Secondary Logo

Journal Logo

The Medication Regimen of Patients With Heart Failure

The Gerontologic Considerations and Anticholinergic Burden

A Mixed-Methods Study

Knecht, Janet G. PhD, RN; Neafsey, Patricia J. PhD (Pharmacology)

doi: 10.1097/JCN.0000000000000302
ARTICLES: Heart Failure
Free

Background: Although prescription medication adherence has been studied in the population living with heart failure (HF), little attention has focused on the patient’s overall medication practices including over-the-counter medications. Patients with HF live with the certainty that their quality of life depends on the proper management of multiple medications. Failure to properly manage prescription medications increases the risk of exacerbation of HF and increased rates of rehospitalization.

Objectives: The aim of the quantitative component of this study was to identify medication practices in patients with HF. The aim of the qualitative component was to identify themes of patients with high and low HF medication self-efficacy.

Methods: A convergent parallel mixed-methods design was followed. Quantitative interviews were conducted by telephone with 41 patients living with HF around their medication-taking and lifestyle behaviors. Immediately thereafter, qualitative interviews were conducted to elicit the patient’s perspective of their therapeutic regimen.

Results: Patients are prescribed medications not recommended for the gerontologic population and/or risk anticholinergic burden. Although highly confident, patients admit to a plethora of errors.

Conclusion: Future study is required to ensure safe transitions to home and enhance technology to provide seamless communication between patients and providers.

Janet G. Knecht, PhD, RN Assistant Professor of Nursing, University of Saint Joseph, West Hartford, Connecticut.

Patricia J. Neafsey, PhD (Pharmacology) Professor Emeritus, University of Connecticut, Storrs; and Cofounder and Chief Scientific Officer, ActualMeds Corp, East Hartford, Connecticut.

The University of Connecticut granted an exclusive license for the survey software used in this study to ActualMeds Corp in 2009. P.J.N. receives a percentage of the software license fee charged by the University of Connecticut. The University of Connecticut and she are shareholders of ActualMeds Corp. J.G.K. has no conflicts of interest to disclose.

Correspondence Janet G. Knecht, PhD, RN, University of Saint Joseph, 1678 Asylum Ave, West Hartford, CT 06117 (jknecht@usj.edu).

Back to Top | Article Outline

Defining the Problem

Heart failure (HF) is a major public health problem in the United States. It is defined as a clinical syndrome that results from any structural or functional disorder that decreases the ability of the ventricles to fill or eject blood. Heart failure is diagnosed from a myriad of symptoms that result in dyspnea and fatigue.1 More than 5 million people have received a diagnosis of HF, with more than 650 000 people receiving a diagnosis of HF annually.1 Approximately 5.7 million Americans are living with HF.2 The cost of HF is estimated at $35 billion annually from 2008 to 2011,3 and more Medicare dollars are spent on HF than any other diagnosis-related group,4,5 Heart failure is responsible for more than 1 million admissions a year and is the most common cause for hospitalization in persons older than 65 years. A hospital stay usually lasts 7 to 10 days.4 Patients with HF have the highest readmission rates, most often within weeks of discharge.6 Unfortunately, HF carries a 50% mortality rate in 5 years.7

Heart failure has been labeled a cardiovascular disorder of aging, with the incidence approaching 10 per 1000 after 65 years of age. More than half of all patients who received an HF diagnosis are older than 75 years.7,8 Sixty-nine percent of total HF expenditures in 2012 involved patients 65 years or older.3

Failure of older adults (≥65 years) to take medications properly is estimated to be a factor in more than a quarter of emergency department (ED) visits.9 Upon admission, medication reconciliation records are often inaccurate.10,11 Medication nonadherence in the HF population is associated with an increased risk of all-cause mortality and cardiovascular hospitalizations.12–14 Failure of the healthcare system to identify and remediate poor adherence and adverse medication behaviors adds to the overall cost of treatment as providers typically intensify therapy and add additional agents to the regimen, further increasing the risk of drug adverse effects as well as cost.4,15 Seventy-three percent of HF patients have hypertension upon admission to the hospital.12 An estimated $100 billion is spent annually in the United States on healthcare for patients with poorly controlled blood pressure in part due to poor antihypertensive medication adherence and other adverse self-medication behaviors.16 Uncontrolled hypertensive HF leads to high rates of intensive care admission, prolonged hospitalization, and increased 90-day readmission.4,6,17

Back to Top | Article Outline

Background and Significance

The disease prevention agenda for the United States, Healthy People 2020,18 calls for the identification of significant, preventable threats to US health and establishes national goals to reduce these threats. This project identifies adverse self-medication practices in patients with HF as a significant threat to the health of the nation’s elderly. Older adults have significant knowledge deficits with respect to interactions of prescription and over-the-counter (OTC) medications and have low self-efficacy levels in how to avoid serious interactions.19 Healthy People 202018 also promotes the public’s rights to be adequately informed about their medication therapy.

Patients with HF live with the certainty that their quality of life depends on the proper management of multiple medications with potential interactions and also innumerable therapeutic regimens. Failure to properly manage prescription medications increases the risk of exacerbation of HF and rates of rehospitalization.12–14 Although prescription medication adherence has been studied in the HF population, little attention has focused on patients’ overall self-medication practices and their confidence in following the prescribed therapeutic regimen.

Prescription medication adherence has been addressed,13,14,20,21 as well as adherence to medical advice22,23 in the HF population. Wu et al14 first sought to describe predictors of medication adherence in HF. The authors used the World Health Organization’s multidimensional adherence model to measure 3 indicators: dose count, the percentage of prescribed doses taken; dose days, the percentage of days the correct number of doses was taken; and dose time, the percentage of doses taken at the prescribed time. They cited barriers to each measurement and identified perceived social and financial support as having the greatest impact. In a follow-up study, Wu et al13 sought to identify at what point the degree of medication adherence impacts current HF status, rehospitalization, and morbidity. They concluded that patients were significantly more event-free when adherence to the prescribed regimen was greater than 88%.

Snyder et al24 conducted a pharmacist-led medication therapy management program with 700 patients on Medicaid. They demonstrated that the total number of medications, obesity, dyslipidemia, and 1 or more ED visits in the past 3 months were significant predictors of medication-related problems. They concluded that the number of medications was a significant predictor of medication-related problems in that population.

