liu J,Koleva Y,Fonseca V,Kalsekar A,Pawaskar M.Concordance of adherence measurement using self‐reported adherence questionnaires and medication monitoring devices.Pharmacoeconomics.2010;28(12):10971107.
  39. Grant RW,Devita NG,Singer DE,Meigs JB.Polypharmacy and medication adherence in patients with type 2 diabetes.Diabetes Care.2003;26(5):14081412.
  40. Grant RW,Devita NG,Singer DE,Meigs JB.Improving adherence and reducing medication discrepancies in patients with diabetes.Ann Pharmacother.2003;37(7–8):962969.
References
  1. Osterberg L,Blaschke T.Adherence to medication.N Engl J Med.2005;353(5):487497.
  2. Coleman EA,Smith JD,Raha D,Min SJ.Posthospital medication discrepancies: prevalence and contributing factors.Arch Intern Med.2005;165(16):18421847.
  3. Cua YM,Kripalani S.Medication use in the transition from hospital to home.Ann Acad Med Singapore.2008;37(2):136141.
  4. Moore C,Wisnivesky J,Williams S,McGinn T.Medical errors related to discontinuity of care from an inpatient to an outpatient setting.J Gen Intern Med.2003;18(8):646651.
  5. Forster AJ,Murff HJ,Peterson JF,Gandhi TK,Bates DW.The incidence and severity of adverse events affecting patients after discharge from the hospital.Ann Intern Med.2003;138(3):161167.
  6. Forster AJ,Murff HJ,Peterson JF,Gandhi TK,Bates DW.Adverse drug events occurring following hospital discharge.J Gen Intern Med.2005;20(4):317323.
  7. Schnipper JL,Kirwin JL,Cotugno MC, et al.Role of pharmacist counseling in preventing adverse drug events after hospitalization.Arch Intern Med.2006;166(5):565571.
  8. Vira T,Colquhoun M,Etchells E.Reconcilable differences: correcting medication errors at hospital admission and discharge.Qual Saf Health Care.2006;15(2):122126.
  9. Hassan M,Lage MJ.Risk of rehospitalization among bipolar disorder patients who are nonadherent to antipsychotic therapy after hospital discharge.Am J Health Syst Pharm.2009;66(4):358365.
  10. Mansur N,Weiss A,Hoffman A,Gruenewald T,Beloosesky Y.Continuity and adherence to long‐term drug treatment by geriatric patients after hospital discharge: a prospective cohort study.Drugs Aging.2008;25(10):861870.
  11. Kripalani S,Henderson LE,Jacobson TA,Vaccarino V.Medication use among inner‐city patients after hospital discharge: patient‐reported barriers and solutions.Mayo Clin Proc.2008;83(5):529535.
  12. Lindquist LA,Go L,Fleisher J,Jain N,Friesema E,Baker DW.Relationship of health literacy to intentional and unintentional non‐adherence of hospital discharge medications.J Gen Intern Med.2012;27(2):173178.
  13. Office of Disease Prevention and Health Promotion, US Department of Health and Human Services.Healthy People 2010. Available at: http://www.healthypeople.gov/Document/pdf/uih/2010uih.pdf. Accessed February 15,2012.
  14. Davis TC,Wolf MS,Bass PF, et al.Literacy and misunderstanding prescription drug labels.Ann Intern Med.2006;145(12):887894.
  15. Kripalani S,Henderson LE,Chiu EY,Robertson R,Kolm P,Jacobson TA.Predictors of medication self‐management skill in a low‐literacy population.J Gen Intern Med.2006;21(8):852856.
  16. Schnipper JL,Roumie CL,Cawthorn C, et al;for the PILL‐CVD Study Group.Rationale and design of the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL‐CVD) study.Circ Cardiovasc Qual Outcomes.2010;3(2):212219.
  17. Borson S,Scanlan JM,Watanabe J,Tu SP,Lessig M.Simplifying detection of cognitive impairment: comparison of the Mini‐Cog and Mini‐Mental State Examination in a multiethnic sample.J Am Geriatr Soc.2005;53(5):871874.
  18. Nurss JR.Short Test of Functional Health Literacy in Adults.Snow Camp, NC:Peppercorn Books and Press;1998.
  19. Morisky DE,Ang A,Krousel‐Wood M,Ward HJ.Predictive validity of a medication adherence measure in an outpatient setting.J Clin Hypertens (Greenwich).2008;10(5):348354.
  20. Marvanova M,Roumie CL,Eden SK,Cawthon C,Schnipper JL,Kripalani S.Health literacy and medication understanding among hospitalized adults.J Hosp Med. In press.
  21. Marvanova M,Roumie CL,Eden SK,Cawthon C,Schnipper JL,Kripalani S.Health literacy and medication understanding among hospitalized adults.J Hosp Med.2011;6(9):488493.
  22. Toobert DJ,Hampson SE,Glasgow RE.The summary of diabetes self‐care activities measure: results from 7 studies and a revised scale.Diabetes Care.2000;23(7):943950.
  23. Rubin DB.Multiple Imputation for Nonresponse in Surveys.New York, NY:John Wiley 1987.
  24. Hinkin CH,Hardy DJ,Mason KI, et al.Medication adherence in HIV‐infected adults: effect of patient age, cognitive status, and substance abuse.AIDS.2004;18(suppl 1):S19S25.
  25. Wong MC,Jiang JY,Griffiths SM.Factors associated with antihypertensive drug compliance in 83,884 Chinese patients: a cohort study.J Epidemiol Community Health.2010;64(10):895901.
  26. Wong MC,Kong AP,So WY,Jiang JY,Chan JC,Griffiths SM.Adherence to oral hypoglycemic agents in 26,782 Chinese patients: a cohort study.J Clin Pharmacol.2011;51(10):14741482.
  27. Gazmararian J,Jacobson KL,Pan Y,Schmotzer B,Kripalani S.Effect of a pharmacy‐based health literacy intervention and patient characteristics on medication refill adherence in an urban health system.Ann Pharmacother.2010;44(1):8087.
  28. Kalichman SC,Ramachandran B,Catz S.Adherence to combination antiretroviral therapies in HIV patients of low health literacy.J Gen Intern Med.1999;14(5):267273.
  29. Gazmararian JA,Kripalani S,Miller MJ,Echt KV,Ren J,Rask K.Factors associated with medication refill adherence in cardiovascular‐related diseases: a focus on health literacy.J Gen Intern Med.2006;21(12):12151221.
  30. Persell SD,Osborn CY,Richard R,Skripkauskas S,Wolf MS.Limited health literacy is a barrier to medication reconciliation in ambulatory care.J Gen Intern Med.2007;22(11):15231526.
  31. Chew LD,Bradley KA,Flum DR,Cornia PB,Koepsell TD.The impact of low health literacy on surgical practice.Am J Surg.2004;188(3):250253.
  32. Gatti ME,Jacobson KL,Gazmararian JA,Schmotzer B,Kripalani S.Relationships between beliefs about medications and adherence.Am J Health Syst Pharm.2009;66(7):657664.
  33. Fang MC,Machtinger EL,Wang F,Schillinger D.Health literacy and anticoagulation‐related outcomes among patients taking warfarin.J Gen Intern Med.2006;21(8):841846.
  34. Paasche‐Orlow MK,Cheng DM,Palepu A,Meli S,Faber V,Samet JH.Health literacy, antiretroviral adherence, and HIV‐RNA suppression: a longitudinal perspective.J Gen Intern Med.2006;21(8):835840.
  35. Platt AB,Localio AR,Brensinger CM, et al.Risk factors for nonadherence to warfarin: results from the IN‐RANGE study.Pharmacoepidemiol Drug Saf.2008;17(9):853860.
  36. Muntner P,Mann DM,Woodward M, et al.Predictors of low clopidogrel adherence following percutaneous coronary intervention.Am J Cardiol.2011;108(6):822827.
  37. Shi L,Liu J,Fonseca V,Walker P,Kalsekar A,Pawaskar M.Correlation between adherence rates measured by MEMS and self‐reported questionnaires: a meta‐analysis.Health Qual Life Outcomes.2010;8:99.
  38. Shi L,Liu J,Koleva Y,Fonseca V,Kalsekar A,Pawaskar M.Concordance of adherence measurement using self‐reported adherence questionnaires and medication monitoring devices.Pharmacoeconomics.2010;28(12):10971107.
  39. Grant RW,Devita NG,Singer DE,Meigs JB.Polypharmacy and medication adherence in patients with type 2 diabetes.Diabetes Care.2003;26(5):14081412.
  40. Grant RW,Devita NG,Singer DE,Meigs JB.Improving adherence and reducing medication discrepancies in patients with diabetes.Ann Pharmacother.2003;37(7–8):962969.
Issue
Journal of Hospital Medicine - 7(6)
Issue
Journal of Hospital Medicine - 7(6)
Page Number
470-475
Page Number
470-475
Article Type
Display Headline
Predictors of medication adherence postdischarge: The impact of patient age, insurance status, and prior adherence
Display Headline
Predictors of medication adherence postdischarge: The impact of patient age, insurance status, and prior adherence
Sections
Article Source

Copyright © 2012 Society of Hospital Medicine

Disallow All Ads
Correspondence Location
General Medicine Unit, Massachusetts General Hospital, 50 Staniford St, 9th Floor, Boston, MA 02114
Content Gating
No Gating (article Unlocked/Free)
Alternative CME
Article PDF Media
Media Files

Press Ganey Analyst Explains Implications of Hospital Value-Based Purchasing

Article Type
Changed
Fri, 09/14/2018 - 12:24
Display Headline
Press Ganey Analyst Explains Implications of Hospital Value-Based Purchasing

Seven percent of Medicare hospital DRGs: That is, potentially, how much Medicare reimbursement will be in play from CMS' hospital value-based purchasing (HVBP) quality initiatives by Fiscal Year 2017, Nell Buhlman, MBA, vice president of clinical products for Press Ganey Associates, said during a Sunday pre-course at SHM 2012. How many of you know your hospital’s profit margin on Medicare?” she posed to the audience. “Is it 7%?”

Buhlman outlined various components of CMS’ quality initiatives for hospitals, which could add up to millions of dollars per year for an average-sized hospital. By 2017 quality measures impacting on reimbursement will include the HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems), core clinical measures, penalties for higher-than-expected 30-day readmission rates, and meaningful-use reductions.

Hospitals are capable of significant quality improvement, “but the importance of hitting the hospital quality targets every single time will grow,” she said.

“Improving quality is fantastic, but even better is improving faster than everyone else,” Buhlman said, adding that the smallest things can sometimes make a big difference on outcomes scores. She offered the example of giving a notepad and pen to hospitalized patients so they can write down the questions they want to ask their doctor for the next visit.

Issue
The Hospitalist - 2012(04)
Publications
Sections

Seven percent of Medicare hospital DRGs: That is, potentially, how much Medicare reimbursement will be in play from CMS' hospital value-based purchasing (HVBP) quality initiatives by Fiscal Year 2017, Nell Buhlman, MBA, vice president of clinical products for Press Ganey Associates, said during a Sunday pre-course at SHM 2012. How many of you know your hospital’s profit margin on Medicare?” she posed to the audience. “Is it 7%?”

Buhlman outlined various components of CMS’ quality initiatives for hospitals, which could add up to millions of dollars per year for an average-sized hospital. By 2017 quality measures impacting on reimbursement will include the HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems), core clinical measures, penalties for higher-than-expected 30-day readmission rates, and meaningful-use reductions.

Hospitals are capable of significant quality improvement, “but the importance of hitting the hospital quality targets every single time will grow,” she said.

“Improving quality is fantastic, but even better is improving faster than everyone else,” Buhlman said, adding that the smallest things can sometimes make a big difference on outcomes scores. She offered the example of giving a notepad and pen to hospitalized patients so they can write down the questions they want to ask their doctor for the next visit.

Seven percent of Medicare hospital DRGs: That is, potentially, how much Medicare reimbursement will be in play from CMS' hospital value-based purchasing (HVBP) quality initiatives by Fiscal Year 2017, Nell Buhlman, MBA, vice president of clinical products for Press Ganey Associates, said during a Sunday pre-course at SHM 2012. How many of you know your hospital’s profit margin on Medicare?” she posed to the audience. “Is it 7%?”

Buhlman outlined various components of CMS’ quality initiatives for hospitals, which could add up to millions of dollars per year for an average-sized hospital. By 2017 quality measures impacting on reimbursement will include the HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems), core clinical measures, penalties for higher-than-expected 30-day readmission rates, and meaningful-use reductions.

Hospitals are capable of significant quality improvement, “but the importance of hitting the hospital quality targets every single time will grow,” she said.

“Improving quality is fantastic, but even better is improving faster than everyone else,” Buhlman said, adding that the smallest things can sometimes make a big difference on outcomes scores. She offered the example of giving a notepad and pen to hospitalized patients so they can write down the questions they want to ask their doctor for the next visit.

Issue
The Hospitalist - 2012(04)
Issue
The Hospitalist - 2012(04)
Publications
Publications
Article Type
Display Headline
Press Ganey Analyst Explains Implications of Hospital Value-Based Purchasing
Display Headline
Press Ganey Analyst Explains Implications of Hospital Value-Based Purchasing
Sections
Disallow All Ads
Content Gating
No Gating (article Unlocked/Free)

Applicable Themes, Big-Picture Thinking Take Root at HM12

Article Type
Changed
Fri, 09/14/2018 - 12:24
Display Headline
Applicable Themes, Big-Picture Thinking Take Root at HM12

Sitting in the back of a conference room at the San Diego Convention Center on Sunday morning, Benjamin Frizner, MD, listens intently as a panel of HM experts debates the finer points of how best to implement and manage multidisciplinary rounds. The conversation, one of dozens to be tackled at a daylong pre-course on practice management, gave Dr. Frizner and dozens of physicians around him applicable advice, new viewpoints, and time to think about the big picture.

Welcome to HM12.

"The topics are focused to a lot of the problems we are facing," says Dr. Frizner, director of the hospitalist program at Saint Agnes Hospital in Baltimore. "It really gives us the whole day to just focus."

Of course, the four-day meeting is only just starting. Sunday is dedicated to eight pre-courses that offer CME credits, including a new session, "How to Improve Performance in CMS' Value-Based Purchasing Program." Other pre-courses dealt with ABIM Maintenance of Certification, critical care, perioperative care, and hands-on training in ultrasound and other medical procedures.

The annual meeting continues Monday, April 2, and includes the Research, Innovations, and Clinical Vignettes (RIV) poster competition and plenary addresses from Patrick Conway, MD, MSc, FAAP, SFHM, a pediatric hospitalist and chief medical officer of the Centers for Medicare & Medicaid Services (CMS); political commentator Norman Ornstein, PhD, MA, BA; and HM pioneer Robert Wachter, MD, MHM.

"This is the best meeting I've ever been to," says Madonna Ringswald, DO, medical director of the hospitalist program at Baptist Hospital Northeast in La Grange, Ky., who attended the practice management pre-course. "If you can’t find a lecture [that appeals to you], there’s something wrong with you."

Issue
The Hospitalist - 2012(04)
Publications
Sections

Sitting in the back of a conference room at the San Diego Convention Center on Sunday morning, Benjamin Frizner, MD, listens intently as a panel of HM experts debates the finer points of how best to implement and manage multidisciplinary rounds. The conversation, one of dozens to be tackled at a daylong pre-course on practice management, gave Dr. Frizner and dozens of physicians around him applicable advice, new viewpoints, and time to think about the big picture.

Welcome to HM12.

"The topics are focused to a lot of the problems we are facing," says Dr. Frizner, director of the hospitalist program at Saint Agnes Hospital in Baltimore. "It really gives us the whole day to just focus."

Of course, the four-day meeting is only just starting. Sunday is dedicated to eight pre-courses that offer CME credits, including a new session, "How to Improve Performance in CMS' Value-Based Purchasing Program." Other pre-courses dealt with ABIM Maintenance of Certification, critical care, perioperative care, and hands-on training in ultrasound and other medical procedures.

The annual meeting continues Monday, April 2, and includes the Research, Innovations, and Clinical Vignettes (RIV) poster competition and plenary addresses from Patrick Conway, MD, MSc, FAAP, SFHM, a pediatric hospitalist and chief medical officer of the Centers for Medicare & Medicaid Services (CMS); political commentator Norman Ornstein, PhD, MA, BA; and HM pioneer Robert Wachter, MD, MHM.

"This is the best meeting I've ever been to," says Madonna Ringswald, DO, medical director of the hospitalist program at Baptist Hospital Northeast in La Grange, Ky., who attended the practice management pre-course. "If you can’t find a lecture [that appeals to you], there’s something wrong with you."

Sitting in the back of a conference room at the San Diego Convention Center on Sunday morning, Benjamin Frizner, MD, listens intently as a panel of HM experts debates the finer points of how best to implement and manage multidisciplinary rounds. The conversation, one of dozens to be tackled at a daylong pre-course on practice management, gave Dr. Frizner and dozens of physicians around him applicable advice, new viewpoints, and time to think about the big picture.

Welcome to HM12.

"The topics are focused to a lot of the problems we are facing," says Dr. Frizner, director of the hospitalist program at Saint Agnes Hospital in Baltimore. "It really gives us the whole day to just focus."

Of course, the four-day meeting is only just starting. Sunday is dedicated to eight pre-courses that offer CME credits, including a new session, "How to Improve Performance in CMS' Value-Based Purchasing Program." Other pre-courses dealt with ABIM Maintenance of Certification, critical care, perioperative care, and hands-on training in ultrasound and other medical procedures.

The annual meeting continues Monday, April 2, and includes the Research, Innovations, and Clinical Vignettes (RIV) poster competition and plenary addresses from Patrick Conway, MD, MSc, FAAP, SFHM, a pediatric hospitalist and chief medical officer of the Centers for Medicare & Medicaid Services (CMS); political commentator Norman Ornstein, PhD, MA, BA; and HM pioneer Robert Wachter, MD, MHM.

"This is the best meeting I've ever been to," says Madonna Ringswald, DO, medical director of the hospitalist program at Baptist Hospital Northeast in La Grange, Ky., who attended the practice management pre-course. "If you can’t find a lecture [that appeals to you], there’s something wrong with you."

Issue
The Hospitalist - 2012(04)
Issue
The Hospitalist - 2012(04)
Publications
Publications
Article Type
Display Headline
Applicable Themes, Big-Picture Thinking Take Root at HM12
Display Headline
Applicable Themes, Big-Picture Thinking Take Root at HM12
Sections
Disallow All Ads
Content Gating
No Gating (article Unlocked/Free)

ABIM Maintenance of Certification (MOC) Pre-Course Is Learning Experience

Article Type
Changed
Fri, 09/14/2018 - 12:24
Display Headline
ABIM Maintenance of Certification (MOC) Pre-Course Is Learning Experience

Recertification is a fact of life for physicians. But at today’s ABIM Maintenance of Certification pre-course, the conversation is about more than just answers. It’s about the questions.

“When you take the actual recertification exam, it’s an exam,” Ethan Cumbler, MD, FACP, of University of Colorado Denver, says between leading question-and-answer sessions at the Hm12 pre-course this afternoon. “You find out whether it’s pass or fail. But when you go through this process of getting to look at the questions, look at all the answers, ask questions, discuss it as a group for the controversies - that’s an entirely different process."

Dr. Cumbler also tells pre-course attendees that the MOC is more than an evaluation.

“What this is is a learning process," he says, "and I think the people who come want to be part of that."

Issue
The Hospitalist - 2012(04)
Publications
Sections

Recertification is a fact of life for physicians. But at today’s ABIM Maintenance of Certification pre-course, the conversation is about more than just answers. It’s about the questions.

“When you take the actual recertification exam, it’s an exam,” Ethan Cumbler, MD, FACP, of University of Colorado Denver, says between leading question-and-answer sessions at the Hm12 pre-course this afternoon. “You find out whether it’s pass or fail. But when you go through this process of getting to look at the questions, look at all the answers, ask questions, discuss it as a group for the controversies - that’s an entirely different process."

Dr. Cumbler also tells pre-course attendees that the MOC is more than an evaluation.

“What this is is a learning process," he says, "and I think the people who come want to be part of that."

Recertification is a fact of life for physicians. But at today’s ABIM Maintenance of Certification pre-course, the conversation is about more than just answers. It’s about the questions.

“When you take the actual recertification exam, it’s an exam,” Ethan Cumbler, MD, FACP, of University of Colorado Denver, says between leading question-and-answer sessions at the Hm12 pre-course this afternoon. “You find out whether it’s pass or fail. But when you go through this process of getting to look at the questions, look at all the answers, ask questions, discuss it as a group for the controversies - that’s an entirely different process."

Dr. Cumbler also tells pre-course attendees that the MOC is more than an evaluation.

“What this is is a learning process," he says, "and I think the people who come want to be part of that."

Issue
The Hospitalist - 2012(04)
Issue
The Hospitalist - 2012(04)
Publications
Publications
Article Type
Display Headline
ABIM Maintenance of Certification (MOC) Pre-Course Is Learning Experience
Display Headline
ABIM Maintenance of Certification (MOC) Pre-Course Is Learning Experience
Sections
Disallow All Ads
Content Gating
No Gating (article Unlocked/Free)

Talking Shop: Hospitalists eager to adopt computerized physician order entry (CPOE)

Article Type
Changed
Fri, 09/14/2018 - 12:24
Display Headline
Talking Shop: Hospitalists eager to adopt computerized physician order entry (CPOE)

With pre-course participants finishing up lunch today, four hospitalists from three hospitals are talking shop. Two of the hospitals are in the process of implementing computerized physician order entry (CPOE), and the third hospital is on track to do the same but got delayed. Each of the hospitalists expresses cautious optimism about the outcomes.

“It’s a necessary evil,” says Gaurav T. Parikh, MD, a Cogent HMG hospitalist practicing at City Hospital in Martinsburg, W.V. “Once we start using it—if it really makes things easier—then it should give us more flexibility. You can use it anywhere in the hospital.

"Sometimes I leave a patient’s room and go to another patient, and then remember, gee, I forget to order something for that first patient.”

With CPOE, Dr. Parikh can enter the additional prescription at a computer terminal or try to reach a nurse on that floor by phone, who then places the order.

Jaydeep Patel, MD, MBA, hospitalist at Grant Medical Center in Columbus,Ohio, says he used CPOE in residency and eagerly is awaiting its implementation at Grant. “I really liked it. There’s just less chance for error, as opposed to 15 charts on the rack,” he says.

Dr. Parikh says his hospital tried for two years to get physicians to enter the date and time on prescriptions. “It didn’t happen,” he says, noting CPOE puts an electronic signature on every prescription. He also says the hospital will have an easier time pulling data on practice. “In a time of increased demands for data and quality, it will help you big time," he adds. "But it won’t necessarily save us time.”

Larry Beresford is a freelance writer covering HM12.

Issue
The Hospitalist - 2012(04)
Publications
Sections

With pre-course participants finishing up lunch today, four hospitalists from three hospitals are talking shop. Two of the hospitals are in the process of implementing computerized physician order entry (CPOE), and the third hospital is on track to do the same but got delayed. Each of the hospitalists expresses cautious optimism about the outcomes.

“It’s a necessary evil,” says Gaurav T. Parikh, MD, a Cogent HMG hospitalist practicing at City Hospital in Martinsburg, W.V. “Once we start using it—if it really makes things easier—then it should give us more flexibility. You can use it anywhere in the hospital.

"Sometimes I leave a patient’s room and go to another patient, and then remember, gee, I forget to order something for that first patient.”

