Abstract
Background:
Guideline-directed medical therapy (GDMT) dramatically improves outcomes in heart failure with reduced ejection fraction (HFrEF). Our goal was to examine GDMT use in community patients with newly diagnosed HFrEF.
Methods and Results:
We performed a population-based, retrospective cohort study of all Olmsted County, Minnesota residents with newly diagnosed HFrEF (EF≤40%) 2007–2017. We excluded patients with contraindications to medication initiation. We examined use of beta blockers, HF beta blockers (metoprolol succinate, carvedilol, bisoprolol), ACEi/ARB/ARNI, and MRA in the first year after HFrEF diagnosis. We used Cox models to evaluate the association of being seen in a HF clinic with initiation of GDMT. From 2007–2017, 1160 patients were diagnosed with HFrEF (mean age 69.7 years, 65.6% men). Most eligible patients received beta blockers (92.6%) and ACEi/ARB/ARNI (87.0%) in the first year. However, only 63.8% of patients were treated with a HF beta blocker, and few received MRAs (17.6%). In models accounting for the role of HF clinic in initiation of these medications, being seen in a HF clinic was independently associated with initiation of new GDMT across all medication classes, with HR (95% CI) of 1.54 (1.15–2.06)for any beta blocker, 2.49 (1.95–3.20) for HF beta blockers, 1.97 (1.46–2.65) for ACEi/ARB/ARNI, and 2.14 (1.49–3.08) for MRAs.
Conclusions:
In this population-based study, most patients with newly diagnosed HFrEF received beta blockers and ACEi/ARB/ARNIs. GDMT use was higher in patients seen in a HF clinic, suggesting potential benefit of referral to a HF clinic for patients with newly diagnosed HFrEF.
Keywords: guideline directed medical therapy, heart failure, medications
Lay summary:
While most patients with heart failure with low ejection fraction receive some medications to treat their heart failure in the first year after diagnosis, there is still opportunity for improvement. This will become even more complex with approval of new medications to treat heart failure.
Patients who are newly diagnosed with heart failure and a low ejection fraction have higher use of heart failure medications if they are seen in a heart failure specialty clinic.
INTRODUCTION
Guideline directed medical therapy (GDMT) is the cornerstone of management of heart failure with reduced ejection fraction (HFrEF).1 Clinical trials have consistently demonstrated the effectiveness of GDMT including beta blockers, antagonists of the renin-angiotensin system (angiotensin converting enzyme inhibitors; ACEi; angiotensin receptor antagonists; ARB; angiotensin receptor neprilysin inhibitors; ARNI) and mineralocorticoid receptor antagonists (MRAs) in patients with HFrEF.2–4 Use of GDMT been demonstrated to improve cardiac function, quality of life, and functional status, and to decrease risks of hospitalization and mortality.1 While societal guidelines strongly recommend the use of GDMT in HFrEF,1, 5 prior studies have demonstrated gaps in use and dosing of GDMT in outpatient clinical practice.6, 7 In one registry of outpatient primary care and (primarily general) cardiology practices, 27% of eligible patients were not prescribed ACEi/ARB/ARNI and 33% were not prescribed beta blockers.7 Data from an academic tertiary referral HF clinic suggested much higher overall rates of use, with titration to goal doses limited by physiologic constraints.8
The overall use of GDMT, achievement of goal doses, and impact of HF specialty clinic care at a population level remains unclear. We undertook the present study using our population-based cohort comprised of all residents with newly diagnosed HFrEF in a single community to address these gaps in knowledge.
METHODS
This was a population-based retrospective cohort study conducted in Olmsted County, Minnesota that leveraged the resources of the Rochester Epidemiology Project (REP).9 The REP enables epidemiologic research to be conducted, as all local care for county’s residents is captured and linked. Patients who declined to provide Minnesota Research Authorization were excluded from analysis. The study was approved by the Mayo and Olmsted Medical Center Institutional Review Boards.
HFrEF Cohort.
We identified all Olmsted County residents with newly diagnosed HF from 2007–2017 using methods previously described.10 In brief, we reviewed the medical records of all patients with at least one billing code for HF (ICD-9 428 or ICD-10 I50) to confirm new, symptomatic HF.11 We then restricted the population to those with an EF≤40% assessed by echocardiogram closest to HF diagnosis, and within 1 year prior or up to 3 months post diagnosis.
GDMT Use.
We assessed use of GDMT as documented in the electronic health record. Medications assessed are shown in Table S1, and included beta blockers, HF-specific beta blockers (metoprolol succinate, carvedilol, bisoprolol), ACEi/ARB/ARNI (sacubitril-valsartan was FDA approved for HFrEF in 2015), and mineralocorticoid receptor antagonists. The doses were as documented in the patient’s chart. We defined use of each medication class at baseline (pre-HF diagnosis) as those who were documented as taking the medication at least 1 week prior to HFrEF diagnosis. We defined initiation as those who were started on a medication within 1 week prior to HFrEF diagnosis until 1 year post HFrEF diagnosis. We assessed peak dose of each medication as the highest dose documented in the medical record within the first year after HFrEF diagnosis. Goal daily doses were as documented based on prior studies (Table S1).
