Medical Therapy for Heart Failure With Reduced Ejection Fraction: The CHAMP-HF Registry
Original Investigation
Central Illustration

Abstract
Background:
Guidelines strongly recommend patients with heart failure with reduced ejection fraction (HFrEF) be treated with multiple medications proven to improve clinical outcomes, as tolerated. The degree to which gaps in medication use and dosing persist in contemporary outpatient practice is unclear.
Objectives:
This study sought to characterize patterns and factors associated with use and dose of HFrEF medications in current practice.
Methods:
The CHAMP-HF (Change the Management of Patients with Heart Failure) registry included outpatients in the United States with chronic HFrEF receiving at least 1 oral medication for management of HF. Patients were characterized by baseline use and dose of angiotensin-converting enzyme inhibitor (ACEI)/angiotensin II receptor blocker (ARB), angiotensin receptor neprilysin inhibitor (ARNI), beta-blocker, and mineralocorticoid receptor antagonist (MRA). Patient-level factors associated with medication use were examined.
Results:
Overall, 3,518 patients from 150 primary care and cardiology practices were included. Mean age was 66 ± 13 years, 29% were female, and mean EF was 29 ± 8%. Among eligible patients, 27%, 33%, and 67% were not prescribed ACEI/ARB/ARNI, beta-blocker, and MRA therapy, respectively. When medications were prescribed, few patients were receiving target doses of ACEI/ARB (17%), ARNI (14%), and beta-blocker (28%), whereas most patients were receiving target doses of MRA therapy (77%). Among patients eligible for all classes of medication, 1% were simultaneously receiving target doses of ACE/ARB/ARNI, beta-blocker, and MRA. In adjusted models, older age, lower blood pressure, more severe functional class, renal insufficiency, and recent HF hospitalization generally favored lower medication utilization or dose. Social and economic characteristics were not independently associated with medication use or dose.
Conclusions:
In this contemporary outpatient HFrEF registry, significant gaps in use and dose of guideline-directed medical therapy remain. Multiple clinical factors were associated with medication use and dose prescribed. Strategies to improve guideline-directed use of HFrEF medications remain urgently needed, and these findings may inform targeted approaches to optimize outpatient medical therapy.
Introduction
For patients with chronic heart failure with reduced ejection fraction (HFrEF), contemporary therapy includes multiple medications proven to decrease mortality and hospitalization rates in large randomized controlled trials (1,2). The robust survival benefits of these medical therapies in clinical trials have been generally shown to apply in routine clinical practice (3). Accordingly, these medications form the cornerstone of contemporary evidence-based HFrEF care and are supported by class I indications in clinical treatment guidelines (1,2). Guidelines also recommend target doses for each medication, as tolerated, based on doses tested in landmark studies (1,2).
Despite proven benefits and strong guideline recommendations, medication use and dosing in routine clinical practice have traditionally fallen short of levels achieved in clinical trials (4,5). As rates of mortality and morbidity for the general HFrEF population remain high, improving real-world use of proven medications remains a critically important quality improvement initiative (6–8). However, the utilization patterns of chronic HFrEF medical therapies and the barriers to their uptake and dosing in contemporary U.S. outpatient practice are poorly understood. Such information is even more relevant now in light of recent regulatory approval of sacubitril/valsartan and well-described concerns over traditionally slow adoption of novel medical therapies (9,10). Better understanding of current practice patterns, gaps in medication delivery, and barriers to receiving guideline-directed medical therapy (GDMT) are critical to development of targeted initiatives aimed at improving patient outcomes and quality of care. In this context, the CHAMP-HF (Change the Management of Patients with Heart Failure) registry offers the opportunity to study a contemporary U.S. outpatient HFrEF cohort and explore patterns of GDMT use and dosing, the clinical and social patient profiles associated with GDMT use, and the patient-level factors associated with medication use and target dosing.
Methods
Study design
The design of the CHAMP-HF registry has been published previously (11). Briefly, CHAMP-HF was a prospective, observational, nonrandomized study of adult outpatients with HFrEF. Eligible patients had a diagnosis of chronic HF, a left ventricular ejection fraction (EF) ≤40% according to imaging performed within 12 months of enrollment, and were receiving ≥1 oral medication for HF at study enrollment (including diuretics, angiotensin-converting enzyme inhibitors [ACEI], angiotensin II receptor blockers [ARB], angiotensin receptor-neprilysin inhibitors [ARNI], beta-blockers, mineralocorticoid receptor antagonists [MRA], antihypertensives, vasoactive/inotropic agents, or other cardiovascular medications). Key exclusion criteria included current or anticipated participation in a clinical trial, currently receiving comfort care or enrolled in hospice, life expectancy <1 year, or history of or plan for heart transplantation, left ventricular assist device, or dialysis. Clinical data were collected at baseline and at specified time intervals during study follow-up, when data were abstracted from the medical record (i.e., no study-specific mandatory follow-up visits) and were recorded in an electronic case report form. Baseline patient data reported by health care providers included patient demographics, HF history, comorbidities, vital signs and laboratory values, EF, and concurrent cardiovascular medication doses. Baseline sociodemographic data were self-reported by patients. The registry was conducted in accordance with Declaration of Helsinki tenets and with institutional review board/ethics committee approval at all sites. All patients provided written informed consent.
Baseline medication data
Baseline use and dose of the following HFrEF medication categories were examined within the baseline case report form: ACEI/ARB, ARNI, evidence-based beta-blocker, and MRA. For each medication class, the presence and absence of absolute contraindications were determined based on documentation in the medical record or as ascertained by study investigators. For each patient and each medication, available dose information was reviewed in reference to recommended target doses by clinical practice guidelines (Online Table 1), and patients were divided into 1 of 4 groups according to prescribed dose: patients not receiving medication, patients treated with <50% target dose, patients treated with 50 to <100% target dose, and patients treated with ≥100% target dose (1,2,12).
Statistical analysis
For each medication class, baseline characteristics of patients without an absolute contraindication were compared across the 4 dose groups. Continuous variables were reported as median (25th percentile, 75th percentile), and categorical variables were recorded as frequencies and percentages.
To assess for independent associations between patient-level characteristics and medical therapy use and dose among eligible patients, logistic regression models were constructed for the probability of being treated; and then among treated patients only, for the probability of receiving a higher dose. For ACEI/ARB and evidence-based beta-blocker analyses, a total of 3 separate regression models were used to assess the probability of: 1) being treated with any dose of medication; 2) being treated with ≥50% target dose; and 3) being treated with ≥100% target dose. For ARNI and MRA analyses, due to lower numbers of treated patients and the distribution of doses, 2 separate regression models were used to assess: 1) being treated with any dose of medication; and 2) being treated with ≥50% target dose for ARNI or treated with ≥100% target dose for MRA. Referent categories were either the “not treated” group or the lowest-dose group, as applicable. To account for clustering of patients within individual study sites, which may be correlated with patient factors and medication doses, hierarchical models were used, including a random effect for site. Model selection was based on backwards elimination, and variables with p > 0.05 were removed based on highest p value first. A new model was assessed using the remaining variables. Rates of missing data for most variables were <1% with few exceptions (New York Heart Association [NYHA] functional class 4.4%; systolic blood pressure 5.4%; heart rate 6.7%). Complete case analysis was performed, resulting in slightly different sample sizes for different models. All statistical analyses were performed using SAS version 9.4 software (Cary, North Carolina). Two-tailed p value <0.05 was considered statistically significant.
Results
Baseline medication use and dose
The present analysis included 3,518 HFrEF patients with available baseline medication data from 150 U.S. primary care and cardiology practices. Overall, mean age of the study cohort was 66.4 ± 12.6 years, 29.2% were females, 74.9% were white, and mean EF was 29 ± 8%. For each class of medical therapy, proportions of patients: 1) with a contraindication; 2) treated; and 3) without a contraindication but not treated are displayed in the Central Illustration. For all therapies, the percentage of patients with a documented absolute contraindication was low, with the highest rate of 1.8% for ACEI/ARB/ARNI. Among eligible patients, 2,536 patients (73.4%), 2,351 patients (67.0%), and 1,163 patients (33.4%) were treated with ACEI/ARB/ARNI, beta-blocker, and MRA, respectively. ACEI/ARB was prescribed to 2,107 eligible patients (60.5%) and ARNI to 452 patients (13.0%). Among patients receiving each medication, <30% of patients were prescribed target doses of ACEI/ARB/ARNI or beta-blocker therapy (Central Illustration). In contrast, >75% of patients receiving an MRA were prescribed the target dose. Among patients eligible for all classes of medication, 755 patients (22.1%) were simultaneously prescribed some dose of ACEI/ARB/ARNI, beta-blocker, and MRA therapy, and 37 patients (1.1%) were simultaneously prescribed target doses of all 3 therapies. Medication use and doses among patients enrolled in the first half (December 15, 2015 to August 22, 2016) were similar to those among patients enrolled in the second half (August 23, 2016 to March 6, 2017) (Online Tables 2 and 3). In contrast, with the exception of ACEI/ARB, use of medication was substantially lower among patients enrolled from family medicine or internal medicine practices (n = 485) than among patients enrolled from cardiology outpatient practices (n = 2821) (Online Table 4). Among patients treated with each medication, doses were relatively similar by practice type (Online Table 5).