With CPOE, Dr. Parikh can enter the additional prescription at a computer terminal or try to reach a nurse on that floor by phone, who then places the order.

Jaydeep Patel, MD, MBA, hospitalist at Grant Medical Center in Columbus,Ohio, says he used CPOE in residency and eagerly is awaiting its implementation at Grant. “I really liked it. There’s just less chance for error, as opposed to 15 charts on the rack,” he says.

Dr. Parikh says his hospital tried for two years to get physicians to enter the date and time on prescriptions. “It didn’t happen,” he says, noting CPOE puts an electronic signature on every prescription. He also says the hospital will have an easier time pulling data on practice. “In a time of increased demands for data and quality, it will help you big time," he adds. "But it won’t necessarily save us time.”

Larry Beresford is a freelance writer covering HM12.

With pre-course participants finishing up lunch today, four hospitalists from three hospitals are talking shop. Two of the hospitals are in the process of implementing computerized physician order entry (CPOE), and the third hospital is on track to do the same but got delayed. Each of the hospitalists expresses cautious optimism about the outcomes.

“It’s a necessary evil,” says Gaurav T. Parikh, MD, a Cogent HMG hospitalist practicing at City Hospital in Martinsburg, W.V. “Once we start using it—if it really makes things easier—then it should give us more flexibility. You can use it anywhere in the hospital.

"Sometimes I leave a patient’s room and go to another patient, and then remember, gee, I forget to order something for that first patient.”

With CPOE, Dr. Parikh can enter the additional prescription at a computer terminal or try to reach a nurse on that floor by phone, who then places the order.

Jaydeep Patel, MD, MBA, hospitalist at Grant Medical Center in Columbus,Ohio, says he used CPOE in residency and eagerly is awaiting its implementation at Grant. “I really liked it. There’s just less chance for error, as opposed to 15 charts on the rack,” he says.

Dr. Parikh says his hospital tried for two years to get physicians to enter the date and time on prescriptions. “It didn’t happen,” he says, noting CPOE puts an electronic signature on every prescription. He also says the hospital will have an easier time pulling data on practice. “In a time of increased demands for data and quality, it will help you big time," he adds. "But it won’t necessarily save us time.”

Larry Beresford is a freelance writer covering HM12.

Issue
The Hospitalist - 2012(04)
Issue
The Hospitalist - 2012(04)
Publications
Publications
Article Type
Display Headline
Talking Shop: Hospitalists eager to adopt computerized physician order entry (CPOE)
Display Headline
Talking Shop: Hospitalists eager to adopt computerized physician order entry (CPOE)
Sections
Disallow All Ads
Content Gating
No Gating (article Unlocked/Free)

HM12 Kicks Off: Hundreds Dive into Pre-Courses; SHM Expects 3,000 Attendees

Article Type
Changed
Fri, 09/14/2018 - 12:24
Display Headline
HM12 Kicks Off: Hundreds Dive into Pre-Courses; SHM Expects 3,000 Attendees

HM12 kicked off this morning with hundreds attending eight pre-courses and many more registering for the annual meeting. SHM expects more than 3,000 to attend the four-day gathering at the San Diego Convention Center.

The Hospitalist will be delivering daily news updates and analysis throughout the annual meeting. Check back or download the HM12 at Hand web application to stay abreast of the latest news.

Today's pre-courses are as follows:

  • ABIM MOC learning session, 6.5 credits;
  • Advanced Interactive Critical Care, 7.75 credits;
  • CMS’s Value-Based Purchasing Program, 3.75 credits;
  • Medical Procedures, 7.5 credits;
  • Portable Ultrasounds, 3.75 credits;
  • Perioperative Medicine, 7.75 credits; and
  • Practice Management, 7.5 credits.
Issue
The Hospitalist - 2012(04)
Publications
Sections

HM12 kicked off this morning with hundreds attending eight pre-courses and many more registering for the annual meeting. SHM expects more than 3,000 to attend the four-day gathering at the San Diego Convention Center.

The Hospitalist will be delivering daily news updates and analysis throughout the annual meeting. Check back or download the HM12 at Hand web application to stay abreast of the latest news.

Today's pre-courses are as follows:

  • ABIM MOC learning session, 6.5 credits;
  • Advanced Interactive Critical Care, 7.75 credits;
  • CMS’s Value-Based Purchasing Program, 3.75 credits;
  • Medical Procedures, 7.5 credits;
  • Portable Ultrasounds, 3.75 credits;
  • Perioperative Medicine, 7.75 credits; and
  • Practice Management, 7.5 credits.

HM12 kicked off this morning with hundreds attending eight pre-courses and many more registering for the annual meeting. SHM expects more than 3,000 to attend the four-day gathering at the San Diego Convention Center.

The Hospitalist will be delivering daily news updates and analysis throughout the annual meeting. Check back or download the HM12 at Hand web application to stay abreast of the latest news.

Today's pre-courses are as follows:

  • ABIM MOC learning session, 6.5 credits;
  • Advanced Interactive Critical Care, 7.75 credits;
  • CMS’s Value-Based Purchasing Program, 3.75 credits;
  • Medical Procedures, 7.5 credits;
  • Portable Ultrasounds, 3.75 credits;
  • Perioperative Medicine, 7.75 credits; and
  • Practice Management, 7.5 credits.
Issue
The Hospitalist - 2012(04)
Issue
The Hospitalist - 2012(04)
Publications
Publications
Article Type
Display Headline
HM12 Kicks Off: Hundreds Dive into Pre-Courses; SHM Expects 3,000 Attendees
Display Headline
HM12 Kicks Off: Hundreds Dive into Pre-Courses; SHM Expects 3,000 Attendees
Sections
Disallow All Ads
Content Gating
No Gating (article Unlocked/Free)

Building an Innovative Model for Personalized Healthcare

Article Type
Changed
Wed, 04/10/2019 - 11:27
Display Headline
Building an Innovative Model for Personalized Healthcare

Supplement Editor:
Kathryn Teng, MD

Contents

Building an innovative model for personalized healthcare
Kathryn Teng, MD; Charis Eng, MD, PhD; Caryl A. Hess, PhD, MBA; Meredith A. Holt, MBA; Rocio T. Moran, MD; Richard R. Sharp, PhD; and Elias I. Traboulsi, MD

Article PDF
Issue
Cleveland Clinic Journal of Medicine - 79(4)
Publications
Topics
Page Number
S1-S9
Sections
Article PDF
Article PDF

Supplement Editor:
Kathryn Teng, MD

Contents

Building an innovative model for personalized healthcare
Kathryn Teng, MD; Charis Eng, MD, PhD; Caryl A. Hess, PhD, MBA; Meredith A. Holt, MBA; Rocio T. Moran, MD; Richard R. Sharp, PhD; and Elias I. Traboulsi, MD

Supplement Editor:
Kathryn Teng, MD

Contents

Building an innovative model for personalized healthcare
Kathryn Teng, MD; Charis Eng, MD, PhD; Caryl A. Hess, PhD, MBA; Meredith A. Holt, MBA; Rocio T. Moran, MD; Richard R. Sharp, PhD; and Elias I. Traboulsi, MD

Issue
Cleveland Clinic Journal of Medicine - 79(4)
Issue
Cleveland Clinic Journal of Medicine - 79(4)
Page Number
S1-S9
Page Number
S1-S9
Publications
Publications
Topics
Article Type
Display Headline
Building an Innovative Model for Personalized Healthcare
Display Headline
Building an Innovative Model for Personalized Healthcare
Sections
Citation Override
Cleveland Clinic Journal of Medicine 2012 April;79(4 suppl 1):S1-S9
Disallow All Ads
Content Gating
No Gating (article Unlocked/Free)
Alternative CME
Disqus Comments
Default
Use ProPublica
Hide sidebar & use full width
render the right sidebar.
Article PDF Media

Benzodiazepines: A versatile clinical tool

Article Type
Changed
Wed, 09/26/2018 - 13:42
Display Headline
Benzodiazepines: A versatile clinical tool

Issue
Current Psychiatry - 11(04)
Publications
Topics
Sections

Issue
Current Psychiatry - 11(04)
Issue
Current Psychiatry - 11(04)
Publications
Publications
Topics
Article Type
Display Headline
Benzodiazepines: A versatile clinical tool
Display Headline
Benzodiazepines: A versatile clinical tool
Sections
Disallow All Ads
Alternative CME
Use ProPublica

Building an innovative model for personalized healthcare

Article Type
Changed
Fri, 03/23/2018 - 09:21
Display Headline
Building an innovative model for personalized healthcare

Personalized healthcare is the tailoring of medical management and patient care to the individual characteristics of each patient. This is achieved by incorporating the genetic and genomic makeup of an individual and his or her family medical history, environment, health-related behaviors, culture, and values into a complete health picture that can be used to customize care. Another level of personalization, often called personalized medicine, involves the selection of drug therapy through the use of tests to determine the genes and gene interactions that can reliably predict an individual’s response to a given therapy. This white paper focuses largely on the use of personalized healthcare as a risk prediction tool.

CURRENT STATUS OF PERSONALIZED HEALTHCARE

Practitioners and consumers in today’s healthcare setting do not yet fully recognize the potential benefits of personalized healthcare (Table 11). Further, proposals for reform tend to be reactive rather than proactive. Family history is well validated as a tool to predict risk for disease, but, in some instances, genomic information may enhance risk prediction provided by family history. The trial-and-error approach now used to treat disease is costly, but genomic testing has the potential to save money through more effective use of diagnostic tests, counseling about medical management based on gene test results, and prescribing of medications.

The case for personalized healthcare: Seeking value

To fully appreciate the need to advance the adoption of personalized healthcare into the delivery of medicine, one must consider the operation of our current healthcare system and its inefficiencies in terms of delivery and cost, its imprecision in the selection of therapies, and its inability to optimize outcomes. The framework of the US healthcare system as it is now constructed is expensive, disease-directed (instead of health- and wellness-directed), fragmented, and complex. While gross domestic product (GDP) in the United States has increased by approximately 3% per year,2 the compounded growth rate of healthcare expenditures is 6.1% per year. Healthcare in the aggregate now represents 17.6% of GDP and 27% of spending by the federal government and consumes 28% of the average household’s discretionary spending, surpassed only by housing.3

Personalized healthcare can potentially address the need for value consistent with the healthcare system’s prominent share of the US economy. The growth in healthcare spending is certain to be a target of the newly created Joint Select Committee on Deficit Reduction (created by the Budget Control Act of 2011), which is tasked with deficit reduction of at least $1.5 trillion over a 10-year period.

The need to address healthcare costs has been recognized in the Patient Protection and Affordable Care Act, a central feature of which is the creation of integrated health systems that pay for value based on quality, cost containment, and consumer experience. The legislation was enacted to transform healthcare in a variety of ways to make it more sustainable. The Patient Protection and Affordable Care Act seeks to end fragmentation by expanding the use of information technology to reorganize the delivery system and to prevent errors, shifting from volume-based incentives to incentives based on performance and outcomes, and rewarding effective healthcare delivery measures and good patient outcomes.

A shift from reactive to proactive

The premise behind personalized healthcare is the potential for more efficient healthcare, with the assumption that efficiency translates to lower cost and improved patient care.

Although healthcare reform is most often referred to in the context of improving access to care through insurance coverage mandates, true healthcare reform shifts current healthcare models from the practice of reactive medicine to the practice of proactive medicine, in which the tools of personalized healthcare (ie, genetics, genomics, and other molecular diagnostics) enable not only better quality of care but also less expensive care.

Several personalized tools have long been accepted into mainstream medicine. Two examples are the family history, which is the least expensive and most available genetic evaluation tool, and ABO blood typing for safe transfusions (as ABO blood types are alleles of a gene). In fact, much of what is now considered mainstream medical management was at one time considered new. To allow further evolution of medical practice, our challenge is to open our minds to the possibility that personalized proactive medicine can improve healthcare.

The new vision: More precise management

The trial-and-error approach to treating disease is inefficient and costly. Many drugs are effective for only about 50% of patients, often leading to switching or intensification of therapy that requires multiple patient visits.

Personalized medicine considers pharmacokinetic and other characteristics in selection of drug dosages. Genomic testing has the potential to provide clearer insight into the more successful use of currently available medicines. Treatment decisions (ie, drug and drug dosage choice) made on the basis of pharmacogenomic testing should increase adherence through greater effectiveness and fewer adverse drug reactions.

A massive amount of waste is related to pharmaceutical nonadherence and noncompliance. The New England Healthcare Institute has estimated that medication nonadherence costs the healthcare system $290 billion annually.4 Methodologies targeted at individual patients to improve adherence to drug regimens could save the healthcare system a tremendous amount of money.

Cancer management as a model for personalized healthcare. Personalization of therapy is especially suited to cancer management, given that the response to nonspecific cancer chemotherapy is suboptimal in most patients yet exposes them to adverse effects.5 Large-scale sequencing of human cancer genomes is rapidly changing the understanding of cancer biology and is identifying new targets in difficult-to-treat diseases and causes of drug resistance. Applying this information can achieve cost savings by avoiding the use of treatments that are ineffective in particular patients.

Overexpression of genetic mutations renders some cancers less susceptible to certain treatments, but has opened the door to individualized molecularly guided treatment strategies. For example, among patients with non–small cell lung cancer, mutations in the epidermal growth factor receptor (EGFR) tyrosine kinase domain predict response to EGFR tyrosine kinase inhibitors, and anaplastic lymphoma kinase (ALK) inhibitors induce response in patients harboring a mutation in EML4-ALK genes. The recognition that human epidermal growth factor receptor (HER)-2 overexpression as a result of ERBB2 gene amplification occurs in as many as 20% of human breast cancers paved the way for the development of HER-2–targeted therapies. Patients with advanced colorectal cancer whose tumors express the KRAS gene mutation do not benefit from an EGFR inhibitor, whereas those with wild-type KRAS have improved survival with EGFR inhibitor treatment.6

 

 

BARRIERS TO THE APPLICATION OF PERSONALIZED HEALTHCARE

The availability and potential of personalized healthcare services and technology is not universally recognized or appreciated by consumers and clinicians. This lack of awareness contributes to a shortage of public support and limited demand for such services. Other barriers include misperceptions regarding the impact of personalized healthcare on disease management, limited incentives to use the available technology, and a knowledge gap among healthcare providers.

Lack of awareness and support

As applications of personalized healthcare advance to the point of clinical relevance, it is important to consider strategies for effective implementation into healthcare practice. Personalized healthcare, when more fully implemented, promises to accelerate the progress that healthcare reform hopes to achieve.

A major challenge to widespread adoption of personalized healthcare is limited recognition by the public and some healthcare providers that personalized healthcare can help to achieve better value. For personalized medicine to be embraced, the concept of “helix to health,” or translation of knowledge to the clinical setting, must resonate with the general public. Despite lack of public and provider awareness, the Personalized Medicine Coalition (PMC) has documented the existence of 56 personalized treatment and diagnostic products. Further, more than 200 product labels now recommend genetic testing prior to use to identify likely responders or inform of the influence of genetic variation on safety and effectiveness.

Consumers’ confidence in the efficacy and safety of medicines they take might contribute to the absence of public support for personalized healthcare. Similarly, despite the availability of genomic tests and tools, many physicians who might be advocates for personalized healthcare do not see the relevance of genomic medicine to their practices in terms of direct benefit to patient care.7

Apart from clinicians and consumers, support is also weak among health insurers and employers, even though the return on investment for personalized healthcare may be profound. Payers await the economic outcomes data that are crucial for their commitment to personalized healthcare. In addition, some have concerns about the ethical implications of personalized healthcare (see “Managing Genomic Information Responsibly”).

Perception of impact on treatment and prevention

A frequent criticism of genomics in medicine is that a genetic diagnosis does not help with patient management. In fact, surveillance and management of patients and family members often changes in response to a genetic diagnosis; knowing which gene is involved personalizes medical management. An example is the management of hereditary nonpolyposis colorectal cancer (HNPCC), or Lynch syndrome, which is the most common form of hereditary colon cancer. For a person with HNPCC, the lifetime risk of developing colorectal cancer is approximately 80%. Lynch syndrome is caused by germline mutations in one of three major mismatch repair (MMR) genes (MLH1, MSH2, and MSH6), and it predisposes to other cancers—uterine, stomach, and ovarian—as well. In women with Lynch syndrome, the lifetime risk for uterine cancer is 40%, compared with 4% in the general population.

At least 90% of patients with Lynch syndrome can be detected through MMR testing via microsatellite instability (MSI) or immunohistochemistry (IHC).8 MSI is a cellular phenotype that indicates a deficiency in at least one DNA MMR protein.

Although 5-fluorouracil–based chemo therapy is the standard of care for treatment of colorectal cancer, it confers no survival advantage in patients with MMR-IHC null (lack of expression of the gene) or MSI-high sporadic colorectal cancer.9,10 Knowing the status of MMR proteins, therefore, would alter the decision regarding neoadjuvant and adjuvant chemotherapy.

Perception of value

Implementation of pharmacogenomics into clinical practice has lagged. One major reason is the lack of an obvious business model for a product that may only be required once in an individual patient’s lifetime.11

A second barrier to integration lies in the limited demand for pharmacogenomics from physicians. This may be related partly to limited expertise in genetics among many physicians and to significant pushback from payers against today’s costs. Without reimbursement, little incentive exists for pharmacogenomics diagnostics. The incentive for physicians is further depressed, perhaps appropriately, when randomized controlled studies fail to demonstrate improved clinical outcomes with the use of pharmacogenomicbased treatment strategies. Two such examples are genotype-guided warfarin dosing, which failed in a randomized controlled trial to improve the proportion of international normalized ratios in the therapeutic range,12 and dosing of clopidogrel based on platelet reactivity, which did not improve outcomes after percutaneous coronary intervention compared with standard dosing in a randomized double-blind clinical trial.13

A significant delay in obtaining the results of pharmacogenomics testing, which also postpones the prescribing encounter, is another major drawback.

A knowledge gap persists

At present, delivery of personalized healthcare is not part of the usual training of physicians and other healthcare providers who are the gatekeepers of medicine. Few medical schools incorporate human and medical genetics, genomics, and pharmacogenomics into their curricula. Genetics is inadequately emphasized in residency curricula outside of pediatrics, family medicine, and obstetrics/gynecology.

The resulting knowledge gap is a fundamental factor in the lack of interest in using genomics in clinical medicine. Educating consumers and physicians at all levels, including specialty societies as well as insurers, will be key to expanding utilization of personalized healthcare. Educating payers and providing them with more data on economic outcomes associated with personalized healthcare will be necessary for adoption into clinical practice; implementation will lag as long as reimbursement decisions do not support personalized approaches to medicine.

As DNA sequencing technology has become less expensive and more powerful, companies have begun to market personal genomic testing. As a result, patients who use these services will increasingly want to discuss the results with their physicians. A significant number of clinicians are unfamiliar with personal genomic testing and emerging genetic testing options. In one survey of physicians who attended educational sessions that discussed recent developments in clinical genetics, only 37% indicated that they were familiar with recent genetic research that affected their patients.14

Targeted education will enhance physicians’ understanding of probabilities and risk estimates from the use of genomic testing; it will also improve recognition of potential causes of patient anxiety, gene variants of unknown significance, and follow-up tests and procedures that can add to expense. Nonphysician healthcare providers (ie, nurses and physician assistants) of direct care also will benefit from education.

 

 

INTEGRATING PERSONALIZED HEALTHCARE INTO CLINICAL PRACTICE

Practice standardization and an overhaul of the health information technology (HIT) infrastructure are needed if we are to reap the potential benefits of personalized healthcare. Creative approaches to practitioner education, which are being used in some institutions, must become more widespread. Similarly, the models for successful integration of personalized healthcare that have been achieved in some settings also can be implemented in other institutions.

Data collection and integration must be prioritized

Personalized healthcare can be both predictive and preventive, but moving past the disruptive phase of personalized healthcare will require a radical transformation of the healthcare “ecosystem” and HIT infrastructure.

Although data collection in the current system is extensive, data sharing and data management are inadequate. The pace at which HIT links clinical and genetic information must be accelerated. HIT will expedite innovation and implementation of personalized healthcare, allowing greater integration of data to permit improved data analysis capability. The ultimate goal is to create an interoperable system that connects these data across hospitals and clinicians to help clinicians interpret genomic and other risk information to better inform patient care.

Fully integrated health systems support better coordination of care and optimize the treatment of individual patients: linking research findings, treatment guidelines, treatment outcomes based on genetic profiles, and the individual patient’s own genetic profile will help to personalize treatments. Genomic information added to an individual’s electronic medical record along with improved data-sharing will facilitate clinicians’ ability to retrieve outcomes data based on patient characteristics.

Care models must be standardized, evidence-based practices must be executed, and care must be coordinated yet decentralized. In this way, clinicians can use the electronic medical record as an interoperable patient record to determine a personalized pathway to patient management. Standardization reduces variability in practice and permits seamless execution of care. Automation is imperative to achieving standardization, irrespective of the care supervisor. Investments must therefore be made to stimulate electronic medical record decision support.

In addition, larger data sets will be needed to identify the types of patients likely to respond to a treatment. Ideal data sets would be large enough to have adequate statistical power, be publicly available, standardize the collection of data with respect to response to therapy and toxicity, and contain data on concomitant collections of biologic samples.

Reimbursement must keep pace with medical advances

Payer willingness to reimburse for genomic tests and treatments will determine the pace of integration of personalized healthcare into clinical practice. Evidence that enhanced value can be derived from personalized approaches to medicine must be generated before personalized healthcare gains widespread acceptance by payers.

In addition, care-coordinated models must be developed to promote a value-based agenda that facilitates physician accountability and encourages clinical integration.

Innovative approaches are needed to educate providers

Development of point-of-care tools. Because information overload and lack of time are obstacles to clinicians’ efforts to incorporate genomic information into clinical practice, emphasis must be placed on genomic applications that have demonstrated utility. Engaging busy clinicians with point-of-care tools will maximize the relevance of the genomic information they receive and encourage effective use of their time. Decision-making should be supported through automatic risk assessment and management recommendations.

Educational tools. The National Coalition for Health Professional Education in Genetics (NCHPEG) was borne out of the recognition that the pace of genomic discovery far exceeds the pace at which healthcare providers can be educated. Its vision is to improve healthcare through informed use of genomic resources. NCHPEG is a member-based organization whose stakeholders include professional societies, hospitals, advocacy groups, and industry; it attempts to identify the specific educational needs for particular target audiences and then address these needs. It achieves its goals through the use of point-of-care tools and educational programs for continuing medical education credit.

One NCHPEG tool is the Pregnancy and Health Profile, which is a risk assessment and screening tool that attempts to improve the identification of women and babies at risk of developing genetic disease. It collects personal and family history information, performs a risk assessment for the clinician, and provides clinical decision support and education.

Another example of an educational tool is the “Genes to Society” curriculum initiated by The Johns Hopkins University School of Medicine in August 2009. The curriculum is being used as “the foundation for the scientific and clinical career development of future physicians.”15

Using personal genomic testing for education. The number of direct-to-consumer genomic tests is growing, and their market penetration will only increase as the cost of supplying a personal genome continues to decline. Whole genome scanning is being offered with the promise of identifying genetic predisposition to multiple diseases.

Participation in personal genomic testing may be a useful educational tool. Medical students, residents, and practicing physicians who participate in testing may be better equipped to advise patients about the processes involved and the potential utility and limitations of direct-to-consumer genotyping.14

Some companies that offer direct-to-consumer genomic testing provide telephone support from genetic counselors to help clients and their healthcare providers manage genetic information. Counselor services include identifying hereditary risks and reviewing diagnostic, preventive, and early-detection options.

