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Characteristics and in-hospital outcomes for nonadherent patients with heart failure: Findings from Get With The Guidelines-Heart Failure (GWTG-HF)
Characteristics and in-hospital outcomes for nonadherent patients with heart failure: Findings from Get With The Guidelines-Heart Failure (GWTG-HF) Amrut V. Ambardekar, MD, a Gregg C. Fonarow, MD, b Adrian F. Hernandez, MD, MS, c Wenqin Pan, PhD, c Clyde W. Yancy, MD, d and Mori J. Krantz, MD a,e for the Get With the Guidelines Steering Committee and Hospitals Aurora and Denver CO; Los Angeles, CA; Durham, NC; and Dallas, TX
Background Medication and dietary nonadherence are precipitating factors for heart failure (HF) hospitalization; however, the characteristics, outcomes, and quality of care of patients with nonadherence are unknown. Recognizing features of nonadherent patients may provide a means to reduce rehospitalization for this population. Methods GWTG-HF registry data were collected from 236 hospitals and 54,322 patients from January 1, 2005 to December 30, 2007. Demographics, clinical characteristics, in-hospital outcomes, and quality of care were stratified by precipitating factor for HF admission. Multivariate logistic regression analysis was used to determine the association of nonadherence with length of stay (LOS) and in-hospital mortality. Results Clinicians documented dietary and/or medication nonadherence as the reason for admission in 5576 (10.3%) of HF hospitalizations. Nonadherent patients were younger and more likely to be male, minority, uninsured, and have nonischemic HF. These patients had lower ejection fractions (34.9% vs 39.6%, P b .0001), more frequent previous HF hospitalizations, higher brain natriuretic peptide levels (1813 vs 1371 pg/mL, P b .0001), and presented with greater signs of congestion. Despite this, nonadherent patients had shorter LOS (odds ratio 0.94, 95% CI 0.92-0.97) and lower in-hospital mortality (odds ratio 0.65, 95% CI 0.51-0.83) in multivariate analysis. Although nonadherent patients received high rates of Joint Commission core measures, rates of other evidence-based treatments were less optimal. Conclusions Nonadherence is a common precipitant for HF admission. Despite a higher risk profile, nonadherent patients had lower in-hospital mortality and LOS, suggesting that it may be easier to stabilize nonadherent patients by reinstituting sodium and/or fluid restriction and resuming medical therapy. (Am Heart J 2009;158:644-52.) Heart failure (HF) is the leading cause of hospital admissions in the adult population.1,2 Repeat HF hospitalization is a burden on the health care system and adversely impacts long-term patient outcomes.3,4 Both dietary and medication nonadherence are candidate factors that trigger HF rehospitalization, yet the burden of these factors as precipitants for HF admission remains unclear. Less is known about the impact of dietary versus medication nonadherence on process of care and clinical outcomes.
From the aDenver Health Medical Center and University of Colorado Denver, Aurora, CO, b Ahmanson-UCLA Cardiomyopathy Center, Los Angeles, CA, cDuke Clinical Research Institute, Durham, NC, and dBaylor Heart and Vascular Institute, Dallas, TX, eColorado Prevention Center, Denver, CO. Dr. Hernandez received American Heart Association Pharmaceutical Roundtable grant 0675060N. Jack V. Tu, MD, PhD served as guest editor on this manuscript. Submitted May 30, 2009; accepted July 28, 2009. Reprint requests: Amrut V. Ambardekar MD, Division of Cardiology, University of Colorado Denver, 12631 E. 17th Avenue, Room 7102, Campus Box B-130, Aurora, CO 80045. E-mail: [email protected] 0002-8703/$ - see front matter © 2009, Mosby, Inc. All rights reserved. doi:10.1016/j.ahj.2009.07.034
There are a number of evidence-based therapies for HF that improve morbidity and mortality, and clear practice guidelines have been developed.4 Moreover, when therapies are initiated in the hospital, outpatient adherence is improved as well as long-term outcomes.5,6 Predischarge identification of patients with risk factors for nonadherence may provide opportunities to target preventable admissions through disease management plans. The goal of this study was to determine the characteristics, treatments, quality of care, and in-hospital outcomes of patients identified as having dietary and medication nonadherence as precipitating factors for HF hospitalization in a large contemporary group of patients. Understanding patient characteristics associated with nonadherence as well as differences with regard to quality of care may provide insights into the role of nonadherence as a mediator of HF outcomes creating a framework for instituting interventions to improve adherence.
Methods The GWTG-HF program is overseen by the American Heart Association and is an ongoing, prospective observational data
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collection and quality improvement initiative.7 Hospitals participating in this registry include institutions from all regions of the United States and represent community hospitals as well as tertiary referral centers. Trained individuals at each site submitted clinical information regarding medical history, hospital care, and outcomes for consecutive patients hospitalized for HF using an online, interactive Patient Management Tool (Outcome Sciences, Inc, Cambridge, MA). Variables entered included demographic and clinical characteristics, medical history, previous treatments and HF hospitalizations, contraindications to evidence-based therapies, quality and performance measures, and in-hospital outcomes. Data on factors contributing to exacerbation of the patients' HF were prespecified and collected as follows: ischemia/acute coronary syndromes, uncontrolled hypertension, pneumonia/respiratory process, worsening renal function, arrhythmia, nonadherence— diet, nonadherence—medications, and other. More than one factor could be selected if applicable. Nonadherence data were based on clinician interview and patient self-report. All participating institutions were required to comply with local regulatory and privacy guidelines and, if appropriate, to submit the GWTG-HF protocol for review and approval by their institutional review board. Because data were used primarily at the local site for quality improvement, sites were granted a waiver of informed consent under the common rule. Outcome Sciences Inc served as the clinical coordinating center for GWTG. The Duke Clinical Research Institute (Durham, NC) served as the data analysis center, and institutional review board approval was granted to analyze aggregate deidentified data for research purposes.
Patient eligibility The analysis cohort included patients reported in the GWTGHF database from January 1, 2005, to December 30, 2007, who were N18 years old. To be included in the study, participating institutions needed to provide information regarding the precipitating cause of the HF admission. As we were interested in analyzing adherence to HF therapy as a cause for hospital admissions, patients with new diagnoses of HF were not included in the analysis. Only sites and variables with a high degree of completeness were used in the analysis.
Outcome measures Demographics and clinical characteristics were stratified by precipitating factor for HF admission. Patients were categorized into 2 groups: those in whom nonadherence (including either dietary nonadherence, medication nonadherence, or both) contributed to HF admission and those without nonadherence. Within the subgroup of patients identified as having nonadherence as their HF precipitant, differences between patients with dietary nonadherence alone, mediation nonadherence alone, and combined dietary and medication nonadherence were also analyzed. Hospital outcomes and quality of care were then assessed among patients with nonadherence versus patients without nonadherence. Hospital outcomes included length of stay (LOS) and mortality. Quality of care was assessed by measuring compliance with Joint Commission HF core measures8 as well as 8 additional prespecified evidence-based quality measures in the GWTG-HF registry. Rates of compliance with core
Ambardekar et al 645
measures and evidence-based guidelines were analyzed only for eligible patients in whom no therapeutic contraindication was identified. The 4 Joint Commission HF core measures include (1) providing HF specific discharge instructions (activity level, low sodium diet, weight monitoring, symptom recognition, follow-up appointment, and medication counseling), (2) assessment and documentation of left ventricular ejection fraction (EF), (3) use of an angiotensin-converting enzyme inhibitor or angiotensin receptor blocker in patients with left ventricular systolic dysfunction (LVSD), and (4) smoking cessation counseling. The 8 additional GWTG-HF quality measures include (1) use of a β-blocker in patients with LVSD, (2) anticoagulation for patients with atrial fibrillation, (3) use of an aldosterone antagonist for patients with LVSD, (4) a guideline-recommended β-blocker (bisoprolol, carvedilol, or extended-release metoprolol) for patients with LVSD, (5) use of hydralazine/nitrate therapy in African American patients with LVSD, (6) documentation of appropriate blood pressure (BP) control (systolic BP b140 mm Hg and diastolic BP b90 mm Hg) at discharge, (7) lipid-lowering therapy in HF patients with coronary artery disease (CAD), and (8) implantable cardioverter defibrillator (ICD) implanted or planned for eligible patients with an EF of b30%.