Albert et al25 revealed that patients seeking emergency care for HF decompensation demonstrated inaccurate HF beliefs and poor self-care adherence. Participants included 195 adults living with HF who presented to the ED for exacerbation. Validated instruments for HF illness beliefs and self-care adherence were used. Illness beliefs were based on the perceived level of danger by the patient. There was a lack of association between HF beliefs and self-care adherence, which reflects a need for improved HF education.

Lama Tamang et al22 studied 371 patients with HF to describe risk factor control and adherence to recommended therapies. A majority (83.3%) were taking only 1 drug of the recommended 3-drug regimen (β-blocker, angiotensin converting enzyme inhibitors/angiotensin receptor blocker (ACEI/ARB), diuretic). Additionally, only 69.1% were at goal for blood pressure. The authors concluded a significant gap between adherence to medication regimen and control of significant risk factors in the population of HF patients living at home.

Dunlay et al26 studied 209 patients with HF to determine factors associated with poor adherence, which was defined as the proportion of days covered less than 80%. The median number of medications in the population was 11 (interquartile range, 8–17), with 26 patients (12%) filling more than 20 medications. Nineteen percent of patients had poor adherence to β-blockers and ACEIs/ARBs. Patients with poor adherence to ACEIs/ARBs were younger (P < .05), and men had lower ACEIs/ARBs adherence than did women (P < .04). Cost related to statin medications was the most often cited issue by patients for this category. Other factors cited included prior history of depression, marital status, and dosing frequency of medications. Depression remains underdiagnosed and undertreated in this population. It is estimated that up to 60% to 77% of patients living with HF have depression.27–29 Worsening HF symptoms increase depressive symptoms, which further exacerbates HF symptoms.27

Back to Top | Article Outline

Purpose

This mixed-methods study addressed medication practices and HF management self-efficacy. The primary aim of this study was 2-fold: (1) measure and gain a more complete understanding of HF medication management and self-efficacy and (2) quantify self-medication practices. Immediately following the completion of the quantitative interviews, qualitative interviews were conducted to discover the patients’ perspective of their therapeutic regimen. The results of these qualitative interviews were meant to inform the researcher and provide insight into the quantitative strand. It was to provide greater understanding and depth to the process of patients’ self-medication practices and adherence along with self-efficacy when living with HF.

Back to Top | Article Outline

Research Questions

Quantitative:

  • What are the HF medication self-efficacy (MSE) scores of patients following an HF medication regimen?
  • What are the medication practices of patients living with HF?
  • What is the relationship between self-efficacy scores and the number of medications or risk score of HF patients in this study?

Qualitative:

  • What themes can be derived from interviews with patients living with HF who score high and low on an MSE instrument?

Mixed-methods:

  • To what extent do the quantitative scores of patients living with HF, who scored high and low in self-efficacy, inform the themes derived from qualitative interviews?
Back to Top | Article Outline

Methods

Study Design and Study Population

This study used a convergent parallel mixed-methods design to determine patient medication behaviors and self-efficacy of their medication regimen in the home30 (Figure 1). The philosophical foundation of the mixed-methods approach to research is pragmatism.31 Within a pragmatic approach, one recognizes that there is both “a single ‘real world’ and that all individuals have their own unique interpretations of that world.”32(p73) It evolved from the constructivist perspective, which sought to describe a problem within the social political landscape.32 Mixed-methods research blends the singular and multiple views of reality.30 Morgan32 advocates that the abduction-intersubjectivity-transferability approach provides an opportunity to “move back and forth between”(p71) the induction/deduction and subjectivity/objectivity throughout the research process. It is through this working back and forth between the theory and approaches to knowledge that the depth of the pragmatic approach is discovered.

FIGURE 1

FIGURE 1

Patients who received a diagnosis of HF, 50 years or older, and recently discharged from home care following hospitalization for any cause were recruited for the study. Patients were not eligible if they (1) had cognitive or psychological impairment or (2) were terminal and had been referred to hospice. Approval of the institutional review board from a central Connecticut urban hospital was sought and obtained prior to initiation of the study. Reported within are the results including the post hoc analysis of quantitative strand related to patients’ medication regimen with discussion from the qualitative and mixed-methods strands.

Back to Top | Article Outline

Procedures

After receiving a description of the study, patients who wished to participate were contacted by the primary researcher on the telephone. Following consent, the nurse researcher (1) collected demographic information including age, sex, race, and last year of completed education; (2) completed the ActualMeds structured interview (that queries recent symptoms, lifestyle behaviors, prescription medications, self-medication, and OTC behaviors); (3) compared patient-reported medication behavior to a paper medication adherence record obtained electronically from the primary care nurse (including all prescriptions and OTC medications the patient reported taking in the previous month); and (4) completed the HF MSE instrument.

After completion of the quantitative strand, participants who scored higher (≥4 = confident) and lower (≤3 = not as confident) in self-efficacy were asked to participate in the qualitative interviews, which were audiotaped. The participants were asked to answer 2 open-ended questions. The first question was intended to uncover the patient’s experience living with HF and is aligned with a qualitative method of inquiry. The second is congruent with self-efficacy theory.

  • (1) Please describe your experience of living with HF since your diagnosis. Please describe for me in as much detail as possible a day that you feel represents what it means to live with your diagnosis.
  • (1) Can you tell me about your level of confidence related to following your prescribed regimen? Probing questions were asked when the researcher wanted the patient to elaborate on a scenario the participant recounted.
Back to Top | Article Outline

Instruments

ActualMeds

ActualMeds is a medication use instrument that was developed from The Personal Education Program–Next Generation, an interactive computer-based educational intervention.33 The instrument includes a structured patient interview and risk rules/scoring that were redeveloped for secure, Health Insurance Portability and Accountability Act–compliant commercial delivery in a variety of healthcare settings. A rules engine analyzes patient self-reported prescription and OTC medication data (including what, when, and how often taken) and lifestyle behavior (including tobacco use, alcohol consumption, and hours of sleep) to calculate risks (high, ≥25; medium, ≥10; low, <10). The rules engine delivers a stratified list of patient-reported behaviors with the highest risk scores. A total risk score is generated which is the sum of all of the risks identified. A total risk score is generated that is the sum of all of the risks identified.

