Long-term adherence with cardiovascular drug regimens

Long-term adherence with cardiovascular drug regimens Sonali P. Kulkarni, MD, MPH,a Karen P. Alexander, MD,a Barbara Lytle, MS,a Gerardo Heiss, PhD,b and Eric D. Peterson, MD, MPHa Durham and Chapel Hill, NC

Background An increasing number of medications are prescribed for patients with coronary artery disease, but poor adherence may limit realization of their benefits. Objective

To characterize adherence to evidence-based cardiovascular medications prescribed at hospital discharge

at 1 year.

Methods We studied 1326 patients with coronary artery disease undergoing cardiac catheterization between 1998 and 2001. We examined adherence to angiotensin-converting enzyme (ACE) inhibitors, aspirin, h-blockers (BBs), and statins by comparing baseline prescription at hospital discharge to self-reported medical regimen at 12 months. Patients who reported use of each cardiac medication at 1 year were considered adherent. Clinical and demographic predictors of nonadherence are described. Results The population had a mean age of 65.7 F 10.5 years, and 36% were women. At discharge, aspirin was prescribed in 95%, BBs in 86%, ACE inhibitors in 65%, and statins in 55%. The proportion of patients who discontinued medications was lowest for aspirin (18%) and BBs (22%) and highest for ACE inhibitors/angiotensin receptor blockers (28%) and statins (28%). Only 54% were adherent to all of their initial medications. Patients who discontinued medications were more likely to be older, women, unmarried, and less educated. Multivariable predictors of better adherence were higher mental health, education level, marital status, and no antidepressant use. A higher number of prescribed medications were associated with lower adherence to the recommended regimen. Insurance coverage and physical function did not correlate with adherence. Conclusions Patients frequently stop medications within 1 year of prescription. Adherence is influenced by marital status, mental health, education, and total number of medications prescribed. Physicians need to be aware of patient factors which influence adherence to facilitate higher use of evidence-based medications. (Am Heart J 2006;151:185 -91.) The number of evidence-based medications with proven benefit for patients with coronary artery disease (CAD) continues to expand.1-3 Improving the prescription of guideline-recommended medications for the secondary prevention of CAD has been associated with decreased mortality of 20% at 1-year follow-up.4 These medications are not intended for short-term use, and many require time to realize benefits, yet the majority of the literature on cardiac medication use focuses on prescription of therapies at a single point in time.5-7 Physicians have appropriately responded to this infor-

From the aDuke Clinical Research Institute, Durham, NC, and bDepartment of Epidemiology, School of Public Health, University of North Carolina, Chapel Hill, NC. Dr Alexander is a recipient of a Doris Duke Foundation Development Award; Dr Peterson is a Paul Beeson Faculty Scholar in Aging. Submitted December 18, 2004; accepted February 28, 2005. Reprint requests: Karen P. Alexander, MD, Box 3411 Duke University Medical Center, Durham NC 27710. 0002-8703/$ - see front matter n 2005, Mosby, Inc. All rights reserved. doi:10.1016/j.ahj.2005.02.038

mation by increasing the number of prescriptions for evidence-based medications over the years, but this may not truly reflect increased use given nonadherence.8 Recent statistics suggest a sobering 50% of patients with recurrent myocardial infarction were not taking aspirin, h-blockers (BBs), or lipid-lowering medications at the time of their readmission.5-9 In fact, medication adherence of 50% is considered to be average with reported variation from 25% to 85%.10-12 Thus, more information is needed on the variables which influence adherence to prescribed medications, specifically modifiable factors. We describe adherence to a prescribed regimen at 12 months and identify patient factors associated with greater likelihood of stopping a prescribed medication over follow-up.