Statistical analysis All statistical analyses were performed centrally at the Duke Clinical Research Institute. Data were reported as means for continuous variables and as percentages of nonmissing values for categorical variables. Univariate comparisons between patients with different precipitating factors were performed using χ2 analysis for categorical variables and Wilcoxon rank sum test (or Kruskal-Wallis test) for continuous variables. Prespecified subgroup analysis was also performed within the nonadherence group as a whole—comparing differences between dietary nonadherence alone, medication nonadherence alone, and dietary and medication nonadherence combined. Potential confounders that may have impacted outcomes were identified in the univariate analysis and included demographics (age, gender, race), insurance status, medical comorbidities (anemia, ischemic history, diabetes, hyperlipidemia, hypertension, cerebrovascular disease, peripheral arterial disease, chronic pulmonary disease, renal insufficiency and tobacco use), and clinical data at admission (weight, heart rate, blood pressure, respiratory rate, blood urea nitrogen [BUN], serum creatinine, and EF). The generalized estimating equation method was used to perform logistic multivariable regression models to adjust for these confounders in the analysis of in-hospital outcomes and quality of care measures. In addition, the adjusted multivariate model for in-hospital mortality incorporated LOS. Estimated adjusted odds ratio for death, quality and performance measures, and estimated mean ratios for LOS were then computed. SAS statistical software (version 9.1, SAS Institute Inc, Cary, NC) was used for all statistical analyses.
Sources of funding The GWTG-HF program is supported by the American Heart Association, which received funding in part from unrestricted educational grants from GlaxoSmithKline, Philadelphia, PA and Medtronic, Minneapolis, MN. The authors are solely responsible
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646 Ambardekar et al
Figure 1
Precipitating factors for HF admission.
for the design and conduct of this study, all analyses, and the drafting and editing of the paper and its final contents.
Results A total of 95,127 patients were identified among 333 hospitals from January 1, 2005 to December 30, 2007. Ninety-seven hospitals either did not provide information with regard to the precipitating cause of HF or had a high missing rate (N25%) on reporting patient medical history; thus, 32,495 patients were excluded. In addition, 8,310 patients were admitted with a first time diagnosis of HF and were also excluded. The final analysis cohort included 54,322 patients from 236 hospitals. Clinicians documented dietary and/or medication nonadherence as a reason for admission in 5576 (10.3%) of all HF hospitalizations. Other precipitating factors identified for the HF hospitalization are shown in Figure 1. Patients with nonadherence were more likely to be young, male, minority, and uninsured (Table I). In addition, nonadherent patients were more likely to have nonischemic cardiomyopathy, lower EF, and more frequent prior HF admissions. Physical examination and laboratory findings at admission among nonadherent patients were notable for greater volume overload as evidenced by higher body weight, increased presence of jugular venous distention, rales, and edema on examination as well as higher brain natriuretic peptide (BNP) levels. Some HF prognostic variables such as blood pressure, BUN, and troponin, appeared to reflect lower disease severity in nonadherent patients, whereas other prognostic measurements such as BNP level, EF, and number of prior HF admissions were less favorable in nonadherent patients. In multivariate analysis, younger age, male gender, minority race, and
lack of insurance were independently associated with nonadherence (Table II). Within the population of patients with nonadherence, differences between dietary only, medication only, and both dietary and medication nonadherence were examined (Table III). Patients with medication nonadherence as a component of their nonadherence versus dietary nonadherence alone were more likely to be young, male, minority, and uninsured. This subpopulation also tended to have increased rates of nonischemic cardiomyopathy, alcohol abuse, tobacco dependence, as well as lower EF. By contrast, diabetes and higher body mass index were more prevalent among those with dietary nonadherence. Patients with combined medication and dietary nonadherence were almost twice as likely to have had ≥2 HF admissions in the previous 6 months as well as considerably higher BNP levels. In-hospital outcomes differed for patients identified as having nonadherence as a precipitating factor for hospitalization compared to those who did not. Nonadherent patients were observed to have lower inhospital mortality and shorter LOS (Table IV). Even after adjusting for multiple variables of prognostic importance, nonadherence appeared to be independently associated with better in-hospital outcomes. Among the different subtypes of nonadherence, patients with dietary nonadherence alone tended to have the lowest unadjusted inhospital mortality (Figure 2 and Table V). Nonadherent patients were as likely to receive Joint Commission HF performance measures compared to patients without nonadherence (Table IV). With regard to additional GWTG quality measures, slightly higher rates of aldosterone antagonist utilization were noted among nonadherent patients. However, they were less likely to have optimal BP control, be on lipid-lowering therapy in
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Table I. Baseline patient characteristics
Table I (continued ) Without Nonadherence nonadherence (n = 48 746, Univariate (n = 5576, 89.7%) P 10.3%)
Without Nonadherence nonadherence (n = 48 746, Univariate (n = 5576, 89.7%) P 10.3%) Demographics Male (%) 60.4 Mean age (y) 64.2 White (%) 53.0 Minority race (%) 47.0 No health 11.7 insurance (%) Baseline HF history (%) Nonischemic 57.4 cardiomyopathy ≥2 HF admissions 18.2 in preceding 6 mo Atrial fibrillation 24.4 ICD in place 11.8 Medical comorbidities (%) Alcohol abuse 6.5 Anemia 16.9 Chronic lung 31.0 disease Cerebrovascular 12.5 disease Depression 10.3 Diabetes 46.1 18.9 Chronic renal insufficiency (creatinine N2 mg/dL) Hypertension 77.8 Hyperlipidemia 38.0 Cigarette smoking 32.6 Prescribed medications at admission (%) β-Blocker 80.0 ACE 47.8 ARB 14.3 Aldosterone 13.4 antagonist Diuretic 74.4 Clinical data at presentation Body mass 31.5 index (kg/m2) Weight (kg) 90.7 Heart rate 89.4 (beat/min) Systolic blood 146.8 pressure (mm Hg) Diastolic blood 84.5 pressure (mm Hg) Jugular venous 30.0 distention present (%) S3 gallop 9.6 present (%) S4 gallop 5.3 present (%) Rales present (%) 59.6 Edema present (%) 69.6 LDL (mg/dL) 93.8
48.4 73.6 74.6 25.4 3.3
b.0001 b.0001 b.0001 b.0001 b.0001
50.6
b.0001
11.3
b.0001
31.0 10.1
b.0001 .0002
1.3 17.1 29.2
b.0001 .7493 .0044
14.5
b.0001
10.1 43.5 20.2
.7289 .0002 .0183
71.7 38.1 14.3
b.0001 .9007 b.0001
79.1 41.4 15.1 10.7
.2003 b.0001 .1625 b.0001
71.2
b.0001
29.3
b.0001
82.1 84.2
b.0001 b.0001
138.6
b.0001
74.9
b.0001
21.5
b.0001
6.1
b.0001
3.2
b.0001
52.3 59.0 86.7
b.0001 b.0001 b.0001
Sodium (mEq/L) Hemoglobin (g/dL) BNP (pg/mL) Serum creatinine (mg/dL) BUN (mg/dL) Troponin (ng/mL) Hemoglobin A1c (%) Mean EF LVSD present (EF b40%)
136.8 12.7
136.6 12.2
.0010 b.0001
1813 1.93
1371 1.90
b.0001 .0002
28.4 0.21 7.97
31.9 0.35 7.37
b.0001 .3206 b.0001
34.9 60.2
39.6 48.5
b.0001 b.0001
ACE, Angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; LDL, low-density lipoprotein.