Implementing pharmacogenomics into practice: Decision support systems are needed

A genomic decision support system that guides medication prescribing is needed to implement pharmacogenomic diagnostics. For such a system to achieve the goal of selecting the best medication for each individual, it must do the following:

  • Test all polymorphisms relevant to the prescribing of any medication
  • Be completed with no out-of-pocket cost to the patient
  • Be performed before the patient requires the medication
  • Provide results that will be interpreted as part of an individualized pharmacogenomics consult.11

Many useful pharmacogenomic tests are based on cytochrome P450 metabolism phenotypes that are responsible for variance in response to drugs metabolized by this pathway. Others use human leukocyte antigen screening for hypersensitivity reactions to abacavir, carbamazepine, and allopurinol. Examples of pharmacogenomics tests appear in Table 2.

The 1200 Patients Project, a pilot research study under way at the Center for Personalized Therapeutics at the University of Chicago, is attempting to demonstrate the feasibility of incorporating pharmacogenomic testing into routine clinical practice for medication treatment decisions. DNA samples from patients who are taking at least one prescription medication are being tested for differences in genes that may suggest greater effectiveness or an increased risk of side effects from certain medications.

 

 

Solutions in practice

Cleveland Clinic’s genetics-based management of Lynch syndrome, the integration of genetics services during patient appointments at Cleveland Clinic, and a coordinated approach at The Ohio State University Medical Center are examples of practical applications of personalized healthcare.

Colorectal cancer management. One example of a personalized approach to medicine that improves health outcome while achieving cost savings is the genetics-based approach to HNPCC (Lynch syndrome) at Cleveland Clinic.

Early identification of Lynch syndrome by screening all colorectal cancer patients has been shown to save $250,000 per life-year gained in the United States.16 All colorectal cancers resected at the Cleveland Clinic main campus are routinely screened for MSI and IHC, and the process is embedded into the routine pathology workflow. With the patients’ foreknowledge, a gastrointestinal cancer genetics counselor scans the list of MSI and IHC results each week. Patients who are MSI-high or IHC-null are invited to receive genetic counseling and consider germline single-gene testing guided by the IHC results. With this active approach, patient uptake is 80%; in comparison, with a passive approach (MSI/IHC results are placed in the pathology report), the uptake is 14%17 (B. Leach and C. Eng, unpublished data, 2011).The successful application of the active approach requires the close cooperation of multiple disciplines, including members of the Cleveland Clinic Genomic Medicine, Pathology & Laboratory Medicine, and Digestive Disease Institutes.18

Integrating genetics-based care at Cleveland Clinic. Time delays for genetics services and limited collaboration with managing physicians who are not genetics specialists reduces genetics-based access and availability. Broad access to genetics clinical services is a means of clinical integration of genetics-enabled care. Providing patients and healthcare providers with easy access and short wait times is vital for clinical integration of genetics-enabled personalized healthcare.

As part of a patient-centered focus on medicine, clinical genetics services have been integrated throughout Cleveland Clinic. The system has two genetics clinics at its main campus and has embedded multiple genetics satellites within its nongenetics clinics, easing access. Genetics counselors are stationed in the same areas of practice as referring providers. Although patient encounters have increased at the medical genetics clinic in the Genomic Medicine Institute, genetics consultations no longer require an extra trip to the clinic since they are integrated into existing appointments. With this approach, large numbers of patients can be seen with no wait times.

Coordinated care at The Ohio State University Medical Center. The Center for Personalized Health Care at The Ohio State University Medical Center (OSUMC) embraces a systems-based care-coordinated model that improves care by executing standardized processes and automating routine tasks. The Institute for Systems Biology, which was established to develop genomics, wellness, and chronic disease biomarkers, collaborates with OSUMC on pilot projects in chronic disease, including cancer.

The OSUMC has a closed system in which it is the payer, employer, and provider of healthcare. This closed system serves as an ideal testing ground for reform. Goals include intervention in disease before symptoms appear and maintenance of wellness. The data from these demonstration projects should facilitate adoption of personalized healthcare by improving physician acceptance of personalized approaches and satisfying payers that personalized healthcare is cost-effective.

References
  1. Personalized medicine. Coriell Institute for Medical Research Web site. http://www.coriell.org/personalized-medicine. Updated 2011. Accessed December 27, 2011.
  2. The 2012 Statistical Abstract. U.S. Census Bureau Web site. http://www.census.gov/compendia/statab/cats/income_expenditures_poverty_wealth/gross_domestic_product_gdp.html. Updated September 27, 2011. Accessed December 22, 2011.
  3. National health expenditure fact sheet. Center for Medicare & Medicaid Services (CMS) Web site. https://www.cms.gov/NationalHealthExpendData/25_NHE_Fact_Sheet.asp. Updated November 4, 2011. Accessed December 22, 2011.
  4. New England Healthcare Institute (NEHI). Thinking outside the pillbox: A system-wide approach to improving patient medication adherence for chronic disease. NEHI Web site. http://www.nehi.net/publications/44/thinking_outside_the_pillbox_a_systemwide_approach_to_improving_patient_medication_adherence_for_chronic_disease. Published August 12, 2009. Accessed December 22, 2011.
  5. Spear BB, Heath-Chiozzi M, Huff J. Clinical application of pharmacogenetics. Trends Mol Med 2001; 7:201204.
  6. Karapetis CS, Khambata-Ford S, Jonker DJ, et al. K-ras mutations and benefit from cetuximab in advanced colorectal cancer. N Engl J Med 2008; 359:17571765.
  7. Feero WG, Green ED. Genomics education for healthcare professionals in the 21st century. JAMA 2011; 306:989990.
  8. Meyers M, Wagner MW, Hwang HS, Kinsella TJ, Boothman DA. Role of the hMLH1 DNA mismatch repair protein in flouropyrimidine-mediated cell death and cell cycle responses. Cancer Res 2001; 61:51935201.
  9. Carethers JM, Chauhan DP, Fink D, et al. Mismatch repair proficiency and in vitro response to 5-fluorouracil. Gastroenterology 1999; 117:123131.
  10. Ribic CM, Sargent DJ, Moore MJ, et al. Tumor microsatellite-instability status as a predictor of benefit from fluorouracil-based adjuvant chemotherapy for colon cancer. N Engl J Med 2003; 349:247257.
  11. Ratain MJ. Personalized medicine: building the GPS to take us there. Clin Pharmacol Ther 2007; 81:321322.
  12. Anderson JL, Horne BD, Stevens SM, et al; Couma-Gen Investigators. Randomized trial of genotype-guided versus standard warfarin dosing in patients initiating oral anticoagulation [published online ahead of print November 7, 2007]. Circulation 2007; 116:25632570. doi: 10.1161/CIRCULATIONAHA.107.737312
  13. Price MJ, Angiolillo DJ, Teirstein PS, et al .Platelet reactivity and cardiovascular outcomes after percutaneous coronary intervention: a time-dependent analysis of the Gauging Responsiveness with a VerifyNow P2Y12 assay: Impact on Thrombosis and Safety (GRAVITAS) trial [published online ahead of print August 29, 2011]. Circulation 2011; 124:11321137. doi: 10.1161/CIRCULATIONAHA.111.029165
  14. Sharp RR, Goldlust ME, Eng C. Addressing gaps in physician education using personal genomic testing. Genet Med 2011; 13:750751.
  15. Wiener CM, Thomas PA, Goodspeed E, Valle D, Nichols DG. “Genes to society”—the logic and process of the new curriculum for the Johns Hopkins University School of Medicine. Acad Med 2010; 85:498506.
  16. Ladabaum U, Wang G, Terdiman J, et al. Strategies to identify the Lynch syndrome among patients with colorectal cancer: a costeffectiveness analysis. Ann Intern Med 2011; 155:6979.
  17. Leach B, Eng C, Kalady M, et al. Sharing the responsibility: multidisciplinary model improves colorectal cancer microsatellite testing. Paper presented at: InSight 2009 Annual Conference: September 2009; Orlando, FL.
  18. Manolio TA, Chisolm R, Ozenberger B, et al. Implementing genomic medicine in the clinic: the future is here. Genet Med Forthcoming.
Article PDF
Author and Disclosure Information

Kathryn Teng, MD, FACP
Director, Center for Personalized Healthcare, Cleveland Clinic, Cleveland, OH

Charis Eng, MD, PhD
Hardis/ACS Professor and Chair, Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH

Caryl A. Hess, PhD, MBA
Director, Cleveland Clinic Academy, Cleveland Clinic, Cleveland, OH

Meredith A. Holt, MBA
Program Director, Center for Personalized Healthcare, Cleveland Clinic, Cleveland, OH

Rocio T. Moran, MD
Medical Director, General Genetics Clinical Service, Cleveland Clinic, Cleveland, OH

Richard R. Sharp, PhD
Director, Bioethics Research, Cleveland Clinic, Cleveland, OH

Elias I. Traboulsi, MD
Director, Graduate Medical Education, Cleveland Clinic, Cleveland, OH

Correspondence: Kathryn Teng, MD, FACP, Director, Center for Personalized Healthcare, Cleveland Clinic, 9500 Euclid Avenue, NE50, Cleveland, OH 44195; [email protected]

All authors have indicated that they have no relationships that, in the context of their contributions to this supplement, could be perceived as a potential conflict of interest.

Publications
Page Number
S1-S9
Author and Disclosure Information

Kathryn Teng, MD, FACP
Director, Center for Personalized Healthcare, Cleveland Clinic, Cleveland, OH

Charis Eng, MD, PhD
Hardis/ACS Professor and Chair, Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH

Caryl A. Hess, PhD, MBA
Director, Cleveland Clinic Academy, Cleveland Clinic, Cleveland, OH

Meredith A. Holt, MBA
Program Director, Center for Personalized Healthcare, Cleveland Clinic, Cleveland, OH

Rocio T. Moran, MD
Medical Director, General Genetics Clinical Service, Cleveland Clinic, Cleveland, OH

Richard R. Sharp, PhD
Director, Bioethics Research, Cleveland Clinic, Cleveland, OH

Elias I. Traboulsi, MD
Director, Graduate Medical Education, Cleveland Clinic, Cleveland, OH

Correspondence: Kathryn Teng, MD, FACP, Director, Center for Personalized Healthcare, Cleveland Clinic, 9500 Euclid Avenue, NE50, Cleveland, OH 44195; [email protected]

All authors have indicated that they have no relationships that, in the context of their contributions to this supplement, could be perceived as a potential conflict of interest.

Author and Disclosure Information

Kathryn Teng, MD, FACP
Director, Center for Personalized Healthcare, Cleveland Clinic, Cleveland, OH

Charis Eng, MD, PhD
Hardis/ACS Professor and Chair, Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH

Caryl A. Hess, PhD, MBA
Director, Cleveland Clinic Academy, Cleveland Clinic, Cleveland, OH

Meredith A. Holt, MBA
Program Director, Center for Personalized Healthcare, Cleveland Clinic, Cleveland, OH

Rocio T. Moran, MD
Medical Director, General Genetics Clinical Service, Cleveland Clinic, Cleveland, OH

Richard R. Sharp, PhD
Director, Bioethics Research, Cleveland Clinic, Cleveland, OH

Elias I. Traboulsi, MD
Director, Graduate Medical Education, Cleveland Clinic, Cleveland, OH

Correspondence: Kathryn Teng, MD, FACP, Director, Center for Personalized Healthcare, Cleveland Clinic, 9500 Euclid Avenue, NE50, Cleveland, OH 44195; [email protected]

All authors have indicated that they have no relationships that, in the context of their contributions to this supplement, could be perceived as a potential conflict of interest.

Article PDF
Article PDF

Personalized healthcare is the tailoring of medical management and patient care to the individual characteristics of each patient. This is achieved by incorporating the genetic and genomic makeup of an individual and his or her family medical history, environment, health-related behaviors, culture, and values into a complete health picture that can be used to customize care. Another level of personalization, often called personalized medicine, involves the selection of drug therapy through the use of tests to determine the genes and gene interactions that can reliably predict an individual’s response to a given therapy. This white paper focuses largely on the use of personalized healthcare as a risk prediction tool.

CURRENT STATUS OF PERSONALIZED HEALTHCARE

Practitioners and consumers in today’s healthcare setting do not yet fully recognize the potential benefits of personalized healthcare (Table 11). Further, proposals for reform tend to be reactive rather than proactive. Family history is well validated as a tool to predict risk for disease, but, in some instances, genomic information may enhance risk prediction provided by family history. The trial-and-error approach now used to treat disease is costly, but genomic testing has the potential to save money through more effective use of diagnostic tests, counseling about medical management based on gene test results, and prescribing of medications.

The case for personalized healthcare: Seeking value

To fully appreciate the need to advance the adoption of personalized healthcare into the delivery of medicine, one must consider the operation of our current healthcare system and its inefficiencies in terms of delivery and cost, its imprecision in the selection of therapies, and its inability to optimize outcomes. The framework of the US healthcare system as it is now constructed is expensive, disease-directed (instead of health- and wellness-directed), fragmented, and complex. While gross domestic product (GDP) in the United States has increased by approximately 3% per year,2 the compounded growth rate of healthcare expenditures is 6.1% per year. Healthcare in the aggregate now represents 17.6% of GDP and 27% of spending by the federal government and consumes 28% of the average household’s discretionary spending, surpassed only by housing.3

Personalized healthcare can potentially address the need for value consistent with the healthcare system’s prominent share of the US economy. The growth in healthcare spending is certain to be a target of the newly created Joint Select Committee on Deficit Reduction (created by the Budget Control Act of 2011), which is tasked with deficit reduction of at least $1.5 trillion over a 10-year period.

The need to address healthcare costs has been recognized in the Patient Protection and Affordable Care Act, a central feature of which is the creation of integrated health systems that pay for value based on quality, cost containment, and consumer experience. The legislation was enacted to transform healthcare in a variety of ways to make it more sustainable. The Patient Protection and Affordable Care Act seeks to end fragmentation by expanding the use of information technology to reorganize the delivery system and to prevent errors, shifting from volume-based incentives to incentives based on performance and outcomes, and rewarding effective healthcare delivery measures and good patient outcomes.

A shift from reactive to proactive

The premise behind personalized healthcare is the potential for more efficient healthcare, with the assumption that efficiency translates to lower cost and improved patient care.

Although healthcare reform is most often referred to in the context of improving access to care through insurance coverage mandates, true healthcare reform shifts current healthcare models from the practice of reactive medicine to the practice of proactive medicine, in which the tools of personalized healthcare (ie, genetics, genomics, and other molecular diagnostics) enable not only better quality of care but also less expensive care.

Several personalized tools have long been accepted into mainstream medicine. Two examples are the family history, which is the least expensive and most available genetic evaluation tool, and ABO blood typing for safe transfusions (as ABO blood types are alleles of a gene). In fact, much of what is now considered mainstream medical management was at one time considered new. To allow further evolution of medical practice, our challenge is to open our minds to the possibility that personalized proactive medicine can improve healthcare.

The new vision: More precise management

The trial-and-error approach to treating disease is inefficient and costly. Many drugs are effective for only about 50% of patients, often leading to switching or intensification of therapy that requires multiple patient visits.

Personalized medicine considers pharmacokinetic and other characteristics in selection of drug dosages. Genomic testing has the potential to provide clearer insight into the more successful use of currently available medicines. Treatment decisions (ie, drug and drug dosage choice) made on the basis of pharmacogenomic testing should increase adherence through greater effectiveness and fewer adverse drug reactions.

A massive amount of waste is related to pharmaceutical nonadherence and noncompliance. The New England Healthcare Institute has estimated that medication nonadherence costs the healthcare system $290 billion annually.4 Methodologies targeted at individual patients to improve adherence to drug regimens could save the healthcare system a tremendous amount of money.

Cancer management as a model for personalized healthcare. Personalization of therapy is especially suited to cancer management, given that the response to nonspecific cancer chemotherapy is suboptimal in most patients yet exposes them to adverse effects.5 Large-scale sequencing of human cancer genomes is rapidly changing the understanding of cancer biology and is identifying new targets in difficult-to-treat diseases and causes of drug resistance. Applying this information can achieve cost savings by avoiding the use of treatments that are ineffective in particular patients.

Overexpression of genetic mutations renders some cancers less susceptible to certain treatments, but has opened the door to individualized molecularly guided treatment strategies. For example, among patients with non–small cell lung cancer, mutations in the epidermal growth factor receptor (EGFR) tyrosine kinase domain predict response to EGFR tyrosine kinase inhibitors, and anaplastic lymphoma kinase (ALK) inhibitors induce response in patients harboring a mutation in EML4-ALK genes. The recognition that human epidermal growth factor receptor (HER)-2 overexpression as a result of ERBB2 gene amplification occurs in as many as 20% of human breast cancers paved the way for the development of HER-2–targeted therapies. Patients with advanced colorectal cancer whose tumors express the KRAS gene mutation do not benefit from an EGFR inhibitor, whereas those with wild-type KRAS have improved survival with EGFR inhibitor treatment.6

 

 

BARRIERS TO THE APPLICATION OF PERSONALIZED HEALTHCARE

The availability and potential of personalized healthcare services and technology is not universally recognized or appreciated by consumers and clinicians. This lack of awareness contributes to a shortage of public support and limited demand for such services. Other barriers include misperceptions regarding the impact of personalized healthcare on disease management, limited incentives to use the available technology, and a knowledge gap among healthcare providers.

Lack of awareness and support

As applications of personalized healthcare advance to the point of clinical relevance, it is important to consider strategies for effective implementation into healthcare practice. Personalized healthcare, when more fully implemented, promises to accelerate the progress that healthcare reform hopes to achieve.

A major challenge to widespread adoption of personalized healthcare is limited recognition by the public and some healthcare providers that personalized healthcare can help to achieve better value. For personalized medicine to be embraced, the concept of “helix to health,” or translation of knowledge to the clinical setting, must resonate with the general public. Despite lack of public and provider awareness, the Personalized Medicine Coalition (PMC) has documented the existence of 56 personalized treatment and diagnostic products. Further, more than 200 product labels now recommend genetic testing prior to use to identify likely responders or inform of the influence of genetic variation on safety and effectiveness.

Consumers’ confidence in the efficacy and safety of medicines they take might contribute to the absence of public support for personalized healthcare. Similarly, despite the availability of genomic tests and tools, many physicians who might be advocates for personalized healthcare do not see the relevance of genomic medicine to their practices in terms of direct benefit to patient care.7

Apart from clinicians and consumers, support is also weak among health insurers and employers, even though the return on investment for personalized healthcare may be profound. Payers await the economic outcomes data that are crucial for their commitment to personalized healthcare. In addition, some have concerns about the ethical implications of personalized healthcare (see “Managing Genomic Information Responsibly”).

Perception of impact on treatment and prevention

A frequent criticism of genomics in medicine is that a genetic diagnosis does not help with patient management. In fact, surveillance and management of patients and family members often changes in response to a genetic diagnosis; knowing which gene is involved personalizes medical management. An example is the management of hereditary nonpolyposis colorectal cancer (HNPCC), or Lynch syndrome, which is the most common form of hereditary colon cancer. For a person with HNPCC, the lifetime risk of developing colorectal cancer is approximately 80%. Lynch syndrome is caused by germline mutations in one of three major mismatch repair (MMR) genes (MLH1, MSH2, and MSH6), and it predisposes to other cancers—uterine, stomach, and ovarian—as well. In women with Lynch syndrome, the lifetime risk for uterine cancer is 40%, compared with 4% in the general population.

At least 90% of patients with Lynch syndrome can be detected through MMR testing via microsatellite instability (MSI) or immunohistochemistry (IHC).8 MSI is a cellular phenotype that indicates a deficiency in at least one DNA MMR protein.

Although 5-fluorouracil–based chemo therapy is the standard of care for treatment of colorectal cancer, it confers no survival advantage in patients with MMR-IHC null (lack of expression of the gene) or MSI-high sporadic colorectal cancer.9,10 Knowing the status of MMR proteins, therefore, would alter the decision regarding neoadjuvant and adjuvant chemotherapy.

Perception of value

Implementation of pharmacogenomics into clinical practice has lagged. One major reason is the lack of an obvious business model for a product that may only be required once in an individual patient’s lifetime.11

A second barrier to integration lies in the limited demand for pharmacogenomics from physicians. This may be related partly to limited expertise in genetics among many physicians and to significant pushback from payers against today’s costs. Without reimbursement, little incentive exists for pharmacogenomics diagnostics. The incentive for physicians is further depressed, perhaps appropriately, when randomized controlled studies fail to demonstrate improved clinical outcomes with the use of pharmacogenomicbased treatment strategies. Two such examples are genotype-guided warfarin dosing, which failed in a randomized controlled trial to improve the proportion of international normalized ratios in the therapeutic range,12 and dosing of clopidogrel based on platelet reactivity, which did not improve outcomes after percutaneous coronary intervention compared with standard dosing in a randomized double-blind clinical trial.13

A significant delay in obtaining the results of pharmacogenomics testing, which also postpones the prescribing encounter, is another major drawback.

A knowledge gap persists

At present, delivery of personalized healthcare is not part of the usual training of physicians and other healthcare providers who are the gatekeepers of medicine. Few medical schools incorporate human and medical genetics, genomics, and pharmacogenomics into their curricula. Genetics is inadequately emphasized in residency curricula outside of pediatrics, family medicine, and obstetrics/gynecology.

The resulting knowledge gap is a fundamental factor in the lack of interest in using genomics in clinical medicine. Educating consumers and physicians at all levels, including specialty societies as well as insurers, will be key to expanding utilization of personalized healthcare. Educating payers and providing them with more data on economic outcomes associated with personalized healthcare will be necessary for adoption into clinical practice; implementation will lag as long as reimbursement decisions do not support personalized approaches to medicine.

As DNA sequencing technology has become less expensive and more powerful, companies have begun to market personal genomic testing. As a result, patients who use these services will increasingly want to discuss the results with their physicians. A significant number of clinicians are unfamiliar with personal genomic testing and emerging genetic testing options. In one survey of physicians who attended educational sessions that discussed recent developments in clinical genetics, only 37% indicated that they were familiar with recent genetic research that affected their patients.14

Targeted education will enhance physicians’ understanding of probabilities and risk estimates from the use of genomic testing; it will also improve recognition of potential causes of patient anxiety, gene variants of unknown significance, and follow-up tests and procedures that can add to expense. Nonphysician healthcare providers (ie, nurses and physician assistants) of direct care also will benefit from education.

 

 

INTEGRATING PERSONALIZED HEALTHCARE INTO CLINICAL PRACTICE

Practice standardization and an overhaul of the health information technology (HIT) infrastructure are needed if we are to reap the potential benefits of personalized healthcare. Creative approaches to practitioner education, which are being used in some institutions, must become more widespread. Similarly, the models for successful integration of personalized healthcare that have been achieved in some settings also can be implemented in other institutions.

Data collection and integration must be prioritized

Personalized healthcare can be both predictive and preventive, but moving past the disruptive phase of personalized healthcare will require a radical transformation of the healthcare “ecosystem” and HIT infrastructure.

Although data collection in the current system is extensive, data sharing and data management are inadequate. The pace at which HIT links clinical and genetic information must be accelerated. HIT will expedite innovation and implementation of personalized healthcare, allowing greater integration of data to permit improved data analysis capability. The ultimate goal is to create an interoperable system that connects these data across hospitals and clinicians to help clinicians interpret genomic and other risk information to better inform patient care.