Patients were considered not eligible for use of a specific medication class if they had documented contraindication, allergy, or intolerance. For beta blockers, this included heart rate <50 bpm, systolic blood pressure <80mm Hg, or allergy/ intolerance in the electronic health record. For ACEi/ARB/ARNI, this was defined as systolic blood pressure <80 mm Hg,1 creatinine >3 mg/dL within 3 months prior to HF,1 potassium >5 mEq/L within 3 months prior to HF, and documented allergy or intolerance other than cough with ACEi (as ARB/ARNI can be used instead). MRA exclusion criteria included creatinine >2.5 mg/dL in men or 2.0 mg/dL in women in the 3 months prior to HF,1 potassium >5.0 mEq/L in the 3 months prior to HF,1 or allergy/intolerance.
Patient Characteristics.
Patient baseline characteristics were as documented in the electronic health record. Heart rate, systolic blood pressure, and BMI values were used closest to the date of HF diagnosis. Comorbid conditions were assessed using the Charlson comorbidity index12 and defined by 2 or more codes within the five years prior to diagnosis. Laboratory data (serum sodium, creatinine, potassium, hemoglobin) were obtained from labs drawn closest to the incident HF date and within 3 months prior in all cases. Anemia was defined using WHO criteria. Echocardiographic data were obtained from transthoracic echocardiograms obtained closest to the incident HF date and within 1 year in all cases. There is only one HF clinic in the county (Mayo Clinic HF Clinic). The HF clinic sees patients referred for diagnosis, evaluation, and management of HF. Patients are seen by heart failure cardiologists and nurse practitioners. The number of annual visits is approximately 5000–6000. Patients were defined as being seen in the HF clinic within the 7 days prior through 1 year post incident HF diagnosis.
Statistical Analysis.
Patient baseline characteristics were summarized using mean (standard deviation; SD), N(%), or median (25th–75th percentile) where appropriate. Differences in baseline characteristics in those seen in the HF clinic versus not were examined using t-tests, Fisher’s exact test, or Wilcoxon rank sum tests for continuous variables and chi-square tests for categorical variables. The percent of goal dose achieved was calculated as the peak dose for each medication within a class divided by the goal dose of each medication. We examined use of each class of GDMT (beta blockers, HF beta blockers, ACEi/ARB/ARNI, MRA) in the first year after new HFrEF diagnosis using logistic regression. The multivariable model included age, gender, BMI, diabetes, cerebrovascular disease, hypertension, coronary artery disease, chronic obstructive pulmonary disease, estimated glomerular filtration rate, EF, location of incident HF diagnosis (inpatient or outpatient), and an indicator of whether the patient had been seen in the HF clinic in the first year after HF diagnosis. However, recognizing that this logistic regression model does not account for the role of HF clinic in initiating new medications, we next ran a Cox regression model predicting each class of GDMT with HF clinic as a time-dependent covariate. That model was adjusted for the variables included in the logistic regression models. A p value of <0.05 was used as the level of significance for all analyses. All analyses were performed in SAS Version 9.4.
RESULTS
A total of 1160 adult Olmsted County residents with incident HFrEF were identified in the study period. Their baseline characteristics are shown in Table 1. On average, they were older (mean age 69.7 years), 65.6% were men, and the mean EF was 29.7%.
Table 1.