Use and Dosing of Guideline-Directed Medical Therapy Among Patients With Chronic HFrEF in Contemporary U.S. Outpatient Practice
(A) For each medication category, data reflect the proportion of the overall study population with an absolute contraindication, treated with any dose, and without an absolute contraindication but not treated. (B) Data show the proportion of eligible patients with available dosage data treated with <50% target dose, 50% to <100% target dose, or ≥100% target dose. The number of patients with contraindications to ACEI, ARB, or ARNI (N = 62) included those with contraindications to ACEI and ARB (N = 37) or ARNI (N = 37) and does not equal the sum of the 2 groups. HFrEF = heart failure with reduced ejection fraction.
Patient characteristics by dosing of guideline-directed medical therapy
Angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker
Compared to patients receiving <50% target dose, patients not receiving ACEI/ARB therapy tended to be older with similar blood pressure and worse renal function (Table 1). Across all dosing groups, NYHA functional class and history of HF hospitalization were inversely related to ACEI/ARB dose, with more severe functional class and highest rate of HF hospitalization among patients receiving the lowest doses. Social and economic characteristics were approximately similar by dose group.
| None (n = 1,374) | <50% Target Dose (n = 1,261) | 50% to <100% Target Dose (n = 466) | ≥100% Target Dose (n = 366) | |
|---|---|---|---|---|
| Age, yrs | 69 (59–76) | 67 (58–75) | 67 (58–75) | 67 (58–74) |
| Female | 428 (31.3) | 359 (28.5) | 133 (28.5) | 90 (24.7) |
| Race | ||||
| White | 1,018 (74.5) | 984 (78.2) | 323 (69.3) | 260 (71.2) |
| Black | 237 (17.3) | 154 (12.2) | 93 (20.0) | 84 (23.0) |
| Other | 111 (8.1) | 120 (9.5) | 50 (10.7) | 21 (5.8) |
| Hispanic ethnicity | 203 (14.9) | 216 (17.2) | 97 (20.8) | 67 (18.4) |
| Ejection fraction, % | 30 (23–35) | 30 (23–35) | 31 (25–36) | 32 (25–37) |
| NYHA functional class | ||||
| I | 97 (7.1) | 140 (11.1) | 55 (11.8) | 52 (14.2) |
| II | 713 (52.4) | 697 (55.4) | 257 (55.3) | 226 (61.7) |
| III | 455 (33.5) | 345 (27.4) | 123 (26.5) | 65 (17.8) |
| IV | 50 (3.7) | 20 (1.6) | 8 (1.7) | 9 (2.5) |
| Not available | 45 (3.3) | 56 (4.5) | 22 (4.7) | 14 (3.8) |
| Vital sign and laboratory findings | ||||
| Systolic blood pressure, mm Hg | 120 (108–130) | 120 (109–130) | 122 (110–134) | 128 (115–140) |
| Diastolic blood pressure, mm Hg | 70 (64–80) | 70 (64–80) | 74 (67–80) | 76 (70–84) |
| Heart rate, beats/min | 72 (66–82) | 73 (66–82) | 72 (65–81) | 72 (64–80) |
| Obese, BMI ≥30 kg/m2 | 547 (40.2) | 492 (39.1) | 220 (47.3) | 168 (45.9) |
| Hemoglobin, g/dl∗ | 13.0 (11.8–14.3) | 13.3 (12.0–14.6) | 13.3 (12.2–14.5) | 13.4 (12.1–14.5) |
| Serum sodium, mmol/l† | 139 (137–141) | 139 (137–141) | 140 (138–142) | 140 (138–142) |
| BUN, mg/dl‡ | 21 (16–30) | 19 (15–26) | 20 (15–26) | 19 (15–26) |
| eGFR, ml/min per 1.73 m2§ | ||||
| <30 | 74 (8.9) | 22 (2.9) | 7 (2.5) | 10 (4.8) |
| 30–44 | 159 (19.2) | 81 (10.5) | 32 (11.4) | 27 (12.9) |
| 45–60 | 188 (22.7) | 186 (24.2) | 65 (23.1) | 44 (21.1) |
| >60 | 409 (49.3) | 479 (62.4) | 177 (63.0) | 128 (61.2) |
| NT-proBNP, pg/ml‖ | 2,308 (804–5,530) | 2,613 (919–5,348) | 1,320 (695–2,106) | 1,120 (396–2,790) |
| Hemoglobin A1c, %# | 6.6 (5.8–7.7) | 6.4 (5.8–7.5) | 6.4 (5.9–7.7) | 6.4 (6.0–7.5) |
| Medical history∗∗ | ||||
| HF hospitalization within past 12 months | 566 (41.4) | 495 (39.3) | 155 (33.3) | 94 (25.7) |
| Coronary artery disease | 848 (62.2) | 795 (63.0) | 284 (61.1) | 229 (62.6) |
| Hypertension | 1,112 (81.5) | 984 (78.0) | 401 (86.2) | 341 (93.2) |
| Hyperlipidemia | 1,014 (74.3) | 954 (75.7) | 352 (75.7) | 298 (81.4) |
| Diabetes mellitus | 581 (42.7) | 473 (37.6) | 196 (42.2) | 163 (44.5) |
| Atrial fibrillation | 557 (40.9) | 439 (34.8) | 137 (29.5) | 115 (31.4) |
| Chronic renal insufficiency | 333 (24.5) | 208 (16.5) | 79 (17.0) | 59 (16.1) |
| Asthma/COPD | 422 (31.0) | 389 (30.9) | 142 (30.5) | 86 (23.5) |
| History of ventricular tachycardia/fibrillation | 257 (18.8) | 270 (21.4) | 71 (15.3) | 57 (15.6) |
| Depression | 359 (26.4) | 328 (26.1) | 105 (22.6) | 72 (19.7) |
| Active cigarette smoking | 246 (18.1) | 275 (21.8) | 100 (21.5) | 66 (18.0) |
| Heart failure device therapy | ||||
| Implantable cardioverter-defibrillator | 573 (42.1) | 551 (43.8) | 170 (36.6) | 144 (39.3) |
| Cardiac resynchronization therapy | 92 (6.8) | 90 (7.1) | 25 (5.4) | 19 (5.2) |
| Social and economic characteristics | ||||
| Insurance status | ||||
| Managed care (HMO, PPO) | 206 (15.1) | 224 (17.8) | 90 (19.3) | 50 (13.7) |
| Private insurance | 133 (9.7) | 115 (9.1) | 41 (8.8) | 36 (9.9) |
| Medicare | 817 (59.8) | 722 (57.4) | 259 (55.6) | 214 (58.6) |
| Medicaid | 134 (9.8) | 104 (8.3) | 45 (9.7) | 33 (9.0) |
| Military health care | 26 (1.9) | 27 (2.1) | 10 (2.1) | 9 (2.5) |
| Uninsured | 19 (1.4) | 35 (2.8) | 5 (1.1) | 10 (2.7) |
| Other | 31 (2.3) | 31 (2.5) | 16 (3.4) | 13 (3.6) |
| Highest level of education | ||||
| Less than high school | 168 (12.3) | 145 (11.5) | 65 (13.9) | 40 (11.0) |
| High school/GED | 477 (34.9) | 417 (33.1) | 159 (34.1) | 119 (32.6) |
| Some college | 409 (29.9) | 400 (31.8) | 149 (32.0) | 125 (34.2) |
| 4-yr college (baccalaureate) | 171 (12.5) | 167 (13.3) | 49 (10.5) | 50 (13.7) |
| Graduate or other professional degree | 141 (10.3) | 129 (10.3) | 44 (9.4) | 31 (8.5) |
| Total household income | ||||
| <$25,000 | 411 (30.1) | 383 (30.4) | 155 (33.3) | 113 (31.0) |
| $25,000–$49,999 | 264 (19.3) | 259 (20.6) | 86 (18.5) | 70 (19.2) |
| $50,000–$74,999 | 185 (13.5) | 143 (11.4) | 49 (10.5) | 35 (9.6) |
| $75,000–$99,999 | 78 (5.7) | 75 (6.0) | 27 (5.8) | 27 (7.4) |
| $100,000–$149,999 | 66 (4.8) | 72 (5.7) | 26 (5.6) | 18 (4.9) |
| ≥$150,000 | 41 (3.0) | 37 (2.9) | 9 (1.9) | 10 (2.7) |
| Prefer not to answer | 321 (23.5) | 289 (23.0) | 114 (24.5) | 92 (25.2) |
| Employment status | ||||
| Full-time employee (≥35 h/week) | 181 (13.3) | 173 (13.8) | 77 (16.5) | 63 (17.3) |
| Part-time employee (<35 h/week) | 94 (6.9) | 94 (7.5) | 26 (5.6) | 33 (9.0) |
| Disability for medical reasons | 342 (25.0) | 315 (25.0) | 115 (24.7) | 95 (26.0) |
| Not employed for other reasons (e.g., retired, student, unemployed) | 749 (54.8) | 676 (53.7) | 248 (53.2) | 174 (47.7) |
Angiotensin receptor neprilysin inhibitor
Patients receiving <50% target ARNI dose tended to have similar renal function but lower blood pressure than those not receiving therapy (Table 2). Among patients receiving ARNI, higher doses correlated with milder NYHA functional class, higher systolic blood pressure, higher rates of blacks and obesity, and lower rates of prior HF hospitalization and coronary artery disease. Patients receiving higher doses of ARNI therapy had greater likelihood of having full-time employment, a graduate or professional degree, and private insurance.