Fully integrated health systems support better coordination of care and optimize the treatment of individual patients: linking research findings, treatment guidelines, treatment outcomes based on genetic profiles, and the individual patient’s own genetic profile will help to personalize treatments. Genomic information added to an individual’s electronic medical record along with improved data-sharing will facilitate clinicians’ ability to retrieve outcomes data based on patient characteristics.

Care models must be standardized, evidence-based practices must be executed, and care must be coordinated yet decentralized. In this way, clinicians can use the electronic medical record as an interoperable patient record to determine a personalized pathway to patient management. Standardization reduces variability in practice and permits seamless execution of care. Automation is imperative to achieving standardization, irrespective of the care supervisor. Investments must therefore be made to stimulate electronic medical record decision support.

In addition, larger data sets will be needed to identify the types of patients likely to respond to a treatment. Ideal data sets would be large enough to have adequate statistical power, be publicly available, standardize the collection of data with respect to response to therapy and toxicity, and contain data on concomitant collections of biologic samples.

Reimbursement must keep pace with medical advances

Payer willingness to reimburse for genomic tests and treatments will determine the pace of integration of personalized healthcare into clinical practice. Evidence that enhanced value can be derived from personalized approaches to medicine must be generated before personalized healthcare gains widespread acceptance by payers.

In addition, care-coordinated models must be developed to promote a value-based agenda that facilitates physician accountability and encourages clinical integration.

Innovative approaches are needed to educate providers

Development of point-of-care tools. Because information overload and lack of time are obstacles to clinicians’ efforts to incorporate genomic information into clinical practice, emphasis must be placed on genomic applications that have demonstrated utility. Engaging busy clinicians with point-of-care tools will maximize the relevance of the genomic information they receive and encourage effective use of their time. Decision-making should be supported through automatic risk assessment and management recommendations.

Educational tools. The National Coalition for Health Professional Education in Genetics (NCHPEG) was borne out of the recognition that the pace of genomic discovery far exceeds the pace at which healthcare providers can be educated. Its vision is to improve healthcare through informed use of genomic resources. NCHPEG is a member-based organization whose stakeholders include professional societies, hospitals, advocacy groups, and industry; it attempts to identify the specific educational needs for particular target audiences and then address these needs. It achieves its goals through the use of point-of-care tools and educational programs for continuing medical education credit.

One NCHPEG tool is the Pregnancy and Health Profile, which is a risk assessment and screening tool that attempts to improve the identification of women and babies at risk of developing genetic disease. It collects personal and family history information, performs a risk assessment for the clinician, and provides clinical decision support and education.

Another example of an educational tool is the “Genes to Society” curriculum initiated by The Johns Hopkins University School of Medicine in August 2009. The curriculum is being used as “the foundation for the scientific and clinical career development of future physicians.”15

Using personal genomic testing for education. The number of direct-to-consumer genomic tests is growing, and their market penetration will only increase as the cost of supplying a personal genome continues to decline. Whole genome scanning is being offered with the promise of identifying genetic predisposition to multiple diseases.

Participation in personal genomic testing may be a useful educational tool. Medical students, residents, and practicing physicians who participate in testing may be better equipped to advise patients about the processes involved and the potential utility and limitations of direct-to-consumer genotyping.14

Some companies that offer direct-to-consumer genomic testing provide telephone support from genetic counselors to help clients and their healthcare providers manage genetic information. Counselor services include identifying hereditary risks and reviewing diagnostic, preventive, and early-detection options.

Implementing pharmacogenomics into practice: Decision support systems are needed

A genomic decision support system that guides medication prescribing is needed to implement pharmacogenomic diagnostics. For such a system to achieve the goal of selecting the best medication for each individual, it must do the following:

  • Test all polymorphisms relevant to the prescribing of any medication
  • Be completed with no out-of-pocket cost to the patient
  • Be performed before the patient requires the medication
  • Provide results that will be interpreted as part of an individualized pharmacogenomics consult.11

Many useful pharmacogenomic tests are based on cytochrome P450 metabolism phenotypes that are responsible for variance in response to drugs metabolized by this pathway. Others use human leukocyte antigen screening for hypersensitivity reactions to abacavir, carbamazepine, and allopurinol. Examples of pharmacogenomics tests appear in Table 2.

The 1200 Patients Project, a pilot research study under way at the Center for Personalized Therapeutics at the University of Chicago, is attempting to demonstrate the feasibility of incorporating pharmacogenomic testing into routine clinical practice for medication treatment decisions. DNA samples from patients who are taking at least one prescription medication are being tested for differences in genes that may suggest greater effectiveness or an increased risk of side effects from certain medications.

 

 

Solutions in practice

Cleveland Clinic’s genetics-based management of Lynch syndrome, the integration of genetics services during patient appointments at Cleveland Clinic, and a coordinated approach at The Ohio State University Medical Center are examples of practical applications of personalized healthcare.

Colorectal cancer management. One example of a personalized approach to medicine that improves health outcome while achieving cost savings is the genetics-based approach to HNPCC (Lynch syndrome) at Cleveland Clinic.

Early identification of Lynch syndrome by screening all colorectal cancer patients has been shown to save $250,000 per life-year gained in the United States.16 All colorectal cancers resected at the Cleveland Clinic main campus are routinely screened for MSI and IHC, and the process is embedded into the routine pathology workflow. With the patients’ foreknowledge, a gastrointestinal cancer genetics counselor scans the list of MSI and IHC results each week. Patients who are MSI-high or IHC-null are invited to receive genetic counseling and consider germline single-gene testing guided by the IHC results. With this active approach, patient uptake is 80%; in comparison, with a passive approach (MSI/IHC results are placed in the pathology report), the uptake is 14%17 (B. Leach and C. Eng, unpublished data, 2011).The successful application of the active approach requires the close cooperation of multiple disciplines, including members of the Cleveland Clinic Genomic Medicine, Pathology & Laboratory Medicine, and Digestive Disease Institutes.18

Integrating genetics-based care at Cleveland Clinic. Time delays for genetics services and limited collaboration with managing physicians who are not genetics specialists reduces genetics-based access and availability. Broad access to genetics clinical services is a means of clinical integration of genetics-enabled care. Providing patients and healthcare providers with easy access and short wait times is vital for clinical integration of genetics-enabled personalized healthcare.

As part of a patient-centered focus on medicine, clinical genetics services have been integrated throughout Cleveland Clinic. The system has two genetics clinics at its main campus and has embedded multiple genetics satellites within its nongenetics clinics, easing access. Genetics counselors are stationed in the same areas of practice as referring providers. Although patient encounters have increased at the medical genetics clinic in the Genomic Medicine Institute, genetics consultations no longer require an extra trip to the clinic since they are integrated into existing appointments. With this approach, large numbers of patients can be seen with no wait times.

Coordinated care at The Ohio State University Medical Center. The Center for Personalized Health Care at The Ohio State University Medical Center (OSUMC) embraces a systems-based care-coordinated model that improves care by executing standardized processes and automating routine tasks. The Institute for Systems Biology, which was established to develop genomics, wellness, and chronic disease biomarkers, collaborates with OSUMC on pilot projects in chronic disease, including cancer.

The OSUMC has a closed system in which it is the payer, employer, and provider of healthcare. This closed system serves as an ideal testing ground for reform. Goals include intervention in disease before symptoms appear and maintenance of wellness. The data from these demonstration projects should facilitate adoption of personalized healthcare by improving physician acceptance of personalized approaches and satisfying payers that personalized healthcare is cost-effective.

Personalized healthcare is the tailoring of medical management and patient care to the individual characteristics of each patient. This is achieved by incorporating the genetic and genomic makeup of an individual and his or her family medical history, environment, health-related behaviors, culture, and values into a complete health picture that can be used to customize care. Another level of personalization, often called personalized medicine, involves the selection of drug therapy through the use of tests to determine the genes and gene interactions that can reliably predict an individual’s response to a given therapy. This white paper focuses largely on the use of personalized healthcare as a risk prediction tool.

CURRENT STATUS OF PERSONALIZED HEALTHCARE

Practitioners and consumers in today’s healthcare setting do not yet fully recognize the potential benefits of personalized healthcare (Table 11). Further, proposals for reform tend to be reactive rather than proactive. Family history is well validated as a tool to predict risk for disease, but, in some instances, genomic information may enhance risk prediction provided by family history. The trial-and-error approach now used to treat disease is costly, but genomic testing has the potential to save money through more effective use of diagnostic tests, counseling about medical management based on gene test results, and prescribing of medications.

The case for personalized healthcare: Seeking value

To fully appreciate the need to advance the adoption of personalized healthcare into the delivery of medicine, one must consider the operation of our current healthcare system and its inefficiencies in terms of delivery and cost, its imprecision in the selection of therapies, and its inability to optimize outcomes. The framework of the US healthcare system as it is now constructed is expensive, disease-directed (instead of health- and wellness-directed), fragmented, and complex. While gross domestic product (GDP) in the United States has increased by approximately 3% per year,2 the compounded growth rate of healthcare expenditures is 6.1% per year. Healthcare in the aggregate now represents 17.6% of GDP and 27% of spending by the federal government and consumes 28% of the average household’s discretionary spending, surpassed only by housing.3

Personalized healthcare can potentially address the need for value consistent with the healthcare system’s prominent share of the US economy. The growth in healthcare spending is certain to be a target of the newly created Joint Select Committee on Deficit Reduction (created by the Budget Control Act of 2011), which is tasked with deficit reduction of at least $1.5 trillion over a 10-year period.

The need to address healthcare costs has been recognized in the Patient Protection and Affordable Care Act, a central feature of which is the creation of integrated health systems that pay for value based on quality, cost containment, and consumer experience. The legislation was enacted to transform healthcare in a variety of ways to make it more sustainable. The Patient Protection and Affordable Care Act seeks to end fragmentation by expanding the use of information technology to reorganize the delivery system and to prevent errors, shifting from volume-based incentives to incentives based on performance and outcomes, and rewarding effective healthcare delivery measures and good patient outcomes.

A shift from reactive to proactive

The premise behind personalized healthcare is the potential for more efficient healthcare, with the assumption that efficiency translates to lower cost and improved patient care.

Although healthcare reform is most often referred to in the context of improving access to care through insurance coverage mandates, true healthcare reform shifts current healthcare models from the practice of reactive medicine to the practice of proactive medicine, in which the tools of personalized healthcare (ie, genetics, genomics, and other molecular diagnostics) enable not only better quality of care but also less expensive care.

Several personalized tools have long been accepted into mainstream medicine. Two examples are the family history, which is the least expensive and most available genetic evaluation tool, and ABO blood typing for safe transfusions (as ABO blood types are alleles of a gene). In fact, much of what is now considered mainstream medical management was at one time considered new. To allow further evolution of medical practice, our challenge is to open our minds to the possibility that personalized proactive medicine can improve healthcare.

The new vision: More precise management

The trial-and-error approach to treating disease is inefficient and costly. Many drugs are effective for only about 50% of patients, often leading to switching or intensification of therapy that requires multiple patient visits.

Personalized medicine considers pharmacokinetic and other characteristics in selection of drug dosages. Genomic testing has the potential to provide clearer insight into the more successful use of currently available medicines. Treatment decisions (ie, drug and drug dosage choice) made on the basis of pharmacogenomic testing should increase adherence through greater effectiveness and fewer adverse drug reactions.

A massive amount of waste is related to pharmaceutical nonadherence and noncompliance. The New England Healthcare Institute has estimated that medication nonadherence costs the healthcare system $290 billion annually.4 Methodologies targeted at individual patients to improve adherence to drug regimens could save the healthcare system a tremendous amount of money.

Cancer management as a model for personalized healthcare. Personalization of therapy is especially suited to cancer management, given that the response to nonspecific cancer chemotherapy is suboptimal in most patients yet exposes them to adverse effects.5 Large-scale sequencing of human cancer genomes is rapidly changing the understanding of cancer biology and is identifying new targets in difficult-to-treat diseases and causes of drug resistance. Applying this information can achieve cost savings by avoiding the use of treatments that are ineffective in particular patients.

Overexpression of genetic mutations renders some cancers less susceptible to certain treatments, but has opened the door to individualized molecularly guided treatment strategies. For example, among patients with non–small cell lung cancer, mutations in the epidermal growth factor receptor (EGFR) tyrosine kinase domain predict response to EGFR tyrosine kinase inhibitors, and anaplastic lymphoma kinase (ALK) inhibitors induce response in patients harboring a mutation in EML4-ALK genes. The recognition that human epidermal growth factor receptor (HER)-2 overexpression as a result of ERBB2 gene amplification occurs in as many as 20% of human breast cancers paved the way for the development of HER-2–targeted therapies. Patients with advanced colorectal cancer whose tumors express the KRAS gene mutation do not benefit from an EGFR inhibitor, whereas those with wild-type KRAS have improved survival with EGFR inhibitor treatment.6

 

 

BARRIERS TO THE APPLICATION OF PERSONALIZED HEALTHCARE

The availability and potential of personalized healthcare services and technology is not universally recognized or appreciated by consumers and clinicians. This lack of awareness contributes to a shortage of public support and limited demand for such services. Other barriers include misperceptions regarding the impact of personalized healthcare on disease management, limited incentives to use the available technology, and a knowledge gap among healthcare providers.

Lack of awareness and support

As applications of personalized healthcare advance to the point of clinical relevance, it is important to consider strategies for effective implementation into healthcare practice. Personalized healthcare, when more fully implemented, promises to accelerate the progress that healthcare reform hopes to achieve.

A major challenge to widespread adoption of personalized healthcare is limited recognition by the public and some healthcare providers that personalized healthcare can help to achieve better value. For personalized medicine to be embraced, the concept of “helix to health,” or translation of knowledge to the clinical setting, must resonate with the general public. Despite lack of public and provider awareness, the Personalized Medicine Coalition (PMC) has documented the existence of 56 personalized treatment and diagnostic products. Further, more than 200 product labels now recommend genetic testing prior to use to identify likely responders or inform of the influence of genetic variation on safety and effectiveness.

Consumers’ confidence in the efficacy and safety of medicines they take might contribute to the absence of public support for personalized healthcare. Similarly, despite the availability of genomic tests and tools, many physicians who might be advocates for personalized healthcare do not see the relevance of genomic medicine to their practices in terms of direct benefit to patient care.7

Apart from clinicians and consumers, support is also weak among health insurers and employers, even though the return on investment for personalized healthcare may be profound. Payers await the economic outcomes data that are crucial for their commitment to personalized healthcare. In addition, some have concerns about the ethical implications of personalized healthcare (see “Managing Genomic Information Responsibly”).

Perception of impact on treatment and prevention

A frequent criticism of genomics in medicine is that a genetic diagnosis does not help with patient management. In fact, surveillance and management of patients and family members often changes in response to a genetic diagnosis; knowing which gene is involved personalizes medical management. An example is the management of hereditary nonpolyposis colorectal cancer (HNPCC), or Lynch syndrome, which is the most common form of hereditary colon cancer. For a person with HNPCC, the lifetime risk of developing colorectal cancer is approximately 80%. Lynch syndrome is caused by germline mutations in one of three major mismatch repair (MMR) genes (MLH1, MSH2, and MSH6), and it predisposes to other cancers—uterine, stomach, and ovarian—as well. In women with Lynch syndrome, the lifetime risk for uterine cancer is 40%, compared with 4% in the general population.

At least 90% of patients with Lynch syndrome can be detected through MMR testing via microsatellite instability (MSI) or immunohistochemistry (IHC).8 MSI is a cellular phenotype that indicates a deficiency in at least one DNA MMR protein.

Although 5-fluorouracil–based chemo therapy is the standard of care for treatment of colorectal cancer, it confers no survival advantage in patients with MMR-IHC null (lack of expression of the gene) or MSI-high sporadic colorectal cancer.9,10 Knowing the status of MMR proteins, therefore, would alter the decision regarding neoadjuvant and adjuvant chemotherapy.

Perception of value

Implementation of pharmacogenomics into clinical practice has lagged. One major reason is the lack of an obvious business model for a product that may only be required once in an individual patient’s lifetime.11

A second barrier to integration lies in the limited demand for pharmacogenomics from physicians. This may be related partly to limited expertise in genetics among many physicians and to significant pushback from payers against today’s costs. Without reimbursement, little incentive exists for pharmacogenomics diagnostics. The incentive for physicians is further depressed, perhaps appropriately, when randomized controlled studies fail to demonstrate improved clinical outcomes with the use of pharmacogenomicbased treatment strategies. Two such examples are genotype-guided warfarin dosing, which failed in a randomized controlled trial to improve the proportion of international normalized ratios in the therapeutic range,12 and dosing of clopidogrel based on platelet reactivity, which did not improve outcomes after percutaneous coronary intervention compared with standard dosing in a randomized double-blind clinical trial.13

A significant delay in obtaining the results of pharmacogenomics testing, which also postpones the prescribing encounter, is another major drawback.

A knowledge gap persists

At present, delivery of personalized healthcare is not part of the usual training of physicians and other healthcare providers who are the gatekeepers of medicine. Few medical schools incorporate human and medical genetics, genomics, and pharmacogenomics into their curricula. Genetics is inadequately emphasized in residency curricula outside of pediatrics, family medicine, and obstetrics/gynecology.

The resulting knowledge gap is a fundamental factor in the lack of interest in using genomics in clinical medicine. Educating consumers and physicians at all levels, including specialty societies as well as insurers, will be key to expanding utilization of personalized healthcare. Educating payers and providing them with more data on economic outcomes associated with personalized healthcare will be necessary for adoption into clinical practice; implementation will lag as long as reimbursement decisions do not support personalized approaches to medicine.

As DNA sequencing technology has become less expensive and more powerful, companies have begun to market personal genomic testing. As a result, patients who use these services will increasingly want to discuss the results with their physicians. A significant number of clinicians are unfamiliar with personal genomic testing and emerging genetic testing options. In one survey of physicians who attended educational sessions that discussed recent developments in clinical genetics, only 37% indicated that they were familiar with recent genetic research that affected their patients.14

Targeted education will enhance physicians’ understanding of probabilities and risk estimates from the use of genomic testing; it will also improve recognition of potential causes of patient anxiety, gene variants of unknown significance, and follow-up tests and procedures that can add to expense. Nonphysician healthcare providers (ie, nurses and physician assistants) of direct care also will benefit from education.

 

 

INTEGRATING PERSONALIZED HEALTHCARE INTO CLINICAL PRACTICE

Practice standardization and an overhaul of the health information technology (HIT) infrastructure are needed if we are to reap the potential benefits of personalized healthcare. Creative approaches to practitioner education, which are being used in some institutions, must become more widespread. Similarly, the models for successful integration of personalized healthcare that have been achieved in some settings also can be implemented in other institutions.

Data collection and integration must be prioritized

Personalized healthcare can be both predictive and preventive, but moving past the disruptive phase of personalized healthcare will require a radical transformation of the healthcare “ecosystem” and HIT infrastructure.

Although data collection in the current system is extensive, data sharing and data management are inadequate. The pace at which HIT links clinical and genetic information must be accelerated. HIT will expedite innovation and implementation of personalized healthcare, allowing greater integration of data to permit improved data analysis capability. The ultimate goal is to create an interoperable system that connects these data across hospitals and clinicians to help clinicians interpret genomic and other risk information to better inform patient care.

Fully integrated health systems support better coordination of care and optimize the treatment of individual patients: linking research findings, treatment guidelines, treatment outcomes based on genetic profiles, and the individual patient’s own genetic profile will help to personalize treatments. Genomic information added to an individual’s electronic medical record along with improved data-sharing will facilitate clinicians’ ability to retrieve outcomes data based on patient characteristics.

Care models must be standardized, evidence-based practices must be executed, and care must be coordinated yet decentralized. In this way, clinicians can use the electronic medical record as an interoperable patient record to determine a personalized pathway to patient management. Standardization reduces variability in practice and permits seamless execution of care. Automation is imperative to achieving standardization, irrespective of the care supervisor. Investments must therefore be made to stimulate electronic medical record decision support.

In addition, larger data sets will be needed to identify the types of patients likely to respond to a treatment. Ideal data sets would be large enough to have adequate statistical power, be publicly available, standardize the collection of data with respect to response to therapy and toxicity, and contain data on concomitant collections of biologic samples.

Reimbursement must keep pace with medical advances

Payer willingness to reimburse for genomic tests and treatments will determine the pace of integration of personalized healthcare into clinical practice. Evidence that enhanced value can be derived from personalized approaches to medicine must be generated before personalized healthcare gains widespread acceptance by payers.

In addition, care-coordinated models must be developed to promote a value-based agenda that facilitates physician accountability and encourages clinical integration.

Innovative approaches are needed to educate providers

Development of point-of-care tools. Because information overload and lack of time are obstacles to clinicians’ efforts to incorporate genomic information into clinical practice, emphasis must be placed on genomic applications that have demonstrated utility. Engaging busy clinicians with point-of-care tools will maximize the relevance of the genomic information they receive and encourage effective use of their time. Decision-making should be supported through automatic risk assessment and management recommendations.

Educational tools. The National Coalition for Health Professional Education in Genetics (NCHPEG) was borne out of the recognition that the pace of genomic discovery far exceeds the pace at which healthcare providers can be educated. Its vision is to improve healthcare through informed use of genomic resources. NCHPEG is a member-based organization whose stakeholders include professional societies, hospitals, advocacy groups, and industry; it attempts to identify the specific educational needs for particular target audiences and then address these needs. It achieves its goals through the use of point-of-care tools and educational programs for continuing medical education credit.

One NCHPEG tool is the Pregnancy and Health Profile, which is a risk assessment and screening tool that attempts to improve the identification of women and babies at risk of developing genetic disease. It collects personal and family history information, performs a risk assessment for the clinician, and provides clinical decision support and education.

Another example of an educational tool is the “Genes to Society” curriculum initiated by The Johns Hopkins University School of Medicine in August 2009. The curriculum is being used as “the foundation for the scientific and clinical career development of future physicians.”15

Using personal genomic testing for education. The number of direct-to-consumer genomic tests is growing, and their market penetration will only increase as the cost of supplying a personal genome continues to decline. Whole genome scanning is being offered with the promise of identifying genetic predisposition to multiple diseases.

Participation in personal genomic testing may be a useful educational tool. Medical students, residents, and practicing physicians who participate in testing may be better equipped to advise patients about the processes involved and the potential utility and limitations of direct-to-consumer genotyping.14

Some companies that offer direct-to-consumer genomic testing provide telephone support from genetic counselors to help clients and their healthcare providers manage genetic information. Counselor services include identifying hereditary risks and reviewing diagnostic, preventive, and early-detection options.

Implementing pharmacogenomics into practice: Decision support systems are needed

A genomic decision support system that guides medication prescribing is needed to implement pharmacogenomic diagnostics. For such a system to achieve the goal of selecting the best medication for each individual, it must do the following:

  • Test all polymorphisms relevant to the prescribing of any medication
  • Be completed with no out-of-pocket cost to the patient
  • Be performed before the patient requires the medication
  • Provide results that will be interpreted as part of an individualized pharmacogenomics consult.11

Many useful pharmacogenomic tests are based on cytochrome P450 metabolism phenotypes that are responsible for variance in response to drugs metabolized by this pathway. Others use human leukocyte antigen screening for hypersensitivity reactions to abacavir, carbamazepine, and allopurinol. Examples of pharmacogenomics tests appear in Table 2.