Drug interactions/lifestyle behaviors that generate risk scores are derived from a Lexicomp database (Wolters-Kluwer) rule set, with additional rules derived from published guidelines from the Beers Criteria for Potentially Inappropriate Medication Use in Older Adults34; American Diabetes Association; American Heart Association; Word Health Organization; Centers for Disease Control; National Heart, Lung and Blood Institute; US Food and Drug Administration; and US Department of Agriculture.

Back to Top | Article Outline

The HF Medication Self-efficacy Instrument

The HF Medication Self-efficacy Instrument (J. Knecht, “The HF Medications Self-efficacy Instrument” [unpublished paper], 2012) was developed based on previously validated instruments that measure self-efficacy related to medication adherence in different populations: older women with osteoporosis35 and persons living with HIV.36 Patients were given a set of 5 response options, with 1 being “not at all confident” and 5 being “totally confident.” Lower scores were associated with negative feelings related to confidence, and higher scores were associated with increased confidence. This 15-item instrument (Figure 2) was pilot tested on patients 60 years or older taking cardiac medications recruited from senior centers throughout central Connecticut: 6 participants for each item, 93 participants total.

Principal component analysis was performed using SPSS (version 19) on the 15 items. Using Kaiser’s criterion (eigenvalues >1) and examination of the scree plot, 3 components were derived that accounted for 82.85% of the total variance. Cronbach’s α for the data from component 1, outcome expectations, was .98, with a mean interitem correlation of .89. The second component, MSE, reported a Cronbach’s α for the data of .91 and a mean interitem correlation of .72.

Back to Top | Article Outline

Statistical Methods

Coded data were downloaded via the secure, Health Insurance Portability and Accountability Act–compliant ActualMeds database and entered into an SPSS (version 19) data file. Descriptive analyses were conducted on all variables organized by gender, race, and age. Frequency distributions of the HF MSE, ActualMeds risk score, and blood pressure were analyzed. Correlation statistical analyses were conducted between SE scores and ActualMeds risk scores.

Back to Top | Article Outline

Qualitative Data Analysis

Audiotaped sessions recorded during the qualitative interviews were transcribed verbatim by the researcher. Krippendorff’s37 method of content analysis for qualitative study was conducted to derive themes from qualitative interviews. According to Krippendorff, qualitative content analysis enables the researcher to select text that is relevant, interpreting that text and returning to earlier interpretations based on later readings and interpretations. The researcher “settles for nothing less than interpretations that do justice to a whole body of texts.”(p88) Interpretations were supported by quotes of participants.37

Back to Top | Article Outline

Mixed-Methods Data Analysis

Following a convergent parallel design, data from the quantitative and qualitative strands were analyzed independently using procedures suited for the appropriate methodological approaches.30 As the quantitative scores and the qualitative transcripts were examined, dimensions were identified and compared. Constructs were defined. A side-by-side comparison was conducted during the merging process. Quotes from the qualitative interviews were used to support quantitative results.

Back to Top | Article Outline

Sample

Forty-one patients participated in the study. Data collection was at a single point in time. Phone interviews ranged in duration from 21 to 49 minutes. The participants were primarily white (95%) females (58%) ranging in age from 52 to 94 years (Table 1). African Americans represented the other 5%. Fifteen percent reported less than 12 years of education, whereas 54% reported high school as their terminal degree. The mean age was 81 (SD, 8) years. The participants were English speaking, living independently at home.

TABLE 1

TABLE 1

Back to Top | Article Outline

Results

Patients reported a mean number of 12.6 (SD, 5) medications (range, 6–25 medications), with 38 of 41 patients (93%) with multiple dose times and multiple prescribers. Over-the-counter medications accounted for 29% of medications. All patients reported taking 5 or more prescription medications. A total of 16 medications were prescribed for HF in the participants interviewed. The most commonly prescribed categories of medications are represented in Table 2. Despite the extensive number of medications that patients were prescribed, only 46% met 3 recommended concurrent medications for HF per prescription guidelines: ACEI or ARB, β-blocker, and loop diuretic.1

TABLE 2

TABLE 2

The mean medication risk score calculated from the total medication profile by the ActualMeds system was 63 (SD, 50; range, 17–276). There was a strong positive correlation between the risk score and the number of medications (r = 0.70, n = 41, P < .001) (Figure 3). The ActualMeds instrument generated a high-risk total score for 86% of patients, indicating the patient’s regimen had multiple, high-risk medications and/or drug-drug interactions. Ninety-five percent had at least 1 urgent need for medication reconciliation and review. Most of the high risks were related to medications that appear on the Beers list of potentially harmful drugs34 for the gerontologic population. It is a consensus-based list citing medications deemed inappropriate in the elderly with level of evidence referenced. The Beers list was originally intended for use in the long-term-care setting, but it has been adapted to all environments.34 The results demonstrated 29 patients (71%) took at least 1 Beers medication; 51% had 2 or more Beers medications (Table 3). Of those taking medications on the Beers list, 48% had related symptoms. In particular, 5 patients (17%) reported they had fallen in the last year. Other symptoms reported that were related to Beers medications included unsteadiness, near fall, fatigue, dizziness, leg cramps, and constipation. Self-reported cognitive changes also present as an adverse effect in patients prescribed Beers medications with anticholinergic adverse effects (see the following paragraph).