| None (n = 3,029) | <50% Target Dose (n = 255) | 50% to <100% Target Dose (n = 133) | ≥100% Target Dose (n = 63) | |
|---|---|---|---|---|
| Age, yrs | 68 (59–76) | 65 (55–73) | 63 (54–71) | 62 (52–68) |
| Female | 873 (28.9) | 68 (26.7) | 47 (35.6) | 26 (41.3) |
| Race | ||||
| White | 2,261 (74.9) | 194 (76.1) | 97 (73.5) | 40 (63.5) |
| Black | 475 (15.7) | 49 (19.2) | 27 (20.5) | 19 (30.2) |
| Other | 281 (9.3) | 12 (4.7) | 8 (6.1) | 4 (6.3) |
| Hispanic ethnicity | 557 (18.5) | 22 (8.6) | 6 (4.5) | 3 (4.8) |
| Ejection fraction, % | 30 (23–35) | 27 (20–33) | 28 (22–32) | 29 (23–35) |
| NYHA functional class | ||||
| I | 314 (10.4) | 20 (7.8) | 7 (5.3) | 6 (9.5) |
| II | 1,656 (55.0) | 131 (51.4) | 78 (59.1) | 35 (55.6) |
| III | 837 (27.8) | 97 (38.0) | 41 (31.1) | 20 (31.7) |
| IV | 79 (2.6) | 5 (2.0) | 2 (1.5) | 1 (1.6) |
| Not available | 125 (4.2) | 2 (0.8) | 4 (3.0) | 1 (1.6) |
| Vital signs and laboratory findings | ||||
| Systolic blood pressure, mm Hg | 120 (110–132) | 116 (104–124) | 120 (110–130) | 120 (110–136) |
| Diastolic blood pressure, mm Hg | 72 (64–80) | 70 (62–78) | 72 (64–80) | 75 (68–82) |
| Heart rate, beats/min | 72 (66–81) | 73 (66–82) | 70 (64–82) | 74 (62–80) |
| Obese, BMI ≥30 kg/m2 | 1217 (40.4) | 114 (44.9) | 63 (47.7) | 38 (60.3) |
| Hemoglobin, g/dl∗ | 13.3 (11.9–14.5) | 13.3 (12.1–14.5) | 13.1 (12.1–14.5) | 13.5 (12.3–14.3) |
| Serum sodium, mmol/l† | 139 (137–141) | 139 (137–141) | 140 (138–142) | 140 (138–142) |
| BUN, mg/dl‡ | 20 (16–27) | 20 (15–28) | 18 (14–24) | 20 (16–25) |
| eGFR, ml/min per 1.73 m2§ | ||||
| <30 | 109 (6.0) | 3 (1.9) | 3 (4.3) | 0 (0.0) |
| 30–44 | 260 (14.2) | 28 (17.6) | 6 (8.6) | 7 (16.7) |
| 45–60 | 419 (23.0) | 39 (24.5) | 18 (25.7) | 9 (21.4) |
| >60 | 1037 (56.8) | 89 (56.0) | 43 (61.4) | 26 (61.9) |
| NT-proBNP, pg/ml‖ | 1,984 (800–,5210) | 1,400 (528–4,299) | 1,135 (737–2,622) | 2,944 (751–6,289) |
| Hemoglobin A1c, %# | 6.4 (5.8–7.6) | 6.6 (6.1–7.8) | 6.7 (5.8–7.7) | 6.9 (6.6–9.3) |
| Medical history∗∗ | ||||
| HF hospitalization within past 12 months | 1,118 (37.0) | 115 (45.1) | 55 (41.4) | 23 (36.5) |
| Coronary artery disease | 1,878 (62.2) | 172 (67.5) | 77 (57.9) | 31 (49.2) |
| Hypertension | 2,486 (82.4) | 197 (77.3) | 109 (82.0) | 56 (88.9) |
| Hyperlipidemia | 2,288 (75.8) | 196 (76.9) | 96 (72.2) | 45 (71.4) |
| Diabetes mellitus | 1,234 (40.9) | 104 (40.9) | 53 (40.2) | 29 (46.0) |
| Atrial fibrillation | 1,078 (35.7) | 93 (36.5) | 52 (39.1) | 24 (38.1) |
| Chronic renal insufficiency | 613 (20.3) | 42 (16.5) | 17 (12.9) | 9 (14.3) |
| Asthma/COPD | 929 (30.8) | 65 (25.6) | 37 (28.0) | 17 (27.0) |
| History of ventricular tachycardia/fibrillation | 533 (17.7) | 77 (30.2) | 26 (19.5) | 16 (25.4) |
| Depression | 758 (25.1) | 58 (22.8) | 33 (25.0) | 17 (27.0) |
| Active cigarette smoking | 597 (19.8) | 51 (20.1) | 27 (20.5) | 12 (19.0) |
| Heart failure device therapy | ||||
| Implantable cardioverter-defibrillator | 1,195 (39.6) | 140 (55.1) | 74 (56.1) | 32 (50.8) |
| Cardiac resynchronization therapy | 185 (6.1) | 29 (11.4) | 11 (8.3) | 5 (7.9) |
| Social and economic characteristics | ||||
| Insurance status | ||||
| Managed care (HMO, PPO) | 478 (15.8) | 59 (23.1) | 20 (15.2) | 13 (20.6) |
| Private insurance | 265 (8.8) | 30 (11.8) | 16 (12.1) | 11 (17.5) |
| Medicare | 1,794 (59.5) | 127 (49.8) | 77 (58.3) | 29 (46.0) |
| Medicaid | 271 (9.0) | 26 (10.2) | 13 (9.8) | 6 (9.5) |
| Military health care | 66 (2.2) | 2 (0.8) | 2 (1.5) | 1 (1.6) |
| Uninsured | 58 (1.9) | 8 (3.1) | 2 (1.5) | 1 (1.6) |
| Other | 85 (2.8) | 3 (1.2) | 2 (1.5) | 2 (3.2) |
| Highest level of education | ||||
| Less than high school | 381 (12.6) | 27 (10.6) | 7 (5.3) | 6 (9.5) |
| High school/GED | 1,032 (34.2) | 87 (34.1) | 40 (30.3) | 19 (30.2) |
| Some college | 938 (31.1) | 74 (29.0) | 56 (42.4) | 18 (28.6) |
| 4-yr college (baccalaureate) | 373 (12.4) | 38 (14.9) | 18 (13.6) | 7 (11.1) |
| Graduate or other professional degree | 293 (9.7) | 29 (11.4) | 11 (8.3) | 13 (20.6) |
| Total household income | ||||
| <$25,000 | 947 (31.4) | 70 (27.5) | 30 (22.7) | 20 (31.7) |
| $25,000–$49,999 | 596 (19.8) | 41 (16.1) | 28 (21.2) | 13 (20.6) |
| $50,000–$74,999 | 337 (11.2) | 38 (14.9) | 27 (20.5) | 10 (15.9) |
| $75,000–$99,999 | 181 (6.0) | 20 (8.2) | 11 (8.3) | 3 (4.8) |
| $100,000–$149,999 | 149 (4.9) | 21 (8.2) | 11 (8.3) | 3 (4.8) |
| ≥$150,000 | 78 (2.6) | 7 (2.7) | 6 (4.5) | 4 (6.3) |
| Prefer not to answer | 729 (24.2) | 58 (22.7) | 24 (18.2) | 9 (14.3) |
| Employment status | ||||
| Full-time employee (≥35 h/week) | 400 (13.3) | 48 (18.8) | 25 (18.9) | 16 (25.4) |
| Part-time employee (<35 h/week) | 209 (6.9) | 18 (7.1) | 16 (12.1) | 5 (7.9) |
| Disability for medical reasons | 737 (24.4) | 70 (27.5) | 42 (31.8) | 23 (36.5) |
| Not employed for other reasons (e.g., retired, student, unemployed) | 1,671 (55.4) | 119 (46.7) | 49 (37.1) | 19 (30.2) |
Evidence-based beta-blocker
Compared with patients receiving <50% target dose, patients not receiving beta-blockers tended to be older and were more likely to be female, white, and have a history of asthma or chronic obstructive pulmonary disease (COPD) but less likely to have prior HF hospitalization (Table 3). Among patients receiving beta-blocker therapy, heart rate and NYHA functional class were similar across dose groups, and blood pressure tended to be higher among patients receiving higher doses. Rates of prior HF hospitalization and coronary artery disease declined with increasing dose group. Social and economic characteristics were approximately similar by dose group, with potential exception of lower rates of Medicare insurance, higher rates of medical disability, and lower rates of employment for other reasons among patients receiving target doses.