Table II. Multivariate analysis of characteristics associated with nonadherence
Demographics Younger age (per each year decrease) Male gender (vs female) Nonwhite race (vs white) No health insurance (vs health insurance) Medical comorbidities Hypertension (vs no hypertension) Chronic lung disease (vs no chronic lung disease) Cigarette smoking (vs no smoking) Clinical data at presentation Higher weight (per each kilogram increase) Higher heart rate (per each beat/min increase) Higher blood pressure (per each mm Hg increase) Lower BUN (per each mg/dL decrease) Lower left ventricular EF (per each unit decrease in left ventricular EF)
Odds ratio (95% CI)
Multivariate P
1.022 (1.019-1.026)
b.0001
1.274 (1.196-1.358) 1.489 (1.358-1.632) 1.421 (1.236-1.633)
b.0001 b.0001 b.0001
1.183 (1.078-1.298)
.0004
1.070 (1.006-1.138)
.0322
1.683 (1.562-1.814)
b.0001
1.005 (1.003-1.007)
b.0001
1.005 (1.004-1.007)
b.0001
1.007 (1.006-1.008)
b.0001
1.003 (1.001-1.005)
.0025
1.008 (1.006-1.010)
b.0001
Odds ratio N1.0 indicates the variable is associated with increased frequency of nonadherence.
the setting of CAD, and receive consideration for ICD implantation. Within each subgroup of nonadherent patients, Joint Commission core measures did not differ (Table V). By contrast, patients with medication nonadherence as a component of their nonadherence compared to dietary
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648 Ambardekar et al
Table III. Baseline characteristics by types of nonadherence Dietary nonadherence Medication Dietary and medication (n = 1302) nonadherence (n = 2996) nonadherence (n = 1278) Univariate P Demographics Male (%) Mean age (y) White (%) Minority race (%) No health insurance (%) Baseline HF history (%) Nonischemic cardiomyopathy ≥2 HF admissions in preceding 6 mo Atrial fibrillation ICD in place Medical comorbidities (%) Alcohol abuse Anemia Chronic lung disease Cerebrovascular disease Depression Diabetes Chronic renal insufficiency (creatinine N2 mg/dL) Hypertension Hyperlipidemia Cigarette smoking Clinical data at presentation Body mass index (kg/m2) Weight (kg) Heart rate (beat/min) Systolic blood pressure (mm Hg) Diastolic blood pressure (mm Hg) Jugular venous distention present (%) S3 gallop present (%) S4 gallop present (%) Rales present (%) Edema present (%) LDL (mg/dL) Sodium (mEq/L) Hemoglobin (g/dL) BNP (pg/mL) Serum creatinine (mg/dL) BUN (mg/dL) Troponin (ng/mL) Hemoglobin A1c (%) Mean EF LVSD present (EF b40%)
56.7 69.1 67.2 32.8 2.6
60.1 63.8 51.9 48.1 13.8
64.9 60.3 40.9 59.1 16.0
.0001 b.0001 b.0001 b.0001 b.0001
51.3 15.5
58.7 15.1
60.7 28.2
b.0001 b.0001
29.2 13.1
23.7 10.9
21.0 12.4
b.0001 .099
3.3 20.2 32.5 14.1 10.1 55.2 21.4
5.9 15.5 30.3 12.1 10.2 40.6 16.8
11.1 16.9 31.2 11.9 10.8 49.4 21.0
b.0001 .0008 .3528 .1502 .8215 b.0001 .0002
76.3 46.2 21.0
78.1 35.0 34.0
78.6 36.7 41.0
.3405 b.0001 b.0001
32.9 93.7 85.1 141.0 77.2 30.7 6.4 5.1 60.5 74.3 81.5 136.7 12.1 1811.6 2.0 31.4 0.15 7.82 38.4 50.3
30.6 88.3 90.7 148.0 86.4 28.9 10.0 5.2 59.1 67.3 97.3 136.9 13.0 1622.6 1.8 26.9 0.23 7.96 34.1 63.2
32.1 93.1 91.1 149.9 87.8 31.5 12.0 5.9 59.7 69.8 96.3 136.4 12.3 2259.2 2.2 28.9 0.21 8.11 33.2 63.0
b.0001 b.0001 b.0001 b.0001 b.0001 .1479 b.0001 .0010 .4449 .0003 b.0001 .0514 b.0001 b.0001 b.0001 b.0001 .0023 .6357 b.0001 b.0001
nonadherence alone were less likely to be prescribed anticoagulation therapy for atrial fibrillation, evidencebased β-blockers, and lipid-lowering therapy in the setting of CAD, and they were less likely to have an ICD when indicated.
Discussion We investigated a broad cohort of US patients admitted with acute decompensated HF to evaluate the influence of nonadherence on quality of care and outcomes. This study has 3 main findings. First, nonadherence is a common precipitant for HF admission, and such patients are sociodemographically disadvantaged relative to
patients without nonadherence. In addition, medication nonadherence was more commonly noted among younger patients, ethnic minorities, and the uninsured, whereas dietary nonadherence was observed more frequently in older, overweight, and diabetic patients. Second, nonadherent patients present with evidence of lower EF and greater volume overload, yet have an inhospital course characterized by a shorter LOS and lower mortality. This suggests greater acuity of presentation but lower short-term disease severity, less difficulty in achieving stability with reinstitution of sodium restriction, fluid restriction, and/or medication, and potentially preventable admission. Finally, nonadherent patients
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Table IV. Hospital outcomes and quality of care with multivariate analysis⁎ Nonadherence Mortality (%) Mean length of stay (d) Joint Commission HF core measures (% compliance) Discharge instructions Documentation of left ventricular EF ACE or ARB for LVSD Smoking cessation counseling Additional GWTG-HF quality measures (% compliance) β-Blocker for LVSD Anticoagulation for atrial fibrillation Aldosterone antagonist for LVSD Evidence-based β-blocker for LVSD Hydralazine/nitrate for AA patients with LVSD Discharge BP b140/90 mm Hg Lipid-lowering therapy for CAD ICD therapy for left ventricular EF b30%
1.55 4.99
Without nonadherence
Univariate P
Adjusted odds ratio (95% CI)
Multivariate P
3.49 5.63
b.0001 b.0001
0.66 (0.51-0.86) 0.94 (0.92-0.97)
.0017 b.0001
82.6 94.5 89.9 91.5
81.7 93.9 85.0 90.0
.1533 .0936 b.0001 .0700
1.06 0.95 1.14 1.07
(0.97-1.16) (0.85-1.06) (0.98-1.31) (0.93-1.22)
.1725 .3554 .0873 .3680
91.6 66.9 34.5 71.8 8.6 70.9 55.9 35.7
89.2 66.6 27.3 71.7 7.0 75.5 58.2 41.4
.0002 .8621 b.0001 .8994 .0599 b.0001 .0145 b.0001
1.12 0.91 1.14 0.95 1.18 0.92 0.82 0.80
(0.97-1.29) (0.78-1.06) (1.03-1.27) (0.84-1.06) (0.91-1.52) (0.85-0.99) (0.74-0.91) (0.69-0.93)
.1271 .2287 .0151 .3351 .2043 .0252 b.0001 .0034
AA, African American. ⁎ Adjusted for age, gender, race, insurance status, medical comorbidities, clinical data at admission, and hospital sites. Mortality multivariate analysis additionally adjusted for length of stay.
Figure 2
Unadjusted in-hospital mortality rates.