Patient Characteristics
| Characteristic | Overall (N=1160) |
Not seen in HF clinic (N=780) | Seen in HF clinic (N=380) | P-value |
|---|---|---|---|---|
| Age, Mean (SD) | 69.7 (15.7) | 71.2 (15.5) | 66.4 (15.5) | <.001* |
| Male, n (%) | 761 (65.6) | 506 (64.9) | 255 (67.1) | 0.45† |
| Race/Ethnicity, n (%) | 0.03† | |||
| White, non-Hispanic | 1051 (90.6) | 715 (91.7) | 336 (88.4) | |
| Hispanic | 31 (2.7) | 19 (2.4) | 12 (3.2) | |
| Black | 33 (2.8) | 14 (1.8) | 19 (5.0) | |
| Asian | 22 (1.9) | 15 (1.9) | 7 (1.8) | |
| Other race | 23 (2.0) | 17 (2.2) | 6 (1.6) | |
| Smoking status, n (%)¶ | 0.86† | |||
| Never | 437 (39.8) | 290 (39.5) | 147 (40.4) | |
| Current | 169 (15.4) | 116 (15.8) | 53 (14.6) | |
| Former | 492 (44.8) | 328 (44.7) | 164 (45.1) | |
| Heart rate, bpm¶, mean (SD) | 70.5 (19.2) | 69.3 (18.7) | 72.9 (19.9) | 0.004* |
| Systolic BP, mm Hg, mean (SD) ¶ | 112.2 (23.5) | 110.5 (24.3) | 115.5 (21.5) | <.001‡ |
| BMI, kg/m2, mean (SD) ¶ | 29.6 (7.3) | 29.2 (7.1) | 30.4 (7.6) | 0.012* |
| Hypertension, n (%) | 753 (64.9) | 528 (67.7) | 225 (59.2) | 0.004† |
| Hyperlipidemia, n (%) | 657 (56.6) | 444 (56.9) | 213 (56.1) | 0.78† |
| CAD, n (%) | 444 (38.3) | 315 (40.4) | 129 (33.9) | 0.034† |
| Diabetes, n (%) | 316 (27.2) | 214 (27.4) | 102 (26.8) | 0.83† |
| COPD, n (%) | 238 (20.5) | 167 (21.4) | 71 (18.7) | 0.28† |
| Peripheral vascular disease, n (%) | 273 (23.5) | 192 (24.6) | 81 (21.3) | 0.21† |
| Cerebrovascular disease, n (%) | 128 (11.0) | 96 (12.3) | 32 (8.4) | 0.047† |
| Dementia, n (%) | 53 (4.6) | 44 (5.6) | 9 (2.4) | 0.012† |
| Depression, n (%) | 215 (18.5) | 150 (19.2) | 65 (17.1) | 0.38† |
| Arrhythmia, n (%) | 250 (21.6) | 175 (22.4) | 75 (19.7) | 0.29† |
| Charlson comorbidity index, n (%) | 0.081* | |||
| 0–2 | 613 (52.8) | 397 (50.9) | 216 (56.8) | |
| 3–4 | 302 (26.0) | 205 (26.3) | 97 (25.5) | |
| 5 or more | 245 (21.1) | 178 (22.8) | 67 (17.6) | |
| Sodium, Mean (SD) ¶ | 138.3 (4.3) | 138.1 (4.4) | 138.7 (4.1) | 0.049* |
| eGFR, Mean (SD) | 67.3 (42.2) | 65.5 (27.1) | 70.9 (62.6) | 0.111‡ |
| Potassium, Mean (SD) ¶ | 4.3 (0.5) | 4.3 (0.6) | 4.3 (0.5) | 0.038* |
| Anemia, n (%) | 489 (42.4) | 363 (46.8) | 126 (33.2) | <.001† |
| Ejection fraction, Mean (SD) | 29.7 (7.3) | 30.6 (7.1) | 28.0 (7.4) | <.001* |
| ICD prior to HF diagnosis, n(%) | 29 (2.5) | 21 (2.7) | 8 (2.1) | 0.69† |
| HF diagnosed as inpatient | 524 (45.2) | 367 (47.1) | 157 (41.3) | 0.065† |
Equal variance two sample t-test
Chi-Square p-value. For ICD prior to HF diagnosis, the Fisher’s exact test was calculated.
Unequal variance two sample t-test
Wilcoxon rank sum p-value
Missing data: 62 were missing smoking status, 64 were missing heart rate, 128 systolic blood pressure, 3 BMI, 3 serum sodium, and 78 serum potassium. All other variables were complete in all cases
BMI= body mass index, BP= blood pressure, CAD= coronary artery disease; COPD= chronic obstructive pulmonary disease; eGFR= estimated glomerular filtration rate; HF= heart failure; SD= standard deviation
Prior to HFrEF diagnosis, 345 (29.7%) patients were taking a beta blocker, and 148 (12.8%) were not eligible due to contraindication/ allergy/ intolerance (Table 2 and Table S2). In total, among those eligible, 937 (92.6%) were taking a beta blocker in the year after HFrEF diagnosis. Only 133 (11.5%) patients were taking one of the three HF beta blockers prior to HFrEF. Among those eligible, 494 (42.6%) were initiated on a HF beta blocker, such that in total, 63.8% of those eligible were taking a HF beta blocker in the year after HFrEF. ACEi/ARB/ARNI use was also high. Prior to HFrEF, 342 (29.5%) patients were taking an ACEi/ARB/ARNI, and an additional 544 (46.9%) patients initiated one in the year after HFrEF. Among those eligible, 87.0% took an ACEi/ARB/ARNI in the year after HFrEF. MRA use was much lower than the other medication groups. In total, 24 (2.1%) patients were taking an MRA prior to HFrEF and 156 (13.5%) initiated one, such that 180 (17.6%) of those eligible took an MRA in the first year after HFrEF.
Table 2.