| None (n = 1,159) | <50% Target Dose (n = 1,071) | 50% to <100% Target Dose (n = 628) | ≥100% Target Dose (n = 646) | |
|---|---|---|---|---|
| Age, yrs | 70 (61–77) | 68 (59–76) | 67 (58–74) | 65 (56–72) |
| Female | 379 (32.9) | 305 (28.5) | 167 (26.6) | 170 (26.4) |
| Race | ||||
| White | 911 (79.1) | 808 (75.6) | 474 (75.5) | 420 (65.3) |
| Black | 152 (13.2) | 151 (14.1) | 109 (17.4) | 160 (24.9) |
| Other | 88 (7.6) | 110 (10.3) | 45 (7.2) | 63 (9.8) |
| Hispanic ethnicity | 272 (23.6) | 165 (15.4) | 74 (11.8) | 77 (12.0) |
| Ejection fraction, % | 33 (25–38) | 28 (21–35) | 30 (23–35) | 30 (23–35) |
| NYHA functional class | ||||
| I | 85 (7.4) | 120 (11.2) | 65 (10.4) | 76 (11.8) |
| II | 662 (57.9) | 575 (53.7) | 335 (53.5) | 347 (53.8) |
| III | 300 (26.2) | 320 (29.9) | 190 (30.4) | 187 (29.0) |
| IV | 38 (3.3) | 22 (2.1) | 14 (2.2) | 12 (1.9) |
| Not available | 59 (5.2) | 33 (3.1) | 22 (3.5) | 23 (3.6) |
| Vital sign and laboratory findings | ||||
| Systolic blood pressure, mm Hg | 120 (110–131) | 118 (106–130) | 120 (110–130) | 121 (110–134) |
| Diastolic blood pressure, mm Hg | 72 (66–80) | 70 (63–80) | 71 (64–80) | 74 (66–80) |
| Heart rate, beats/min | 72 (67–80) | 73 (66–83) | 72 (64–82) | 72 (65–80) |
| Obese, BMI ≥30 kg/m2 | 461 (40.2) | 360 (33.6) | 273 (43.6) | 346 (53.6) |
| Hemoglobin, g/dl∗ | 13.3 (12.0–14.5) | 13.3 (12.0–14.5) | 13.1 (11.7–14.4) | 13.2 (12.0–14.4) |
| Serum sodium, mmol/l† | 139 (137–141) | 139 (137–141) | 139 (137–141) | 139 (137–141) |
| BUN, mg/dl‡ | 20 (15–28) | 18 (15–23) | 20 (16–27) | 20 (15–28) |
| eGFR, ml/min per 1.73 m2§ | ||||
| <30 | 40 (5.8) | 36 (5.5) | 21 (5.5) | 25 (6.5) |
| 30–44 | 92 (13.4) | 88 (13.4) | 66 (17.3) | 58 (15.1) |
| 45–60 | 143 (20.8) | 164 (24.9) | 83 (21.7) | 99 (25.7) |
| >60 | 413 (60.0) | 371 (56.3) | 212 (55.5) | 203 (52.7) |
| NT-proBNP, pg/ml‖ | 2,309 (1,074–5,620) | 2,613 (1,184–5,570) | 1,832 (664–5,136) | 932 (528–3,059) |
| Hemoglobin A1c, %# | 6.3 (5.8–7.4) | 6.3 (5.7–7.5) | 6.5 (5.8–7.5) | 6.7 (6.0–8.2) |
| Medical history∗∗ | ||||
| HF hospitalization within past 12 months | 411 (35.7) | 481 (44.9) | 236 (37.6) | 194 (30.0) |
| Coronary artery disease | 697 (60.7) | 702 (65.5) | 411 (65.4) | 370 (57.4) |
| Hypertension | 944 (82.2) | 857 (80.0) | 524 (83.4) | 543 (84.2) |
| Hyperlipidemia | 863 (75.2) | 807 (75.4) | 485 (77.2) | 486 (75.3) |
| Diabetes mellitus | 454 (39.6) | 402 (37.6) | 264 (42.2) | 311 (48.2) |
| Atrial fibrillation | 424 (37.0) | 353 (33.0) | 256 (40.8) | 220 (34.1) |
| Chronic renal insufficiency | 208 (18.2) | 216 (20.2) | 128 (20.4) | 140 (21.7) |
| Asthma/COPD | 400 (34.9) | 327 (30.6) | 170 (27.2) | 157 (24.3) |
| History of ventricular tachycardia/fibrillation | 178 (15.5) | 199 (18.6) | 140 (22.3) | 143 (22.2) |
| Depression | 321 (28.0) | 270 (25.2) | 132 (21.1) | 151 (23.4) |
| Active cigarette smoking | 223 (19.5) | 223 (20.8) | 141 (22.5) | 103 (16.0) |
| Heart failure device therapy | ||||
| Implantable cardioverter-defibrillator | 333 (29.1) | 458 (42.8) | 314 (50.2) | 350 (54.3) |
| Cardiac resynchronization therapy | 56 (4.9) | 74 (6.9) | 53 (8.5) | 51 (7.9) |
| Social and economic characteristics | ||||
| Insurance status | ||||
| Managed care (HMO, PPO) | 151 (13.1) | 194 (18.1) | 110 (17.5) | 120 (18.7) |
| Private insurance | 109 (9.5) | 98 (9.2) | 61 (9.7) | 62 (9.6) |
| Medicare | 741 (64.4) | 597 (55.8) | 352 (56.1) | 346 (53.6) |
| Medicaid | 92 (8.0) | 104 (9.7) | 57 (9.1) | 63 (9.8) |
| Military health care | 18 (1.6) | 30 (2.8) | 13 (2.1) | 12 (1.9) |
| Uninsured | 17 (1.5) | 17 (1.6) | 14 (2.2) | 21 (3.3) |
| Other | 23 (2.0) | 29 (2.7) | 21 (3.3) | 19 (3.0) |
| Highest level of education | ||||
| Less than high school | 157 (13.6) | 136 (12.7) | 58 (9.2) | 72 (11.2) |
| High school/GED | 405 (35.2) | 339 (31.7) | 207 (33.0) | 239 (37.2) |
| Some college | 343 (29.8) | 333 (31.2) | 210 (33.4) | 204 (31.7) |
| 4-yr college (baccalaureate degree) | 135 (11.7) | 141 (13.2) | 85 (13.5) | 78 (12.1) |
| Graduate or other professional degree | 111 (9.6) | 120 (11.2) | 68 (10.8) | 50 (7.8) |
| Total household income | ||||
| <$25,000 | 389 (33.8) | 309 (28.9) | 188 (29.9) | 188 (29.2) |
| $25,000–$49,999 | 209 (18.2) | 222 (20.8) | 117 (18.6) | 137 (21.3) |
| $50,000–$74,999 | 142 (12.3) | 120 (11.2) | 77 (12.3) | 76 (11.8) |
| $75,000–$99,999 | 70 (6.1) | 77 (7.2) | 31 (4.9) | 35 (5.4) |
| $100,000–$149,999 | 57 (5.0) | 59 (5.5) | 37 (5.9) | 32 (5.0) |
| ≥$150,000 | 25 (2.2) | 36 (3.4) | 24 (3.8) | 11 (1.7) |
| Prefer not to answer | 259 (22.5) | 246 (23.0) | 154 (24.5) | 164 (25.5) |
| Employment status | ||||
| Full–time employee (≥35 h/week) | 131 (11.4) | 158 (14.8) | 106 (16.9) | 102 (15.9) |
| Part-time employee (<35 h/week) | 75 (6.5) | 84 (7.9) | 45 (7.2) | 48 (7.5) |
| Disability for medical reasons | 246 (21.4) | 255 (23.9) | 167 (26.6) | 211 (32.8) |
| Not employed for other reasons (e.g., retired, student, unemployed) | 699 (60.7) | 572 (53.5) | 310 (49.4) | 282 (43.9) |
Mineralocorticoid receptor antagonist
Compared with patients receiving target doses, patients not receiving MRA therapy were more likely to be older, white, male and have worse renal function (Table 4). Patients not prescribed therapy also tended to have milder NYHA functional class, higher systolic blood pressure, and lower rates of prior HF hospitalization. Patient receiving higher doses of MRA had higher rates of managed care or private insurance and lower rates of Medicare, as well as higher rates of medical disability and lower rates of unemployment for other reasons.