Methods Study population Patients with significant CAD (defined as z70% occlusion in 1 or more vessels) at cardiac catheterization were enrolled

American Heart Journal January 2006

186 Kulkarni et al

from August 1998 to April 2001, and consent for long-term follow-up was obtained. Exclusion criteria were inability to give consent (ie, altered mental status, mental retardation, and inability to speak English), prior coronary artery bypass graft, percutaneous coronary intervention in the previous 6 months, history of congenital heart disease, history of heart transplant, history of primary valvular disease, or age b45 years. From this long-term follow-up cohort of 2097 patients enrolled in the functional outcomes study, we excluded a total of 771 patients for whom baseline and 12 months’ medication use was not available. Of the 771 patients excluded, 155 died between enrollment and 12 months, 158 did not have 12-month functional data, and 364 did not have 12-month medication data. An additional 94 patients were missing medication data at baseline. Hence, we had a final population of 1326 patients for this analysis (Figure 1).

Figure 1 Functional Outcomes Population (n = 2097) Patient died (n = 155) Alive at 12 m (n = 1942) Incomplete 12 mo survey (n = 158)†

Missing Baseline Meds ‡ (n = 94)

Baseline Meds Available (n = 1690)

Outcomes We selected 4 medications: aspirin, BB, angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blockers, and HMG-CoA (3-hydroxy-3-methylglutaryl coenzyme A) reductase inhibitors (statins)—indicated for CAD patients based on the 2002 American College of Cardiology/American Heart Association guidelines for stable angina.3 Use of medications at discharge was examined among patients eligible for that medication (those with indications and no contraindications for a particular medication class) (see Appendix). Patients were classified as badherentQ if they were taking a regimen at 12 months which was similar to that recommended at hospital discharge. Patients were adherent if they continued all medication classes they were prescribed at discharge. Adherence was measured for each individual therapy and as an overall composite (continuation of all of the baseline therapies). Only medications prescribed at discharge were deemed part of the baseline medical regimen, and those started during follow-up period were not considered in our end point of adherence. However, new prescription of medications between baseline discharge and 1 year was determined and displayed.

Data collection Two trained interviewers enrolled patients in the catheterization laboratory. The interviewers collected the baseline information on functional status, comorbidity, and insurance status from the patient or chart. Baseline medications were collected using the medical charts and the electronic medical record. When no discharge medication list was available, a clinic note within 3 months of discharge was used as a proxy. One-year follow-up information was obtained by telephone interview using a structured questionnaire. Functional status data were collected using the Medical Outcomes Study ShortForm 36-Item (SF-36) health survey. For patients aged z70 years, cognitive status was assessed at baseline and follow-up using the Mini-Mental State Examination (MMSE), a brief cognitive screen that has been validated for administration both in person and via telephone.13-18 Medication use was obtained by asking patients to get their medication bottles and respond to the question bWhat medications do you take now?Q Previous pharmacy studies have shown that patient self-report of medication use correlates with medication records without underestimating actual use.19,20

Completed 12 m survey (n = 1784)

Missing 12 mo Meds§ (n = 364) 12-m Available (n = 1326)

Adherence Study Population (n = 1326)

Functional outcomes and adherence population descriptions. *Patients in the functional outcomes population who died between enrollment and 12 months. yLost to follow-up, refused, or unable to complete interview secondary to illness or deafness. zBaseline medications not available in medical record. §Twelve-month medication not given by patient in interview.

Covariates We describe the demographics, education, primary insurance status, and level of assisted living of study patients. Primary insurance status was separated into 2 categories—private (any health maintenance organization or private health plan) and nonprivate (Medicare, Medicaid, and self-pay). We describe cognitive function with the MMSE.13,17 Comorbidities include diabetes, hypertension, hyperlipidemia, and emotional disorders, as well as heart, liver, and renal disease. To capture functional status, we describe perceived mental and physical health by using the SF-36 health survey, which has been validated for use in patients with CAD.14,18 The SF-36 assesses different dimensions of health through the use of 8 scales. Two summary scores can then be calculated. The physical component summary (PCS) and the mental component summary (MCS) were constructed using factor analysis of the correlations among the 8 SF-36 scales.18 Higher MCS scores indicate frequent positive mood, absence of psychological distress, and less emotional distress–related limitations to activities. Higher PCS scores are associated with better physical function, absence of physical limitations, and high energy level. The SF-36 survey questionnaire data were coded and analyzed according to the methodology specified by the SF-36 Manual and Interpretation guides.15,16 We measured the number of