received care with high rates of conformity with Joint Commission core measures but lower rates of conformity with other guideline-based therapies. Nonadherence as clinically identified and documented in the medical record is a common reason for HF admission accounting for 10.3% of admissions in this contemporary registry. The burden of nonadherence reported in the literature is variable depending on the population studied and how nonadherence was defined. Among patients hospitalized for HF exacerbation participating in clinical trials, medication nonadherence contributed to HF admission in 3% to 11% of patients.9,10 By contrast, among a population of urban African American patients hospitalized for HF exacerbation,
nonadherence identified by detailed patient interview accounted for 64% of HF admissions.11 The results of our study support the notion that nonadherence is more commonly identified and is a critical factor in socioeconomically disadvantaged populations. Nonadherence rates in our study were comparable to another large contemporary registry that also relied on clinical history for defining nonadherence. In the OPTIMIZE-HF registry, dietary nonadherence contributed to HF admission in 5.2% of patients and medication nonadherence contributed to HF admission in 8.9% of patients.12 Given the sheer volume of HF admissions, the overall burden of clinically reported nonadherence is quite high even if these estimates are conservative. As HF
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Table V. Hospital outcome measures by type of nonadherence Dietary nonadherence Mortality (%) Length of stay (d) Joint Commission HF core measures (% compliance) Discharge instructions Documentation of left ventricular EF ACE or ARB for LVSD Smoking cessation counseling Additional GWTG-HF quality measures (% compliance) β-Blocker for LVSD Anticoagulation for atrial fibrillation Aldosterone antagonist for LVSD Evidence-based β-blocker for LVSD Hydralazine/nitrate for AA patients with LVSD Discharge BP b140/90 mm Hg Lipid-lowering therapy for CAD ICD therapy for left ventricular EF b30%
Medication nonadherence
Dietary and medication nonadherence
Univariate P
1.6 4.9
1.8 5.5
b.0001 .2942
82.5 94.1 92.1 91.5
81.7 94.5 89.0 90.3
84.9 94.8 90.4 94.1
.0727 .8046 .1659 .0756
92.1 74.5 32.0 76.2 8.2
91.0 64.2 34.9 69.9 8.7
92.7 62.2 35.4 72.7 8.6
.4203 .0043 .4417 .0251 .9758
73.8 64.5 46.1
70.8 52.1 31.8
68.4 54.4 36.9
.0231 b.0001 b.0001
0.92 4.7
admission due to nonadherence may be preventable, addressing this precipitant has the potential to lower the economic burden of HF. In examining long-term outcomes, nonadherence has been associated with higher mortality and more frequent hospitalizations.10,13 We similarly noted some poor prognostic markers among nonadherent patients including lower EF, more frequent prior admissions, and higher BNP levels. However, despite these markers, the nonadherent patients in our study had lower in-hospital mortality and shorter LOS. The reasons for why nonadherent patients had better in-hospital outcomes are unclear. Nonadherent patients were younger, had higher BP, and lower BUN levels—all markers of better prognosis in HF. It is plausible that it is easier to stabilize such patients by reinstituting sodium restriction and resuming medical therapy. This suggests that if the cycle of nonadherence is broken, clinicians may be able to improve long-term outcomes in this high-risk population. In this study, rates of compliance with the each of the Joint Commission core measures for patients with nonadherence ranged from 83% to 94%. This is higher than what was reported in the OPTIMIZE-HF registry14 from 2003 to 2004 where the compliance rates for each of the core measures ranged from 54% to 87% and the ADHERE registry15 from 2002 to 2003 where the compliance rates ranged from 24% to 86%. Improved compliance in the GWTG-HF database may reflect more contemporary trends as well as an emerging emphasis on documentation of these measures. It is also possible that a quality improvement program like GWTG-HF may enhance evidence-based care by providing clinicians with real-time guideline reminders. In addition, the GWTG-HF program gives periodic updates to institutions comparing their individual facility to other similar institutions. Such provider- and institution-specific feedback has been
shown to improve care in other diseases such as acute coronary syndromes.16 It is noteworthy that the rates of compliance with additional quality measures assessed in the GWTG-HF database varied between patients with nonadherence versus those without nonadherence. The reasons for these differences are unclear. Nonadherent patients were less often considered for ICD therapy, which may reflect a sense from clinicians that if patients were to become adherent with medications that improve cardiac remodeling, functional status and EF would improve obviating the need for ICD therapy. It is important to note that the rates of compliance with these additional quality measures in the total cohort of patients were not ideal, and improvement in these areas for all patients may improve long-term outcomes. This is one of the few studies in HF to compare differences between subtypes of nonadherence. Patients with dietary nonadherence appeared to have the best inhospital outcomes, whereas those with combined dietary and medication nonadherence had the highest in-hospital mortality and greatest LOS. In addition, patients with medication nonadherence were less likely to receive several key evidence-based medical therapies. The reasons for these differences are not clear. One possibility may be that medication nonadherence influences clinicians to be less likely to prescribe guideline-based therapies, thus potentiating a cycle of nonadherence and hospital readmission. This represents another potential area for investigation.
Limitations We acknowledge a number of limitations to this study. It is possible that the rates of nonadherence were underestimated. Relying on patient-reported nonadherence during a clinician history has lower sensitivity for
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detecting nonadherence compared with more objective measures such as pill counts13; however, such measures are rarely used in routine clinical practice. Furthermore, there may have been physician bias based on patient characteristics in identifying and documenting nonadherence. The underlying reasons for medication nonadherence and different subtypes of dietary nonadherence (fluid vs salt vs food) were not investigated. The GWTG-HF is a voluntary program and could overrepresent high-performing hospitals. Data were collected by chart review and thus depend on the accuracy and completeness of documentation. Multivariate analysis was performed to attempt to adjust for confounding differences between patients with and without nonadherence, but it is possible that there were residual unmeasured confounders unaccounted for in the analysis. In addition, the GWTG-HF database does not track inpatient provider specialty, and this may influence both in-hospital mortality and LOS.17 Finally, only in-hospital measures were tracked, and thus, longterm follow-up and outcomes are unknown. Despite these limitations, the current study has several advantages over other HF studies. This analysis was conducted from a large, contemporary, prospectively collected database from multiple hospitals across the United States. Consecutive patients were enrolled from small institutions to large referral centers. It also reflects real-world experience as opposed to studies involving highly selected patients enrolled in a clinical trial.
Conclusions Among GWTG-HF hospitals, patients with nonadherence as a factor for HF hospitalization tended to be younger and more sociodemographically disadvantaged. Despite evidence of greater volume overload and lower EF, this population had better in-hospital outcomes. This lower risk-adjusted mortality and LOS suggests that it may be easier to stabilize nonadherent patients by reinstituting sodium and/or fluid restriction and resuming appropriate medical therapy. Patients with nonadherence were equally or more likely to receive the Joint Commission HF core measures at discharge. However, rates of other guideline-based care were suboptimal, which may contribute to poor long-term outcomes. Given the high resource utilization in patients with nonadherence, targeted efforts to assess predictors of HF readmission and to improve rates of compliance with all evidencebased care for this vulnerable population have the potential to reduce health disparities, improve quality of care, and lower hospital costs.
Disclosures Dr Fonarow reports receiving research grants and honoraria from GlaxoSmithKline and Medtronic and
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serves as a consultant for GlaxoSmithKline, Medtronic, and Novartis. He serves as chair of the American Heart Association’s Get With the Guidelines Steering Committee. He is supported by the Ahmanson and Elliot Corday Foundations. Dr Hernandez reports receiving research grants from Scios, Medtronic, GlaxoSmithKline, and Roche Diagnostics and has served on the speaker’s bureau or has received honoraria in the past 5 years from Novartis. Dr Yancy reports no active consulting or speaker bureau relationships. He continues to hold a noncompensated research relationship with Medtronic and has an editorial relationship with theheart.org and several cardiology journals. Dr Yancy does have contracts with the FDA and NIH. He currently holds a volunteer leadership role with the American Heart Association. Dr Krantz has received investigator initiated research grant support from GlaxoSmithKline and serves on their speaker’s bureau. He has received speaker honorarium in the last five years from Pfizer, Novartis, and Sanofi-BMS. He volunteers for the American Heart Association locally and serves on the national Get With the Guidelines Steering Committee. Dr Ambardekar is supported by a 2009 Research Fellowship Award from the Heart Failure Society of America.
References 1. American Heart Association. Heart disease and stroke statistics— 2006 update. Dallas (Tex): American Heart Association; 2006. 2. Koelling TM, Chen RS, Lubwama RN, et al. The expanding national burden of heart failure in the United States: the influence of heart failure in women. Am Heart J 2004;147:74-8. 3. Gheorghiade M, Zannad F, Sopko G, et al. International Working Group on Acute Heart Failure Syndromes. Acute heart failure syndromes: current state and framework for future research. Circulation 2005;112:3958-68. 4. Adams KF, Lindenfeld J, Arnold JMO, et al. Executive summary: HFSA 2006 comprehensive heart failure practice guideline. J Cardiac Failure 2006;12:10-38. 5. Krantz MJ, Havranek EP, Haynes DK, et al. Inpatient initiation of βblockade plus nurse management in vulnerable heart failure patients: a randomized study. J Cardiac Fail 2008;14:303-9. 6. Fonarow GC, Abraham WT, Albert NM, et al. Carvedilol use at discharge in patients hospitalized for heart failure is associated with improved survival: an analysis from Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure (OPTIMIZE-HF). Am Heart J 2007;153:82.e1-82.e11. 7. Hong Y, LaBresh KA. Overview of the American Heart Association “Get With The Guidelines” programs: Coronary heart disease, stroke, and heart failure. Crit Pathways in Cardiol 2006;5:179-86. 8. The Joint Commission. Specification manual for national implementation of hospital core measures: version 2.5. Available at: http://www.jointcommission.org/PerformanceMeasurement/ PerformanceMeasurement/Current+NHQM+Manual.htm. Last accessed November 8, 2008. 9. Tsuyuki RT, McKelvie RS, Arnold MO, et al. Acute precipitants of congestive heart failure exacerbations. Arch Intern Med 2001;161: 2337-42.
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10. Granger BB, Swedbery K, Ikman I, et al. Adherence to candesartan and placebo and outcomes in chronic heart failure in the CHARM programme: double-blind, randomized, controlled clinical trial. Lancet 2005;366:2005-11. 11. Ghali JK, Kadakia S, Cooper R, et al. Precipitating factors leading to decompensation in heart failure: traits among urban blacks. Arch Intern Med 1988;148:2013-6. 12. Fonarow GC, Abraham WT, Albert NM, et al. Factors identified as precipitating hospital admission for heart failure and clinical outcomes: findings from OPTIMIZE-HF. Arch Intern Med 2008;168: 847-54. 13. Wu JR, Moser DK, Chung ML, et al. Objectively measured, but not self-reported, medication adherence independently predicts event-
O
14.