| None (n = 2,317) | <50% Target Dose (n = 21) | 50% to <100% Target Dose (n = 250) | ≥100% Target Dose (n = 885) | |
|---|---|---|---|---|
| Age, yrs | 69 (61–77) | 71 (64–76) | 65 (56–74) | 63 (55–71) |
| Female | 653 (28.3) | 7 (33.3) | 62 (24.8) | 290 (32.8) |
| Race | ||||
| White | 1,767 (76.7) | 18 (85.7) | 197 (78.8) | 606 (68.6) |
| Black | 334 (14.5) | 2 (9.5) | 33 (13.2) | 199 (22.5) |
| Other | 204 (8.9) | 1 (4.8) | 20 (8.0) | 79 (8.9) |
| Hispanic ethnicity | 465 (20.2) | 0 (0.0) | 17 (6.8) | 106 (12.0) |
| Ejection fraction, % | 31 (25–37) | 32 (29–39) | 30 (20–35) | 28 (20–33) |
| NYHA functional class | ||||
| I | 229 (10.0) | 2 (9.5) | 27 (10.8) | 84 (9.5) |
| II | 1,295 (56.3) | 10 (47.6) | 138 (55.2) | 454 (51.4) |
| III | 624 (27.1) | 8 (38.1) | 71 (28.4) | 290 (32.8) |
| IV | 62 (2.7) | 0 (0.0) | 4 (1.6) | 20 (2.3) |
| Not available | 90 (3.9) | 1 (4.8) | 10 (4.0) | 35 (4.0) |
| Vital sign and laboratory findings | ||||
| Systolic blood pressure, mm Hg | 120 (110–132) | 121 (102–133) | 118 (104–128) | 118 (105–129) |
| Diastolic blood pressure, mm Hg | 72 (66–80) | 72 (64–79) | 70 (62–79) | 70 (63–80) |
| Heart rate, beats/min | 72 (65–80) | 74 (65–86) | 72 (64–82) | 74 (67–83) |
| Obese, BMI ≥30 kg/m2 | 896 (38.9) | 11 (52.4) | 102 (40.8) | 416 (47.1) |
| Hemoglobin, g/dl∗ | 13.2 (11.8–14.4) | 13.5 (12.3–14.9) | 13.5 (12.3–14.7) | 13.3 (12.1–14.5) |
| Serum sodium, mmol/l† | 140 (138–142) | 138 (137–141) | 139 (137–141) | 139 (137–141) |
| BUN, mg/dl‡ | 20 (15–28) | 21 (17–35) | 21 (16–27) | 20 (16–26) |
| eGFR, ml/min per 1.73 m2§ | ||||
| <30 | 83 (6.3) | 3 (23.1) | 7 (4.0) | 22 (3.8) |
| 30–44 | 208 (15.7) | 2 (15.4) | 19 (10.8) | 66 (11.4) |
| 45–60 | 297 (22.5) | 3 (23.1) | 41 (23.3) | 142 (24.6) |
| >60 | 734 (55.5) | 5 (38.5) | 109 (61.9) | 347 (60.1) |
| NT-proBNP, pg/ml‖ | 2,642 (960–6,070) | 3,247 (1,246–5,248) | 1,400 (591–3,640) | 1,608 (638–3,671) |
| Hemoglobin A1c, %# | 6.4 (5.8–7.5) | 6.1 (5.8–6.5) | 6.4 (5.9–7.6) | 6.6 (5.8–7.9) |
| Medical history∗∗ | ||||
| HF hospitalization within past 12 months | 786 (34.0) | 8 (38.1) | 104 (41.6) | 416 (47.0) |
| Coronary artery disease | 1,484 (64.4) | 11 (52.4) | 155 (62.0) | 507 (57.3) |
| Hypertension | 1,960 (85.0) | 17 (81.0) | 167 (66.8) | 702 (79.3) |
| Hyperlipidemia | 1,794 (77.8) | 18 (85.7) | 178 (71.2) | 630 (71.2) |
| Diabetes mellitus | 966 (42.0) | 6 (28.6) | 84 (33.6) | 362 (41.0) |
| Atrial fibrillation | 830 (36.0) | 15 (71.4) | 103 (41.2) | 303 (34.2) |
| Chronic renal insufficiency | 473 (20.5) | 4 (19.0) | 39 (15.6) | 157 (17.8) |
| Asthma/COPD | 712 (30.9) | 6 (28.6) | 56 (22.4) | 267 (30.2) |
| History of ventricular tachycardia/fibrillation | 382 (16.6) | 8 (38.1) | 72 (28.8) | 192 (21.7) |
| Depression | 602 (26.2) | 5 (23.8) | 54 (21.6) | 207 (23.4) |
| Active cigarette smoking | 453 (19.7) | 2 (9.5) | 42 (16.8) | 187 (21.2) |
| Heart failure device therapy | ||||
| Implantable cardioverter-defibrillator | 840 (36.5) | 17 (81.0) | 136 (54.4) | 442 (50.1) |
| Cardiac resynchronization therapy | 128 (5.6) | 1 (4.8) | 26 (10.4) | 75 (8.5) |
| Social and economic characteristics | ||||
| Insurance status | ||||
| Managed care (HMO, PPO) | 354 (15.4) | 1 (4.8) | 45 (18.0) | 171 (19.3) |
| Private insurance | 197 (8.5) | 3 (14.3) | 28 (11.2) | 100 (11.3) |
| Medicare | 1,418 (61.5) | 17 (81.0) | 139 (55.6) | 438 (49.5) |
| Medicaid | 191 (8.3) | 0 (0.0) | 22 (8.8) | 103 (11.7) |
| Military health care | 49 (2.1) | 0 (0.0) | 7 (2.8) | 17 (1.9) |
| Uninsured | 42 (1.8) | 0 (0.0) | 6 (250) | 21 (2.4) |
| Other | 54 (2.3) | 0 (0.0) | 3 (1.2) | 34 (3.8) |
| Highest level of education | ||||
| Less than high school | 305 (13.2) | 1 (4.8) | 17 (6.8) | 95 (10.7) |
| High school/GED | 779 (33.8) | 5 (23.8) | 73 (29.2) | 321 (36.3) |
| Some college | 736 (31.9) | 9 (42.9) | 77 (30.8) | 267 (30.2) |
| 4-yr college (baccalaureate degree) | 270 (11.7) | 3 (14.3) | 48 (19.2) | 111 (12.6) |
| Graduate or other professional degree | 215 (9.3) | 3 (14.3) | 35 (14.0) | 90 (10.2) |
| Total household income | ||||
| <$25,000 | 735 (31.9) | 5 (23.8) | 52 (20.8) | 275 (31.1) |
| $25,000–$49,999 | 439 (19.0) | 5 (23.8) | 47 (18.8) | 188 (21.3) |
| $50,000–$74,999 | 266 (11.5) | 3 (14.3) | 32 (12.8) | 110 (12.4) |
| $75,000–$99,999 | 135 (5.9) | 1 (4.8) | 20 (8.0) | 54 (6.1) |
| $100,000–$149,999 | 112 (4.9) | 2 (9.5) | 24 (9.6) | 45 (5.1) |
| ≥$150,000 | 62 (2.7) | 0 (0.0) | 8 (3.2) | 23 (2.6) |
| Prefer not to answer | 556 (24.1) | 5 (23.8) | 67 (26.8) | 189 (21.4) |
| Employment status | ||||
| Full-time employee (≥35 h/week) | 305 (13.2) | 0 (0.0) | 43 (17.2) | 146 (16.5) |
| Part-time employee (<35 h/week) | 152 (6.6) | 2 (9.5) | 29 (11.6) | 60 (6.8) |
| Disability for medical reasons | 486 (21.1) | 5 (23.8) | 71 (28.4) | 312 (35.3) |
| Not employed for other reasons (e.g., retired, student, unemployed) | 1,362 (59.1) | 14 (66.7) | 107 (42.8) | 366 (41.4) |
Factors associated with medication use and doses
Independent associations among baseline patient characteristics, medication use, and medication dose are displayed in Table 5.