The 1200 Patients Project, a pilot research study under way at the Center for Personalized Therapeutics at the University of Chicago, is attempting to demonstrate the feasibility of incorporating pharmacogenomic testing into routine clinical practice for medication treatment decisions. DNA samples from patients who are taking at least one prescription medication are being tested for differences in genes that may suggest greater effectiveness or an increased risk of side effects from certain medications.

 

 

Solutions in practice

Cleveland Clinic’s genetics-based management of Lynch syndrome, the integration of genetics services during patient appointments at Cleveland Clinic, and a coordinated approach at The Ohio State University Medical Center are examples of practical applications of personalized healthcare.

Colorectal cancer management. One example of a personalized approach to medicine that improves health outcome while achieving cost savings is the genetics-based approach to HNPCC (Lynch syndrome) at Cleveland Clinic.

Early identification of Lynch syndrome by screening all colorectal cancer patients has been shown to save $250,000 per life-year gained in the United States.16 All colorectal cancers resected at the Cleveland Clinic main campus are routinely screened for MSI and IHC, and the process is embedded into the routine pathology workflow. With the patients’ foreknowledge, a gastrointestinal cancer genetics counselor scans the list of MSI and IHC results each week. Patients who are MSI-high or IHC-null are invited to receive genetic counseling and consider germline single-gene testing guided by the IHC results. With this active approach, patient uptake is 80%; in comparison, with a passive approach (MSI/IHC results are placed in the pathology report), the uptake is 14%17 (B. Leach and C. Eng, unpublished data, 2011).The successful application of the active approach requires the close cooperation of multiple disciplines, including members of the Cleveland Clinic Genomic Medicine, Pathology & Laboratory Medicine, and Digestive Disease Institutes.18

Integrating genetics-based care at Cleveland Clinic. Time delays for genetics services and limited collaboration with managing physicians who are not genetics specialists reduces genetics-based access and availability. Broad access to genetics clinical services is a means of clinical integration of genetics-enabled care. Providing patients and healthcare providers with easy access and short wait times is vital for clinical integration of genetics-enabled personalized healthcare.

As part of a patient-centered focus on medicine, clinical genetics services have been integrated throughout Cleveland Clinic. The system has two genetics clinics at its main campus and has embedded multiple genetics satellites within its nongenetics clinics, easing access. Genetics counselors are stationed in the same areas of practice as referring providers. Although patient encounters have increased at the medical genetics clinic in the Genomic Medicine Institute, genetics consultations no longer require an extra trip to the clinic since they are integrated into existing appointments. With this approach, large numbers of patients can be seen with no wait times.

Coordinated care at The Ohio State University Medical Center. The Center for Personalized Health Care at The Ohio State University Medical Center (OSUMC) embraces a systems-based care-coordinated model that improves care by executing standardized processes and automating routine tasks. The Institute for Systems Biology, which was established to develop genomics, wellness, and chronic disease biomarkers, collaborates with OSUMC on pilot projects in chronic disease, including cancer.

The OSUMC has a closed system in which it is the payer, employer, and provider of healthcare. This closed system serves as an ideal testing ground for reform. Goals include intervention in disease before symptoms appear and maintenance of wellness. The data from these demonstration projects should facilitate adoption of personalized healthcare by improving physician acceptance of personalized approaches and satisfying payers that personalized healthcare is cost-effective.

References
  1. Personalized medicine. Coriell Institute for Medical Research Web site. http://www.coriell.org/personalized-medicine. Updated 2011. Accessed December 27, 2011.
  2. The 2012 Statistical Abstract. U.S. Census Bureau Web site. http://www.census.gov/compendia/statab/cats/income_expenditures_poverty_wealth/gross_domestic_product_gdp.html. Updated September 27, 2011. Accessed December 22, 2011.
  3. National health expenditure fact sheet. Center for Medicare & Medicaid Services (CMS) Web site. https://www.cms.gov/NationalHealthExpendData/25_NHE_Fact_Sheet.asp. Updated November 4, 2011. Accessed December 22, 2011.
  4. New England Healthcare Institute (NEHI). Thinking outside the pillbox: A system-wide approach to improving patient medication adherence for chronic disease. NEHI Web site. http://www.nehi.net/publications/44/thinking_outside_the_pillbox_a_systemwide_approach_to_improving_patient_medication_adherence_for_chronic_disease. Published August 12, 2009. Accessed December 22, 2011.
  5. Spear BB, Heath-Chiozzi M, Huff J. Clinical application of pharmacogenetics. Trends Mol Med 2001; 7:201204.
  6. Karapetis CS, Khambata-Ford S, Jonker DJ, et al. K-ras mutations and benefit from cetuximab in advanced colorectal cancer. N Engl J Med 2008; 359:17571765.
  7. Feero WG, Green ED. Genomics education for healthcare professionals in the 21st century. JAMA 2011; 306:989990.
  8. Meyers M, Wagner MW, Hwang HS, Kinsella TJ, Boothman DA. Role of the hMLH1 DNA mismatch repair protein in flouropyrimidine-mediated cell death and cell cycle responses. Cancer Res 2001; 61:51935201.
  9. Carethers JM, Chauhan DP, Fink D, et al. Mismatch repair proficiency and in vitro response to 5-fluorouracil. Gastroenterology 1999; 117:123131.
  10. Ribic CM, Sargent DJ, Moore MJ, et al. Tumor microsatellite-instability status as a predictor of benefit from fluorouracil-based adjuvant chemotherapy for colon cancer. N Engl J Med 2003; 349:247257.
  11. Ratain MJ. Personalized medicine: building the GPS to take us there. Clin Pharmacol Ther 2007; 81:321322.
  12. Anderson JL, Horne BD, Stevens SM, et al; Couma-Gen Investigators. Randomized trial of genotype-guided versus standard warfarin dosing in patients initiating oral anticoagulation [published online ahead of print November 7, 2007]. Circulation 2007; 116:25632570. doi: 10.1161/CIRCULATIONAHA.107.737312
  13. Price MJ, Angiolillo DJ, Teirstein PS, et al .Platelet reactivity and cardiovascular outcomes after percutaneous coronary intervention: a time-dependent analysis of the Gauging Responsiveness with a VerifyNow P2Y12 assay: Impact on Thrombosis and Safety (GRAVITAS) trial [published online ahead of print August 29, 2011]. Circulation 2011; 124:11321137. doi: 10.1161/CIRCULATIONAHA.111.029165
  14. Sharp RR, Goldlust ME, Eng C. Addressing gaps in physician education using personal genomic testing. Genet Med 2011; 13:750751.
  15. Wiener CM, Thomas PA, Goodspeed E, Valle D, Nichols DG. “Genes to society”—the logic and process of the new curriculum for the Johns Hopkins University School of Medicine. Acad Med 2010; 85:498506.
  16. Ladabaum U, Wang G, Terdiman J, et al. Strategies to identify the Lynch syndrome among patients with colorectal cancer: a costeffectiveness analysis. Ann Intern Med 2011; 155:6979.
  17. Leach B, Eng C, Kalady M, et al. Sharing the responsibility: multidisciplinary model improves colorectal cancer microsatellite testing. Paper presented at: InSight 2009 Annual Conference: September 2009; Orlando, FL.
  18. Manolio TA, Chisolm R, Ozenberger B, et al. Implementing genomic medicine in the clinic: the future is here. Genet Med Forthcoming.
References
  1. Personalized medicine. Coriell Institute for Medical Research Web site. http://www.coriell.org/personalized-medicine. Updated 2011. Accessed December 27, 2011.
  2. The 2012 Statistical Abstract. U.S. Census Bureau Web site. http://www.census.gov/compendia/statab/cats/income_expenditures_poverty_wealth/gross_domestic_product_gdp.html. Updated September 27, 2011. Accessed December 22, 2011.
  3. National health expenditure fact sheet. Center for Medicare & Medicaid Services (CMS) Web site. https://www.cms.gov/NationalHealthExpendData/25_NHE_Fact_Sheet.asp. Updated November 4, 2011. Accessed December 22, 2011.
  4. New England Healthcare Institute (NEHI). Thinking outside the pillbox: A system-wide approach to improving patient medication adherence for chronic disease. NEHI Web site. http://www.nehi.net/publications/44/thinking_outside_the_pillbox_a_systemwide_approach_to_improving_patient_medication_adherence_for_chronic_disease. Published August 12, 2009. Accessed December 22, 2011.
  5. Spear BB, Heath-Chiozzi M, Huff J. Clinical application of pharmacogenetics. Trends Mol Med 2001; 7:201204.
  6. Karapetis CS, Khambata-Ford S, Jonker DJ, et al. K-ras mutations and benefit from cetuximab in advanced colorectal cancer. N Engl J Med 2008; 359:17571765.
  7. Feero WG, Green ED. Genomics education for healthcare professionals in the 21st century. JAMA 2011; 306:989990.
  8. Meyers M, Wagner MW, Hwang HS, Kinsella TJ, Boothman DA. Role of the hMLH1 DNA mismatch repair protein in flouropyrimidine-mediated cell death and cell cycle responses. Cancer Res 2001; 61:51935201.
  9. Carethers JM, Chauhan DP, Fink D, et al. Mismatch repair proficiency and in vitro response to 5-fluorouracil. Gastroenterology 1999; 117:123131.
  10. Ribic CM, Sargent DJ, Moore MJ, et al. Tumor microsatellite-instability status as a predictor of benefit from fluorouracil-based adjuvant chemotherapy for colon cancer. N Engl J Med 2003; 349:247257.
  11. Ratain MJ. Personalized medicine: building the GPS to take us there. Clin Pharmacol Ther 2007; 81:321322.
  12. Anderson JL, Horne BD, Stevens SM, et al; Couma-Gen Investigators. Randomized trial of genotype-guided versus standard warfarin dosing in patients initiating oral anticoagulation [published online ahead of print November 7, 2007]. Circulation 2007; 116:25632570. doi: 10.1161/CIRCULATIONAHA.107.737312
  13. Price MJ, Angiolillo DJ, Teirstein PS, et al .Platelet reactivity and cardiovascular outcomes after percutaneous coronary intervention: a time-dependent analysis of the Gauging Responsiveness with a VerifyNow P2Y12 assay: Impact on Thrombosis and Safety (GRAVITAS) trial [published online ahead of print August 29, 2011]. Circulation 2011; 124:11321137. doi: 10.1161/CIRCULATIONAHA.111.029165
  14. Sharp RR, Goldlust ME, Eng C. Addressing gaps in physician education using personal genomic testing. Genet Med 2011; 13:750751.
  15. Wiener CM, Thomas PA, Goodspeed E, Valle D, Nichols DG. “Genes to society”—the logic and process of the new curriculum for the Johns Hopkins University School of Medicine. Acad Med 2010; 85:498506.
  16. Ladabaum U, Wang G, Terdiman J, et al. Strategies to identify the Lynch syndrome among patients with colorectal cancer: a costeffectiveness analysis. Ann Intern Med 2011; 155:6979.
  17. Leach B, Eng C, Kalady M, et al. Sharing the responsibility: multidisciplinary model improves colorectal cancer microsatellite testing. Paper presented at: InSight 2009 Annual Conference: September 2009; Orlando, FL.
  18. Manolio TA, Chisolm R, Ozenberger B, et al. Implementing genomic medicine in the clinic: the future is here. Genet Med Forthcoming.
Page Number
S1-S9
Page Number
S1-S9
Publications
Publications
Article Type
Display Headline
Building an innovative model for personalized healthcare
Display Headline
Building an innovative model for personalized healthcare
Citation Override
Cleveland Clinic Journal of Medicine 2012 April;79(suppl 1):S1-S9
Disallow All Ads
Alternative CME
Article PDF Media

Nonallergic rhinitis: Common problem, chronic symptoms

Article Type
Changed
Mon, 10/02/2017 - 12:30
Display Headline
Nonallergic rhinitis: Common problem, chronic symptoms

A 55-year-old woman has come to the clinic because of clear rhinorrhea and nasal congestion, which occur year-round but are worse in the winter. She reports that at times her nose runs continuously. Nasal symptoms have been present for 4 to 5 years but are worsening. The clear discharge is not associated with sneezing or itching. Though she lives with a cat, her symptoms are not exacerbated by close contact with it.

One year ago, an allergist performed skin testing but found no evidence of allergies as a cause of her rhinitis. A short course of intranasal steroids did not seem to improve her nasal symptoms.

The patient also has hypertension, hypothyroidism, and hot flashes due to menopause; these conditions are well controlled with lisinopril (Zestril), levothyroxine (Synthroid), and estrogen replacement. She has no history of asthma and has had no allergies to drugs, including nonsteroidal anti-inflammatory drugs (NSAIDs.)

How should this patient be evaluated and treated?

COMMON, OFTEN OVERLOOKED

Many patients suffer from rhinitis, but this problem can be overshadowed by other chronic diseases seen in a medical clinic, especially during a brief office visit. When a patient presents with rhinitis, a key question is whether it is allergic or nonallergic.

This review will discuss the different forms of nonallergic rhinitis and their causes, and give recommendations about therapy.

RHINITIS: ALLERGIC OR NONALLERGIC?

While allergic rhinitis affects 30 and 60 million Americans annually, or between 10% to 30% of US adults,1 how many have nonallergic rhinitis has been difficult to determine.

In a study in allergy clinics, 23% of patients with rhinitis had the nonallergic form, 43% had the allergic form, and 34% had both forms (mixed rhinitis).2 Other studies have suggested that up to 52% of patients presenting to allergy clinics with rhinitis have nonallergic rhinitis.3

Over time, patients may not stay in the same category. One study found that 24% of patients originally diagnosed with nonallergic rhinitis developed positive allergy tests when retested 3 or more years after their initial evaluation.4

Regardless of the type, untreated or uncontrolled symptoms of rhinitis can significantly affect the quality of life.

All forms of rhinitis are characterized by one or more of the following symptoms: nasal congestion, clear rhinorrhea, sneezing, and itching. These symptoms can be episodic or chronic and can range from mild to debilitating. In addition, rhinitis can lead to systemic symptoms of fatigue, headache, sleep disturbance, and cognitive impairment and can be associated with respiratory symptoms such as sinusitis and asthma.1

Mechanisms are mostly unknown

While allergic rhinitis leads to symptoms when airborne allergens bind with specific immunoglobulin E (IgE) in the nose, the etiology of most forms of nonallergic rhinitis is unknown. However, several mechanisms have been proposed. These include entopy (local nasal IgE synthesis with negative skin tests),5 nocioceptive dysfunction (hyperactive sensory receptors),6 and autonomic nervous system abnormalities (hypoactive or hyperactive dysfunction of sympathetic or parasympathetic nerves in the nose).7

Does this patient have an allergic cause of rhinitis?

When considering a patient with rhinitis, the most important question is, “Does this patient have an allergic cause of rhinitis?” Allergic and nonallergic rhinitis have similar symptoms, making them difficult to distinguish. However, their mechanisms and treatment differ. By categorizing a patient’s type of rhinitis, the physician can make specific recommendations for avoidance and can initiate treatment with the most appropriate therapy. Misclassification can lead to treatment failure, multiple visits, poor adherence, and frustration for patients with uncontrolled symptoms.

Patients for whom an allergic cause cannot be found by allergy skin testing or serum specific IgE immunoassay (Immunocap/RAST) for environmental aeroallergens are classified as having nonallergic rhinitis.

 

 

CLUES POINTING TO NONALLERGIC VS ALLERGIC RHINITIS

Nonallergic rhinitis encompasses a range of syndromes with overlapping symptoms. While tools such as the Rhinitis Diagnostic Worksheet are available to help differentiate allergic from nonallergic rhinitis, debate continues about whether it is necessary to characterize different forms of rhinitis before initiating treatment.8

The diagnosis of nonallergic rhinitis depends on a thorough history and physical examination. Key questions relate to the triggers that bring on the rhinitis, which will assist the clinician in determining which subtype of rhinitis a patient may be experiencing and therefore how to manage it. Clues:

  • Patients with nonallergic rhinitis more often report nasal congestion and rhinorrhea, rather than sneezing and itching, which are predominant symptoms of allergic rhinitis.
  • Patients with nonallergic rhinitis tend to develop symptoms at a later age.
  • Common triggers of nonallergic rhinitis are changes in weather and temperature, food, perfumes, odors, smoke, and fumes. Animal exposure does not lead to symptoms.
  • Patients with nonallergic rhinitis have few complaints of concomitant symptoms of allergic conjunctivitis (itching, watering, redness, and swelling).
  • Many patients with nonallergic rhinitis find that antihistamines have no benefit. Also, they do not have other atopic diseases such as eczema or food allergies and have no family history of atopy.

PHYSICAL FINDINGS

Some findings on physical examination may help distinguish allergic from nonallergic rhinitis.

  • Patients with long-standing allergic rhinitis may have an “allergic crease,” ie, a horizontal wrinkle near the tip of the nose caused by frequent upward wiping. Another sign may be a gothic arch, which is a narrowing of the hard palate occurring as a child.
  • In allergic rhinitis, the turbinates are often pale, moist, and boggy with a bluish tinge.
  • Findings such as a deviated nasal septum, discolored nasal discharge, atrophic nasal mucosa, or nasal polyps should prompt consideration of the several subtypes of nonallergic rhinitis (Table 1).

CASE CONTINUED

Our patient’s symptoms can be caused by many different factors. Allergic triggers for rhinitis include both indoor and outdoor sources. The most common allergens include cat, dog, dust mite, cockroach, mold, and pollen allergens. The absence of acute sneezing and itching when around her cat and her recent negative skin-prick tests confirm that the rhinitis symptoms are not allergic.

In this patient, who has symptoms throughout the year but no allergic triggers, consideration of the different subtypes of nonallergic rhinitis may help guide further therapy.

SUBTYPES OF NONALLERGIC RHINITIS

Vasomotor rhinitis

Vasomotor rhinitis is thought to be caused by a variety of neural and vascular triggers, often without an inflammatory cause. These triggers lead to symptoms involving nasal congestion and clear rhinorrhea more than sneezing and itching. The symptoms can be sporadic, with acute onset in relation to identifiable nonallergic triggers, or chronic, with no clear trigger.

Gustatory rhinitis, for example, is a form of vasomotor rhinitis in which clear rhinorrhea occurs suddenly while eating or while drinking alcohol. It may be prevented by using nasal ipratropium (Atrovent) before meals.

Irritant-sensitive vasomotor rhinitis. In some patients, acute vasomotor rhinitis symptoms are brought on by strong odors, cigarette smoke, air pollution, or perfume. When asked, most patients easily identify which of these irritant triggers cause symptoms.

Weather- or temperature-sensitive vasomotor rhinitis. In other patients, a change in temperature, humidity, or barometric pressure or exposure to cold or dry air can cause nasal symptoms.9 These triggers are often hard to identify. Weather- or temperature-sensitive vasomotor rhinitis is often mistaken for seasonal allergic rhinitis because weather changes occur in close relation to the peak allergy seasons in the spring and fall. However, this subtype does not respond as well to intranasal steroids.9

Other nonallergic triggers of vasomotor rhinitis may include exercise, emotion, and sexual arousal (honeymoon rhinitis).10

Some triggers, such as tobacco smoke and perfume, are easy to avoid. Other triggers, such as weather changes, are unavoidable. If avoidance measures fail or are inadequate, medications (described below) can be used for prophylaxis and symptomatic treatment.

Drug-induced rhinitis

Drugs of various classes are known to cause either acute or chronic rhinitis. Drug-induced rhinitis has been divided into different types based on the mechanism involved.11

The local inflammatory type occurs in aspirin-exacerbated respiratory disease, which is characterized by nasal polyposis with chronic rhinosinusitis, hyposmia, and moderate to severe persistent asthma. Aspirin and other NSAIDs induce an acute local inflammation, leading to severe rhinitis and asthma symptoms. Avoiding all NSAID products is recommended; aspirin desensitization may lead to improvement in rhinosinusitis and asthma control.

The neurogenic type of drug-induced rhinitis can occur with sympatholytic drugs such as alpha receptor agonists (eg, clonidine [Cat-apres]) and antagonists (eg, prazosin [Minipress]).11 Vasodilators, including phosphodiesterase-5 inhibitors such as sildenafil (Viagra), can lead to acute rhinitis symptoms (“anniversary rhinitis”).

Unknown mechanisms. Many other medications can lead to rhinitis by unknown mechanisms, usually with normal findings on physical examination. These include beta-blockers, angiotensin-converting enzyme inhibitors, calcium channel blockers, exogenous estrogens, oral contraceptives, antipsychotics, and gabapentin (Neurontin).

Correlating the initiation of a drug with the onset of rhinitis can help identify offending medications. Stopping the suspected medication, if feasible, is the first-line treatment.

Rhinitis medicamentosa, typically caused by overuse of over-the-counter topical nasal decongestants, is also classified under drug-induced rhinitis. Patients may not think of nasal decongestants as medications, and the physician may need to ask specifically about their use.

On examination, the nasal mucosa appears beefy red without mucous. Once a diagnosis is made, the physician should identify and treat the original etiology of the nasal congestion that led the patient to self-treat.

Patients with rhinitis medicamentosa often have difficulty discontinuing use of topical decongestants. They should be educated that the withdrawal symptoms can be severe and that more than one attempt at quitting may be needed. To break the cycle of rebound congestion, topical intranasal steroids should be used, though 5 to 7 days of oral steroids may be necessary.1

Cocaine is a potent vasoconstrictor. Its illicit use should be suspected, especially if the patient presents with symptoms of chronic irritation such as frequent nosebleeds, crusting, and scabbing.12

Infectious rhinitis

One of the most common causes of acute rhinitis is upper respiratory infection.

Acute viral upper respiratory infection often presents with thick nasal discharge, sneezing, and nasal obstruction that usually clears in 7 to 10 days but can last up to 3 weeks. Acute bacterial sinusitis can follow, typically in fewer than 2% of patients, with symptoms of persistent nasal congestion, discolored mucus, facial pain, cough, and sometimes fever.

Chronic rhinosinusitis is a syndrome with sinus mucosal inflammation with multiple causes. It is clinically defined as persistent nasal and sinus symptoms lasting longer than 12 weeks and confirmed with computed tomography (CT).13 The CT findings of chronic rhinosinusitis include thickening of the lining of the sinus cavities or complete opacification of the pneumatized sinuses.

Major symptoms to consider for diagnosis include facial pain, congestion, obstruction, purulent discharge on examination, and changes in olfaction. Minor symptoms are cough, fatigue, headache, halitosis, fever, ear symptoms, and dental pain.

Treatment may involve 3 or more weeks of an oral antibiotic and a short course of an oral steroid, a daily nasal steroid spray, or both oral and nasal steroids. Most patients can be managed in the primary care setting, but they can be referred to an ear, nose, and throat specialist, an allergist, or an immunologist if their symptoms do not respond to initial therapy.

 

 

Nonallergic rhinitis eosinophilic syndrome

Patients with nonallergic rhinitis eosinophilic syndrome (NARES) are typically middle-aged and have perennial symptoms of sneezing, itching, and rhinorrhea with intermittent exacerbations. They occasionally have associated hyposmia (impaired sense of smell).1 The diagnosis is made when eosinophils account for more than 5% of cells on a nasal smear and allergy testing is negative.

Patients may develop nasal polyposis and aspirin sensitivity.1 Entopy has been described in some.14

Because of the eosinophilic inflammation, this form of nonallergic rhinitis responds well to intranasal steroids.