American Heart Journal Volume 151, Number 1

Kulkarni et al 187

Table I. Characteristics of patients, overall and by adherence

Characteristic Age, y (mean F SD) Age = 65 y (%) Women (%) Nonwhite race (%) Unmarried (%) Nonprivate insuranceT (%) Education Vhigh school (%) Assisted living — no help (%) MMSEy MCS PCS Antidepressant use (%) Total no. of medications prescribed

Overall

Adherent

Nonadherent

n = 1326

n = 720

n = 606

P value

65.7 F 10.5 56.2 35.5 14.9 29.9 36.5 58.1 79.3 17.1 F 3.5 48.8 F 14.9 32.7 F 6.4 12.0 6.1 F 2.5

65.5 F 10.8 56.0 31.1 13.8 24.7 63.0 54.6 81.7 17.2 F 3.5 50.2 F 14.9 32.7 F 6.5 10.6% 5.9 F 2.4

66.0 F 10.2 56.4 40.8 16.3 36.1 64.2 62.4 76.4 17.0 F 3.5 47.5 F 14.9 32.8 F 6.2 13.7% 6.3 F 2.5

NS NS b.001 NS b.001 NS .004 .02 NS .002 NS .09 b.001

NS, Not significant. P value for adherent compared with nonadherent. zPatients who did not complete 12-month interview or had missing baseline or 12-month medication information. TNonprivate: Medicare, Medicaid, and self-pay. yAdult lifestyles and function interview MMSE baseline score among patients z 70 years, scale 0 to 22.

Statistical analysis Continuous variables were reported using their mean values, and categorical variables were reported using percentages obtained from cross tabulations. Dichotomous and categorical forms of all independent variables were evaluated. Statistical significance was determined using v 2 tests and t tests for categorical and continuous variables, respectively. Two-tailed P values were used for all descriptive tests. Associations between the variables of interest and adherence to medication regimen were assessed using logistic regression. Significant univariate predictors were included in multivariate regression analysis. We included 11 covariates in the model: age, sex, race, marital status, education, primary insurance, SF-36 scores (MCS and PCS), total number of medications, and 2 dummy variables to account for missing MCS scores and insurance information. All calculations were performed with STATA software version 8.0 (Stata Corp, College Station, TX).

Figure 2 Patients taking at each interval (%)

medications prescribed at discharge by counting all medications that were prescribed for use longer than 30 days and were not specified to be used bonly as needed.Q

100 90 80 70 60 50 40 30 20 10 0

Baseline Adherent at One Year Total at One Year

Aspirin

Beta Blocker

Ace Inhibitor

Statin

95%

86% 67% 72%

65% 47% 56%

55% 40% 56%

78% 83%

Adherence by medication type. Baseline use (top of gray bar), patients still taking each agent at 1 year (top of black bar), and discontinued use between baseline and 1 year (gray bar). A small percentage of patients has new prescription for each medication between baseline and 1 year (arrows) which adds to adherent population (black bars) for total use at 1 year.

Results The mean age of the population was 66 years; 36% were women; 30% were unmarried, and 15% were nonwhite. Over half had b12th-grade education. Seventy-nine percent reported living independently without assistance from family or other caregivers. One third of patients had nonprivate primary insurance such as Medicare or Medicaid. The mean baseline MMSE score was N17 of a maximum possible score of 22, which indicates good cognitive function. Antidepressants were prescribed at discharge in 12%. The mean number of medications prescribed to patients at discharge was 6 (Table I).