FIGURE 2

FIGURE 2

FIGURE 3

FIGURE 3

Ninety-five percent of patients surveyed in this study reported medications with anticholinergic adverse effects and risk of cognitive impairment38,39 (Table 4). Applying the Anticholinergic Cognitive Burden (ACB) Scale40 to the current results, 78% had at least a moderate anticholinergic burden (ACB) score (≥2), and 54% had a high anticholinergic burden score (≥3; range, 3–10). Of the 41 patients interviewed in this study, 28 reported they were prescribed the loop diuretic furosemide, which has an ACB score of 1 and is one of the medications recommended for HF in the ACC guidelines. Another 12 patients reported the β-blocker metoprolol, which also carries an ACB score of 1 and is one of the ACC-recommended medications for HF. Finally, 12 patients were prescribed warfarin for atrial fibrillation. Patients prescribed these 3 medications for HF would carry an ACB burden of 3 if they were taking nothing else. Six patients (15%) were prescribed this combination. Patients were also prescribed other medications with anticholinergic adverse effects including those with ACB scores of 3: Bentyl (dicyclomine), Ditropan (oxybutynin), and Paxil (paroxetine), and an ACB score of 2: Tegretol (carbamazepine), Flexeril (cyclobenzaprine), and various opioid-containing pain medications. A total of 14 other medications with ACB scores of 1 were prescribed to patients. Also, one of these patients self-medicated with OTC diphenhydramine (ACB score = 3), and 2 patients took other OTC antihistamines (cetirizine and loratadine) (ACB score = 2). Only 1 patient was prescribed donepezil, the recommended treatment to allay the impairment caused by the anticholinergic adverse effects.

TABLE 4

TABLE 4

TABLE 3

TABLE 3

Back to Top | Article Outline

Medication Regimen Self-efficacy

The average MSE scores of a patient measured the patient’s confidence that he/she was managing the regimen correctly (5 = totally confident and 1 = not at all confident). The participants in this sample were highly confident that they were following their regimen correctly. Of the 41 patients, 34 (83%) were very confident (MSE = 4) to totally confident (MSE = 5) that they were following their regimen correctly. Eleven patients (17%) scored 3 or less on the MSE.

Back to Top | Article Outline

Outcome Expectation Scores

There was a small, not significant negative correlation between outcome expectation self-efficacy (OESE) scores and risk scores (r = −0.27, n = 41, P < .095) (Figure 4). The relationship between age and outcome expectation score was investigated using the Pearson product-moment correlation coefficient. Because outcome expectation scores were not normally distributed, a nonparametric correlation was conducted. The test yielded no significant correlation between age and outcome expectation score in the sample measured; however, on average, the outcome expectation score went down as the participant’s age increased. In addition, both the mean and median outcome expectation scores were somewhat lower for men than those for women (male mean, 3.6 [SD, 1.3]; female mean, 4.2 [SD, .85]; male median, 3.8; female median, 4.5).

FIGURE 4

FIGURE 4

Back to Top | Article Outline

Qualitative Results

A total of 21 patients were interviewed to obtain qualitative data. Ten of these participants had low self-efficacy scores, and 11 had high scores. Analysis of the transcripts revealed 3 themes, which were identified as social support, attitudes and beliefs, and formulating coping strategies. All patients told a story of either a positive or negative transition learning to manage their new reality.41 Social support as well as the patient’s own philosophy of life promoted either a positive or negative experience during this transition. Patients often described symptoms within the context of a situation. Those with higher self-efficacy scores related a sense of confidence in their ability to manage daily life; however, patients with lower self-efficacy scores recounted feelings of discouragement. Patients with low HF MSE and OESE presented a narrative of being lost and alone. They were often overwhelmed because of the complexity of routine care. These factors and the themes derived became clearly articulated in the words of patients.

Back to Top | Article Outline

Social Support

Patients with high self-efficacy scores described support systems and the comfort they provided. Social support included family, friends, neighbors, and peers. Patients who did live alone but who felt they could depend on a family member nearby related more positive self-efficacy scores. Family included spouses, partners, sons and daughters, nephews, and grandchildren. Patients relied on different individuals for assistance, but in all cases, they were aware of how important this support was to their well-being. A woman living with her domestic partner simply stated:

I could not do it without her.

Others told stories of “fantastic neighbors” who would shovel their driveway in the snow. One participant called these friends “angels.” For all participants, social networks were carefully constructed to enhance their ability to continue to function successfully within the confines of their limitations.

We have a good support system through the church. Thursday we are going to XXXX… to see if a pacemaker or defibrillator might help me. A fellow from the church and his wife are going to drive us….

In order to maintain social connections, participants made adjustments to their daily routines and set goals to maintain social connections.

I know on Monday mornings I have to take it easy, because I like to go to tai chi. If I start cleaning up, I will get too tired. I go mostly to see my friends.

A grandmother articulated the concepts of adjustment and goal setting by stating that she stayed home on Fridays, which allowed her to attend her grandchildren’s sports games.

I like to get out with a goal… because I will see my grandchildren in their soccer games.

Patients with lower self-efficacy scores often lived alone, and their story related a sense of discouragement. One gentleman described the effect of being alone on his healthcare practices by saying:

I live alone; my children don’t live near me. I use my medicine box, but I still find that I miss some medications. I don’t remember, the days run together….

A widower apologized for his sense of discouragement:

I am home most of the time; I don’t go anywhere… I am legally blind. I cannot read print. I am sorry I cannot be more positive.

Apologizing for feeling discouraged was not limited to 1 participant. A recovering alcoholic who had been sober for 17 years and lived alone without family support also apologized when he self-rated his health as poor. He was fairly confident he took his medication correctly; most were taken early in the morning, but when asked if he felt they were helping, he stated:

I am not sure… I’ll be honest with you; I am not sure how much good they are… I really don’t know. I am overwhelmed really; I am taking so much stuff.

It is clear that social support improved the self-efficacy and facilitated the transition of the patients interviewed in this study.

Back to Top | Article Outline

Attitudes and Beliefs

The patient’s perspective of life was influenced by his/her own attitudes toward spirituality, life’s purpose, and current quality of life. Patients offered a myriad of thoughts and feelings encountered within the framework of transitioning to their altered daily lives. Attitudes and beliefs, as well as personal characteristics, played a role in the adaptation of patients to living with HF. Confronted with unfamiliar situations, patients drew on inner strength and personal beliefs to successfully live with HF. Patients with high self-efficacy related that one could not expect to feel well every day.

Patients with high OESE believed that their medications were of benefit to them; they believed that they helped them feel better and improved their quality of life. They described positive attitudes and beliefs. One woman stated, “I have a very good attitude toward the whole thing. I think you have to think positively, not dwell on what you think might happen.” Another participant summed up the feeling of many who believed that they had to maintain a positive attitude to maintain their current health status: “I don’t let anything stress me out; I can’t have that in my life.”