Immunologic causes

Systemic diseases can affect the nose and cause variable nasal symptoms that can be mistaken for rhinitis. Wegener granulomatosis, sarcoidosis, relapsing polychondritis, midline granulomas, Churg-Strauss syndrome, and amyloidosis can all affect the structures in the nose even before manifesting systemic symptoms. Granulomatous infections in the nose may lead to crusting, bleeding, and nasal obstruction.1

A lack of a response to intranasal steroids or oral antibiotics should lead to consideration of these conditions, and treatment should be tailored to the specific disease.

Occupational rhinitis

Occupational exposure to chemicals, biologic aerosols, flour, and latex can lead to rhinitis, typically through an inflammatory mechanism. Many patients present with associated occupational asthma. The symptoms improve when the patient is away from work and worsen throughout the work week.

Avoiding the triggering agent is necessary to treat these symptoms.

Hormonal rhinitis

Hormonal rhinitis, ie, rhinitis related to metabolic and endocrine conditions, is most commonly associated with high estrogen states. Nasal congestion has been reported with pregnancy, menses, menarche, and the use of oral contraceptives.15 The mechanism for congestion in these conditions still needs clarification.

When considering drug therapy, only intranasal budesonide (Rhinocort) has a pregnancy category B rating.

While hypothyroidism and acromegaly have been mentioned in reviews of nonallergic rhinitis, evidence that these disorders cause nonallergic rhinitis is not strong.16,17

Structurally related rhinitis

Anatomic abnormalities that can cause persistent nasal congestion include nasal septal deviation, turbinate hypertrophy, enlarged adenoids, tumors, and foreign bodies. These can be visualized by simple anterior nasal examination, nasal endoscopy, or radiologic studies. If structural causes lead to impaired quality of life or chronic rhinosinusitis, then consider referral to a specialist for possible surgical treatment.

Clear spontaneous rhinorrhea, with or without trauma, can be caused by cerebrospinal fluid leaking into the nasal cavity.18 A salty, metallic taste in the mouth can be a clue that the fluid is cerebrospinal fluid. A definitive diagnosis of cerebrospinal fluid leak is made by finding beta-2-transferrin in nasal secretions.

Atrophic rhinitis

Atrophic rhinitis is categorized as primary or secondary.

Primary (idiopathic) atrophic rhinitis is characterized by atrophy of the nasal mucosa and mucosal colonization with Klebsiella ozaenae associated with a foul-smelling nasal discharge.19,20 This disorder has been primarily reported in young people who present with nasal obstruction, dryness, crusting, and epistaxis. They are from areas with warm climates, such as the Middle East, Southeast Asia, India, Africa, and the Mediterranean.

Secondary atrophic rhinitis can be a complication of nasal or sinus surgery, trauma, granulomatous disease, or exposure to radiation.21 This disorder is typically diagnosed with nasal endoscopy and treated with daily saline rinses with or without topical antibiotics.21

CASE CONTINUED

Questioned further, our patient says her symptoms are worse when her husband smokes, but that she continues to have congestion and rhinorrhea when he is away on business trips. She notes that her symptoms are often worse on airplanes (dry air with an acute change in barometric pressure), with weather changes, and in cold, dry environments. Symptoms are not induced by eating.

We note that she started taking lisinopril 2 years ago and conjugated equine estrogens 8 years ago. Review of systems reveals no history of facial or head trauma, polyps, or hyposmia.

The rhinitis and congestion are bilateral, and she denies headaches, acid reflux, and conjunctivitis. She has a mild throat-clearing cough that she attributes to postnasal drip.

On physical examination, her blood pressure is 118/76 mm Hg and her pulse is 64. Her turbinates are congested with clear rhinorrhea. The rest of the examination is normal.

AVOID TRIGGERS, PRETREAT BEFORE EXPOSURE

Figure 1.
While treatment for nonallergic rhinitis varies according to the cause, there are some general guidelines for therapy (Figure 1).

People with known environmental, non-immunologic, and irritant triggers should be reminded to avoid these exposures if possible.

If triggers are unavoidable, patients can pretreat themselves with topical nasal sprays before exposure. For example, if symptoms occur while on airplanes, then intranasal steroids or antihistamine sprays should be used before getting on the plane.

 

 

Many drugs available

Fortunately, many effective drugs are available to treat nonallergic rhinitis. These have few adverse effects or drug interactions.

Intranasal steroid sprays are considered first-line therapy, as there are studies demonstrating effectiveness in nonallergic rhinitis.22 Intranasal fluticasone propionate (Flonase) and beclomethasone dipropionate (Beconase AQ) are approved by the US Food and Drug Administration (FDA) for treating nonallergic rhinitis. Intranasal mometasone (Nasonex) is approved for treating nasal polyps.

Nasal steroid sprays are most effective if the dominant nasal symptom is congestion, but they have also shown benefit for rhinorrhea, sneezing, and itching.

Side effects of nasal steroid sprays include nasal irritation (dryness, burning, and stinging) and epistaxis, the latter occurring in 5% to 10% of patients.23

Intranasal antihistamines include azelastine (Astelin, Astepro) and olopatadine (Patanase). They are particularly useful for treating sneezing, congestion, and rhinorrhea.24 Astelin is the only intranasal antihistamine with FDA approval for nonallergic rhinitis.

Side effects of this drug class include bitter taste (with Astelin), sweet taste (with Astepro), headache, and somnolence.

Oral antihistamines such as loratadine (Claritin), cetirizine (Zyrtec), and fexofenadine (Allegra) are now available over the counter, and many patients try them before seeking medical care. These drugs may be helpful for those bothered by sneezing. However, no study has demonstrated their effectiveness for nonallergic rhinitis.25 First-generation antihistamines may help with rhinorrhea via their anticholinergic effects.

Ipratropium, an antimuscarinic agent, decreases secretions by inhibiting the nasal parasympathetic mucous glands. Intranasal ipratropium 0.03% (Atrovent 0.03%) should be considered first-line if the dominant symptom is rhinorrhea. Higher-dose ipratropium 0.06% is approved for rhinorrhea related to the common cold or allergic rhinitis. Because it is used topically, little is absorbed. Its major side effect is nasal dryness.

Decongestants, either oral or topical, can relieve the symptoms of congestion and rhinorrhea in nonallergic rhinitis. They should only be used short-term, as there is little evidence to support their chronic use.

Phenylpropanolamine, a decongestant previously found in over-the-counter cough medicines, was withdrawn from the market in 2000 owing to concern that the drug, especially when used for weight suppression, was linked to hemorrhagic stroke in young women.26,27 Other oral decongestants, ie, pseudoephedrine and phenylephrine, are still available, but there are no definitive guidelines for their use. Their side effects include tachycardia, increase in blood pressure, and insomnia.

Nasal saline irrigation has been used for centuries to treat rhinitis and sinusitis, despite limited evidence of benefit. A Cochrane review concluded that saline irrigation was well tolerated, had minor side effects, and could provide some relief of rhinosinusitis symptoms either as the sole therapeutic measure or as adjunctive treatment.28 Hypertonic saline solutions, while possibly more effective than isotonic saline in improving mucociliary clearance, are not as well tolerated since they can cause nasal burning and irritation. Presumed benefits of saline irrigation are clearance of nasal secretions, improvement of nasociliary function, and removal of irritants and pollen from the nose.

A strategy

Initial therapy (Table 2) should be based on the presentation. If the patient has a limited response to the therapy at follow-up in 2 to 4 weeks, the physician should consider using adjunctive medications, address patient adherence and technique, and reassess the accuracy of the initial diagnosis. At this point, one can consider referral to a specialist such as an allergist or otolaryngologist, especially if there are comorbid conditions such as asthma or polyps.

Imaging the sinuses with CT, which has replaced standard nasal radiography, may help if one is concerned about chronic rhinosinusitis, nasal polyps, or other anatomic condition that could contribute to persistent symptoms. Cost and radiation exposure should enter into the decision to obtain this study because a diagnosis based on the patient’s report of symptoms may be equally accurate.29,30

CASE CONTINUED

Our patient has a number of potential causes of her symptoms. Exposure to second-hand tobacco smoke at home and to the air in airplanes could be acute triggers. Weather and temperature changes could explain her chronic symptoms in the spring and fall. Use of an angiotensin-converting enzyme inhibitor (in her case, lisinopril) and estrogen replacement therapy may contribute to perennial symptoms, but the onset of her nonallergic rhinitis does not correlate with the use of these drugs. There are no symptoms to suggest chronic rhinosinusitis or anatomic causes of her symptoms.

This case is typical of vasomotor rhinitis of the weather- or temperature-sensitive type. This diagnosis may explain her lack of improvement with intranasal steroids, though adherence and spray technique should be assessed. At this point, we would recommend trying topical antihistamines daily when chronic symptoms are present or as needed for acute symptoms.

References
  1. Wallace DV, Dykewicz MS, Bernstein DI, et al. The diagnosis and management of rhinitis: an updated practice parameter. J Allergy Clin Immunol 2008; 122( suppl 2):S1S84.
  2. Settipane RA, Charnock DR. Epidemiology of rhinitis: allergic and nonallergic. Clin Allergy Immunol 2007; 19:2334.
  3. Settipane RA, Lieberman P. Update on nonallergic rhinitis. Ann Allergy Asthma Immunol 2001; 86:494507.
  4. Rondón C, Doña I, Torres MJ, Campo P, Blanca M. Evolution of patients with nonallergic rhinitis supports conversion to allergic rhinitis. J Allergy Clin Immunol 2009; 123:10981102.
  5. Forester JP, Calabria CW. Local production of IgE in the respiratory mucosa and the concept of entopy: does allergy exist in nonallergic rhinitis? Ann Allergy Asthma Immunol 2010; 105:249255.
  6. Silvers WS. The skier’s nose: a model of cold-induced rhinorrhea. Ann Allergy 1991; 67:3236.
  7. Jaradeh SS, Smith TL, Torrico L, et al. Autonomic nervous system evaluation of patients with vasomotor rhinitis. Laryngoscope 2000; 110:18281831.
  8. Quan M, Casale TB, Blaiss MS. Should clinicians routinely determine rhinitis subtype on initial diagnosis and evaluation? A debate among experts. Clin Cornerstone 2009; 9:5460.
  9. Jacobs R, Lieberman P, Kent E, Silvey M, Locantore N, Philpot EE. Weather/temperature-sensitive vasomotor rhinitis may be refractory to intranasal corticosteroid treatment. Allergy Asthma Proc 2009; 30:120127.
  10. Monteseirin J, Camacho MJ, Bonilla I, Sanchez-Hernandez C, Hernandez M, Conde J. Honeymoon rhinitis. Allergy 2001; 56:353354.
  11. Varghese M, Glaum MC, Lockey RF. Drug-induced rhinitis. Clin Exp Allergy 2010; 40:381384.
  12. Schwartz RH, Estroff T, Fairbanks DN, Hoffmann NG. Nasal symptoms associated with cocaine abuse during adolescence. Arch Otolaryngol Head Neck Surg 1989; 115:6364.
  13. Meltzer EO, Hamilos DL, Hadley JA, et al; American Academy of Allergy, Asthma and Immunology (AAAAI); American Academy of Otolaryngic Allergy (AAOA); American Academy of Otolaryngology--Head and Neck Surgery (AAO-HNS); American College of Allergy, Asthma and Immunology (ACAAI); American Rhinologic Society (ARS). Rhinosinusitis: establishing definitions for clinical research and patient care. J Allergy Clin Immunol 2004; 114( suppl 6):155212.
  14. Powe DG, Huskisson RS, Carney AS, Jenkins D, Jones NS. Evidence for an inflammatory pathophysiology in idiopathic rhinitis. Clin Exp Allergy 2001; 31:864872.
  15. Philpott CM, Robinson AM, Murty GE. Nasal pathophysiology and its relationship to the female ovarian hormones. J Otolaryngol Head Neck Surg 2008; 37:540546.
  16. Dykewicz MS, Fineman S, Skoner DP, et al. Diagnosis and management of rhinitis: complete guidelines of the Joint Task Force on Practice Parameters in Allergy, Asthma and Immunology. American Academy of Allergy, Asthma, and Immunology. Ann Allergy Asthma Immunol 1998; 81:478518.
  17. Ellegård EK, Karlsson NG, Ellegård LH. Rhinitis in the menstrual cycle, pregnancy, and some endocrine disorders. Clin Allergy Immunol 2007; 19:305321.
  18. Dunn CJ, Alaani A, Johnson AP. Study on spontaneous cerebrospinal fluid rhinorrhoea: its aetiology and management. J Laryngol Otol 2005; 119:1215.
  19. Bunnag C, Jareoncharsri P, Tansuriyawong P, Bhothisuwan W, Chantarakul N. Characteristics of atrophic rhinitis in Thai patients at the Siriraj Hospital. Rhinology 1999; 37:125130.
  20. Dutt SN, Kameswaran M. The aetiology and management of atrophic rhinitis. J Laryngol Otol 2005; 119:843852.
  21. deShazo RD, Stringer SP. Atrophic rhinosinusitis: progress toward explanation of an unsolved medical mystery. Curr Opin Allergy Clin Immunol 2011; 11:17.
  22. Greiner AN, Meltzer EO. Overview of the treatment of allergic rhinitis and nonallergic rhinopathy. Proc Am Thorac Soc 2011; 8:121131.
  23. Corren J. Intranasal corticosteroids for allergic rhinitis: how do different agents compare? J Allergy Clin Immunol 1999; 104:S144S149.
  24. Lieberman P, Meltzer EO, LaForce CF, Darter AL, Tort MJ. Two-week comparison study of olopatadine hydrochloride nasal spray 0.6% versus azelastine hydrochloride nasal spray 0.1% in patients with vasomotor rhinitis. Allergy Asthma Proc 2011; 32:151158.
  25. Bousquet J, Khaltaev N, Cruz AA, et al; World Health Organization; GA(2)LEN. Allergic Rhinitis and its Impact on Asthma (ARIA) 2008 update (in collaboration with the World Health Organization, GA(2)LEN and AllerGen). Allergy 2008; 63( suppl 86):8160.
  26. SoRelle R. FDA warns of stroke risk associated with phenylpropanolamine; cold remedies and drugs removed from store shelves. Circulation 2000; 102:E9041E9043.
  27. Kernan WN, Viscoli CM, Brass LM, et al. Phenylpropanolamine and the risk of hemorrhagic stroke. N Engl J Med 2000; 343:18261832.
  28. Harvey R, Hannan SA, Badia L, Scadding G. Nasal saline irrigations for the symptoms of chronic rhinosinusitis. Cochrane Database Syst Rev 2007;CD006394.
  29. Bhattacharyya N. The role of CT and MRI in the diagnosis of chronic rhinosinusitis. Curr Allergy Asthma Rep 2010; 10:171174.
  30. Kenny TJ, Duncavage J, Bracikowski J, Yildirim A, Murray JJ, Tanner SB. Prospective analysis of sinus symptoms and correlation with paranasal computed tomography scan. Otolaryngol Head Neck Surg 2001; 125:4043.
Article PDF
Author and Disclosure Information

Brian Schroer, MD
Center for Pediatric Allergy, and Department of Pulmonary, Allergy, and Critical Care Medicine, Cleveland Clinic

Lily C. Pien, MD
Department of Pulmonary, Allergy, and Critical Care Medicine, and Center for Medical Education Research and Development (CMERAD), Education Institute, Cleveland Clinic

Address: Brian Schroer, MD, Center for Pediatric Allergy, A120, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195; e-mail [email protected]

Issue
Cleveland Clinic Journal of Medicine - 79(4)
Publications
Topics
Page Number
285-293
Sections
Author and Disclosure Information

Brian Schroer, MD
Center for Pediatric Allergy, and Department of Pulmonary, Allergy, and Critical Care Medicine, Cleveland Clinic

Lily C. Pien, MD
Department of Pulmonary, Allergy, and Critical Care Medicine, and Center for Medical Education Research and Development (CMERAD), Education Institute, Cleveland Clinic

Address: Brian Schroer, MD, Center for Pediatric Allergy, A120, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195; e-mail [email protected]

Author and Disclosure Information

Brian Schroer, MD
Center for Pediatric Allergy, and Department of Pulmonary, Allergy, and Critical Care Medicine, Cleveland Clinic

Lily C. Pien, MD
Department of Pulmonary, Allergy, and Critical Care Medicine, and Center for Medical Education Research and Development (CMERAD), Education Institute, Cleveland Clinic

Address: Brian Schroer, MD, Center for Pediatric Allergy, A120, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195; e-mail [email protected]

Article PDF
Article PDF

A 55-year-old woman has come to the clinic because of clear rhinorrhea and nasal congestion, which occur year-round but are worse in the winter. She reports that at times her nose runs continuously. Nasal symptoms have been present for 4 to 5 years but are worsening. The clear discharge is not associated with sneezing or itching. Though she lives with a cat, her symptoms are not exacerbated by close contact with it.

One year ago, an allergist performed skin testing but found no evidence of allergies as a cause of her rhinitis. A short course of intranasal steroids did not seem to improve her nasal symptoms.

The patient also has hypertension, hypothyroidism, and hot flashes due to menopause; these conditions are well controlled with lisinopril (Zestril), levothyroxine (Synthroid), and estrogen replacement. She has no history of asthma and has had no allergies to drugs, including nonsteroidal anti-inflammatory drugs (NSAIDs.)

How should this patient be evaluated and treated?

COMMON, OFTEN OVERLOOKED

Many patients suffer from rhinitis, but this problem can be overshadowed by other chronic diseases seen in a medical clinic, especially during a brief office visit. When a patient presents with rhinitis, a key question is whether it is allergic or nonallergic.

This review will discuss the different forms of nonallergic rhinitis and their causes, and give recommendations about therapy.

RHINITIS: ALLERGIC OR NONALLERGIC?

While allergic rhinitis affects 30 and 60 million Americans annually, or between 10% to 30% of US adults,1 how many have nonallergic rhinitis has been difficult to determine.

In a study in allergy clinics, 23% of patients with rhinitis had the nonallergic form, 43% had the allergic form, and 34% had both forms (mixed rhinitis).2 Other studies have suggested that up to 52% of patients presenting to allergy clinics with rhinitis have nonallergic rhinitis.3

Over time, patients may not stay in the same category. One study found that 24% of patients originally diagnosed with nonallergic rhinitis developed positive allergy tests when retested 3 or more years after their initial evaluation.4

Regardless of the type, untreated or uncontrolled symptoms of rhinitis can significantly affect the quality of life.

All forms of rhinitis are characterized by one or more of the following symptoms: nasal congestion, clear rhinorrhea, sneezing, and itching. These symptoms can be episodic or chronic and can range from mild to debilitating. In addition, rhinitis can lead to systemic symptoms of fatigue, headache, sleep disturbance, and cognitive impairment and can be associated with respiratory symptoms such as sinusitis and asthma.1

Mechanisms are mostly unknown

While allergic rhinitis leads to symptoms when airborne allergens bind with specific immunoglobulin E (IgE) in the nose, the etiology of most forms of nonallergic rhinitis is unknown. However, several mechanisms have been proposed. These include entopy (local nasal IgE synthesis with negative skin tests),5 nocioceptive dysfunction (hyperactive sensory receptors),6 and autonomic nervous system abnormalities (hypoactive or hyperactive dysfunction of sympathetic or parasympathetic nerves in the nose).7

Does this patient have an allergic cause of rhinitis?

When considering a patient with rhinitis, the most important question is, “Does this patient have an allergic cause of rhinitis?” Allergic and nonallergic rhinitis have similar symptoms, making them difficult to distinguish. However, their mechanisms and treatment differ. By categorizing a patient’s type of rhinitis, the physician can make specific recommendations for avoidance and can initiate treatment with the most appropriate therapy. Misclassification can lead to treatment failure, multiple visits, poor adherence, and frustration for patients with uncontrolled symptoms.

Patients for whom an allergic cause cannot be found by allergy skin testing or serum specific IgE immunoassay (Immunocap/RAST) for environmental aeroallergens are classified as having nonallergic rhinitis.

 

 

CLUES POINTING TO NONALLERGIC VS ALLERGIC RHINITIS

Nonallergic rhinitis encompasses a range of syndromes with overlapping symptoms. While tools such as the Rhinitis Diagnostic Worksheet are available to help differentiate allergic from nonallergic rhinitis, debate continues about whether it is necessary to characterize different forms of rhinitis before initiating treatment.8

The diagnosis of nonallergic rhinitis depends on a thorough history and physical examination. Key questions relate to the triggers that bring on the rhinitis, which will assist the clinician in determining which subtype of rhinitis a patient may be experiencing and therefore how to manage it. Clues:

  • Patients with nonallergic rhinitis more often report nasal congestion and rhinorrhea, rather than sneezing and itching, which are predominant symptoms of allergic rhinitis.
  • Patients with nonallergic rhinitis tend to develop symptoms at a later age.
  • Common triggers of nonallergic rhinitis are changes in weather and temperature, food, perfumes, odors, smoke, and fumes. Animal exposure does not lead to symptoms.
  • Patients with nonallergic rhinitis have few complaints of concomitant symptoms of allergic conjunctivitis (itching, watering, redness, and swelling).
  • Many patients with nonallergic rhinitis find that antihistamines have no benefit. Also, they do not have other atopic diseases such as eczema or food allergies and have no family history of atopy.

PHYSICAL FINDINGS

Some findings on physical examination may help distinguish allergic from nonallergic rhinitis.

  • Patients with long-standing allergic rhinitis may have an “allergic crease,” ie, a horizontal wrinkle near the tip of the nose caused by frequent upward wiping. Another sign may be a gothic arch, which is a narrowing of the hard palate occurring as a child.
  • In allergic rhinitis, the turbinates are often pale, moist, and boggy with a bluish tinge.
  • Findings such as a deviated nasal septum, discolored nasal discharge, atrophic nasal mucosa, or nasal polyps should prompt consideration of the several subtypes of nonallergic rhinitis (Table 1).

CASE CONTINUED

Our patient’s symptoms can be caused by many different factors. Allergic triggers for rhinitis include both indoor and outdoor sources. The most common allergens include cat, dog, dust mite, cockroach, mold, and pollen allergens. The absence of acute sneezing and itching when around her cat and her recent negative skin-prick tests confirm that the rhinitis symptoms are not allergic.

In this patient, who has symptoms throughout the year but no allergic triggers, consideration of the different subtypes of nonallergic rhinitis may help guide further therapy.

SUBTYPES OF NONALLERGIC RHINITIS

Vasomotor rhinitis

Vasomotor rhinitis is thought to be caused by a variety of neural and vascular triggers, often without an inflammatory cause. These triggers lead to symptoms involving nasal congestion and clear rhinorrhea more than sneezing and itching. The symptoms can be sporadic, with acute onset in relation to identifiable nonallergic triggers, or chronic, with no clear trigger.

Gustatory rhinitis, for example, is a form of vasomotor rhinitis in which clear rhinorrhea occurs suddenly while eating or while drinking alcohol. It may be prevented by using nasal ipratropium (Atrovent) before meals.

Irritant-sensitive vasomotor rhinitis. In some patients, acute vasomotor rhinitis symptoms are brought on by strong odors, cigarette smoke, air pollution, or perfume. When asked, most patients easily identify which of these irritant triggers cause symptoms.