Comparisons between excluded and included patients revealed significant differences in baseline characteristics. Excluded patients were more likely to be older (67.6 vs 65.6 years), women (38.6% vs 35.5%), and of nonwhite race (19.5% vs 14.9%). In addition, they were more likely to have nonprivate insurance (71.0% vs 58.1%) and less likely to be independent (no help with assisted living 69.5% vs 79.3%). They were also more likely to have a higher number of medications and lower score on mental

American Heart Journal January 2006

188 Kulkarni et al

Table II. Association patient characteristics and adherence* to all medications at 12 months Univariable

Multivariable

OR for adherence OR for adherence at 12 m at 12 m Age (per 10 y) Womeny Nonwhite race Unmarriedz Primary insurance, nonprivate§ Education bhigh schoolO Assisted living — no help ALFI-MMSET score (continuous) MCSF PCS Antidepressant use Total no. of medications prescribed

0.97 0.65 0.82 0.58 1.00

[0.87-1.07] [0.52-0.82] [0.61-1.11] [0.46-0.74] [0.81-1.24]

– – – 0.65 [0.50-0.85] –

0.73 [0.59-0.91] 1.17 [0.76-1.81] 1.00 [0.99-1.01]

0.76 [0.60-0.96] – –

1.01 1.00 0.74 0.93

1.01 [1.00-1.02] – – 0.94 [0.90-0.98]

[1.00-1.02] [0.98-1.01] [0.53-1.03] [0.88-0.97]

T Adherence defined as taking all cardiac medications prescribed at discharge. y Significant univariates for ACE inhibitor, aspirin, and statins. z Significant univariates for all 4 medication classes. § Adult lifestyles and function interview MMSE baseline score among patients aged z70 years. OSignificant univariates for ACE inhibitor, BB, and statins.

health. The excluded population was more likely to have a lower baseline MMSE and MCS scores, and higher PCS scores and tended to be prescribed more medications at discharge (6.68 vs 6.02) ( P b .001 for all). Only the percentage of women and percentage of patients prescribed antidepressants at discharge did not differ significantly between those included and excluded in the adherence study. At time of discharge, 95% were prescribed aspirin, 86% BBs, 65% ACE inhibitors, and 55% statin therapy among recommended patients (see Appendix). All medication classes had similar adherence trends over time with continuation rates ranging from 72% to 83% (Figure 2). Adherence was lowest for statins and ACE inhibitors and highest for aspirin. With the exception of the statin drug, new prescriptions between baseline and 1 year did not make up for discontinuation (Figure 2). The rate of new prescriptions was between 4% (aspirin) and 16% (statins) over a 1-year interval. Thus, there was a decrement in total use of each class of drugs at 1 year primarily mediated by discontinuation of therapies. For all medication classes, the decrease in use between discharge and 12 months among those prescribed these medications was statistically significant. Adherent patients were significantly more likely to be men, married, and better educated. Adherent patients were also more likely to live without assistance, have higher MCS scores, and have fewer total medications prescribed. MMSE and PCS scores did not significantly

differ between the 2 groups (Table I). Multivariable predictors of better adherence from the patient level were higher educational attainment, married status, and better level of mood on the MCS. In addition, there was an inverse relationship between the number of medications prescribed and likelihood of adherence (odds ratio [OR] 0.93, P b .05) (Table II).