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16. 17.
free survival in patients with heart failure. J Cardiac Failure 2008;14: 203-10. Fonarow GC, Abraham WT, Albert NM, et al. Association between performance measures and clinical outcomes for patients hospitalized with heart failure. JAMA 2007;297:61-70. Fonarow GC, Yancy CW, Heywood J. Adherence to Heart Failure Quality of Care Indicators in US hospitals: Analysis of the ADHERE Registry. Arch Intern Med 2005;165:1469-77. Roe MT. Success stories: how hospitals are improving care. Am Heart J 2004;148:S52-5. Ahmed A, Allman RM, Kiefe CI, et al. Association of consultation between generalists and cardiologists with quality and outcomes of heart failure care. Am Heart J 2003;145:1086-93.
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Background Medication and dietary nonadherence are precipitating factors for heart failure (HF) hospitalization; however, the characteristics, outcomes, and quality of care of patients with nonadherence are unknown. Recognizing features of nonadherent patients may provide a means to reduce rehospitalization for this population. Methods GWTG-HF registry data were collected from 236 hospitals and 54,322 patients from January 1, 2005 to December 30, 2007. Demographics, clinical characteristics, in-hospital outcomes, and quality of care were stratified by precipitating factor for HF admission. Multivariate logistic regression analysis was used to determine the association of nonadherence with length of stay (LOS) and in-hospital mortality. Results Clinicians documented dietary and/or medication nonadherence as the reason for admission in 5576 (10.3%) of HF hospitalizations. Nonadherent patients were younger and more likely to be male, minority, uninsured, and have nonischemic HF. These patients had lower ejection fractions (34.9% vs 39.6%, P b .0001), more frequent previous HF hospitalizations, higher brain natriuretic peptide levels (1813 vs 1371 pg/mL, P b .0001), and presented with greater signs of congestion. Despite this, nonadherent patients had shorter LOS (odds ratio 0.94, 95% CI 0.92-0.97) and lower in-hospital mortality (odds ratio 0.65, 95% CI 0.51-0.83) in multivariate analysis. Although nonadherent patients received high rates of Joint Commission core measures, rates of other evidence-based treatments were less optimal. Conclusions Nonadherence is a common precipitant for HF admission. Despite a higher risk profile, nonadherent patients had lower in-hospital mortality and LOS, suggesting that it may be easier to stabilize nonadherent patients by reinstituting sodium and/or fluid restriction and resuming medical therapy. (Am Heart J 2009;158:644-52.) Heart failure (HF) is the leading cause of hospital admissions in the adult population.1,2 Repeat HF hospitalization is a burden on the health care system and adversely impacts long-term patient outcomes.3,4 Both dietary and medication nonadherence are candidate factors that trigger HF rehospitalization, yet the burden of these factors as precipitants for HF admission remains unclear. Less is known about the impact of dietary versus medication nonadherence on process of care and clinical outcomes.
From the aDenver Health Medical Center and University of Colorado Denver, Aurora, CO, b Ahmanson-UCLA Cardiomyopathy Center, Los Angeles, CA, cDuke Clinical Research Institute, Durham, NC, and dBaylor Heart and Vascular Institute, Dallas, TX, eColorado Prevention Center, Denver, CO. Dr. Hernandez received American Heart Association Pharmaceutical Roundtable grant 0675060N. Jack V. Tu, MD, PhD served as guest editor on this manuscript. Submitted May 30, 2009; accepted July 28, 2009. Reprint requests: Amrut V. Ambardekar MD, Division of Cardiology, University of Colorado Denver, 12631 E. 17th Avenue, Room 7102, Campus Box B-130, Aurora, CO 80045. E-mail: [email protected] 0002-8703/$ - see front matter © 2009, Mosby, Inc. All rights reserved. doi:10.1016/j.ahj.2009.07.034
There are a number of evidence-based therapies for HF that improve morbidity and mortality, and clear practice guidelines have been developed.4 Moreover, when therapies are initiated in the hospital, outpatient adherence is improved as well as long-term outcomes.5,6 Predischarge identification of patients with risk factors for nonadherence may provide opportunities to target preventable admissions through disease management plans. The goal of this study was to determine the characteristics, treatments, quality of care, and in-hospital outcomes of patients identified as having dietary and medication nonadherence as precipitating factors for HF hospitalization in a large contemporary group of patients. Understanding patient characteristics associated with nonadherence as well as differences with regard to quality of care may provide insights into the role of nonadherence as a mediator of HF outcomes creating a framework for instituting interventions to improve adherence.
Methods The GWTG-HF program is overseen by the American Heart Association and is an ongoing, prospective observational data
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collection and quality improvement initiative.7 Hospitals participating in this registry include institutions from all regions of the United States and represent community hospitals as well as tertiary referral centers. Trained individuals at each site submitted clinical information regarding medical history, hospital care, and outcomes for consecutive patients hospitalized for HF using an online, interactive Patient Management Tool (Outcome Sciences, Inc, Cambridge, MA). Variables entered included demographic and clinical characteristics, medical history, previous treatments and HF hospitalizations, contraindications to evidence-based therapies, quality and performance measures, and in-hospital outcomes. Data on factors contributing to exacerbation of the patients' HF were prespecified and collected as follows: ischemia/acute coronary syndromes, uncontrolled hypertension, pneumonia/respiratory process, worsening renal function, arrhythmia, nonadherence— diet, nonadherence—medications, and other. More than one factor could be selected if applicable. Nonadherence data were based on clinician interview and patient self-report. All participating institutions were required to comply with local regulatory and privacy guidelines and, if appropriate, to submit the GWTG-HF protocol for review and approval by their institutional review board. Because data were used primarily at the local site for quality improvement, sites were granted a waiver of informed consent under the common rule. Outcome Sciences Inc served as the clinical coordinating center for GWTG. The Duke Clinical Research Institute (Durham, NC) served as the data analysis center, and institutional review board approval was granted to analyze aggregate deidentified data for research purposes.
Patient eligibility The analysis cohort included patients reported in the GWTGHF database from January 1, 2005, to December 30, 2007, who were N18 years old. To be included in the study, participating institutions needed to provide information regarding the precipitating cause of the HF admission. As we were interested in analyzing adherence to HF therapy as a cause for hospital admissions, patients with new diagnoses of HF were not included in the analysis. Only sites and variables with a high degree of completeness were used in the analysis.
Outcome measures Demographics and clinical characteristics were stratified by precipitating factor for HF admission. Patients were categorized into 2 groups: those in whom nonadherence (including either dietary nonadherence, medication nonadherence, or both) contributed to HF admission and those without nonadherence. Within the subgroup of patients identified as having nonadherence as their HF precipitant, differences between patients with dietary nonadherence alone, mediation nonadherence alone, and combined dietary and medication nonadherence were also analyzed. Hospital outcomes and quality of care were then assessed among patients with nonadherence versus patients without nonadherence. Hospital outcomes included length of stay (LOS) and mortality. Quality of care was assessed by measuring compliance with Joint Commission HF core measures8 as well as 8 additional prespecified evidence-based quality measures in the GWTG-HF registry. Rates of compliance with core
Ambardekar et al 645
measures and evidence-based guidelines were analyzed only for eligible patients in whom no therapeutic contraindication was identified. The 4 Joint Commission HF core measures include (1) providing HF specific discharge instructions (activity level, low sodium diet, weight monitoring, symptom recognition, follow-up appointment, and medication counseling), (2) assessment and documentation of left ventricular ejection fraction (EF), (3) use of an angiotensin-converting enzyme inhibitor or angiotensin receptor blocker in patients with left ventricular systolic dysfunction (LVSD), and (4) smoking cessation counseling. The 8 additional GWTG-HF quality measures include (1) use of a β-blocker in patients with LVSD, (2) anticoagulation for patients with atrial fibrillation, (3) use of an aldosterone antagonist for patients with LVSD, (4) a guideline-recommended β-blocker (bisoprolol, carvedilol, or extended-release metoprolol) for patients with LVSD, (5) use of hydralazine/nitrate therapy in African American patients with LVSD, (6) documentation of appropriate blood pressure (BP) control (systolic BP b140 mm Hg and diastolic BP b90 mm Hg) at discharge, (7) lipid-lowering therapy in HF patients with coronary artery disease (CAD), and (8) implantable cardioverter defibrillator (ICD) implanted or planned for eligible patients with an EF of b30%.