Weather- or temperature-sensitive vasomotor rhinitis. In other patients, a change in temperature, humidity, or barometric pressure or exposure to cold or dry air can cause nasal symptoms.9 These triggers are often hard to identify. Weather- or temperature-sensitive vasomotor rhinitis is often mistaken for seasonal allergic rhinitis because weather changes occur in close relation to the peak allergy seasons in the spring and fall. However, this subtype does not respond as well to intranasal steroids.9

Other nonallergic triggers of vasomotor rhinitis may include exercise, emotion, and sexual arousal (honeymoon rhinitis).10

Some triggers, such as tobacco smoke and perfume, are easy to avoid. Other triggers, such as weather changes, are unavoidable. If avoidance measures fail or are inadequate, medications (described below) can be used for prophylaxis and symptomatic treatment.

Drug-induced rhinitis

Drugs of various classes are known to cause either acute or chronic rhinitis. Drug-induced rhinitis has been divided into different types based on the mechanism involved.11

The local inflammatory type occurs in aspirin-exacerbated respiratory disease, which is characterized by nasal polyposis with chronic rhinosinusitis, hyposmia, and moderate to severe persistent asthma. Aspirin and other NSAIDs induce an acute local inflammation, leading to severe rhinitis and asthma symptoms. Avoiding all NSAID products is recommended; aspirin desensitization may lead to improvement in rhinosinusitis and asthma control.

The neurogenic type of drug-induced rhinitis can occur with sympatholytic drugs such as alpha receptor agonists (eg, clonidine [Cat-apres]) and antagonists (eg, prazosin [Minipress]).11 Vasodilators, including phosphodiesterase-5 inhibitors such as sildenafil (Viagra), can lead to acute rhinitis symptoms (“anniversary rhinitis”).

Unknown mechanisms. Many other medications can lead to rhinitis by unknown mechanisms, usually with normal findings on physical examination. These include beta-blockers, angiotensin-converting enzyme inhibitors, calcium channel blockers, exogenous estrogens, oral contraceptives, antipsychotics, and gabapentin (Neurontin).

Correlating the initiation of a drug with the onset of rhinitis can help identify offending medications. Stopping the suspected medication, if feasible, is the first-line treatment.

Rhinitis medicamentosa, typically caused by overuse of over-the-counter topical nasal decongestants, is also classified under drug-induced rhinitis. Patients may not think of nasal decongestants as medications, and the physician may need to ask specifically about their use.

On examination, the nasal mucosa appears beefy red without mucous. Once a diagnosis is made, the physician should identify and treat the original etiology of the nasal congestion that led the patient to self-treat.

Patients with rhinitis medicamentosa often have difficulty discontinuing use of topical decongestants. They should be educated that the withdrawal symptoms can be severe and that more than one attempt at quitting may be needed. To break the cycle of rebound congestion, topical intranasal steroids should be used, though 5 to 7 days of oral steroids may be necessary.1

Cocaine is a potent vasoconstrictor. Its illicit use should be suspected, especially if the patient presents with symptoms of chronic irritation such as frequent nosebleeds, crusting, and scabbing.12

Infectious rhinitis

One of the most common causes of acute rhinitis is upper respiratory infection.

Acute viral upper respiratory infection often presents with thick nasal discharge, sneezing, and nasal obstruction that usually clears in 7 to 10 days but can last up to 3 weeks. Acute bacterial sinusitis can follow, typically in fewer than 2% of patients, with symptoms of persistent nasal congestion, discolored mucus, facial pain, cough, and sometimes fever.

Chronic rhinosinusitis is a syndrome with sinus mucosal inflammation with multiple causes. It is clinically defined as persistent nasal and sinus symptoms lasting longer than 12 weeks and confirmed with computed tomography (CT).13 The CT findings of chronic rhinosinusitis include thickening of the lining of the sinus cavities or complete opacification of the pneumatized sinuses.

Major symptoms to consider for diagnosis include facial pain, congestion, obstruction, purulent discharge on examination, and changes in olfaction. Minor symptoms are cough, fatigue, headache, halitosis, fever, ear symptoms, and dental pain.

Treatment may involve 3 or more weeks of an oral antibiotic and a short course of an oral steroid, a daily nasal steroid spray, or both oral and nasal steroids. Most patients can be managed in the primary care setting, but they can be referred to an ear, nose, and throat specialist, an allergist, or an immunologist if their symptoms do not respond to initial therapy.

 

 

Nonallergic rhinitis eosinophilic syndrome

Patients with nonallergic rhinitis eosinophilic syndrome (NARES) are typically middle-aged and have perennial symptoms of sneezing, itching, and rhinorrhea with intermittent exacerbations. They occasionally have associated hyposmia (impaired sense of smell).1 The diagnosis is made when eosinophils account for more than 5% of cells on a nasal smear and allergy testing is negative.

Patients may develop nasal polyposis and aspirin sensitivity.1 Entopy has been described in some.14

Because of the eosinophilic inflammation, this form of nonallergic rhinitis responds well to intranasal steroids.

Immunologic causes

Systemic diseases can affect the nose and cause variable nasal symptoms that can be mistaken for rhinitis. Wegener granulomatosis, sarcoidosis, relapsing polychondritis, midline granulomas, Churg-Strauss syndrome, and amyloidosis can all affect the structures in the nose even before manifesting systemic symptoms. Granulomatous infections in the nose may lead to crusting, bleeding, and nasal obstruction.1

A lack of a response to intranasal steroids or oral antibiotics should lead to consideration of these conditions, and treatment should be tailored to the specific disease.

Occupational rhinitis

Occupational exposure to chemicals, biologic aerosols, flour, and latex can lead to rhinitis, typically through an inflammatory mechanism. Many patients present with associated occupational asthma. The symptoms improve when the patient is away from work and worsen throughout the work week.

Avoiding the triggering agent is necessary to treat these symptoms.

Hormonal rhinitis

Hormonal rhinitis, ie, rhinitis related to metabolic and endocrine conditions, is most commonly associated with high estrogen states. Nasal congestion has been reported with pregnancy, menses, menarche, and the use of oral contraceptives.15 The mechanism for congestion in these conditions still needs clarification.

When considering drug therapy, only intranasal budesonide (Rhinocort) has a pregnancy category B rating.

While hypothyroidism and acromegaly have been mentioned in reviews of nonallergic rhinitis, evidence that these disorders cause nonallergic rhinitis is not strong.16,17

Structurally related rhinitis

Anatomic abnormalities that can cause persistent nasal congestion include nasal septal deviation, turbinate hypertrophy, enlarged adenoids, tumors, and foreign bodies. These can be visualized by simple anterior nasal examination, nasal endoscopy, or radiologic studies. If structural causes lead to impaired quality of life or chronic rhinosinusitis, then consider referral to a specialist for possible surgical treatment.

Clear spontaneous rhinorrhea, with or without trauma, can be caused by cerebrospinal fluid leaking into the nasal cavity.18 A salty, metallic taste in the mouth can be a clue that the fluid is cerebrospinal fluid. A definitive diagnosis of cerebrospinal fluid leak is made by finding beta-2-transferrin in nasal secretions.

Atrophic rhinitis

Atrophic rhinitis is categorized as primary or secondary.

Primary (idiopathic) atrophic rhinitis is characterized by atrophy of the nasal mucosa and mucosal colonization with Klebsiella ozaenae associated with a foul-smelling nasal discharge.19,20 This disorder has been primarily reported in young people who present with nasal obstruction, dryness, crusting, and epistaxis. They are from areas with warm climates, such as the Middle East, Southeast Asia, India, Africa, and the Mediterranean.

Secondary atrophic rhinitis can be a complication of nasal or sinus surgery, trauma, granulomatous disease, or exposure to radiation.21 This disorder is typically diagnosed with nasal endoscopy and treated with daily saline rinses with or without topical antibiotics.21

CASE CONTINUED

Questioned further, our patient says her symptoms are worse when her husband smokes, but that she continues to have congestion and rhinorrhea when he is away on business trips. She notes that her symptoms are often worse on airplanes (dry air with an acute change in barometric pressure), with weather changes, and in cold, dry environments. Symptoms are not induced by eating.

We note that she started taking lisinopril 2 years ago and conjugated equine estrogens 8 years ago. Review of systems reveals no history of facial or head trauma, polyps, or hyposmia.

The rhinitis and congestion are bilateral, and she denies headaches, acid reflux, and conjunctivitis. She has a mild throat-clearing cough that she attributes to postnasal drip.

On physical examination, her blood pressure is 118/76 mm Hg and her pulse is 64. Her turbinates are congested with clear rhinorrhea. The rest of the examination is normal.

AVOID TRIGGERS, PRETREAT BEFORE EXPOSURE

Figure 1.
While treatment for nonallergic rhinitis varies according to the cause, there are some general guidelines for therapy (Figure 1).

People with known environmental, non-immunologic, and irritant triggers should be reminded to avoid these exposures if possible.

If triggers are unavoidable, patients can pretreat themselves with topical nasal sprays before exposure. For example, if symptoms occur while on airplanes, then intranasal steroids or antihistamine sprays should be used before getting on the plane.

 

 

Many drugs available

Fortunately, many effective drugs are available to treat nonallergic rhinitis. These have few adverse effects or drug interactions.

Intranasal steroid sprays are considered first-line therapy, as there are studies demonstrating effectiveness in nonallergic rhinitis.22 Intranasal fluticasone propionate (Flonase) and beclomethasone dipropionate (Beconase AQ) are approved by the US Food and Drug Administration (FDA) for treating nonallergic rhinitis. Intranasal mometasone (Nasonex) is approved for treating nasal polyps.

Nasal steroid sprays are most effective if the dominant nasal symptom is congestion, but they have also shown benefit for rhinorrhea, sneezing, and itching.

Side effects of nasal steroid sprays include nasal irritation (dryness, burning, and stinging) and epistaxis, the latter occurring in 5% to 10% of patients.23

Intranasal antihistamines include azelastine (Astelin, Astepro) and olopatadine (Patanase). They are particularly useful for treating sneezing, congestion, and rhinorrhea.24 Astelin is the only intranasal antihistamine with FDA approval for nonallergic rhinitis.

Side effects of this drug class include bitter taste (with Astelin), sweet taste (with Astepro), headache, and somnolence.

Oral antihistamines such as loratadine (Claritin), cetirizine (Zyrtec), and fexofenadine (Allegra) are now available over the counter, and many patients try them before seeking medical care. These drugs may be helpful for those bothered by sneezing. However, no study has demonstrated their effectiveness for nonallergic rhinitis.25 First-generation antihistamines may help with rhinorrhea via their anticholinergic effects.

Ipratropium, an antimuscarinic agent, decreases secretions by inhibiting the nasal parasympathetic mucous glands. Intranasal ipratropium 0.03% (Atrovent 0.03%) should be considered first-line if the dominant symptom is rhinorrhea. Higher-dose ipratropium 0.06% is approved for rhinorrhea related to the common cold or allergic rhinitis. Because it is used topically, little is absorbed. Its major side effect is nasal dryness.

Decongestants, either oral or topical, can relieve the symptoms of congestion and rhinorrhea in nonallergic rhinitis. They should only be used short-term, as there is little evidence to support their chronic use.

Phenylpropanolamine, a decongestant previously found in over-the-counter cough medicines, was withdrawn from the market in 2000 owing to concern that the drug, especially when used for weight suppression, was linked to hemorrhagic stroke in young women.26,27 Other oral decongestants, ie, pseudoephedrine and phenylephrine, are still available, but there are no definitive guidelines for their use. Their side effects include tachycardia, increase in blood pressure, and insomnia.

Nasal saline irrigation has been used for centuries to treat rhinitis and sinusitis, despite limited evidence of benefit. A Cochrane review concluded that saline irrigation was well tolerated, had minor side effects, and could provide some relief of rhinosinusitis symptoms either as the sole therapeutic measure or as adjunctive treatment.28 Hypertonic saline solutions, while possibly more effective than isotonic saline in improving mucociliary clearance, are not as well tolerated since they can cause nasal burning and irritation. Presumed benefits of saline irrigation are clearance of nasal secretions, improvement of nasociliary function, and removal of irritants and pollen from the nose.

A strategy

Initial therapy (Table 2) should be based on the presentation. If the patient has a limited response to the therapy at follow-up in 2 to 4 weeks, the physician should consider using adjunctive medications, address patient adherence and technique, and reassess the accuracy of the initial diagnosis. At this point, one can consider referral to a specialist such as an allergist or otolaryngologist, especially if there are comorbid conditions such as asthma or polyps.

Imaging the sinuses with CT, which has replaced standard nasal radiography, may help if one is concerned about chronic rhinosinusitis, nasal polyps, or other anatomic condition that could contribute to persistent symptoms. Cost and radiation exposure should enter into the decision to obtain this study because a diagnosis based on the patient’s report of symptoms may be equally accurate.29,30

CASE CONTINUED

Our patient has a number of potential causes of her symptoms. Exposure to second-hand tobacco smoke at home and to the air in airplanes could be acute triggers. Weather and temperature changes could explain her chronic symptoms in the spring and fall. Use of an angiotensin-converting enzyme inhibitor (in her case, lisinopril) and estrogen replacement therapy may contribute to perennial symptoms, but the onset of her nonallergic rhinitis does not correlate with the use of these drugs. There are no symptoms to suggest chronic rhinosinusitis or anatomic causes of her symptoms.

This case is typical of vasomotor rhinitis of the weather- or temperature-sensitive type. This diagnosis may explain her lack of improvement with intranasal steroids, though adherence and spray technique should be assessed. At this point, we would recommend trying topical antihistamines daily when chronic symptoms are present or as needed for acute symptoms.

A 55-year-old woman has come to the clinic because of clear rhinorrhea and nasal congestion, which occur year-round but are worse in the winter. She reports that at times her nose runs continuously. Nasal symptoms have been present for 4 to 5 years but are worsening. The clear discharge is not associated with sneezing or itching. Though she lives with a cat, her symptoms are not exacerbated by close contact with it.

One year ago, an allergist performed skin testing but found no evidence of allergies as a cause of her rhinitis. A short course of intranasal steroids did not seem to improve her nasal symptoms.

The patient also has hypertension, hypothyroidism, and hot flashes due to menopause; these conditions are well controlled with lisinopril (Zestril), levothyroxine (Synthroid), and estrogen replacement. She has no history of asthma and has had no allergies to drugs, including nonsteroidal anti-inflammatory drugs (NSAIDs.)

How should this patient be evaluated and treated?

COMMON, OFTEN OVERLOOKED

Many patients suffer from rhinitis, but this problem can be overshadowed by other chronic diseases seen in a medical clinic, especially during a brief office visit. When a patient presents with rhinitis, a key question is whether it is allergic or nonallergic.

This review will discuss the different forms of nonallergic rhinitis and their causes, and give recommendations about therapy.

RHINITIS: ALLERGIC OR NONALLERGIC?

While allergic rhinitis affects 30 and 60 million Americans annually, or between 10% to 30% of US adults,1 how many have nonallergic rhinitis has been difficult to determine.

In a study in allergy clinics, 23% of patients with rhinitis had the nonallergic form, 43% had the allergic form, and 34% had both forms (mixed rhinitis).2 Other studies have suggested that up to 52% of patients presenting to allergy clinics with rhinitis have nonallergic rhinitis.3

Over time, patients may not stay in the same category. One study found that 24% of patients originally diagnosed with nonallergic rhinitis developed positive allergy tests when retested 3 or more years after their initial evaluation.4

Regardless of the type, untreated or uncontrolled symptoms of rhinitis can significantly affect the quality of life.

All forms of rhinitis are characterized by one or more of the following symptoms: nasal congestion, clear rhinorrhea, sneezing, and itching. These symptoms can be episodic or chronic and can range from mild to debilitating. In addition, rhinitis can lead to systemic symptoms of fatigue, headache, sleep disturbance, and cognitive impairment and can be associated with respiratory symptoms such as sinusitis and asthma.1

Mechanisms are mostly unknown

While allergic rhinitis leads to symptoms when airborne allergens bind with specific immunoglobulin E (IgE) in the nose, the etiology of most forms of nonallergic rhinitis is unknown. However, several mechanisms have been proposed. These include entopy (local nasal IgE synthesis with negative skin tests),5 nocioceptive dysfunction (hyperactive sensory receptors),6 and autonomic nervous system abnormalities (hypoactive or hyperactive dysfunction of sympathetic or parasympathetic nerves in the nose).7

Does this patient have an allergic cause of rhinitis?

When considering a patient with rhinitis, the most important question is, “Does this patient have an allergic cause of rhinitis?” Allergic and nonallergic rhinitis have similar symptoms, making them difficult to distinguish. However, their mechanisms and treatment differ. By categorizing a patient’s type of rhinitis, the physician can make specific recommendations for avoidance and can initiate treatment with the most appropriate therapy. Misclassification can lead to treatment failure, multiple visits, poor adherence, and frustration for patients with uncontrolled symptoms.

Patients for whom an allergic cause cannot be found by allergy skin testing or serum specific IgE immunoassay (Immunocap/RAST) for environmental aeroallergens are classified as having nonallergic rhinitis.

 

 

CLUES POINTING TO NONALLERGIC VS ALLERGIC RHINITIS

Nonallergic rhinitis encompasses a range of syndromes with overlapping symptoms. While tools such as the Rhinitis Diagnostic Worksheet are available to help differentiate allergic from nonallergic rhinitis, debate continues about whether it is necessary to characterize different forms of rhinitis before initiating treatment.8

The diagnosis of nonallergic rhinitis depends on a thorough history and physical examination. Key questions relate to the triggers that bring on the rhinitis, which will assist the clinician in determining which subtype of rhinitis a patient may be experiencing and therefore how to manage it. Clues:

  • Patients with nonallergic rhinitis more often report nasal congestion and rhinorrhea, rather than sneezing and itching, which are predominant symptoms of allergic rhinitis.
  • Patients with nonallergic rhinitis tend to develop symptoms at a later age.
  • Common triggers of nonallergic rhinitis are changes in weather and temperature, food, perfumes, odors, smoke, and fumes. Animal exposure does not lead to symptoms.
  • Patients with nonallergic rhinitis have few complaints of concomitant symptoms of allergic conjunctivitis (itching, watering, redness, and swelling).
  • Many patients with nonallergic rhinitis find that antihistamines have no benefit. Also, they do not have other atopic diseases such as eczema or food allergies and have no family history of atopy.

PHYSICAL FINDINGS

Some findings on physical examination may help distinguish allergic from nonallergic rhinitis.

  • Patients with long-standing allergic rhinitis may have an “allergic crease,” ie, a horizontal wrinkle near the tip of the nose caused by frequent upward wiping. Another sign may be a gothic arch, which is a narrowing of the hard palate occurring as a child.
  • In allergic rhinitis, the turbinates are often pale, moist, and boggy with a bluish tinge.
  • Findings such as a deviated nasal septum, discolored nasal discharge, atrophic nasal mucosa, or nasal polyps should prompt consideration of the several subtypes of nonallergic rhinitis (Table 1).

CASE CONTINUED

Our patient’s symptoms can be caused by many different factors. Allergic triggers for rhinitis include both indoor and outdoor sources. The most common allergens include cat, dog, dust mite, cockroach, mold, and pollen allergens. The absence of acute sneezing and itching when around her cat and her recent negative skin-prick tests confirm that the rhinitis symptoms are not allergic.

In this patient, who has symptoms throughout the year but no allergic triggers, consideration of the different subtypes of nonallergic rhinitis may help guide further therapy.

SUBTYPES OF NONALLERGIC RHINITIS

Vasomotor rhinitis

Vasomotor rhinitis is thought to be caused by a variety of neural and vascular triggers, often without an inflammatory cause. These triggers lead to symptoms involving nasal congestion and clear rhinorrhea more than sneezing and itching. The symptoms can be sporadic, with acute onset in relation to identifiable nonallergic triggers, or chronic, with no clear trigger.

Gustatory rhinitis, for example, is a form of vasomotor rhinitis in which clear rhinorrhea occurs suddenly while eating or while drinking alcohol. It may be prevented by using nasal ipratropium (Atrovent) before meals.

Irritant-sensitive vasomotor rhinitis. In some patients, acute vasomotor rhinitis symptoms are brought on by strong odors, cigarette smoke, air pollution, or perfume. When asked, most patients easily identify which of these irritant triggers cause symptoms.

Weather- or temperature-sensitive vasomotor rhinitis. In other patients, a change in temperature, humidity, or barometric pressure or exposure to cold or dry air can cause nasal symptoms.9 These triggers are often hard to identify. Weather- or temperature-sensitive vasomotor rhinitis is often mistaken for seasonal allergic rhinitis because weather changes occur in close relation to the peak allergy seasons in the spring and fall. However, this subtype does not respond as well to intranasal steroids.9

Other nonallergic triggers of vasomotor rhinitis may include exercise, emotion, and sexual arousal (honeymoon rhinitis).10

Some triggers, such as tobacco smoke and perfume, are easy to avoid. Other triggers, such as weather changes, are unavoidable. If avoidance measures fail or are inadequate, medications (described below) can be used for prophylaxis and symptomatic treatment.

Drug-induced rhinitis

Drugs of various classes are known to cause either acute or chronic rhinitis. Drug-induced rhinitis has been divided into different types based on the mechanism involved.11

The local inflammatory type occurs in aspirin-exacerbated respiratory disease, which is characterized by nasal polyposis with chronic rhinosinusitis, hyposmia, and moderate to severe persistent asthma. Aspirin and other NSAIDs induce an acute local inflammation, leading to severe rhinitis and asthma symptoms. Avoiding all NSAID products is recommended; aspirin desensitization may lead to improvement in rhinosinusitis and asthma control.

The neurogenic type of drug-induced rhinitis can occur with sympatholytic drugs such as alpha receptor agonists (eg, clonidine [Cat-apres]) and antagonists (eg, prazosin [Minipress]).11 Vasodilators, including phosphodiesterase-5 inhibitors such as sildenafil (Viagra), can lead to acute rhinitis symptoms (“anniversary rhinitis”).

Unknown mechanisms. Many other medications can lead to rhinitis by unknown mechanisms, usually with normal findings on physical examination. These include beta-blockers, angiotensin-converting enzyme inhibitors, calcium channel blockers, exogenous estrogens, oral contraceptives, antipsychotics, and gabapentin (Neurontin).

Correlating the initiation of a drug with the onset of rhinitis can help identify offending medications. Stopping the suspected medication, if feasible, is the first-line treatment.

Rhinitis medicamentosa, typically caused by overuse of over-the-counter topical nasal decongestants, is also classified under drug-induced rhinitis. Patients may not think of nasal decongestants as medications, and the physician may need to ask specifically about their use.

On examination, the nasal mucosa appears beefy red without mucous. Once a diagnosis is made, the physician should identify and treat the original etiology of the nasal congestion that led the patient to self-treat.

Patients with rhinitis medicamentosa often have difficulty discontinuing use of topical decongestants. They should be educated that the withdrawal symptoms can be severe and that more than one attempt at quitting may be needed. To break the cycle of rebound congestion, topical intranasal steroids should be used, though 5 to 7 days of oral steroids may be necessary.1

Cocaine is a potent vasoconstrictor. Its illicit use should be suspected, especially if the patient presents with symptoms of chronic irritation such as frequent nosebleeds, crusting, and scabbing.12

Infectious rhinitis

One of the most common causes of acute rhinitis is upper respiratory infection.

Acute viral upper respiratory infection often presents with thick nasal discharge, sneezing, and nasal obstruction that usually clears in 7 to 10 days but can last up to 3 weeks. Acute bacterial sinusitis can follow, typically in fewer than 2% of patients, with symptoms of persistent nasal congestion, discolored mucus, facial pain, cough, and sometimes fever.