Use of Guideline-Directed Medical Therapy in the First Year After HFrEF Diagnosis
| Medication Group | Overall (n=1160) | Seen in HF Clinic in 1st Year | P value | |
|---|---|---|---|---|
| No (n=780) | Yes (n=380) | |||
| Beta blocker | ||||
| Prior to HFrEF | 345 (29.7%) | 236 (30.3%) | 109 (28.7%) | <0.001 |
| Not eligible | 148 (12.8%) | 106 (13.6%) | 42 (11.1%) | |
| Initiated in first year | 592 (51.0%) | 370 (47.4%) | 222 (58.4%) | |
| Eligible, did not receive in first year | 75 (6.5%) | 68 (8.7%) | 7 (1.8%) | |
| HF beta blocker | ||||
| Prior to HFrEF | 133 (11.5%) | 84 (10.8%) | 49 (12.9%) | <0.001 |
| Not eligible | 178 (15.3%) | 130 (16.7%) | 48 (12.6%) | |
| Initiated in first year | 494 (42.6%) | 268 (34.4%) | 226 (59.5%) | |
| Eligible, did not receive in first year | 355 (30.6%) | 298 (38.2%) | 57 (15.0%) | |
| ACEi/ARB/ARNI | ||||
| Prior to HFrEF | 342 (29.5%) | 232 (29.7%) | 110 (29.0%) | <0.001 |
| Not eligible | 142 (12.2%) | 102 (13.1%) | 40 (10.5%) | |
| Initiated in first year* | 544 (46.9%) | 328 (42.1%) | 216 (56.8%) | |
| Eligible, did not receive in first year | 132 (11.4%) | 118 (15.1%) | 14 (3.7%) | |
| MRA | ||||
| Prior to HFrEF | 24 (2.1%) | 14 (1.8%) | 10 (2.6%) | <0.001 |
| Not eligible | 135 (11.6%) | 95 (12.2%) | 40 (10.5%) | |
| Initiated in first year | 156 (13.5%) | 68 (8.7%) | 88 (23.2%) | |
| Eligible, did not receive in first year | 845 (72.8%) | 603 (77.3%) | 242 (63.7%) | |
ACEi= angiotensin converting enzyme inhibitor; ARB= angiotensin receptor blocker; ARNI= angiotensin receptor neprilysin inhibitor; MRA= mineralocorticoid receptor antagonist
A total of 13 patients diagnosed with HFrEF after July 2015 (sacubitril valsartan FDA approval date) were initiated on ARNI in the first year
The median (25th–75th percentile) peak dose achieved for each medication class are shown in Table S3. Among those eligible (without a contraindication/ allergy/ intolerance), 128 (13.0%), 229 (22.4%), and 138 (13.5%) patients met target dose of HF beta blockers, ACEi/ARB/ARNI, MRA, respectively, in the year after HFrEF diagnosis. A sensitivity analysis using a target dose of 20mg of lisinopril instead of 40mg (given the range of 20–40mg recommended in the 2013 ACC/AHA guidelines) demonstrated 38.4% of eligible patients reached target doses of ACEi/ARB/ARNI.
A total of 380 (32.8%) patients were seen in the HF clinic in the first year after HFrEF diagnosis and 427 (36.8%) patients not seen in the HF clinic had at least one visit in another cardiology clinic in the first year after diagnosis. The mean number of total visits, other cardiology (not HF clinic), and primary care visits in the first year after HFrEF diagnosis for the cohort are included in Table S4. Patients seen in HF clinic were younger, had slightly lower mean ejection fraction, and less often had hypertension, coronary artery disease, cerebrovascular disease, dementia, and anemia than patients not seen in HF clinic (Table 1). They had slightly higher baseline heart rate and systolic blood pressure, and statistically significant, but not clinically significant differences in baseline serum sodium and potassium. Overall, patients seen in HF clinic had higher use of all GDMT compared with those not seen in HF clinic (Table 2 and Figure 1A). The percentage that achieved goal doses in the first year of those taking a medication in that class (versus all those eligible) was higher for those seen in a HF clinic for HF beta blockers and ACEi/ARB/ARNI (Figure 1B). The proportion who reached at least 50% of their goal dose for HF beta blockers (66.9% vs. 41.2%, p<0.001) and ACEi/ARB/ARNI (61.4% vs. 47.1%, p<0.001) was also higher in those seen in HF clinic vs. not, respectively.
Figure 1A.
Use of Guideline-Directed Medical Therapy in Eligible Patients with Heart Failure and Reduced Ejection Fraction in the First Year After Diagnosis
Figure 1B.
Percentage of Patients Reaching Target Doses of Guideline-Directed Medical Therapy in the First Year After Diagnosis The percentage of those achieving target doses, of those taking each medication class, are shown. For lisinopril, we used a goal dose of 20mg.
The unadjusted associations of patient characteristics with use of GDMT in the first year after diagnosis are shown in Table S5. For each medication group (beta blockers, HF beta blockers, ACEi/ARB/ARNI, and MRAs) the multivariable models are shown in Table 3. Older age was independently associated with less use of beta blockers, HF beta blockers, and ACEi/ARB/ARNI. Higher EF was associated with less use of HF beta blockers. Hypertension and better renal function were associated with increased ACEi/ARB/ARNI use. Male gender and higher BMI were associated with increased MRA use.