Statistical analysis All statistical analyses were performed centrally at the Duke Clinical Research Institute. Data were reported as means for continuous variables and as percentages of nonmissing values for categorical variables. Univariate comparisons between patients with different precipitating factors were performed using χ2 analysis for categorical variables and Wilcoxon rank sum test (or Kruskal-Wallis test) for continuous variables. Prespecified subgroup analysis was also performed within the nonadherence group as a whole—comparing differences between dietary nonadherence alone, medication nonadherence alone, and dietary and medication nonadherence combined. Potential confounders that may have impacted outcomes were identified in the univariate analysis and included demographics (age, gender, race), insurance status, medical comorbidities (anemia, ischemic history, diabetes, hyperlipidemia, hypertension, cerebrovascular disease, peripheral arterial disease, chronic pulmonary disease, renal insufficiency and tobacco use), and clinical data at admission (weight, heart rate, blood pressure, respiratory rate, blood urea nitrogen [BUN], serum creatinine, and EF). The generalized estimating equation method was used to perform logistic multivariable regression models to adjust for these confounders in the analysis of in-hospital outcomes and quality of care measures. In addition, the adjusted multivariate model for in-hospital mortality incorporated LOS. Estimated adjusted odds ratio for death, quality and performance measures, and estimated mean ratios for LOS were then computed. SAS statistical software (version 9.1, SAS Institute Inc, Cary, NC) was used for all statistical analyses.
Sources of funding The GWTG-HF program is supported by the American Heart Association, which received funding in part from unrestricted educational grants from GlaxoSmithKline, Philadelphia, PA and Medtronic, Minneapolis, MN. The authors are solely responsible
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646 Ambardekar et al
Figure 1
Precipitating factors for HF admission.
for the design and conduct of this study, all analyses, and the drafting and editing of the paper and its final contents.
Results A total of 95,127 patients were identified among 333 hospitals from January 1, 2005 to December 30, 2007. Ninety-seven hospitals either did not provide information with regard to the precipitating cause of HF or had a high missing rate (N25%) on reporting patient medical history; thus, 32,495 patients were excluded. In addition, 8,310 patients were admitted with a first time diagnosis of HF and were also excluded. The final analysis cohort included 54,322 patients from 236 hospitals. Clinicians documented dietary and/or medication nonadherence as a reason for admission in 5576 (10.3%) of all HF hospitalizations. Other precipitating factors identified for the HF hospitalization are shown in Figure 1. Patients with nonadherence were more likely to be young, male, minority, and uninsured (Table I). In addition, nonadherent patients were more likely to have nonischemic cardiomyopathy, lower EF, and more frequent prior HF admissions. Physical examination and laboratory findings at admission among nonadherent patients were notable for greater volume overload as evidenced by higher body weight, increased presence of jugular venous distention, rales, and edema on examination as well as higher brain natriuretic peptide (BNP) levels. Some HF prognostic variables such as blood pressure, BUN, and troponin, appeared to reflect lower disease severity in nonadherent patients, whereas other prognostic measurements such as BNP level, EF, and number of prior HF admissions were less favorable in nonadherent patients. In multivariate analysis, younger age, male gender, minority race, and
lack of insurance were independently associated with nonadherence (Table II). Within the population of patients with nonadherence, differences between dietary only, medication only, and both dietary and medication nonadherence were examined (Table III). Patients with medication nonadherence as a component of their nonadherence versus dietary nonadherence alone were more likely to be young, male, minority, and uninsured. This subpopulation also tended to have increased rates of nonischemic cardiomyopathy, alcohol abuse, tobacco dependence, as well as lower EF. By contrast, diabetes and higher body mass index were more prevalent among those with dietary nonadherence. Patients with combined medication and dietary nonadherence were almost twice as likely to have had ≥2 HF admissions in the previous 6 months as well as considerably higher BNP levels. In-hospital outcomes differed for patients identified as having nonadherence as a precipitating factor for hospitalization compared to those who did not. Nonadherent patients were observed to have lower inhospital mortality and shorter LOS (Table IV). Even after adjusting for multiple variables of prognostic importance, nonadherence appeared to be independently associated with better in-hospital outcomes. Among the different subtypes of nonadherence, patients with dietary nonadherence alone tended to have the lowest unadjusted inhospital mortality (Figure 2 and Table V). Nonadherent patients were as likely to receive Joint Commission HF performance measures compared to patients without nonadherence (Table IV). With regard to additional GWTG quality measures, slightly higher rates of aldosterone antagonist utilization were noted among nonadherent patients. However, they were less likely to have optimal BP control, be on lipid-lowering therapy in
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Table I. Baseline patient characteristics
Table I (continued ) Without Nonadherence nonadherence (n = 48 746, Univariate (n = 5576, 89.7%) P 10.3%)
Without Nonadherence nonadherence (n = 48 746, Univariate (n = 5576, 89.7%) P 10.3%) Demographics Male (%) 60.4 Mean age (y) 64.2 White (%) 53.0 Minority race (%) 47.0 No health 11.7 insurance (%) Baseline HF history (%) Nonischemic 57.4 cardiomyopathy ≥2 HF admissions 18.2 in preceding 6 mo Atrial fibrillation 24.4 ICD in place 11.8 Medical comorbidities (%) Alcohol abuse 6.5 Anemia 16.9 Chronic lung 31.0 disease Cerebrovascular 12.5 disease Depression 10.3 Diabetes 46.1 18.9 Chronic renal insufficiency (creatinine N2 mg/dL) Hypertension 77.8 Hyperlipidemia 38.0 Cigarette smoking 32.6 Prescribed medications at admission (%) β-Blocker 80.0 ACE 47.8 ARB 14.3 Aldosterone 13.4 antagonist Diuretic 74.4 Clinical data at presentation Body mass 31.5 index (kg/m2) Weight (kg) 90.7 Heart rate 89.4 (beat/min) Systolic blood 146.8 pressure (mm Hg) Diastolic blood 84.5 pressure (mm Hg) Jugular venous 30.0 distention present (%) S3 gallop 9.6 present (%) S4 gallop 5.3 present (%) Rales present (%) 59.6 Edema present (%) 69.6 LDL (mg/dL) 93.8
48.4 73.6 74.6 25.4 3.3
b.0001 b.0001 b.0001 b.0001 b.0001
50.6
b.0001
11.3
b.0001
31.0 10.1
b.0001 .0002
1.3 17.1 29.2
b.0001 .7493 .0044
14.5
b.0001
10.1 43.5 20.2
.7289 .0002 .0183
71.7 38.1 14.3
b.0001 .9007 b.0001
79.1 41.4 15.1 10.7
.2003 b.0001 .1625 b.0001
71.2
b.0001
29.3
b.0001
82.1 84.2
b.0001 b.0001
138.6
b.0001
74.9
b.0001
21.5
b.0001
6.1
b.0001
3.2
b.0001
52.3 59.0 86.7
b.0001 b.0001 b.0001
Sodium (mEq/L) Hemoglobin (g/dL) BNP (pg/mL) Serum creatinine (mg/dL) BUN (mg/dL) Troponin (ng/mL) Hemoglobin A1c (%) Mean EF LVSD present (EF b40%)
136.8 12.7
136.6 12.2
.0010 b.0001
1813 1.93
1371 1.90
b.0001 .0002
28.4 0.21 7.97
31.9 0.35 7.37
b.0001 .3206 b.0001
34.9 60.2
39.6 48.5
b.0001 b.0001
ACE, Angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; LDL, low-density lipoprotein.