Chronic rhinosinusitis is a syndrome with sinus mucosal inflammation with multiple causes. It is clinically defined as persistent nasal and sinus symptoms lasting longer than 12 weeks and confirmed with computed tomography (CT).13 The CT findings of chronic rhinosinusitis include thickening of the lining of the sinus cavities or complete opacification of the pneumatized sinuses.

Major symptoms to consider for diagnosis include facial pain, congestion, obstruction, purulent discharge on examination, and changes in olfaction. Minor symptoms are cough, fatigue, headache, halitosis, fever, ear symptoms, and dental pain.

Treatment may involve 3 or more weeks of an oral antibiotic and a short course of an oral steroid, a daily nasal steroid spray, or both oral and nasal steroids. Most patients can be managed in the primary care setting, but they can be referred to an ear, nose, and throat specialist, an allergist, or an immunologist if their symptoms do not respond to initial therapy.

 

 

Nonallergic rhinitis eosinophilic syndrome

Patients with nonallergic rhinitis eosinophilic syndrome (NARES) are typically middle-aged and have perennial symptoms of sneezing, itching, and rhinorrhea with intermittent exacerbations. They occasionally have associated hyposmia (impaired sense of smell).1 The diagnosis is made when eosinophils account for more than 5% of cells on a nasal smear and allergy testing is negative.

Patients may develop nasal polyposis and aspirin sensitivity.1 Entopy has been described in some.14

Because of the eosinophilic inflammation, this form of nonallergic rhinitis responds well to intranasal steroids.

Immunologic causes

Systemic diseases can affect the nose and cause variable nasal symptoms that can be mistaken for rhinitis. Wegener granulomatosis, sarcoidosis, relapsing polychondritis, midline granulomas, Churg-Strauss syndrome, and amyloidosis can all affect the structures in the nose even before manifesting systemic symptoms. Granulomatous infections in the nose may lead to crusting, bleeding, and nasal obstruction.1

A lack of a response to intranasal steroids or oral antibiotics should lead to consideration of these conditions, and treatment should be tailored to the specific disease.

Occupational rhinitis

Occupational exposure to chemicals, biologic aerosols, flour, and latex can lead to rhinitis, typically through an inflammatory mechanism. Many patients present with associated occupational asthma. The symptoms improve when the patient is away from work and worsen throughout the work week.

Avoiding the triggering agent is necessary to treat these symptoms.

Hormonal rhinitis

Hormonal rhinitis, ie, rhinitis related to metabolic and endocrine conditions, is most commonly associated with high estrogen states. Nasal congestion has been reported with pregnancy, menses, menarche, and the use of oral contraceptives.15 The mechanism for congestion in these conditions still needs clarification.

When considering drug therapy, only intranasal budesonide (Rhinocort) has a pregnancy category B rating.

While hypothyroidism and acromegaly have been mentioned in reviews of nonallergic rhinitis, evidence that these disorders cause nonallergic rhinitis is not strong.16,17

Structurally related rhinitis

Anatomic abnormalities that can cause persistent nasal congestion include nasal septal deviation, turbinate hypertrophy, enlarged adenoids, tumors, and foreign bodies. These can be visualized by simple anterior nasal examination, nasal endoscopy, or radiologic studies. If structural causes lead to impaired quality of life or chronic rhinosinusitis, then consider referral to a specialist for possible surgical treatment.

Clear spontaneous rhinorrhea, with or without trauma, can be caused by cerebrospinal fluid leaking into the nasal cavity.18 A salty, metallic taste in the mouth can be a clue that the fluid is cerebrospinal fluid. A definitive diagnosis of cerebrospinal fluid leak is made by finding beta-2-transferrin in nasal secretions.

Atrophic rhinitis

Atrophic rhinitis is categorized as primary or secondary.

Primary (idiopathic) atrophic rhinitis is characterized by atrophy of the nasal mucosa and mucosal colonization with Klebsiella ozaenae associated with a foul-smelling nasal discharge.19,20 This disorder has been primarily reported in young people who present with nasal obstruction, dryness, crusting, and epistaxis. They are from areas with warm climates, such as the Middle East, Southeast Asia, India, Africa, and the Mediterranean.

Secondary atrophic rhinitis can be a complication of nasal or sinus surgery, trauma, granulomatous disease, or exposure to radiation.21 This disorder is typically diagnosed with nasal endoscopy and treated with daily saline rinses with or without topical antibiotics.21

CASE CONTINUED

Questioned further, our patient says her symptoms are worse when her husband smokes, but that she continues to have congestion and rhinorrhea when he is away on business trips. She notes that her symptoms are often worse on airplanes (dry air with an acute change in barometric pressure), with weather changes, and in cold, dry environments. Symptoms are not induced by eating.

We note that she started taking lisinopril 2 years ago and conjugated equine estrogens 8 years ago. Review of systems reveals no history of facial or head trauma, polyps, or hyposmia.

The rhinitis and congestion are bilateral, and she denies headaches, acid reflux, and conjunctivitis. She has a mild throat-clearing cough that she attributes to postnasal drip.

On physical examination, her blood pressure is 118/76 mm Hg and her pulse is 64. Her turbinates are congested with clear rhinorrhea. The rest of the examination is normal.

AVOID TRIGGERS, PRETREAT BEFORE EXPOSURE

Figure 1.
While treatment for nonallergic rhinitis varies according to the cause, there are some general guidelines for therapy (Figure 1).

People with known environmental, non-immunologic, and irritant triggers should be reminded to avoid these exposures if possible.

If triggers are unavoidable, patients can pretreat themselves with topical nasal sprays before exposure. For example, if symptoms occur while on airplanes, then intranasal steroids or antihistamine sprays should be used before getting on the plane.

 

 

Many drugs available

Fortunately, many effective drugs are available to treat nonallergic rhinitis. These have few adverse effects or drug interactions.

Intranasal steroid sprays are considered first-line therapy, as there are studies demonstrating effectiveness in nonallergic rhinitis.22 Intranasal fluticasone propionate (Flonase) and beclomethasone dipropionate (Beconase AQ) are approved by the US Food and Drug Administration (FDA) for treating nonallergic rhinitis. Intranasal mometasone (Nasonex) is approved for treating nasal polyps.

Nasal steroid sprays are most effective if the dominant nasal symptom is congestion, but they have also shown benefit for rhinorrhea, sneezing, and itching.

Side effects of nasal steroid sprays include nasal irritation (dryness, burning, and stinging) and epistaxis, the latter occurring in 5% to 10% of patients.23

Intranasal antihistamines include azelastine (Astelin, Astepro) and olopatadine (Patanase). They are particularly useful for treating sneezing, congestion, and rhinorrhea.24 Astelin is the only intranasal antihistamine with FDA approval for nonallergic rhinitis.

Side effects of this drug class include bitter taste (with Astelin), sweet taste (with Astepro), headache, and somnolence.

Oral antihistamines such as loratadine (Claritin), cetirizine (Zyrtec), and fexofenadine (Allegra) are now available over the counter, and many patients try them before seeking medical care. These drugs may be helpful for those bothered by sneezing. However, no study has demonstrated their effectiveness for nonallergic rhinitis.25 First-generation antihistamines may help with rhinorrhea via their anticholinergic effects.

Ipratropium, an antimuscarinic agent, decreases secretions by inhibiting the nasal parasympathetic mucous glands. Intranasal ipratropium 0.03% (Atrovent 0.03%) should be considered first-line if the dominant symptom is rhinorrhea. Higher-dose ipratropium 0.06% is approved for rhinorrhea related to the common cold or allergic rhinitis. Because it is used topically, little is absorbed. Its major side effect is nasal dryness.

Decongestants, either oral or topical, can relieve the symptoms of congestion and rhinorrhea in nonallergic rhinitis. They should only be used short-term, as there is little evidence to support their chronic use.

Phenylpropanolamine, a decongestant previously found in over-the-counter cough medicines, was withdrawn from the market in 2000 owing to concern that the drug, especially when used for weight suppression, was linked to hemorrhagic stroke in young women.26,27 Other oral decongestants, ie, pseudoephedrine and phenylephrine, are still available, but there are no definitive guidelines for their use. Their side effects include tachycardia, increase in blood pressure, and insomnia.

Nasal saline irrigation has been used for centuries to treat rhinitis and sinusitis, despite limited evidence of benefit. A Cochrane review concluded that saline irrigation was well tolerated, had minor side effects, and could provide some relief of rhinosinusitis symptoms either as the sole therapeutic measure or as adjunctive treatment.28 Hypertonic saline solutions, while possibly more effective than isotonic saline in improving mucociliary clearance, are not as well tolerated since they can cause nasal burning and irritation. Presumed benefits of saline irrigation are clearance of nasal secretions, improvement of nasociliary function, and removal of irritants and pollen from the nose.

A strategy

Initial therapy (Table 2) should be based on the presentation. If the patient has a limited response to the therapy at follow-up in 2 to 4 weeks, the physician should consider using adjunctive medications, address patient adherence and technique, and reassess the accuracy of the initial diagnosis. At this point, one can consider referral to a specialist such as an allergist or otolaryngologist, especially if there are comorbid conditions such as asthma or polyps.

Imaging the sinuses with CT, which has replaced standard nasal radiography, may help if one is concerned about chronic rhinosinusitis, nasal polyps, or other anatomic condition that could contribute to persistent symptoms. Cost and radiation exposure should enter into the decision to obtain this study because a diagnosis based on the patient’s report of symptoms may be equally accurate.29,30

CASE CONTINUED

Our patient has a number of potential causes of her symptoms. Exposure to second-hand tobacco smoke at home and to the air in airplanes could be acute triggers. Weather and temperature changes could explain her chronic symptoms in the spring and fall. Use of an angiotensin-converting enzyme inhibitor (in her case, lisinopril) and estrogen replacement therapy may contribute to perennial symptoms, but the onset of her nonallergic rhinitis does not correlate with the use of these drugs. There are no symptoms to suggest chronic rhinosinusitis or anatomic causes of her symptoms.

This case is typical of vasomotor rhinitis of the weather- or temperature-sensitive type. This diagnosis may explain her lack of improvement with intranasal steroids, though adherence and spray technique should be assessed. At this point, we would recommend trying topical antihistamines daily when chronic symptoms are present or as needed for acute symptoms.

References
  1. Wallace DV, Dykewicz MS, Bernstein DI, et al. The diagnosis and management of rhinitis: an updated practice parameter. J Allergy Clin Immunol 2008; 122( suppl 2):S1S84.
  2. Settipane RA, Charnock DR. Epidemiology of rhinitis: allergic and nonallergic. Clin Allergy Immunol 2007; 19:2334.
  3. Settipane RA, Lieberman P. Update on nonallergic rhinitis. Ann Allergy Asthma Immunol 2001; 86:494507.
  4. Rondón C, Doña I, Torres MJ, Campo P, Blanca M. Evolution of patients with nonallergic rhinitis supports conversion to allergic rhinitis. J Allergy Clin Immunol 2009; 123:10981102.
  5. Forester JP, Calabria CW. Local production of IgE in the respiratory mucosa and the concept of entopy: does allergy exist in nonallergic rhinitis? Ann Allergy Asthma Immunol 2010; 105:249255.
  6. Silvers WS. The skier’s nose: a model of cold-induced rhinorrhea. Ann Allergy 1991; 67:3236.
  7. Jaradeh SS, Smith TL, Torrico L, et al. Autonomic nervous system evaluation of patients with vasomotor rhinitis. Laryngoscope 2000; 110:18281831.
  8. Quan M, Casale TB, Blaiss MS. Should clinicians routinely determine rhinitis subtype on initial diagnosis and evaluation? A debate among experts. Clin Cornerstone 2009; 9:5460.
  9. Jacobs R, Lieberman P, Kent E, Silvey M, Locantore N, Philpot EE. Weather/temperature-sensitive vasomotor rhinitis may be refractory to intranasal corticosteroid treatment. Allergy Asthma Proc 2009; 30:120127.
  10. Monteseirin J, Camacho MJ, Bonilla I, Sanchez-Hernandez C, Hernandez M, Conde J. Honeymoon rhinitis. Allergy 2001; 56:353354.
  11. Varghese M, Glaum MC, Lockey RF. Drug-induced rhinitis. Clin Exp Allergy 2010; 40:381384.
  12. Schwartz RH, Estroff T, Fairbanks DN, Hoffmann NG. Nasal symptoms associated with cocaine abuse during adolescence. Arch Otolaryngol Head Neck Surg 1989; 115:6364.
  13. Meltzer EO, Hamilos DL, Hadley JA, et al; American Academy of Allergy, Asthma and Immunology (AAAAI); American Academy of Otolaryngic Allergy (AAOA); American Academy of Otolaryngology--Head and Neck Surgery (AAO-HNS); American College of Allergy, Asthma and Immunology (ACAAI); American Rhinologic Society (ARS). Rhinosinusitis: establishing definitions for clinical research and patient care. J Allergy Clin Immunol 2004; 114( suppl 6):155212.
  14. Powe DG, Huskisson RS, Carney AS, Jenkins D, Jones NS. Evidence for an inflammatory pathophysiology in idiopathic rhinitis. Clin Exp Allergy 2001; 31:864872.
  15. Philpott CM, Robinson AM, Murty GE. Nasal pathophysiology and its relationship to the female ovarian hormones. J Otolaryngol Head Neck Surg 2008; 37:540546.
  16. Dykewicz MS, Fineman S, Skoner DP, et al. Diagnosis and management of rhinitis: complete guidelines of the Joint Task Force on Practice Parameters in Allergy, Asthma and Immunology. American Academy of Allergy, Asthma, and Immunology. Ann Allergy Asthma Immunol 1998; 81:478518.
  17. Ellegård EK, Karlsson NG, Ellegård LH. Rhinitis in the menstrual cycle, pregnancy, and some endocrine disorders. Clin Allergy Immunol 2007; 19:305321.
  18. Dunn CJ, Alaani A, Johnson AP. Study on spontaneous cerebrospinal fluid rhinorrhoea: its aetiology and management. J Laryngol Otol 2005; 119:1215.
  19. Bunnag C, Jareoncharsri P, Tansuriyawong P, Bhothisuwan W, Chantarakul N. Characteristics of atrophic rhinitis in Thai patients at the Siriraj Hospital. Rhinology 1999; 37:125130.
  20. Dutt SN, Kameswaran M. The aetiology and management of atrophic rhinitis. J Laryngol Otol 2005; 119:843852.
  21. deShazo RD, Stringer SP. Atrophic rhinosinusitis: progress toward explanation of an unsolved medical mystery. Curr Opin Allergy Clin Immunol 2011; 11:17.
  22. Greiner AN, Meltzer EO. Overview of the treatment of allergic rhinitis and nonallergic rhinopathy. Proc Am Thorac Soc 2011; 8:121131.
  23. Corren J. Intranasal corticosteroids for allergic rhinitis: how do different agents compare? J Allergy Clin Immunol 1999; 104:S144S149.
  24. Lieberman P, Meltzer EO, LaForce CF, Darter AL, Tort MJ. Two-week comparison study of olopatadine hydrochloride nasal spray 0.6% versus azelastine hydrochloride nasal spray 0.1% in patients with vasomotor rhinitis. Allergy Asthma Proc 2011; 32:151158.
  25. Bousquet J, Khaltaev N, Cruz AA, et al; World Health Organization; GA(2)LEN. Allergic Rhinitis and its Impact on Asthma (ARIA) 2008 update (in collaboration with the World Health Organization, GA(2)LEN and AllerGen). Allergy 2008; 63( suppl 86):8160.
  26. SoRelle R. FDA warns of stroke risk associated with phenylpropanolamine; cold remedies and drugs removed from store shelves. Circulation 2000; 102:E9041E9043.
  27. Kernan WN, Viscoli CM, Brass LM, et al. Phenylpropanolamine and the risk of hemorrhagic stroke. N Engl J Med 2000; 343:18261832.
  28. Harvey R, Hannan SA, Badia L, Scadding G. Nasal saline irrigations for the symptoms of chronic rhinosinusitis. Cochrane Database Syst Rev 2007;CD006394.
  29. Bhattacharyya N. The role of CT and MRI in the diagnosis of chronic rhinosinusitis. Curr Allergy Asthma Rep 2010; 10:171174.
  30. Kenny TJ, Duncavage J, Bracikowski J, Yildirim A, Murray JJ, Tanner SB. Prospective analysis of sinus symptoms and correlation with paranasal computed tomography scan. Otolaryngol Head Neck Surg 2001; 125:4043.
References
  1. Wallace DV, Dykewicz MS, Bernstein DI, et al. The diagnosis and management of rhinitis: an updated practice parameter. J Allergy Clin Immunol 2008; 122( suppl 2):S1S84.
  2. Settipane RA, Charnock DR. Epidemiology of rhinitis: allergic and nonallergic. Clin Allergy Immunol 2007; 19:2334.
  3. Settipane RA, Lieberman P. Update on nonallergic rhinitis. Ann Allergy Asthma Immunol 2001; 86:494507.
  4. Rondón C, Doña I, Torres MJ, Campo P, Blanca M. Evolution of patients with nonallergic rhinitis supports conversion to allergic rhinitis. J Allergy Clin Immunol 2009; 123:10981102.
  5. Forester JP, Calabria CW. Local production of IgE in the respiratory mucosa and the concept of entopy: does allergy exist in nonallergic rhinitis? Ann Allergy Asthma Immunol 2010; 105:249255.
  6. Silvers WS. The skier’s nose: a model of cold-induced rhinorrhea. Ann Allergy 1991; 67:3236.
  7. Jaradeh SS, Smith TL, Torrico L, et al. Autonomic nervous system evaluation of patients with vasomotor rhinitis. Laryngoscope 2000; 110:18281831.
  8. Quan M, Casale TB, Blaiss MS. Should clinicians routinely determine rhinitis subtype on initial diagnosis and evaluation? A debate among experts. Clin Cornerstone 2009; 9:5460.
  9. Jacobs R, Lieberman P, Kent E, Silvey M, Locantore N, Philpot EE. Weather/temperature-sensitive vasomotor rhinitis may be refractory to intranasal corticosteroid treatment. Allergy Asthma Proc 2009; 30:120127.
  10. Monteseirin J, Camacho MJ, Bonilla I, Sanchez-Hernandez C, Hernandez M, Conde J. Honeymoon rhinitis. Allergy 2001; 56:353354.
  11. Varghese M, Glaum MC, Lockey RF. Drug-induced rhinitis. Clin Exp Allergy 2010; 40:381384.
  12. Schwartz RH, Estroff T, Fairbanks DN, Hoffmann NG. Nasal symptoms associated with cocaine abuse during adolescence. Arch Otolaryngol Head Neck Surg 1989; 115:6364.
  13. Meltzer EO, Hamilos DL, Hadley JA, et al; American Academy of Allergy, Asthma and Immunology (AAAAI); American Academy of Otolaryngic Allergy (AAOA); American Academy of Otolaryngology--Head and Neck Surgery (AAO-HNS); American College of Allergy, Asthma and Immunology (ACAAI); American Rhinologic Society (ARS). Rhinosinusitis: establishing definitions for clinical research and patient care. J Allergy Clin Immunol 2004; 114( suppl 6):155212.
  14. Powe DG, Huskisson RS, Carney AS, Jenkins D, Jones NS. Evidence for an inflammatory pathophysiology in idiopathic rhinitis. Clin Exp Allergy 2001; 31:864872.
  15. Philpott CM, Robinson AM, Murty GE. Nasal pathophysiology and its relationship to the female ovarian hormones. J Otolaryngol Head Neck Surg 2008; 37:540546.
  16. Dykewicz MS, Fineman S, Skoner DP, et al. Diagnosis and management of rhinitis: complete guidelines of the Joint Task Force on Practice Parameters in Allergy, Asthma and Immunology. American Academy of Allergy, Asthma, and Immunology. Ann Allergy Asthma Immunol 1998; 81:478518.
  17. Ellegård EK, Karlsson NG, Ellegård LH. Rhinitis in the menstrual cycle, pregnancy, and some endocrine disorders. Clin Allergy Immunol 2007; 19:305321.
  18. Dunn CJ, Alaani A, Johnson AP. Study on spontaneous cerebrospinal fluid rhinorrhoea: its aetiology and management. J Laryngol Otol 2005; 119:1215.
  19. Bunnag C, Jareoncharsri P, Tansuriyawong P, Bhothisuwan W, Chantarakul N. Characteristics of atrophic rhinitis in Thai patients at the Siriraj Hospital. Rhinology 1999; 37:125130.
  20. Dutt SN, Kameswaran M. The aetiology and management of atrophic rhinitis. J Laryngol Otol 2005; 119:843852.
  21. deShazo RD, Stringer SP. Atrophic rhinosinusitis: progress toward explanation of an unsolved medical mystery. Curr Opin Allergy Clin Immunol 2011; 11:17.
  22. Greiner AN, Meltzer EO. Overview of the treatment of allergic rhinitis and nonallergic rhinopathy. Proc Am Thorac Soc 2011; 8:121131.
  23. Corren J. Intranasal corticosteroids for allergic rhinitis: how do different agents compare? J Allergy Clin Immunol 1999; 104:S144S149.
  24. Lieberman P, Meltzer EO, LaForce CF, Darter AL, Tort MJ. Two-week comparison study of olopatadine hydrochloride nasal spray 0.6% versus azelastine hydrochloride nasal spray 0.1% in patients with vasomotor rhinitis. Allergy Asthma Proc 2011; 32:151158.
  25. Bousquet J, Khaltaev N, Cruz AA, et al; World Health Organization; GA(2)LEN. Allergic Rhinitis and its Impact on Asthma (ARIA) 2008 update (in collaboration with the World Health Organization, GA(2)LEN and AllerGen). Allergy 2008; 63( suppl 86):8160.
  26. SoRelle R. FDA warns of stroke risk associated with phenylpropanolamine; cold remedies and drugs removed from store shelves. Circulation 2000; 102:E9041E9043.
  27. Kernan WN, Viscoli CM, Brass LM, et al. Phenylpropanolamine and the risk of hemorrhagic stroke. N Engl J Med 2000; 343:18261832.
  28. Harvey R, Hannan SA, Badia L, Scadding G. Nasal saline irrigations for the symptoms of chronic rhinosinusitis. Cochrane Database Syst Rev 2007;CD006394.
  29. Bhattacharyya N. The role of CT and MRI in the diagnosis of chronic rhinosinusitis. Curr Allergy Asthma Rep 2010; 10:171174.
  30. Kenny TJ, Duncavage J, Bracikowski J, Yildirim A, Murray JJ, Tanner SB. Prospective analysis of sinus symptoms and correlation with paranasal computed tomography scan. Otolaryngol Head Neck Surg 2001; 125:4043.
Issue
Cleveland Clinic Journal of Medicine - 79(4)
Issue
Cleveland Clinic Journal of Medicine - 79(4)
Page Number
285-293
Page Number
285-293
Publications
Publications
Topics
Article Type
Display Headline
Nonallergic rhinitis: Common problem, chronic symptoms
Display Headline
Nonallergic rhinitis: Common problem, chronic symptoms
Sections
Inside the Article

KEY POINTS

  • When evaluating a patient with rhinitis, a key question is whether it is allergic or nonallergic.
  • Identifying triggers that should be avoided is important for controlling symptoms.
  • If symptoms continue, then the first-line treatment for nonallergic rhinitis is intranasal steroids.
  • Failure of intranasal steroids to control symptoms should prompt a consideration of the many potential causes of rhinitis, and further evaluation and treatment can be tailored accordingly.
Disallow All Ads
Alternative CME
Article PDF Media