Table 3.
Multivariable Models Predicting Guideline Directed Medical Therapy Use
| Odds Ratio for Use (95% CI) | ||||
|---|---|---|---|---|
| Predictor | Beta Blocker | HF Beta Blocker | ACEi/ARB/ARNI | MRA |
| Age (per 10 year increase) | 0.74 (0.61–0.91) | 0.81 (0.73–0.90) | 0.70 (0.60–0.82) | 0.95 (0.83–1.08) |
| Male | 0.67 (0.39–1.16) | 0.96 (0.71–1.30) | 0.77 (0.59–1.18) | 1.47 (1.00–2.16) |
| BMI (per 5 kg/m2 increase) | 1.02 (0.84–1.24) | 1.04 (0.94–1.16) | 1.06 (0.91–1.24) | 1.15 (1.02–1.29) |
| Diabetes | 1.30 (0.70–2.38) | 0.89 (0.64–1.24) | 1.59 (0.96–2.62) | 1.10 (0.74–1.65) |
| Cerebrovascular disease | 0.75 (0.38–1.48) | 0.82 (0.53–1.28) | 0.78 (0.44–1.36) | 0.64 (0.34–1.20) |
| Hypertension | 1.75 (0.98–3.12) | 1.01 (0.72–1.43) | 1.96 (1.24–3.11) | 1.26 (0.84–1.90) |
| Coronary artery disease | 1.31 (0.75–2.31) | 1.07 (0.77–1.48) | 1.33 (0.85–2.08) | 0.81 (0.55–1.21) |
| COPD | 0.66 (0.37–1.17) | 0.73 (0.52–1.03) | 0.64 (0.41–1.01) | 1.04 (0.67–1.60) |
| eGFR (per 10 mL/min increase) | 1.07 (0.96–1.19) | 1.00 (0.96–1.03) | 1.09 (1.00–1.19) | 0.96 (0.88–1.03) |
| EF (per 5% increase) | 0.97 (0.82–1.16) | 0.85 (0.77–0.94) | 0.99 (0.86–1.14) | 0.90 (0.81–1.01) |
| Seen in HF Clinic | 4.72 (2.11–10.56) | 3.44 (2.46–4.80) | 4.46 (2.47–8.04) | 2.70 (1.92–3.79) |
| Inpatient diagnosis of HF | 1.08 (0.66–1.78) | 0.75 (0.57–1.00) | 1.18 (0.79–1.75) | 0.74 (0.53–1.05) |
BMI= body mass index, BP= blood pressure, COPD= chronic obstructive pulmonary disease; EF= ejection fraction; eGFR= estimated glomerular filtration rate; HF= heart failure
Patients seen in a HF clinic in the first year after HFrEF diagnosis had higher use of all GDMT, including beta blockers, HF beta blockers, ACEi/ARB/ARNI, and MRAs. In models accounting for the role of HF clinic in initiation of these medications, being seen in a HF clinic was independently associated with initiation of new GDMT across all medication classes, with HR of 1.54 (95% CI 1.15–2.06) for any beta blocker, 2.49 (95% CI 1.95–3.20) for HF beta blockers, 1.97 (95% CI 1.46–2.65) for ACEi/ARB/ARNI, and 2.14 (95% CI 1.49–3.08) for MRAs (Table 4). We found no significant associations between the number of visits with HF clinic providers (HF cardiologist or nurse practitioner) and use of GDMT in the first year after diagnosis (Table S6). However, we were limited in our ability to capture contacts for remote phone titration of GDMT performed by nurses after a patient visited with a HF clinic provider.
Table 4.
Association of Heart Failure Clinic Visit with Guideline-Directed Medical Therapy Initiation in the First Year After HFrEF Diagnosis
| Effect (hazard ratio, 95% CI) | Beta blocker | HF beta blocker | ACEi/ARB/ARNI | MRA |
|---|---|---|---|---|
| HF clinic, unadjusted | 1.49 (1.12–1.97) | 2.56 (2.01–3.28) | 1.82 (1.37–2.43) | 2.50 (1.76–3.57) |
| HF clinic, adjusted* | 1.54 (1.15–2.06) | 2.49 (1.95–3.20) | 1.97 (1.46–2.65) | 2.14 (1.49–3.08) |
ACEi/ARB/ARNI= angiotensin converting enzyme inhibitor/ angiotensin receptor blocker/ angiotensin receptor neprilysin inhibitor; CI= confidence interval; HF= heart failure; MRA= mineralocorticoid receptor antagonist
Adjusted for age, body mass index, sex, diabetes, cerebrovascular disease, hypertension, coronary artery disease, chronic obstructive pulmonary disease, estimated glomerular filtration rate, ejection fraction, inpatient diagnosis of HF
DISCUSSION
In this population-based study of patients with incident HFrEF, most eligible patients received beta blockers and ACEi/ARB/ARNIs in the year after HFrEF diagnosis. Only about 2/3 were prescribed a HF-specific beta blocker, and less than one-quarter of patients received MRAs. Accounting for other variables, patients seen in a HF clinic in the first year after diagnosis were significantly more likely to be started on GDMT compared with those who were not.