Table II. Multivariate analysis of characteristics associated with nonadherence
Demographics Younger age (per each year decrease) Male gender (vs female) Nonwhite race (vs white) No health insurance (vs health insurance) Medical comorbidities Hypertension (vs no hypertension) Chronic lung disease (vs no chronic lung disease) Cigarette smoking (vs no smoking) Clinical data at presentation Higher weight (per each kilogram increase) Higher heart rate (per each beat/min increase) Higher blood pressure (per each mm Hg increase) Lower BUN (per each mg/dL decrease) Lower left ventricular EF (per each unit decrease in left ventricular EF)
Odds ratio (95% CI)
Multivariate P
1.022 (1.019-1.026)
b.0001
1.274 (1.196-1.358) 1.489 (1.358-1.632) 1.421 (1.236-1.633)
b.0001 b.0001 b.0001
1.183 (1.078-1.298)
.0004
1.070 (1.006-1.138)
.0322
1.683 (1.562-1.814)
b.0001
1.005 (1.003-1.007)
b.0001
1.005 (1.004-1.007)
b.0001
1.007 (1.006-1.008)
b.0001
1.003 (1.001-1.005)
.0025
1.008 (1.006-1.010)
b.0001
Odds ratio N1.0 indicates the variable is associated with increased frequency of nonadherence.
the setting of CAD, and receive consideration for ICD implantation. Within each subgroup of nonadherent patients, Joint Commission core measures did not differ (Table V). By contrast, patients with medication nonadherence as a component of their nonadherence compared to dietary
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648 Ambardekar et al
Table III. Baseline characteristics by types of nonadherence Dietary nonadherence Medication Dietary and medication (n = 1302) nonadherence (n = 2996) nonadherence (n = 1278) Univariate P Demographics Male (%) Mean age (y) White (%) Minority race (%) No health insurance (%) Baseline HF history (%) Nonischemic cardiomyopathy ≥2 HF admissions in preceding 6 mo Atrial fibrillation ICD in place Medical comorbidities (%) Alcohol abuse Anemia Chronic lung disease Cerebrovascular disease Depression Diabetes Chronic renal insufficiency (creatinine N2 mg/dL) Hypertension Hyperlipidemia Cigarette smoking Clinical data at presentation Body mass index (kg/m2) Weight (kg) Heart rate (beat/min) Systolic blood pressure (mm Hg) Diastolic blood pressure (mm Hg) Jugular venous distention present (%) S3 gallop present (%) S4 gallop present (%) Rales present (%) Edema present (%) LDL (mg/dL) Sodium (mEq/L) Hemoglobin (g/dL) BNP (pg/mL) Serum creatinine (mg/dL) BUN (mg/dL) Troponin (ng/mL) Hemoglobin A1c (%) Mean EF LVSD present (EF b40%)
56.7 69.1 67.2 32.8 2.6
60.1 63.8 51.9 48.1 13.8
64.9 60.3 40.9 59.1 16.0
.0001 b.0001 b.0001 b.0001 b.0001
51.3 15.5
58.7 15.1
60.7 28.2
b.0001 b.0001
29.2 13.1
23.7 10.9
21.0 12.4
b.0001 .099
3.3 20.2 32.5 14.1 10.1 55.2 21.4
5.9 15.5 30.3 12.1 10.2 40.6 16.8
11.1 16.9 31.2 11.9 10.8 49.4 21.0
b.0001 .0008 .3528 .1502 .8215 b.0001 .0002
76.3 46.2 21.0
78.1 35.0 34.0
78.6 36.7 41.0
.3405 b.0001 b.0001
32.9 93.7 85.1 141.0 77.2 30.7 6.4 5.1 60.5 74.3 81.5 136.7 12.1 1811.6 2.0 31.4 0.15 7.82 38.4 50.3
30.6 88.3 90.7 148.0 86.4 28.9 10.0 5.2 59.1 67.3 97.3 136.9 13.0 1622.6 1.8 26.9 0.23 7.96 34.1 63.2
32.1 93.1 91.1 149.9 87.8 31.5 12.0 5.9 59.7 69.8 96.3 136.4 12.3 2259.2 2.2 28.9 0.21 8.11 33.2 63.0
b.0001 b.0001 b.0001 b.0001 b.0001 .1479 b.0001 .0010 .4449 .0003 b.0001 .0514 b.0001 b.0001 b.0001 b.0001 .0023 .6357 b.0001 b.0001
nonadherence alone were less likely to be prescribed anticoagulation therapy for atrial fibrillation, evidencebased β-blockers, and lipid-lowering therapy in the setting of CAD, and they were less likely to have an ICD when indicated.
Discussion We investigated a broad cohort of US patients admitted with acute decompensated HF to evaluate the influence of nonadherence on quality of care and outcomes. This study has 3 main findings. First, nonadherence is a common precipitant for HF admission, and such patients are sociodemographically disadvantaged relative to
patients without nonadherence. In addition, medication nonadherence was more commonly noted among younger patients, ethnic minorities, and the uninsured, whereas dietary nonadherence was observed more frequently in older, overweight, and diabetic patients. Second, nonadherent patients present with evidence of lower EF and greater volume overload, yet have an inhospital course characterized by a shorter LOS and lower mortality. This suggests greater acuity of presentation but lower short-term disease severity, less difficulty in achieving stability with reinstitution of sodium restriction, fluid restriction, and/or medication, and potentially preventable admission. Finally, nonadherent patients
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Table IV. Hospital outcomes and quality of care with multivariate analysis⁎ Nonadherence Mortality (%) Mean length of stay (d) Joint Commission HF core measures (% compliance) Discharge instructions Documentation of left ventricular EF ACE or ARB for LVSD Smoking cessation counseling Additional GWTG-HF quality measures (% compliance) β-Blocker for LVSD Anticoagulation for atrial fibrillation Aldosterone antagonist for LVSD Evidence-based β-blocker for LVSD Hydralazine/nitrate for AA patients with LVSD Discharge BP b140/90 mm Hg Lipid-lowering therapy for CAD ICD therapy for left ventricular EF b30%
1.55 4.99
Without nonadherence
Univariate P
Adjusted odds ratio (95% CI)
Multivariate P
3.49 5.63
b.0001 b.0001
0.66 (0.51-0.86) 0.94 (0.92-0.97)
.0017 b.0001
82.6 94.5 89.9 91.5
81.7 93.9 85.0 90.0
.1533 .0936 b.0001 .0700
1.06 0.95 1.14 1.07
(0.97-1.16) (0.85-1.06) (0.98-1.31) (0.93-1.22)
.1725 .3554 .0873 .3680
91.6 66.9 34.5 71.8 8.6 70.9 55.9 35.7
89.2 66.6 27.3 71.7 7.0 75.5 58.2 41.4
.0002 .8621 b.0001 .8994 .0599 b.0001 .0145 b.0001
1.12 0.91 1.14 0.95 1.18 0.92 0.82 0.80
(0.97-1.29) (0.78-1.06) (1.03-1.27) (0.84-1.06) (0.91-1.52) (0.85-0.99) (0.74-0.91) (0.69-0.93)
.1271 .2287 .0151 .3351 .2043 .0252 b.0001 .0034
AA, African American. ⁎ Adjusted for age, gender, race, insurance status, medical comorbidities, clinical data at admission, and hospital sites. Mortality multivariate analysis additionally adjusted for length of stay.
Figure 2
Unadjusted in-hospital mortality rates.