In contrast to those who were positive about their situation, patients with low self-efficacy were often ambivalent about their daily routines because of the fatigue that resulted from their daily care. A gentleman articulated this sense of despair when he stated:

It is getting tiresome. I am seeing a psychiatrist once a week. I am 70… I feel beat most of the time.

Back to Top | Article Outline

Formulating Coping Strategies

Patients with high overall self-efficacy scores described behaviors used to adhere to their regimen. A retired army officer related a story of the systematic approach required to successfully maintain a level of wellness, which included daily exercise. Patients related the careful details of charts, pill boxes, and alarms used to remember medications along with anyone who would work with them to ensure the accuracy of the procedure. This confidence was expressed frequently in a very matter-of-fact way.

I have been taking them for such a long time. I know what I am doing.

Another retired officer stated that he knew how to take care of himself. He cooked for himself but confessed he was not a very good housecleaner; cleaning required more energy; it made him too tired. He was a widower and lived alone but expressed that taking his medication was “no problem at all.” He was totally confident that he followed his regimen correctly, but he scored only 2.5 on outcome expectations. He confided that the regimen was restrictive: “I am getting to be a homebody because of the medicine.”

Sadly, this avoidance of activities was described by many patients with low OESE in an attempt to allay the onset of symptoms.

Back to Top | Article Outline

Mixed-Methods Results

Most patients interviewed in this study described both positive and negative feelings; however, those with positive self-efficacy reported a greater sense of control over variables in their lives. The themes described by patients living with HF seemed to parallel Erikson’s42 final stage of development, integrity versus despair. In brief, patients with higher self-efficacy scores demonstrated integrity; they described support systems, positive attitudes, and confidence in their ability to manage their regimen. As opposed to those with lower self-efficacy whose stories illustrated despair, patients with lower self-efficacy scores described themes of being alone and often overwhelmed with daily life. Despair is marked by persons who fear death and have lost independence and significant others. For the patients in this study who have been living with HF for some time, these descriptives seem to uncover the essence of the patients’ experience.

Patients self-identified cognitive changes and admitted to a myriad of errors and omissions. Higher risk scores were generated when patients described behaviors that added to overall risk or were prescribed medications with potential interactions, such as medications published on the Beers list. Although not statistically significant, patients with lower risk scores tended to be more confident that their medications would improve how they feel, and conversely, patients with higher risk scores tended to have less confidence in their medications’ ability to improve how they feel. These data reveal the complexity of daily life of this aging population.

Back to Top | Article Outline

Implications for Future Research

Patients living with HF find themselves in a complex world. The average age of patients in this study was 81 (SD, 8) years, and they took an average of 12.6 medications, many with multiple dose times. All patients reported at least 5 medications, a burden that increases the risk of adverse drug adverse effects as well as cost.43–45 The average computed ActualMeds risk score of the patients sampled was high, 63 (SD, 31). Most (71%) were prescribed medications not recommended for the gerontologic population.34 Patients recognized cognitive changes, and of the patients reporting AGS (2012)34 Beers list of medications, 17% had fallen within the last year. This 17% of patients carried a high ACB, and 4 of the 5 lived alone. Others reported a myriad of associated symptoms including unsteadiness, memory changes, and urinary incontinence.

More than half of the hospitals in the United States have sustained Medicare’s readmission penalties due to patient readmission within 30 days of discharge.46 Polypharmacy has been implicated in ED visits and hospitalizations in older adults.43 Greater than half of those hospitalized for all cause were older than 80 years. Two medications commonly prescribed in HF were implicated in this hospitalization: warfarin accounted for 33.3%, and oral antiplatelet agents, 13.3%.43

The 2015 revision of the Beers Criteria lists medications that increase fall risk; for patients taking 3 or more central nervous system drugs, reduction of central nervous system drugs’ burden is recommended.47 Patients taking medications on the Beers Criteria list and/or following a medication regimen with a high ACB would benefit from a medication reconciliation and medication therapy review by a nurse, pharmacist, or eligible provider at the time of discharge from the hospital and again during home healthcare treatment. In addition, results from the current study demonstrate that patients living with HF require medication oversight of their regimen following discharge from home health. The Centers for Medicare & Medicaid Services recommends reconciliation during transitions of care, such as upon hospitalization and discharge to home.48 In this study, patients were contacted at least 2 weeks following discharge from home care. Contacting patients within 1 week following discharge would provide the prescribing team (eligible provider, nurse, and pharmacist) a good assessment of changes patients are making to their regimen and be early enough to prevent complications. At that point, the nurse or pharmacist would be able to reinforce rationales for the prescribed regimen. Furthermore, when prescribers discontinue or add new medications, a member of the prescribing team should reconcile the medication regimen.

Despite high levels of confidence in their management of their regimen, patients admitted to a plethora of errors, most often because they did not remember. In addition, patients reported discrepancies with their medication record from the home care agency.

Potential risk factors for cognitive impairment include anticholinergic drug use and age.49 Ancelin et al50 found that the only highly significant predictors of mild cognitive impairment were anticholinergic drug use (P < .001) and age (P < .001) after adjustment for other possible causes.

A stunning 95% of patients surveyed in this study were taking medications with anticholinergic adverse effects and risk of cognitive impairment; severity of score needs to be addressed in order to address the patients at greatest risk. Seventy-eight percent had at least a moderate anticholinergic burden (ACB) score (≥2), and 54% had a high anticholinergic burden score (≥3; range, 3–10). It is recommended that patients be prescribed medications with lower levels of anticholinergic activity; however, patients prescribed multiple medications with lower levels of activity would be at risk of additive effects.38,39 Also, patients prescribed a single medication with higher-level activity would be at risk when self-medicating with over-the-counter medications with anticholinergic activity such as loperamide, ranitidine, cimetidine, diphenhydramine, clemastine, and cetirizine. He and Ball51 used relevant evidence-based guidelines to analyze if reduction of ACB was achievable in elderly populations. They concluded that a reduction from a high ACB of 3 was possible in 85% of cases. Their study did not address OTC medications. Patients with recognized cognitive changes or the greatest ACB or risk scores generated from a system such as ActualMeds need to be targeted to address reduction of burden, and support services should be made available to all patients.