| Treated vs. Not Treated | ≥50% Target Dose vs. <50% Target Dose | ≥100% Target Dose vs. <100% Target Dose | |
|---|---|---|---|
| ACEI/ARB | (n = 3,158)† | (n = 1,938)‡ | (n = 1,893)§ |
| Age, per 10-yr increase | 0.90 (0.82–0.98), p = 0.014 | ||
| Female | 0.78 (0.66–0.93), p = 0.005 | ||
| Black vs. white | 1.87 (1.39–2.51), p < 0.001 | 1.57 (1.13–2.18), p = 0.008 | |
| Other vs. white | 0.87 (0.60–1.26), p = 0.457‖ | 0.58 (0.34–1.00), p = 0.048 | |
| Diabetes mellitus | 1.23 (1.01–1.51), p = 0.044 | ||
| Chronic renal insufficiency | 0.62 (0.51–0.75), p < 0.001 | ||
| Asthma/COPD | 0.66 (0.49–0.88), p = 0.006 | ||
| Depression | 0.74 (0.58–0.93), p = 0.012 | ||
| Atrial fibrillation | 0.69 (0.58–0.81), p < 0.001 | ||
| Hypertension | 1.86 (1.39–2.50), p < 0.001 | 2.62 (1.66–4.14), p < 0.001 | |
| HF hospitalization in prior 12 months | 0.73 (0.58–0.91), p = 0.005 | 0.63 (0.47–0.84), p = 0.002 | |
| Systolic blood pressure, per 10-mm Hg increase | 1.06 (1.01–1.11), p = 0.013 | 1.22 (1.15–1.30), p < 0.001 | 1.26 (1.18–1.36), p < 0.001 |
| Heart rate, per 10 beats/min increase | 0.89 (0.82–0.96), p = 0.004 | ||
| NYHA functional class II vs. I | 0.81 (0.61–1.08), p = 0.153‖ | 0.84 (0.58–1.23), p = 0.374‖ | |
| NYHA functional class III/IV vs. I | 0.59 (0.43–0.79), p < 0.001 | 0.57 (0.36–0.88), p = 0.011 | |
| Ejection fraction, per 10% absolute increase | 1.14 (1.00–1.31), p = 0.049 | ||
| ARNI | (n = 3,430)# | (n = 423)∗∗ | – |
| Age, per 10-yr increase | 0.82 (0.75–0.90) p < 0.001 | ||
| Hispanic ethnicity | 0.51 (0.31–0.82), p = 0.006 | ||
| Chronic renal insufficiency | 0.66 (0.48–0.90), p = 0.009 | ||
| Coronary artery disease | 0.45 (0.28–0.74), p = 0.002 | ||
| Hypertension | 1.98 (1.04–3.77), p = 0.037 | ||
| History of ventricular tachycardia/fibrillation | 1.58 (1.20–2.06), p < 0.001 | ||
| Systolic blood pressure, per 10-mm Hg increase | 1.17 (1.03–1.33), p = 0.018 | ||
| Heart rate, per 10-beats/min increase | 0.79 (0.65–0.97), p = 0.026 | ||
| Ejection fraction, per 10% absolute increase | 0.73 (0.63–0.84), p < 0.001 | ||
| Beta-blocker | (n = 3,468)†† | (n = 2,163)‡‡ | (n = 2,163)‡‡ |
| Age, per 10-yr increase | 0.87 (0.81–0.93), p < 0.001 | 0.83 (0.77–0.90), p < 0.001 | 0.87 (0.79–0.95), p = 0.003 |
| Black vs. white | 1.46 (1.13–1.89), p = 0.004 | 1.45 (1.10–1.90), p = 0.008 | |
| Other race vs. white | 0.76 (0.55–1.05), p = 0.091‖ | 1.15 (0.77–1.71), p = 0.490‖ | |
| Hispanic ethnicity | 0.66 (0.46–0.96), p = 0.030 | ||
| Obese, BMI ≥30 kg/m2 | 1.58 (1.31–1.92), p < 0.001 | 1.61 (1.30–1.99), p < 0.001 | |
| High school/GED vs. less than high school | 1.13 (0.80–1.59), p = 0.501‖ | ||
| Some college vs. less than high school | 0.95 (0.66–1.36), p = 0.771‖ | ||
| 4-yr/graduate/professional degree vs. less than high school | 0.75 (0.51–1.10), p = 0.135‖ | ||
| Diabetes mellitus | 1.37 (1.14–1.65), p < 0.001 | 1.50 (1.22–1.85), p < 0.001 | |
| Chronic renal insufficiency | 1.24 (1.01–1.52), p = 0.045 | ||
| Asthma/COPD | 0.81 (0.67–0.96), p = 0.016 | 0.77 (0.62–0.94), p = 0.010 | 0.77 (0.60–0.97), p = 0.030 |
| Active cigarette smoking | 0.66 (0.50–0.87), p = 0.003 | ||
| Atrial fibrillation | 0.76 (0.64–0.90), p = 0.002 | 1.53 (1.26–1.87), p < 0.001 | |
| Coronary artery disease | 0.78 (0.62–0.98), p = 0.032 | ||
| HF hospitalization in prior 12 months | 0.60 (0.49–0.72), p < 0.001 | 0.54 (0.43–0.67), p < 0.001 | |
| Systolic blood pressure, per 10-mm Hg increase | 1.10 (1.05–1.16), p < 0.001 | 1.10 (1.04–1.16), p < 0.001 | |
| Heart rate, per 10-beats/min increase | 0.92 (0.85–0.99), p = 0.027 | 0.91 (0.83–0.99), p = 0.027 | |
| Ejection fraction, per absolute 10% increase | 0.73 (0.66–0.82), p < 0.001 | ||
| MRA | (n = 3,262)§§ | – | (n = 1,146)‖‖ |
| Age, per 10-yr increase | 0.78 (0.74–0.84), p < 0.001 | ||
| Female | 1.27 (1.06–1.52), p = 0.008 | 1.47 (1.06–2.05), p = 0.021 | |
| Hispanic ethnicity | 0.72 (0.53–0.97), p = 0.029 | ||
| Obese, BMI ≥30 kg/m2 | 1.26 (1.07–1.50), p = 0.007 | ||
| Chronic renal insufficiency | 0.78 (0.63–0.96), p = 0.021 | ||
| Atrial fibrillation | 0.69 (0.51–0.94), p = 0.017 | ||
| Hypertension | 1.86 (1.33–2.59), p < 0.001 | ||
| History of ventricular tachycardia/fibrillation | 1.28 (1.04–1.57), p = 0.018 | ||
| HF hospitalization in prior 12 months | 1.21 (1.02–1.44), p = 0.032 | ||
| Systolic blood pressure, per 10-mm Hg increase | 0.91 (0.86–0.95), p < 0.001 | ||
| Ejection fraction, per absolute 10% increase | 0.72 (0.64–0.80), p < 0.001 | 0.80 (0.67–0.97), p = 0.020 |
Angiotensin-converting enzyme inhibitors or angiotensin II receptor blocker
Female sex, chronic renal insufficiency, atrial fibrillation, and NYHA functional class III/IV symptoms were strong independent factors associated with not being prescribed an ACEI/ARB (all p ≤ 0.007). Among those who were treated, factors such as being black, higher levels of systolic blood pressure, and a history of hypertension were associated with target doses (all p ≤ 0.008), whereas prior HF hospitalization within 12 months, asthma/COPD, and NYHA functional class III/IV status were strongly associated with subtarget doses (all p ≤ 0.011).
Angiotensin receptor neprilysin inhibitor
For ARNI therapy, multiple clinical factors were independently associated with lower likelihood of treatment, including older age, Hispanic ethnicity, chronic renal insufficiency, and higher EF (all p ≤ 0.009). Of those treated with an ARNI, higher systolic blood pressure and history of hypertension were associated with ≥50% target dose (all p ≤ 0.037).
Evidence-based beta-blocker
Increasing age, asthma/COPD, atrial fibrillation, and lower EF were associated with not receiving beta-blocker therapy (all p ≤ 0.016), whereas a history of chronic renal insufficiency was associated with greater likelihood of beta-blocker treatment (p = 0.045). Among patients receiving an evidence-based beta-blocker, black patients and patients with obesity and diabetes mellitus were substantially more likely to receive ≥50% of target or target dose (all p ≤ 0.008). In contrast, prior HF hospitalization and asthma/COPD were strongly associated with lower likelihood of receiving ≥50% of target (all p ≤ 0.010) or target dose (all p ≤ 0.030).