Prior work has demonstrated gaps in use of GDMT in HFrEF. In a large multicenter U.S. registry, eligible patients with HFrEF were prescribed beta blockers, ACEi/ARB/ARNI, and MRAs 67%, 73%, and 33% of the time, respectively.7 Findings in Denmark were slightly better with use in 86%, 84%, and 56% of eligible patients, respectively.6 We found that our community had slightly better use of beta blockers (93%), ACEi/ARB/ARNI (87%), but lower use of MRA (18%) in the first year after HFrEF diagnosis than these registries.
Differences in use of MRA may reflect titration of other GDMT (beta blocker, ACEi/ARB/ARNI) first with reassessment of EF and symptoms before addition of MRA. There have been different approaches advocated for initiation of GDMT, including starting all medication classes simultaneously upon initial diagnosis of HFrEF versus sequential initiation over time.13 The former is a more contemporary approach (and not relevant to the study period), with the rationale being that early initiation enables patients to experience early benefits of all GDMT therapies even at suboptimal doses. Furthermore, over half of patients are hospitalized at incident HFrEF diagnosis,14 and initiation of GDMT prior to hospital discharge has been associated with higher rates of post-discharge GDMT use.15 There are randomized data suggesting that in-hospital initiation of beta blockers and ARNIs, separately, is safe in hemodynamically stable patients with HFrEF,16, 17 and non-randomized data suggesting similar for ACEi and ARBs.18 There are less data available on in-hospital initiation of MRAs, particularly in newly diagnosed HFrEF, though initiation of high dose spironolactone (100mg) in patients admitted with acute heart failure was found to be safe.19 Arguments against the early simultaneous initiation approach include the lack of randomized data in GDMT naïve or newly diagnosed HFrEF patients, and new HFrEF therapies (MRA, ARNI, sodium glucose co-transporter 2; SGLT2 inhibitors) have always been studied on a background of standard of care GDMT. The Mayo Clinic Heart Failure Clinic physicians have tended to take the more cautious stepped approach to initiation, with initiation of beta blockers and ACEi/ARB/ARNI and subsequent reassessment before adding MRAs, which could partially explain the lower use of MRAs in our population. This is consistent with the 2013 ACC/AHA HF guidelines, which state that MRAs should be added in patients who are already on ACEi/ARBs and beta blockers.1 While all patients had symptomatic HFrEF at incident diagnosis, it is possible that patients became asymptomatic or had significant EF improvement with other therapies and MRA would then no longer be indicated.
Most patients did not meet target doses of HF beta blockers and ACEi/ARB/ARNI in the first year after HFrEF in our study; this is overall consistent with low rates of target doses achieved in patients with HFrEF in registries.6, 7, 20, 21 In the Change the Management of Patients with Heart Failure (CHAMP-HF) registry,22 19% of patients were receiving target beta blocker (they included non-HF beta blockers) doses and 11% target ACEi/ARB doses, which is slightly lower than the 36% and 25% for beta blockers and ACEi/ARB/ARNI, respectively, we observed in our study. It is possible that patients were still amid titration, further titration was limited by hemodynamics or symptoms, or that higher doses were not being pursued due to lack of knowledge or clinical inertia. Prior work in a tertiary HF clinic suggested that inability to achieve doses of GDMT used in clinical trials was largely due to physiologic constraints.8 In a U.S. registry, however, <20% of patients were using target doses of beta blockers and ACEi/ARB/ARNI; this was consistent even in patients with systolic blood pressure of 110mm Hg or higher.22 However, a European study found that, while achieving target doses was associated with lower hospitalization and mortality risks, those that did not reach target doses due to symptoms, side effects, or organ dysfunction had the highest mortality rate, suggesting that inability to reach target doses is at least, in part, a reflection of HF severity.20
Our findings suggest that patients seen in a HF clinic in the first year after HFrEF diagnosis are significantly more likely to use GDMT, and more likely to be initiated on GDMT, compared to those who are not. This builds upon prior work including a recent report from Egypt that found higher use of GDMT in patients in a HF clinic compared with other cardiology outpatient clinics.23 Cardiologist follow-up in addition to primary care has been associated with improved survival compared with primary care follow-up alone in patients with HF24, and patients with HF seen by physicians with higher HF patient volume also have lower mortality,25 suggesting that specialty care may offer benefit. Our data suggest that patients seen in a HF clinic is associated with increased use of GDMT, which could help to explain some of that potential benefit. While optimization of GDMT is one of the goals of a subspecialty HF clinic, there is heterogeneity in the approach taken to achieve that goal. Over time, the Mayo HF clinic has evolved to utilize a provider (physician or nurse practitioner)- directed medication initiation and titration protocol, that can be used by nurses to remotely titrate medications as instructed based on patient symptoms, blood pressure, laboratory data and heart rate.