Patients with HF are placed in a difficult situation because the HF medication guidelines1 include medications that carry a mild anticholinergic burden. As seen in this study, patients may be prescribed multiple medications according to these guidelines that engender a large anticholinergic burden. Two of the most commonly prescribed medications for the patients, metoprolol and furosemide, together yield a moderate ACB. If the patient also has atrial fibrillation and is prescribed warfarin, the patient already carries a high burden (ACB = 3). Carvedilol has no associated ACB and would be a better choice than metoprolol for patients with HF.52 Because of the compounding effects of additional medications, the regimen warrants review at times of transition, within 1 week of discharge from the hospital and/or home care, and again at a minimum of every 2 months. These reviews require continuity and should be conducted with ongoing assessment of symptoms and education around the importance of adherence to their regimen and avoidance of adverse self-medication behaviors, particularly with over-the-counter medications on the Beers list. Phone interventions could be warranted more often in patients with high-risk scores and/or high anticholinergic burden34 and should be investigated in future research.

Although there are no Healthcare Effectiveness Data and Information Set (HEDIS) performance measures specific to HF, there are HEDIS measures for use of high-risk medications in the elderly.53 The measures include the percentage of Medicare members, 65 years or older, who were prescribed at least 1 high-risk medication and the percentage of patients who received at least 2 different high-risk medications. For both measures, lower rates represent better performance. The Centers for Medicare & Medicaid Services54 is currently considering changes to HEDIS rules in 2016 that would require medication reconciliation for patients inbound on transitions of care and within 30 days after discharge.

Finally, because HF is a disorder of aging and it has been suggested that older (≥73 years) patients living with HF have a decreased ability to detect and interpret shortness of breath and HF symptoms,55 we should be providing seamless electronic communication among the entire healthcare team. Making identified high risks actionable via automatic medication action plans sent to prescribers, including an audit trail for the healthcare team to be able to monitor provider follow-up, can offer a new model of best practices in care management of older adults with HF.

Programs such as The Care Transitions Program56 present a successful option for patients with fewer support systems. Developed through the Division of Health Care Policy and Research at the University of Colorado School of Medicine, it aims to support patients through transitions and increase skills among healthcare providers by enhancing health information technology and implementing system-level interventions to improve the quality of care. One instrument that might prove beneficial for the population in this study is the Medication Discrepancy Instrument.56 The elements in this instrument include the causes and contributing factors related to adherence across the spectrum: patient level, system level, and resolution level. It has been previously validated and is widely used.

Another interdisciplinary program whose aim is to minimize disparities among vulnerable community-based elder populations is the Transitional Care Model (TCM), a nurse-led, team-based care delivery model that focuses on providing synchronous delivery of care among disciplines.57 It calls for enhanced patient engagement with a focus on barriers to elders along with shared decision making in resolution.58 The Hospital Discharge Screening Criteria for High Risk Older Adults instrument could address the need to establish those at greatest risk at the time of discharge.59 The assessment of this instrument would assist nurses in the identification of those in need and trigger post discharge interventions to insure coordination of care to those at risk after discharge. The TCM responds to the greatest threats to our healthcare system: the increasing numbers of chronically ill elderly patients generating a disproportionate amount of healthcare expenditures.60

Interdiciplinary teams can provide support to patients at greatest risk upon discharge from the hospital. One example is CareLink,61 a model that employes students during transitions to optimize patient outcomes. Interventions to support the patient’s self care including adherence to medication, diet, and monitoring in the home are guided under the oversight of faculty and community partners.

Berner62 describes the need for greater numbers of geriatricians who understand the time required to foster and build confidence in the relationship with the aging patient. Iloabuchi, Deming Mi, Wanzhu, and Counsell63 identified independent risk factors of early readmission to the hospital were living alone and poor communication with the primary care provider. Geriatric nurse practitioners could fill the void in number of geriatricians.

Ultimately, it is evident that medication review cannot be left to a primary care visit with a median time of 21 minutes in duration.64 The average length of the focused interview on medications in this study was 15 minutes.

Improving the transitions of patients living with HF is critical in the effective treatment of the disease. Strategies aimed at facilitating supportive care to those who receive a diagnosis of HF would ultimately improve the quality of life in this population. Research to enhance positive relationships and to allay inhibitors of a successful transition is needed. Barriers to a positive transition cannot be ignored. Patients living alone are at risk, and depression remains underdiagnosed and undertreated. Effective strategies would focus on patient concerns and perhaps require gender specificity. The goals to decrease adverse sequelae from medication interactions and improve how patients feel would be achieved.

Back to Top | Article Outline

What’s New and Important?

  • Polypharmacy is pervasive in gerontologic patients living with HF following home care discharge for any cause.
  • The medication regimens of patients living with HF are complex and include medications not recommended for the gerontologic population that can contribute to anticholinergic burden and risk of cognitive impairment.
  • 95% of patients surveyed in this study were taking medications with anticholinergic adverse effects and risk of cognitive impairment.
Back to Top | Article Outline

Acknowledgments

The authors thank Anne Marie Biernacki, chief technology officer and cofounder, ActualMeds Corp, for facilitating the authors’ use of the ActualMeds solution for this study. J.G.K. thanks the University of Connecticut for support in research, and Stephen Walsh, ScD, for his assistance in quantitative statistical analysis. The authors also thank Joyce S. Fontana, PhD, RN, for her editorial assistance in qualitative analysis.