Mineralocorticoid receptor antagonist
Several characteristics were independently associated with not receiving MRA therapy, with the magnitudes of association strongest for Hispanic ethnicity, older age, and chronic renal insufficiency (all p ≤ 0.029). Of those receiving MRA treatment, female sex and history of hypertension were strongly associated with receiving target dose (all p ≤ 0.021), whereas atrial fibrillation was strongly associated with lower likelihood of receiving target dose (p = 0.017).
Patient characteristics by baseline medication use
Baseline clinical characteristics by medication use are presented in Online Table 6. Most patients in all medication groups had NYHA functional class II symptoms, and the highest proportion of class III/IV patients were among patients receiving ARNI (37.1%). Rates of prior HF hospitalization were highest among patients receiving MRA (45.7%) and next highest among patients receiving ARNI (42.7%). Across all medication groups, vital signs, laboratory values, and EF were similar. Likewise, the prevalence of comorbidities was high in all groups, with 58.2% to 63.2% of patients having coronary artery disease, 39.2% to 41.7% having diabetes mellitus, and 33.0% to 37.6% having atrial fibrillation. Rates of implantable cardioverter-defibrillator and cardiac resynchronization therapy were 41.3% to 54.9% and 6.4% to 10.0%, respectively.
Descriptive data regarding baseline social and economic characteristics by medication use are displayed in Online Table 7. Regardless of medication group, >50% of patients were insured by Medicare. Patients receiving ARNI therapy had the highest rate of managed care (20.6%) or private insurance coverage (12.6%). Other socioeconomic characteristics differed with ARNI use, for example, ARNI patients had the lowest rates of less than high school education and household income of <$50,000 and highest rates of full-time employment and a household income >$100,000.
Discussion
In this large contemporary U.S. registry of stable outpatients with HFrEF, significant gaps in guideline-directed use and dosing of evidence-based medications remain. Despite <2% of patients having a documented absolute contraindication to any specific therapy, use of each guideline medication fell below 75%. MRA and ARNI therapies were particularly underused, with only 33% and 13% of eligible patients treated, respectively. When GDMT was used, it tended to be used at lower doses with the majority of patients prescribed subtarget doses of ACEI/ARB/ARNI and beta-blocker, and high proportions prescribed <50% of target dose. Only 1% of eligible patients were simultaneously treated with target does of ACEI/ARB/ARNI, beta-blocker, and MRA therapy, and <25% of patients simultaneously received any dose of all 3 medications. For each medication class, patient profiles varied with dose of medications prescribed. Notably, with exception of MRA therapy, rates of prior HF hospitalization and NYHA functional class III/VI status were generally inversely related to medication dose, with highest rates among patients receiving the lowest doses. In multivariate analysis, for each medication class, several patient characteristics were independently associated with patients receiving each therapy and the specific dose.
To our knowledge, we present the most comprehensive and contemporary analysis of outpatient HFrEF medical therapy dose in U.S. clinical practice. Although prior HF registry data informed previous quality improvement efforts, recent programs have focused on patients hospitalized with HF, a notably different population from the stable outpatients recruited in landmark registration trials for GDMT (13,14). In this respect, the current CHAMP-HF data from stable ambulatory HFrEF patients evaluates use of GDMT in a U.S. cohort more representative of the populations from which clinical trial evidence was derived. In addition, the present analysis offers several strengths that provide more granular insight into the pattern of current GDMT use in the United States. First, the registry included pre-specified and detailed dosing information to allow description of target and subtarget doses, as well as associated patient profiles. Importantly, data capture included ARNI therapy, to be consistent with class I recommendations from the recent treatment guideline update (2). Second, recognizing the potential contributions of social factors to the use of HFrEF therapy, CHAMP-HF offers a comprehensive socioeconomic characterization of the patient population. Third, to mitigate potential influences of medical record accuracy and completeness of registry medication data, CHAMP-HF included an electronic case report form with pre-specified capture of medication data, presence of contraindications, and multiple questions regarding tolerability. Fourth, rigorous multivariate modeling inclusive of both clinical and social factors was performed to assess independent patient-level associations with medication use and dose, and hierarchical models were used to account for patient clustering by enrolling study site.
Prior data examining dose of GDMT in U.S. outpatient clinical practice comes largely from the IMPROVE HF (Improve the Use of Evidence-Based Heart Failure Therapies in the Outpatient Setting) study (15). Specifically, this large-scale quality improvement initiative found rates of baseline target dosing of ACEI/ARB, beta-blocker, and MRA among eligible patients to be 36.1%, 20.5%, and 74.4%, respectively (16). Despite IMPROVE HF data reflecting HFrEF care approximately a decade ago, contemporary target doses in CHAMP-HF are remarkably similar for beta-blockers (27.5%) and MRA (76.6%) and are considerably lower for ACEI/ARB/ARNI (16.7%). Overall use (i.e., any dose) of GDMT within the 2 cohorts was also similarly low (e.g., baseline use of MRA among eligible patients was 34.5% in IMPROVE HF and 33.4% in CHAMP-HF) (15).
Given that significant gaps in provision of HFrEF medical therapy persist, next steps must focus on why underuse and underdosing continues. Recent prospective investigations strongly suggest that inadequate rigor of outpatient follow-up may not be the causative factor. For example, the prospective BIOSTAT-CHF (Biology Study to Tailored Treatment in Chronic Heart Failure) program recruited 2,100 HFrEF patients in Europe, who were either not receiving ACEI/ARB and beta-blocker at baseline or were receiving <50% of target doses (4). Despite the pre-specified intent of the program to encourage medication up-titration, at median follow-up of 21 months, only 22% and 12% of patients achieved target doses of ACEI/ARB and beta-blocker, respectively. Likewise, findings from the GUIDE-IT (Guiding Evidence Based Therapy Using Biomarker Intensified Treatment in Heart Failure) trial showed that, despite an intensive outpatient follow-up regimen that included a median 12 clinic visits over 12 months and care guided by natriuretic peptide levels, only 15% of patients reached target doses of beta-blockers, and 31% reached target doses of ACEI/ARB (17).
As a complement to these recent prospective experiences, the current study from CHAMP-HF directed attention to the role of patient-level factors in the likelihood of receiving target doses. Although results for specific therapies were varied, lower systolic blood pressure and other unfavorable prognostic factors such as more severe NYHA functional class, older age, chronic renal insufficiency, and recent hospitalization for HF generally favored lower medication use or dose. These findings are compatible with the previously described “risk-treatment paradox,” where HF patients with greatest need are less likely to receive appropriate therapy (18). Although these observations from CHAMP-HF may be partly intrinsic to greater severity of disease and potential overlapping side effects of many HFrEF medications (e.g., hypotension, worsening renal function), it is conceivable that GDMT underuse and underdosing in higher risk patients may be driven by disproportionate emphasis among providers on potential patient destabilization with medication changes rather than clinical benefits of therapy. Although clinical judgment remains paramount, randomized evidence supports the relative safety of higher doses of GDMT as compared with lower doses. For example, a meta-analysis of ACEI/ARB trials found that, compared to low-dose ACEI/ARB therapy, patients randomized to higher doses experienced improved survival without excess rates of drug discontinuation (19). Similarly, despite enrolling a severely symptomatic HFrEF population, the COPERNICUS (Carvedilol Prospective Randomized Cumulative Survival) trial demonstrated the relative safety and efficacy of carvedilol initiation among >2,200 patients (20).
Numerous studies have explored interventions to improve provision of medical therapy by providers and/or to improve medication adherence among patients (21–23). Although the broad application of most data remain limited by small numbers of study centers, limited numbers of patients, and heterogeneity of interventions, evidence does support the potential impact of some strategies such as quality metric checklists, pharmacist-driven medication programs, and patient education (21–23). Likewise, the recent American College of Cardiology Expert Consensus Document provides recommendations for addressing challenges to appropriate use of HFrEF medical therapy, including clinical, economic, and social barriers (12). Nonetheless, CHAMP-HF highlights the persistently unmet need for generalizable strategies to improve widespread use and dosing of GDMT. Specifically, the current “risk-treatment paradox” findings suggest that patients with the highest clinical risk may particularly benefit from future quality improvement strategies. Moreover, although most patients received care at cardiology practices, larger gaps in GDMT use among patients enrolled at family medicine and internal medicine clinics suggest such practices may have the most to gain from future efforts to improve quality of care. Aside from more conventional approaches, novel interventions grounded in behavioral economics or technological innovation (e.g., mobile applications) may be particularly promising and are being tested (24).