Discussion Adherence to cardiac medications in our population ranged from 72% to 82% and was highest for aspirin and BBs. Importantly, we found that patient educational and marital status, self-reported depression and anxiety, and the total number of medicines in the regimen are all highly correlated with likelihood of adherence 12 months after hospitalization. Patients excluded from our analysis as a result of missing information were even more likely to have baseline characteristics associated with lower adherence in our study sample; therefore, our results likely represent a conservative estimate of the impact of these factors on adherence and the rate of adherence overall. Information on prescribing patterns is well evolved in the literature, but the prior work on medication adherence is more rudimentary.6,7,21 Prior studies have defined and measured adherence through pill counts and monitoring of medication use on a daily basis.10 Only recently have studies explored adherence as bpersistentQ use of medications over a long period such as that in our study.22 A recent study of Canadian patients discharged between 1996 and 1998 for acute myocardial infarction examined drug compliance, defined by the presence of drug prescription refills during the last month of a 12-month period.23 The authors found that adherence at 12 months among those initially prescribed aspirin, BB, ACE inhibitors, and lipid-lowering drugs were 74%, 74%, 70%, and 84%, respectively. The proportion of patients in our study who continued use of aspirin, BB, ACE inhibitors, and lipid-lowering drugs was 82%, 78%, 72%, and 72%, respectively. Thus, more patients in our study were adherent to aspirin, BBs, and ACE inhibitors, but fewer were adherent to statins. Therefore, our estimates are consistent and show that, for each medication class, approximately 1 in 4 patients will discontinue it by 12 months. The lower adherence to statins in our study may be attributable to differences between the US and Canadian health care systems. In the Canadian study, only 21% of patients were discharged on lipid-lowering therapies, suggesting that patient selection and prescription drug cost-sharing may influence compliance. Studies of statin use from US pharmacy registries have estimated the discontinuation rate at 12 months to be 15% to 60% among the elderly.24-26 Medicare beneficiaries with no supplemental drug coverage have a lower use of BB, nitrates, and

American Heart Journal Volume 151, Number 1

statins at several points in time compared with those with drug coverage.24 Patients with a higher number of total medications prescribed tend to have lower adherence, and those with active disease tend to have higher adherence to medical therapy, specifically statins.22 The prescription of evidence-based medications at discharge seen in our population is similar to that in other registries.8,27 Lower use of ACE inhibitors in our population may reflect the fact that key studies were published after our study interval of 1998 and 2001.28 Statin use cannot be explained in such a manner, as their benefits in a cardiac population have been well documented since the mid-1990s.29 -32 Our study demonstrates that one half of eligible patients did not receive statins at discharge. Because patients are most receptive to cardiac medications if prescribed at discharge from the hospital, this must be the first phase of evidence-based therapy and should remain an area of focus.33 Thus, we emphasize that efforts to improve long-term adherence should start by prescribing at time intervals when patients are most receptive. Relatively few studies have identified patient factors associated with discontinuation of cardiac medications, and the associations between demographic factors (age, sex, and race) and adherence have been inconsistent.33 -35 Our study found that female patients were less likely to be adherent to their cardiac regimen, but sex did not remain significant in the multivariable analysis. Although prior studies have suggested age-influenced adherence, our study did not find an association with patient age and adherence after adjusting for other variables such as total number of medications and other factors such as education and independent living.35 Total number of medications, widowhood, and lower education are more prevalent in elderly populations possibly explaining prior associations between age and adherence. Although primary insurance status was not an independent predictor of adherence in our study, we did not have information on supplementary or prescription drug coverage for patients, which can vary widely. For this reason, we can only conclude that primary insurance status is not a helpful indicator of a patient’s adherence to cardiac medications. We did, however, find associations between adherence and patient-reported education, mental health, marital status, and independent living. Depression among elderly patients, which has been estimated to be between 15% and 22% in older CAD patients, has been shown to be an independent risk factor for 4-month mortality.36,37 Older patients with depression have reported decreased use of prescription medications.38 In addition, major depression adversely affects adherence to aspirin over 3 weeks as gauged by an electronic monitoring device.39 We found that lower mentalcomposite summary scores (MCS) which reflect mood