Back to Top | Article Outline

REFERENCES

1. Yancy CW, Jessup M, Bozkurt B, et al.; American College of Cardiology Foundation; American Heart Association Task Force on Practice Guidelines. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2013; 62(16): e147–e239.
2. Roger VL, Go AS, Lloyd-Jones DM, et al.; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Executive summary: heart disease and stroke statistics—2012 update: a report from the American Heart Association. Circulation. 2012; 125(1): 188–197.
3. Heidenreich PA, Albert NM, Allen LA, et al.; Stroke Council. Forecasting the impact of heart failure in the United States: a policy statement from the American Heart Association. Circ Heart Fail. 2013; 6(3): 606–619.
4. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009; 360(14): 1418–1428.
5. Zhang JX, Rathouz PJ, Chin MH. Comorbidity and the concentration of healthcare expenditures in older patients with heart failure. J Am Geriatr Soc. 2003; 51(4): 476–482.
6. Hernandez AF, Greiner MA, Fonarow GC, et al. Relationship between early physician follow-up and 30-day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA. 2010; 303(17): 1716–1722.
7. Go AS, Mozaffarian D, Roger VL, et al.; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Executive summary: heart disease and stroke statistics—2014 update: a report from the American Heart Association. Circulation. 2014; 129(3): 399–410.
8. Falk H, Ekman I, Anderson R, Fu M, Granger B. Older patients’ experiences of heart failure-an integrative literature review. J Nurs Scholarsh. 2013; 45(3): 247–255.
9. Budnitz DS, Pollock DA, Weidenbach KN, Mendelsohn AB, Schroeder TJ, Annest JL. National surveillance of emergency department visits for outpatient adverse drug events. JAMA. 2006; 296(15): 1858–1866.
10. Balon J, Thomas SA. Comparison of hospital admission medication lists with primary care physician and outpatient pharmacy lists. J Nurs Scholarsh. 2011; 43(3): 292–300.
11. Gurwitz JH, Field TS, Harrold LR, et al. Incidence and preventability of adverse drug events among older persons in the ambulatory setting. JAMA. 2003; 289(9): 1107–1116.
12. Fitzgerald AA, Powers JD, Ho PM, et al. Impact of medication nonadherence on hospitalizations and mortality in heart failure. J Card Fail. 2011; 17(8): 664–669.
13. Wu JR, Moser DK, de Jong M, et al. Defining an evidence-based cutpoint for medication adherence in heart failure. Am Heart J. 2009; 157(2): 285–291.
14. Wu JR, Moser DK, Lennie TA, Peden AR, Chen YC, Heo S. Factors influencing medication adherence in patients with heart failure. Heart Lung. 2008; 37(1): 8–16, 16.e1.
15. Ho PM, Magid DJ, Shetterly SM, et al. Importance of therapy intensification and medication nonadherence for blood pressure control in patients with coronary disease. Arch Intern Med. 2008; 168(3): 271–276.
16. Institute of Medicine of the National Academies. A population-based policy and systems change approach to prevent and control hypertension: consensus report. 2010. http://www.iom.edu/Reports/2010/A-Population-Based-Policy-and-Systems-Change-Approach-to-Prevent-and-Control-Hypertension.aspx. Accessed July 29, 2011.
17. Peacock F, Amin A, Granger CB, et al.; Stat Investigators. Hypertensive heart failure: patient characteristics, treatment, and outcomes. Am J Emerg Med. 2011; 29(8): 855–862.
18. Healthy People 2020. Topics and objectives: heart disease and stroke. http://healthypeople.gov/2020/topicsobjectives2020/ebr.aspx?topicId=21. Accessed October 14, 2015.
19. Neafsey PJ, Strickler Z, Shellman J, Chartier V. An interactive technology approach to educate older adults about drug interactions arising from over-the-counter self-medication practices. Public Health Nurs. 2002; 19(4): 255–262.
20. Böhm M, Schumacher H, Laufs U, et al. Effects of nonpersistence with medication on outcomes in high-risk patients with cardiovascular disease. Am Heart J. 2013; 166(2): 306–314.e7.
21. Cooper J, Hall L, Penland A, Krueger A, May J. Measuring medication adherence. Popul Health Manag. 2009; 12(1): 25–30.
22. Lama Tamang TG, Tang L, Chuang J, Patel RJ, Wong ND. Examining risk factor goal attainment and adherence to treatment among US heart failure patients: the National Health and Nutrition Examination Survey 2007–2010. Am J Cardiovasc Drugs. 2014; 14(1): 41–49.
23. Powell LH, Calvin JE Jr, Richardson D, et al.; HART Investigators. Self-management counseling in patients with heart failure: the heart failure adherence and retention randomized behavioral trial. JAMA. 2010; 304(12): 1331–1338.
24. Snyder ME, Frail CK, Jaynes H, Pater KS, Zillich AJ. Predictors of medication-related problems among medicaid patients participating in a pharmacist-provided telephonic medication therapy management program. Pharmacotherapy. 2014; 34(10): 1022–1032.
25. Albert NM, Levy P, Langlois E, et al. Heart failure beliefs and self-care adherence while being treated in an emergency department. J Emerg Med. 2014; 46(1): 122–129.
26. Dunlay SM, Eveleth JM, Shah ND, McNallan SM, Roger VL. Medication adherence among community-dwelling patients with heart failure. Mayo Clin Proc. 2011; 86(4): 273–281.
27. Bekelman DB, Havranek EP, Becker DM, et al. Symptoms, depression, and quality of life in patients with heart failure. J Card Fail. 2007; 13(8): 643–648.
28. Delaney C, Apostolidis B. Pilot testing of a multicomponent home care intervention for older adults with heart failure. J Cardiovasc Nurs. 2010; 25(5): E27–E40.
29. Jaarsma T, Johansson P, Agren S, Strömberg A. Quality of life and symptoms of depression in advanced heart failure patients and their partners. Curr Opin Support Palliat Care. 2010; 4(4): 233–237.
30. Creswell JW, Plano Clark VL. Designing and Conducting Mixed Methods Research. Washington, DC: Sage Publications; 2011.
31. Creswell JW, Tashakkori A. Editorial: Differing perspectives on mixed methods research. J Mix Methods Res. 2007; 1(4): 303–308.