Study limitations
First, although associations between patient characteristics and medication use reflect rigorous multivariate modeling, the possibility of residual confounding exists, and these observational data cannot definitively determine cause-and-effect relationships. Likewise, this observational study is unable to conclusively discern underlying reasons for such associations. Second, baseline laboratory data are limited, as collection was not specific to the CHAMP-HF registry and was obtained only if recorded in the medical record. Third, although study sites were chosen to include a diverse mix of health care providers and outpatient facilities, data reflect patients from sites who elected to participate in the registry and thus may not be generalizable to all care practices. Fourth, the current analysis defined treatment eligibility by the absence of an absolute contraindication to a given therapy at the discretion of site investigators and did not reflect the potential impact of single or multiple relative contraindications on treatment decisions. Moreover, data regarding the specific nature of contraindications were not collected. Fifth, patients in this voluntary program were predominantly male and white. It is unclear how increased representation of women and racial/ethnic minorities would have influenced results. Sixth, this study did not pre-specify assessment of frailty, and it is possible that a frailty score may correlate with medication use or dose. Last, CHAMP-HF data are based on documentation within the medical record, and, despite aforementioned features designed to lessen any effects of documentation quality and completeness on registry data, inherent limitations remain. Specifically, it is possible that actual treatment rates and doses differed from those recorded and that patients on lower doses were individuals in whom dose titration had been previously attempted but not tolerated. In addition, contraindications might have been present in some instances but not documented.
Conclusions
In this contemporary registry of outpatients with chronic HFrEF, there remain significant gaps in guideline-directed use and dosing of HFrEF medications. Despite guidelines, educational efforts, and quality improvement initiatives, comparison with prior registry data approximately a decade ago show that outpatient use and dosing of GDMT has generally not improved. Several clinical factors show strong independent associations with baseline use and dose of GDMT, and these findings may inform targeted efforts toward optimal implementation of medical therapy for HFrEF. In the setting of continued high rates of morbidity and mortality in the general HFrEF population, effective strategies to improve use and target dosing of outpatient medical therapy remain urgently needed.
COMPETENCY IN PRACTICE-BASED LEARNING: Guideline-directed medical therapies are underused in the care of outpatients who have HFrEF. Most medications are prescribed at doses lower than recommended, particularly among older patients and in those with low systolic blood pressure, severe functional disability, renal insufficiency, and recent hospitalization for HF.
TRANSLATIONAL OUTLOOK: Strategies are needed to improve implementation of guideline-directed therapies at optimum doses for outpatients with HFrEF.
Appendix
1. : "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: 147.
2. : "2017 ACC/AHA/HFSA focused update of the 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America". J Am Coll Cardiol 2017; 70: 776.
3. : "Incremental reduction in risk of death associated with use of guideline-recommended therapies in patients with heart failure: a nested case-control analysis of IMPROVE HF". J Am Heart Assoc 2012; 1: 16.
4. : "Determinants and clinical outcome of uptitration of ACE-inhibitors and beta-blockers in patients with heart failure: a prospective European study". Eur Heart J 2017; 38: 1883.
5. : "Comparison of medical therapy dosing in outpatients cared for in cardiology practices with heart failure and reduced ejection fraction with and without device therapy: report from IMPROVE HF". Circ Heart Fail 2010; 3: 596.
6. : "Trends in prevalence and outcome of heart failure with preserved ejection fraction". N Engl J Med 2006; 355: 251.
7. : "Potential impact of optimal implementation of evidence-based heart failure therapies on mortality". Am Heart J 2011; 161: 1024.
8. : "Potential mortality reduction with optimal implementation of angiotensin receptor neprilysin inhibitor therapy in heart failure". JAMA Cardiol 2016; 1: 714.
9. : "Early adoption of sacubitril/valsartan for patients with heart failure with reduced ejection fraction: insights from Get With the Guidelines-Heart Failure (GWTG-HF)". J Am Coll Cardiol HF 2017; 5: 305.
10. Institute of Medicine. Crossing the quality chasm: a new health system for the 21st century. National Academies of Sciences, Engineering, and Medicine. Available at: http://www.nationalacademies.org/hmd/∼/media/Files/Report%20Files/2001/Crossing-the-Quality-Chasm/Quality%20Chasm%202001%20%20report%20brief.pdf. Accessed January 27, 2017.
11. : "Change the management of patients with heart failure: Rationale and design of the CHAMP-HF registry". Am Heart J 2017; 189: 177.
12. : "2017 ACC expert consensus decision pathway for optimization of heart failure treatment: answers to 10 pivotal issues about heart failure with reduced ejection fraction". J Am Coll Cardiol 2018; 71: 201.
13. : "Trends in patients hospitalized with heart failure and preserved left ventricular ejection fraction: prevalence, therapies, and outcomes". Circulation 2012; 126: 65.
14. : "The American Heart Association Get With The Guidelines program". Am Heart J 2004; 148: S46.
15. : "Improving evidence-based care for heart failure in outpatient cardiology practices: primary results of the registry to Improve the Use of Evidence-Based Heart Failure Therapies in the Outpatient Setting (IMPROVE HF)". Circulation 2010; 122: 585.
16. : "Medication dosing in outpatients with heart failure after implementation of a practice-based performance improvement intervention: findings from IMPROVE HF". Congestive Heart Fail 2012; 18: 9.
17. : "Effect of natriuretic peptide-guided therapy on hospitalization or cardiovascular mortality in high-risk patients with heart failure and reduced ejection fraction: a randomized clinical trial". JAMA 2017; 318: 713.
18. : "Treatment and risk in heart failure: gaps in evidence or quality?". Circ Cardiovasc Qual Outcomes 2010; 3: 309.
19. : "Dose of angiotensin-converting enzyme inhibitors and angiotensin receptor blockers and outcomes in heart failure: a meta-analysis". Circ Heart Fail 2017 Aug; 10. pii: e003956 https://doi.org/10.1161/CIRCHEARTFAILURE.117.003956.
20. : "Effects of initiating carvedilol in patients with severe chronic heart failure: results from the COPERNICUS study". JAMA 2003; 289: 712.
21. : "Medication adherence interventions improve heart failure mortality and readmission rates: systematic review and meta-analysis of controlled trials". J Am Heart Assoc 2016 Jun 17; 5. pii: e002606 https://doi.org/10.1161/JAHA.115.002606.
22. : "Improvements in 1-year cardiovascular clinical outcomes associated with a hospital-based discharge medication program". Ann Intern Med 2004; 141: 446.
23. : "Managed care interventions for improving outcomes in acute heart failure syndromes". Am J Manag Care 2008; 14: S273.
24. : "Leveraging behavioral economics to improve heart failure care and outcomes". Circulation 2017; 136: 765.
Abbreviations and Acronyms
| ACEI | angiotensin-converting enzyme inhibitor |
| ARB | angiotensin II receptor blocker |
| ARNI | angiotensin receptor-neprilysin inhibitor |
| GDMT | guideline-directed medical therapy |
| HF | heart failure |
| HFrEF | heart failure with reduced ejection fraction |
| MRA | mineralocorticoid receptor antagonist |
Footnotes
CHAMP-HF was supported by Novartis Pharmaceuticals. Dr. Greene has received support from U.S. National Institutes of Health (NIH) grant 5T32HL069749-14, Heart Failure Society of America/Emergency Medicine Foundation Acute Heart Failure Young Investigator Award funded by Novartis, and from Novartis. Dr. Butler has received research support from the National Institutes of Health, Patient-Centered Outcomes Research Institute, and European Union; and has consulted for Amgen, Array, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol-Myers Squib, CVRx, G3 Pharmaceutical, Innolife, Janssen, Luitpold, Medtronic, Merck, Novartis, Relypsa, StealthPeptide, SC Pharma, Vifor, and ZS Pharma. Dr. DeVore has received funding from the American Heart Association, Amgen, National Institutes of Health, and Novartis; and has consulted for Novartis. Dr. Albert has consulted for Novartis and Boston Scientific. Drs. Sharma and Duffy and Mr. McCague are employees of Novartis. Dr. Patterson has received research funding from Amgen, Bristol-Myers Squibb, Merck, Novartis, and Otsuka; and has consulted for Novartis. Dr. Spertus has consulted for Novartis; and holds copyright to the Kansas City Cardiomyopathy Questionnaire. Dr. Thomas has received funding from Duke University. Dr. Hernandez has consulted for AstraZeneca, Bayer, Boston Scientific, Merck, Novartis, and Sanofi; and has received support from AstraZeneca, GlaxoSmithKline, Luitpold, Merck, and Novartis. Dr. Fonarow has received funding from the National Institutes of Health; and has consulted for Amgen, Bayer, Medtronic, and Novartis. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
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