Kulkarni et al 189

were independently associated with nonadherence. This trend for MCS was consistent across all medication classes. Supporting this finding, we found an association between antidepressant prescriptions at discharge and decreased adherence to cardiac medication at 12 months. From these results, patients’ mental health appears to be a contributor to adherence to cardiac medications. We found that unmarried individuals tended to have lower adherence, and those requiring assistance for daily living were also less adherent. Both parameters likely reflect the role of others in facilitating adherence (ie, spouse or caregiver). Lower educational status may be a marker of limited financial resources to afford medications or reflects lower health literacy. We also found that as the total number of medications prescribed at discharge increased, patient adherence to the cardiac regimen decreased. This result is consistent with prior work which has suggested that increased number of medications was associated with nonadherence.40 Our study had several limitations, the most significant of which is that the continued use of medications and predictor variables for adherence were obtained at 2 separate time points (12 and 0 months, respectively). As a result, we were not able to examine the temporal relationship between patient factors and adherence. Furthermore, information on the patients’ reasons for nonadherence, such as financial hardship or side effects, was not available. In addition, our study relied on patient self-report of medication use. The literature supports the use of patient self-report as either accurately or overestimating actual medication use when compared with pill count or electronic pharmacy records.19,20,41 If this is true, our results would underestimate the true rate of nonadherence. In addition, the theoretical benefit of asking patients which medications they are taking, over pharmacy records, is obtaining the information directly from the end user of these therapies. We feel that the differences in the population not included in our study would only serve to magnify our findings as all the characteristics associated with adherence were also more common in those patients who did not have complete medication information at the 2 time points (the criterion for our study).

Conclusions Our study shows that medication adherence is influenced by patient educational and marital status and mental health more than demographic factors such as age and sex. Health care providers should be aware of patient mood and support systems as major factors in adherence. In addition, providers should simplify medical regimens where possible and ensure patient understanding of medications enabling them to be partners in their health care.

190 Kulkarni et al

References 1. Soumerai SB, McLaughlin TJ, Spiegelman D, et al. Adverse outcomes of underuse of beta-blockers in elderly survivors of acute myocardial infarction. JAMA 1997;277:115 - 21. 2. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of the National Cholesterol Education program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 2001;285:2486 - 97. 3. ACC/AHA 2002 Guideline Update for the Management of Patient with Chronic Stable Angina. A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee to Update the 1999 Guidelines for the Management of Patients with Chronic Stable Angina). Available at: www.acc.org/clinical/guidelines/unstable/unstable.pdf. 4. Lappe JM, Muhlestein JB, Lappe DL, et al. Improvements in 1-year cardiovascular clinical outcomes associated with a hospital-based discharge medication program. Ann Intern Med 2004;141:446 - 53. 5. Majumdar SR, Gurwitz JH, Soumerai SB. Undertreatment of hyperlipidemia in the secondary prevention of coronary artery disease. J Gen Intern Med 1999;14:711 - 7. 6. Rathore SS, Mehta RH, Wang Y, et al. Effects of age on the quality of care provided to older patients with acute myocardial infarction. Am J Med 2003;114:307 - 15. 7. Krumholz HM, Radford MJ, Ellerbeck EF, et al. Aspirin in the treatment of acute myocardial infarction in elderly Medicare beneficiaries. Patterns of use and outcomes. Circulation 1995;92: 2841 - 7. 8. Burwen DR, Galusha DH, Lewis JM, et al. National and state trends in quality of care for acute myocardial infarction between 1994-1995 and 1998-1999. Arch Intern Med 2003;163:1430 - 9. 9. McCormick D, Gurwitz JH, Lessard D, et al. Use of aspirin, h-blockers, and lipid-lowering medications before recurrent acute myocardial infarction. Arch Intern Med 1999;159:561 - 7. 10. Haynes RB, McDonald HP, Garg AX. Helping patients follow prescribed treatment: clinical applications. JAMA 2002;288:2880 - 3. 11. Simons LA, Levis F, Simons J. Apparent discontinuation rates in patients prescribed lipid-lowering drugs. Med J Aust 1996;164: 208 - 11. 12. Avorn J, Monette J, Lacour A, et al. Persistence of use of lipidlowering medications: a cross-national study. JAMA 1998;279: 1458 - 62. 13. Roccaforte WH, Burke WJ, Bayer BL, et al. Validation of a telephone version of the Mini-Mental Status Examination. J Am Geriatr Soc 1992;40:697 - 702. 14. Failde I, Ramos I. Validity and reliability of the SF-36 Health Survey Questionnaire in patients with coronary artery disease. J Clin Epidemiol 2000;53:359 - 65. 15. Ware JE, Kosinski M, Keller SD. SF-36 physical and mental health summary scales: a user’s manual. Boston (Mass): The Health Institute; 1994. 16. Ware JE. SF-36 health survey: manual and interpretation guide. Boston (Mass): The Health Institute; 1993. 17. Folstein MF, Folstein SE, McHugh PR. Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12:189 - 98. 18. Ware JE, Kosinski M, Bayliss MS, et al. Comparison of methods for the scoring and statistical analysis of SF-36 health profile and summary measures: summary of results from the Medical Outcomes Study. Med Care 1995;33:AS264 - 79.