32. Morgan DL. Paradigms lost and pragmatism regained: Methodological implication of combining qualitative and quantitative methods. J Mix Methods Res. 2007; 1(1): 48–76.
33. Neafsey PJ, M’lan CE, Ge M, Walsh SJ, Lin CA, Anderson E. Reducing adverse self-medication behaviors in older adults with hypertension: results of an e-health clinical efficacy trial. Ageing Int. 2011; 36(2): 159–191.
34. American Geriatrics Society. AGS Beers Criteria for potentially inappropriate medication use in the older adults. 2012. http://www.americangeriatrics.org/files/documents/beers/PrintableBeersPocketCard.pdf. Accessed April 22, 2014.
35. Resnick B, Wehren L, Orwig D. Reliability and validity of the self-efficacy and outcome expectations for osteoporosis medication adherence scales. Orthop Nurs. 2003; 22(2): 139–147.
36. Erlen JA, Cha ES, Kim KH, Caruthers D, Sereika SM. The HIV Medication Taking Self-efficacy Scale. J Adv Nurs. 2010; 66(11): 2560–2572.
37. Krippendorff K. Content Analysis: An introduction to its methodology. 2nd ed. Thousand Oaks, CA: Sage Publications; 2004.
38. Campbell N, Boustani M, Limbil T, et al. The cognitive impact of anticholinergics: a clinical review. Clin Interv Aging. 2009; 4: 225–233.
39. Cancelli I, Gigli GL, Piani A, et al. Drugs with anticholinergic properties as a risk factor for cognitive impairment in elderly people: a population-based study. J Clin Psychopharmacol. 2008; 28(6): 654–659.
40. Cai X, Campbell N, Khan B, Callahan C, Boustani M. Longterm anticholinergic use and the aging brain. Alzheimers Dement. 2013; 9(4): 377–385.
41. Meleis AI, Sawyer LM, Im EO, Messiaas DK, Schumacher K. Experiencing transitions: an emerging middle-range theory. Adv Nurs Sci. 2000; 23: 12–28.
42. Erikson EH. The Life Cycle Completed. New York: W.W. Norton & Company; 1997.
43. Budnitz DS, Lovegrove MC, Shehab N, Richards CL. Emergency hospitalizations for adverse drug events in older americans. N Engl J Med. 2011; 365(21): 2002–2012.
44. Gnjidic D, Le Couteur DG, Kouladjian L, Hilmer SN. Deprescribing trials: methods to reduce polypharmacy and the impact on prescribing and clinical outcomes. Clin Geriatr Med. 2012; 28(2): 237–253.
45. Obreli Neto PR, Nobili A, de Lyra DP Jr, et al. Incidence and predictors of adverse drug reactions caused by drug-drug interactions in elderly outpatients: a prospective cohort study. J Pharm Pharm Sci. 2012; 15(2): 332–343.
46. Rau J Kaiser Health News. Half of nation’s hospitals fail again to escape Medicare’s readmission penalties. 2015. http://khn.org/news/half-of-nations-hospitals-fail-again-to-escape-medicares-readmission-penalties/. Accessed October 14, 2015.
47. Fick DM, Beizer JL, Brandt NJ, Steinman MA. AGS updated 2015 Beers Criteria for potentially inappropriate medication use in older adults. Plenary symposium. Presented at the Annual meeting of the American Geriatrics Society; National Harbor, MD; May 16, 2015.
48. Centers for Medicare & Medicaid Services. Meaningful use. 2014. http://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/downloads/7_Medication_Reconciliation.pdf. Accessed March 22, 2014.
49. Roe CM, Anderson MJ, Spivack B. Use of anticholinergic medications by older adults with Dimentia. J Am Geriatr Soc. 2002; 50(5): 836–842.
50. Ancelin ML, Artero S, Portet F, Dupuy AM, Touchon J, Ritchie K. Non-degenerative mild cognitive impairment in elderly people and use of anticholinergic drugs: longitudinal cohort study. BMJ. 2006; 332(7539): 455–459.
51. He Z, Ball PA. Can medication management review reduce anticholinergic burden (ACB) in the elderly? Encouraging results from a theoretical model. Int Psychogeriatr. 2013; 25(9): 1425–1431.
52. Shaukat A, Habib A, Lane KA, et al. Anticholinergic medications: an additional contributor to cognitive impairment in the heart failure population? Drugs Aging. 2014; 31(10): 749–754.
53. National Committee for Quality Assurance (NCQA). HEDIS and Performance Measurement. 2015. http://www.ncqa.org/Portals/0/HEDISQM/Hedis2015/List_of_HEDIS_2015_Measures.pdf. Accessed October 14, 2015.
54. Centers for Medicare & Medicaid Services. 2015. CMS Proposed Changes to Meaningful Use Measures 2015–2017. http://www.informertsystem.com/wp-content/uploads/2015/05/MU-in-2015-Changes-Proposed-by-CMS-EPs.pdf. Accessed October 14, 2015.
55. Riegel B, Dickson VV, Kuhn L, Page K, Worrall-Carter L. Gender-specific barriers and facilitators to HF self-care: A mixed methods study. Int J Nurs Stud. 2010; 47(7): 888–895.
56. Coleman EA. The Care Transitions Program: health care services for improving quality and safety during care hand-offs. 2014. http://www.caretransitions.org/downloadmdt.asp. Accessed June 8, 2014.
57. Naylor MD. Transitional care for older adults: a cost-effective model. LDI Issue Brief. 2004; 9(6): 1–4.
58. Naylor MD, Hirschman KB, O’Connor M, Barg R, Pauly MV. Engaging older adults in their transitional care: what more needs to be done? J Comp Eff Res. 2013; 2(5): 457–468.
59. Bixby MB, Naylor MD. The Transitional Care Model (TCM): hospital discharge screening criteria for high risk older adults. Medsurg Nurs. 2010; 19(1): 62–63.
60. Naylor MD. Advancing high value transitional care: the central role of nursing and its leadership. Nurs Adm Q. 2012; 36(2): 115–126.
61. Shellman J, Lacey K, Clemmens D. CareLink: partners in a caring model, a cardiac management program for home care. Home Healthc Nurse. 2008; 26(10): 582–588.
62. Berner YN. The challenge of geriatric medicine in the twenty-first century. Harefuah. 2012; 151(9): 518–519, 557.
63. Iloabuchi TC, Mi D, Tu W, Counsell SR. Risk factors for early hospital readmission in low-income elderly adults. J Am Geriatr Soc. 2014; 62(3): 489–494.
64. Donald K, Cherry MS, Esther Hing MPH, et al. Division of Health Care Statistics. http://www.cdc.gov/nchs/data/nhsr/nhsr003.pdf. Accessed October 5, 2015.
Keywords:

anticholinergic burden; geriatrics; health transition; heart failure; medication therapy regimen

Copyright © 2017 Wolters Kluwer Health, Inc. All rights reserved