Our data suggest that referral of patients with HFrEF to HF specialty clinics early after diagnosis may result in increased use of GDMT. As the number of medications shown to improve outcomes in HFrEF continues to increase (e.g., addition of vericiguat, SGLT2 inhibitors), the importance of HF specialty care in remaining up to date on these emerging therapies and applying them to patients in practice may continue to increase. Our data suggest that patients seen in this HF clinic were younger and more often had nonischemic cardiomyopathy as an etiology for their HF compared with those who were not seen in the HF clinic. We recognize that HF specialty clinics may not be available locally to all patients, particularly those in rural settings. Virtual care and/ or GDMT titration protocols may enable safe and effective use of new medications across a wide variety of practice settings; these modalities and their impact on HF care are worthy of further evaluation.
Limitations and Strengths.
There are some limitations to acknowledge to aid in interpretation of these data. First, this represents the experience of a single community, and findings may not generalize to other communities. Second, this is an observational study, and despite adjustment for known potential confounders, residual confounding, particularly of the associations of a HF clinic visit with GDMT, may still exist. As this study focused on the first year after newly diagnosed HFrEF, we did not examine if patients met criteria for implantation of a cardiac resynchronization therapy device after initial GDMT initiation and titration. This study did not examine the association of degree of GDMT utilization with clinical outcomes, which would be an important area for future study. Finally, this study does not address cost barriers and polypharmacy as modifiers of uptake of GDMT. As newer HFrEF therapies are introduced, such as SGLT2 inhibitors and vericiguat, these issues will become increasingly relevant. In addition, these findings do not address other potential reasons why GDMT were not initiated such as patient preference, or why target GDMT dosing was not achieved (e.g. heart rate, blood pressure, renal function). However, there are several strengths. The population-based nature of this study enabled examination of factors associated with GDMT use without issues related to referral bias. As we restricted our study to patients who were residents of the local community, our findings would be representative of community patients with access to a specialty HF clinic. Second, we were able to examine the impact of being seen in a HF clinic on introduction of GDMT in patients with HFrEF. Finally, we focused on a population with newly diagnosed HF. In contrast to other studies relying on patients in various stages of illness, this enabled us to capture changes in medication use in response to a new HFrEF diagnosis.
Conclusions.
In this population-based study, we found that use of beta blockers and ACEi/ARB/ARNI were high in the first year after patients were diagnosed with HFrEF, though patients often did not reach target doses of medications achieved in clinical trials. Patients seen in a HF clinic were more likely to receive appropriate GDMT.
Supplementary Material
HIGHLIGHTS.
Patients seen in a heart failure specialty clinic the first year after new heart failure diagnosis had higher use of guideline-directed medications
Our findings suggest that patients with newly diagnosed heart failure with reduced ejection fraction may benefit from referral to a heart failure clinic for initiation and titration of guideline-directed medications
There is still opportunity for improvement in use of guideline-directed medical therapies, particularly mineralocorticoid receptor antagonists, in community patients with heart failure with reduced ejection fraction
Acknowledgments:
We would like to thank Ellen Koepsell RN, Janet Gatzke RN, and Christina Stenzel for their assistance with data collection.
Funding Sources:
This study was funded by the National Institutes of Health (R01 HL144529, PI: Dunlay) and made possible using the resources of the Rochester Epidemiology Project, which is supported by the National Institute on Aging of the National Institutes of Health (R01 AG034676).
ABBREVIATIONS
- ACEi
angiotensin converting enzyme inhibitors
- ARB
angiotensin receptor blockers
- ARNI
angiotensin receptor neprilysin inhibitors
- EF
ejection fraction
- GDMT
guideline-directed medical therapies
- HF
heart failure
- HFrEF
heart failure with reduced ejection fraction
- MRA
mineralocorticoid receptor antagonists
- SGLT2
sodium glucose co-transporter 2
Footnotes
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Disclosures: The authors have no relationships with industry to disclose.