American Heart Journal January 2006

19. Grymonpre RE, Didur CD, Montgomery PR, et al. Pill count, selfreport, and pharmacy claims data to measure medication adherence in the elderly. Ann Pharmacother 1998;32:749 - 54. 20. Vik SA, Maxwell CJ, Hogan DB. Measurement, correlates and health outcomes of medication adherence among seniors. Ann Pharmcother 2004;38:303 - 12. 21. Krumholz HM, Radford MJ, Wang Y, et al. National use and effectiveness of beta-blockers for the treatment of elderly patients after acute myocardial infarction: National Cooperative Cardiovascular Project. JAMA 1998;280:623 - 9. 22. Jackevvicius CA, Mamdani MM, Tu JV. Adherence with statin therapy in elderly patients with and without acute coronary syndromes. JAMA 2002;288:462 - 7. 23. Simpson E, Beck C, Richard H, et al. Drug prescriptions after acute myocardial infarction: dosage, compliance, and persistence. Am Heart J 2003;143:438 - 44. 24. Federman AD, et al. Supplemental insurance and use of effective cardiovascular drugs among elderly Medicare beneficiaries with coronary heart disease. JAMA 2001;286:1732 - 9. 25. Benner JS, Glynn RJ, Mogun H, et al. Long term persistence in use of statin therapy in elderly patients. JAMA 2002;288:455 - 61. 26. Insull W. The problem of compliance to cholesterol altering therapy. J Intern Med 1997;241:317 - 25. 27. Hoekstra JW, Pollack Jr CV, Roe Jr MT, et al. Improving the care of patients with non–ST-elevation acute coronary syndromes in the emergency department: the CRUSADE initiative. Acad Emerg Med 2002;9:1146 - 55. 28. Yusuf S, Sleight P, Pogue J, et al. Effects of an angiotensinconverting–enzyme inhibitor, ramipril, on cardiovascular events in high-risk patients. The Heart Outcomes Prevention Evaluation Study Investigators. N Engl J Med 2000;342:145 - 53. 29. The Scandinavian Simvastatin Survival Study Group. Randomized trial of cholesterol lowering in 4444 patients with coronary heart disease: the Scandinavian Simvastatin Survival Study (4S). Lancet 1999;344:1383 - 9. 30. Sheperd J, Cobbe SM, Ford I, et al. Prevention of coronary heart disease with pravastatin in men with hypercholesterolemia. N Engl J Med 1995;333:1134 - 5. 31. Sacks FM, Pfeffer MA, Moye LA, et al. The effect of pravastatin on coronary events after myocardial infarction in patients with average cholesterol levels. N Engl J Med 1996;335:1001 - 9. 32. The Long-term Intervention With Pravastatin in Ischemic Disease (LIPID) Study Group. Prevention of cardiovascular events and death with pravastatin in patients with coronary heart disease and a broad range of initial cholesterol levels. N Engl J Med 1998;339: 1349 - 57. 33. Balkrishnan R. Predictors of medication adherence in the elderly. Clin Ther 2001;20:764 - 71. 34. Monane M, Bohn RL, Gurwitz JH, et al. The effects of initial drug choice and comorbidity on anti-hypertensive therapy compliance. Am J Hypertens 1997;10:697 - 704. 35. Avorn J, Monette J, Lacour A, et al. Persistence of use of lipidlowering medications. JAMA 1998;279:1458 - 62. 36. Blazer GD, Hughes DC, George LK. The epidemiology of depression in an elderly community population. Gerontologist 1987;27:281 - 7. 37. Bush DE, Ziegelstein RC, Tayback M, et al. Even minimal symptoms of depression increase mortality risk after acute myocardial infarction. Am J Cardiol 2001;88:337 - 41. 38. Romanelli J, et al. The significance of depression in older patients after myocardial infarction. J Am Geriatr Soc 2002;50:817 - 22.