received care with high rates of conformity with Joint Commission core measures but lower rates of conformity with other guideline-based therapies. Nonadherence as clinically identified and documented in the medical record is a common reason for HF admission accounting for 10.3% of admissions in this contemporary registry. The burden of nonadherence reported in the literature is variable depending on the population studied and how nonadherence was defined. Among patients hospitalized for HF exacerbation participating in clinical trials, medication nonadherence contributed to HF admission in 3% to 11% of patients.9,10 By contrast, among a population of urban African American patients hospitalized for HF exacerbation,
nonadherence identified by detailed patient interview accounted for 64% of HF admissions.11 The results of our study support the notion that nonadherence is more commonly identified and is a critical factor in socioeconomically disadvantaged populations. Nonadherence rates in our study were comparable to another large contemporary registry that also relied on clinical history for defining nonadherence. In the OPTIMIZE-HF registry, dietary nonadherence contributed to HF admission in 5.2% of patients and medication nonadherence contributed to HF admission in 8.9% of patients.12 Given the sheer volume of HF admissions, the overall burden of clinically reported nonadherence is quite high even if these estimates are conservative. As HF
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650 Ambardekar et al
Table V. Hospital outcome measures by type of nonadherence Dietary nonadherence Mortality (%) Length of stay (d) Joint Commission HF core measures (% compliance) Discharge instructions Documentation of left ventricular EF ACE or ARB for LVSD Smoking cessation counseling Additional GWTG-HF quality measures (% compliance) β-Blocker for LVSD Anticoagulation for atrial fibrillation Aldosterone antagonist for LVSD Evidence-based β-blocker for LVSD Hydralazine/nitrate for AA patients with LVSD Discharge BP b140/90 mm Hg Lipid-lowering therapy for CAD ICD therapy for left ventricular EF b30%
Medication nonadherence
Dietary and medication nonadherence
Univariate P
1.6 4.9
1.8 5.5
b.0001 .2942
82.5 94.1 92.1 91.5
81.7 94.5 89.0 90.3
84.9 94.8 90.4 94.1
.0727 .8046 .1659 .0756
92.1 74.5 32.0 76.2 8.2
91.0 64.2 34.9 69.9 8.7
92.7 62.2 35.4 72.7 8.6
.4203 .0043 .4417 .0251 .9758
73.8 64.5 46.1
70.8 52.1 31.8
68.4 54.4 36.9
.0231 b.0001 b.0001
0.92 4.7
admission due to nonadherence may be preventable, addressing this precipitant has the potential to lower the economic burden of HF. In examining long-term outcomes, nonadherence has been associated with higher mortality and more frequent hospitalizations.10,13 We similarly noted some poor prognostic markers among nonadherent patients including lower EF, more frequent prior admissions, and higher BNP levels. However, despite these markers, the nonadherent patients in our study had lower in-hospital mortality and shorter LOS. The reasons for why nonadherent patients had better in-hospital outcomes are unclear. Nonadherent patients were younger, had higher BP, and lower BUN levels—all markers of better prognosis in HF. It is plausible that it is easier to stabilize such patients by reinstituting sodium restriction and resuming medical therapy. This suggests that if the cycle of nonadherence is broken, clinicians may be able to improve long-term outcomes in this high-risk population. In this study, rates of compliance with the each of the Joint Commission core measures for patients with nonadherence ranged from 83% to 94%. This is higher than what was reported in the OPTIMIZE-HF registry14 from 2003 to 2004 where the compliance rates for each of the core measures ranged from 54% to 87% and the ADHERE registry15 from 2002 to 2003 where the compliance rates ranged from 24% to 86%. Improved compliance in the GWTG-HF database may reflect more contemporary trends as well as an emerging emphasis on documentation of these measures. It is also possible that a quality improvement program like GWTG-HF may enhance evidence-based care by providing clinicians with real-time guideline reminders. In addition, the GWTG-HF program gives periodic updates to institutions comparing their individual facility to other similar institutions. Such provider- and institution-specific feedback has been
shown to improve care in other diseases such as acute coronary syndromes.16 It is noteworthy that the rates of compliance with additional quality measures assessed in the GWTG-HF database varied between patients with nonadherence versus those without nonadherence. The reasons for these differences are unclear. Nonadherent patients were less often considered for ICD therapy, which may reflect a sense from clinicians that if patients were to become adherent with medications that improve cardiac remodeling, functional status and EF would improve obviating the need for ICD therapy. It is important to note that the rates of compliance with these additional quality measures in the total cohort of patients were not ideal, and improvement in these areas for all patients may improve long-term outcomes. This is one of the few studies in HF to compare differences between subtypes of nonadherence. Patients with dietary nonadherence appeared to have the best inhospital outcomes, whereas those with combined dietary and medication nonadherence had the highest in-hospital mortality and greatest LOS. In addition, patients with medication nonadherence were less likely to receive several key evidence-based medical therapies. The reasons for these differences are not clear. One possibility may be that medication nonadherence influences clinicians to be less likely to prescribe guideline-based therapies, thus potentiating a cycle of nonadherence and hospital readmission. This represents another potential area for investigation.
Limitations We acknowledge a number of limitations to this study. It is possible that the rates of nonadherence were underestimated. Relying on patient-reported nonadherence during a clinician history has lower sensitivity for
American Heart Journal Volume 158, Number 4
detecting nonadherence compared with more objective measures such as pill counts13; however, such measures are rarely used in routine clinical practice. Furthermore, there may have been physician bias based on patient characteristics in identifying and documenting nonadherence. The underlying reasons for medication nonadherence and different subtypes of dietary nonadherence (fluid vs salt vs food) were not investigated. The GWTG-HF is a voluntary program and could overrepresent high-performing hospitals. Data were collected by chart review and thus depend on the accuracy and completeness of documentation. Multivariate analysis was performed to attempt to adjust for confounding differences between patients with and without nonadherence, but it is possible that there were residual unmeasured confounders unaccounted for in the analysis. In addition, the GWTG-HF database does not track inpatient provider specialty, and this may influence both in-hospital mortality and LOS.17 Finally, only in-hospital measures were tracked, and thus, longterm follow-up and outcomes are unknown. Despite these limitations, the current study has several advantages over other HF studies. This analysis was conducted from a large, contemporary, prospectively collected database from multiple hospitals across the United States. Consecutive patients were enrolled from small institutions to large referral centers. It also reflects real-world experience as opposed to studies involving highly selected patients enrolled in a clinical trial.
Conclusions Among GWTG-HF hospitals, patients with nonadherence as a factor for HF hospitalization tended to be younger and more sociodemographically disadvantaged. Despite evidence of greater volume overload and lower EF, this population had better in-hospital outcomes. This lower risk-adjusted mortality and LOS suggests that it may be easier to stabilize nonadherent patients by reinstituting sodium and/or fluid restriction and resuming appropriate medical therapy. Patients with nonadherence were equally or more likely to receive the Joint Commission HF core measures at discharge. However, rates of other guideline-based care were suboptimal, which may contribute to poor long-term outcomes. Given the high resource utilization in patients with nonadherence, targeted efforts to assess predictors of HF readmission and to improve rates of compliance with all evidencebased care for this vulnerable population have the potential to reduce health disparities, improve quality of care, and lower hospital costs.
Disclosures Dr Fonarow reports receiving research grants and honoraria from GlaxoSmithKline and Medtronic and
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serves as a consultant for GlaxoSmithKline, Medtronic, and Novartis. He serves as chair of the American Heart Association’s Get With the Guidelines Steering Committee. He is supported by the Ahmanson and Elliot Corday Foundations. Dr Hernandez reports receiving research grants from Scios, Medtronic, GlaxoSmithKline, and Roche Diagnostics and has served on the speaker’s bureau or has received honoraria in the past 5 years from Novartis. Dr Yancy reports no active consulting or speaker bureau relationships. He continues to hold a noncompensated research relationship with Medtronic and has an editorial relationship with theheart.org and several cardiology journals. Dr Yancy does have contracts with the FDA and NIH. He currently holds a volunteer leadership role with the American Heart Association. Dr Krantz has received investigator initiated research grant support from GlaxoSmithKline and serves on their speaker’s bureau. He has received speaker honorarium in the last five years from Pfizer, Novartis, and Sanofi-BMS. He volunteers for the American Heart Association locally and serves on the national Get With the Guidelines Steering Committee. Dr Ambardekar is supported by a 2009 Research Fellowship Award from the Heart Failure Society of America.
References 1. American Heart Association. Heart disease and stroke statistics— 2006 update. Dallas (Tex): American Heart Association; 2006. 2. Koelling TM, Chen RS, Lubwama RN, et al. The expanding national burden of heart failure in the United States: the influence of heart failure in women. Am Heart J 2004;147:74-8. 3. Gheorghiade M, Zannad F, Sopko G, et al. International Working Group on Acute Heart Failure Syndromes. Acute heart failure syndromes: current state and framework for future research. Circulation 2005;112:3958-68. 4. Adams KF, Lindenfeld J, Arnold JMO, et al. Executive summary: HFSA 2006 comprehensive heart failure practice guideline. J Cardiac Failure 2006;12:10-38. 5. Krantz MJ, Havranek EP, Haynes DK, et al. Inpatient initiation of βblockade plus nurse management in vulnerable heart failure patients: a randomized study. J Cardiac Fail 2008;14:303-9. 6. Fonarow GC, Abraham WT, Albert NM, et al. Carvedilol use at discharge in patients hospitalized for heart failure is associated with improved survival: an analysis from Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure (OPTIMIZE-HF). Am Heart J 2007;153:82.e1-82.e11. 7. Hong Y, LaBresh KA. Overview of the American Heart Association “Get With The Guidelines” programs: Coronary heart disease, stroke, and heart failure. Crit Pathways in Cardiol 2006;5:179-86. 8. The Joint Commission. Specification manual for national implementation of hospital core measures: version 2.5. Available at: http://www.jointcommission.org/PerformanceMeasurement/ PerformanceMeasurement/Current+NHQM+Manual.htm. Last accessed November 8, 2008. 9. Tsuyuki RT, McKelvie RS, Arnold MO, et al. Acute precipitants of congestive heart failure exacerbations. Arch Intern Med 2001;161: 2337-42.
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