American Heart Journal Volume 151, Number 1

Kulkarni et al 191

39. Carney RM, Freedland KE, Eisen SA, et al. Major depression and medication adherence in elderly patients with coronary artery disease. Health Psychol 1995;14:88 - 90. 40. Col N, Fanale JE, Kornhom P. The role of medication noncompliance and adverse drug reactions in hospitalizations in the elderly. Arch Intern Med 1990;150:841 - 5. 41. Codina C, Martinez M, Tuset M, et al. Comparison of three methods to calculate adherence in patients receiving antiretroviral treatment. Infect Dis Clin Microbiol 2002;20:484 - 90.

Appendix

ACE inhibitor—indicated in patients with ejection fraction b40, history of congestive heart failure, or history of diabetes and hypertension Statins—indicated for all patients

Contraindications for medication use Aspirin—no contraindications measured in our study BBs—contraindicated in patients with chronic obstructive pulmonary disease ACE inhibitor—contraindicated in patients with renal or liver disease Statins—contraindicated in patients with liver disease

Indications for medication use Aspirin—indicated for all patients BBs—indicated for all patients

The following article is a AHJ Online Exclusive. Full text of this article is available at no charge at our website: www.ahjonline.com

Electrophysiology Prevention of implantable defibrillator shocks by cognitive behavioral therapy: A pilot trial Philippe Chevalier, MD, PhD,a Jean Cottraux, MD,b Evelyne Mollard, MD,b Sai NanYao, MD,b Sophie Brun, MD,a Haran Burri, MD,a Lioara Restier, PhD,a and Patrice Adeleine, PhDc Lyon, France

Background

Although psychological stress is known to favor

conventional treatment group. At 3 months, among patients

ventricular arrhythmic events, there is no evidence that stress

without antiarrhythmic drugs, none of the subjects in the CBT group had

management intervention decreases ventricular electrical instability

experienced arrhythmic events requiring ICD intervention, as

in implantable cardioverter-defibrillator (ICD) patients. The aim of the

compared with 4 in the control group ( P b .05). At 12 months, there

study was to determine whether cognitive behavioral therapy (CBT)

was no difference in the number of arrhythmic events requiring

results in a decrease of arrhythmic events requiring ICD intervention

therapy between the CBT group versus the control group. Among heart

through an improvement in sympathovagal balance.

rate variability indexes, daytime pNN 50 and nocturnal SDNN

Methods

improved significantly in the CBT group, as compared with the

Of 253 consecutive ICD patients (age 59 F 10 years,

64 men), 70 were randomly assigned to CBT (n = 35) or conventional

control group.

medical care (n = 35). Measures of heart rate variability, psychological

Conclusions

well-being, and quality of life were assessed at baseline, 3 months, and 1 year. The primary outcome was appropriate ICD shock.

Results

Although, it was not statistically different, the number

of patients requiring shocks was less in the CBT group than in the

By decreasing anxiety and possibly

improving sympathovagal balance, cognitive behavior therapy may decrease the propensity for ventricular arrhythmias in ICD patients. However, these effects appear to be limited over time. (Am Heart J 2006;151:191.e1 -191.e6.)