References
- 1.Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE Jr., Drazner MH, Fonarow GC, Geraci SA, Horwich T, Januzzi JL, Johnson MR, Kasper EK, Levy WC, Masoudi FA, McBride PE, McMurray JJ, Mitchell JE, Peterson PN, Riegel B, Sam F, Stevenson LW, Tang WH, Tsai EJ, Wilkoff BL, American College of Cardiology F and American Heart Association Task Force on Practice G. 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:e147–239. [DOI] [PubMed] [Google Scholar]
- 2.Packer M, Fowler MB, Roecker EB, Coats AJ, Katus HA, Krum H, Mohacsi P, Rouleau JL, Tendera M, Staiger C, Holcslaw TL, Amann-Zalan I, DeMets DL and Carvedilol Prospective Randomized Cumulative Survival Study G. Effect of carvedilol on the morbidity of patients with severe chronic heart failure: results of the carvedilol prospective randomized cumulative survival (COPERNICUS) study. Circulation. 2002;106:2194–9. [DOI] [PubMed] [Google Scholar]
- 3.McMurray JJ, Packer M, Desai AS, Gong J, Lefkowitz MP, Rizkala AR, Rouleau JL, Shi VC, Solomon SD, Swedberg K, Zile MR, Investigators P-H and Committees. Angiotensin-neprilysin inhibition versus enalapril in heart failure. N Engl J Med. 2014;371:993–1004. [DOI] [PubMed] [Google Scholar]
- 4.Pitt B, Zannad F, Remme WJ, Cody R, Castaigne A, Perez A, Palensky J and Wittes J. The effect of spironolactone on morbidity and mortality in patients with severe heart failure. Randomized Aldactone Evaluation Study Investigators. N Engl J Med. 1999;341:709–17. [DOI] [PubMed] [Google Scholar]
- 5.McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Bohm M, Burri H, Butler J, Celutkiene J, Chioncel O, Cleland JGF, Coats AJS, Crespo-Leiro MG, Farmakis D, Gilard M, Heymans S, Hoes AW, Jaarsma T, Jankowska EA, Lainscak M, Lam CSP, Lyon AR, McMurray JJV, Mebazaa A, Mindham R, Muneretto C, Francesco Piepoli M, Price S, Rosano GMC, Ruschitzka F, Kathrine Skibelund A and Group ESCSD. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J. 2021;42:3599–3726. [DOI] [PubMed] [Google Scholar]
- 6.Brunner-La Rocca HP, Linssen GC, Smeele FJ, van Drimmelen AA, Schaafsma HJ, Westendorp PH, Rademaker PC, van de Kamp HJ, Hoes AW, Brugts JJ and Investigators C-H. Contemporary Drug Treatment of Chronic Heart Failure With Reduced Ejection Fraction: The CHECK-HF Registry. JACC Heart Fail. 2019;7:13–21. [DOI] [PubMed] [Google Scholar]
- 7.Greene SJ, Butler J, Albert NM, DeVore AD, Sharma PP, Duffy CI, Hill CL, McCague K, Mi X, Patterson JH, Spertus JA, Thomas L, Williams FB, Hernandez AF and Fonarow GC. Medical Therapy for Heart Failure With Reduced Ejection Fraction: The CHAMP-HF Registry. J Am Coll Cardiol. 2018;72:351–366. [DOI] [PubMed] [Google Scholar]
- 8.Jarjour M, Henri C, de Denus S, Fortier A, Bouabdallaoui N, Nigam A, O’Meara E, Ahnadi C, White M, Garceau P, Racine N, Parent MC, Liszkowski M, Giraldeau G, Rouleau JL and Ducharme A. Care Gaps in Adherence to Heart Failure Guidelines: Clinical Inertia or Physiological Limitations? JACC Heart Fail. 2020;8:725–738. [DOI] [PubMed] [Google Scholar]
- 9.Rocca WA, Grossardt BR, Brue SM, Bock-Goodner CM, Chamberlain AM, Wilson PM, Finney Rutten LJ and St Sauver JL. Data Resource Profile: Expansion of the Rochester Epidemiology Project medical records-linkage system (E-REP). Int J Epidemiol. 2018;47:368–368j. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Subramaniam AV, Weston SA, Killian JM, Schulte PJ, Roger VL, Redfield MM, Blecker S and Dunlay SM. Development of Advanced Heart Failure: A Population-Based Study. Circ Heart Fail. 2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Gerber Y, Weston SA, Redfield MM, Chamberlain AM, Manemann SM, Jiang R, Killian JM and Roger VL. A contemporary appraisal of the heart failure epidemic in Olmsted County, Minnesota, 2000 to 2010. JAMA Intern Med. 2015;175:996–1004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Charlson ME, Pompei P, Ales KL and MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373–83. [DOI] [PubMed] [Google Scholar]
- 13.Greene SJ, Butler J and Fonarow GC. Simultaneous or Rapid Sequence Initiation of Quadruple Medical Therapy for Heart Failure-Optimizing Therapy With the Need for Speed. JAMA Cardiol. 2021;6:743–744. [DOI] [PubMed] [Google Scholar]
- 14.Dunlay SM, Redfield MM, Weston SA, Therneau TM, Hall Long K, Shah ND and Roger VL. Hospitalizations after heart failure diagnosis a community perspective. J Am Coll Cardiol. 2009;54:1695–702. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Bhagat AA, Greene SJ, Vaduganathan M, Fonarow GC and Butler J. Initiation, Continuation, Switching, and Withdrawal of Heart Failure Medical Therapies During Hospitalization. JACC Heart Fail. 2019;7:1–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Gattis WA, O’Connor CM, Gallup DS, Hasselblad V, Gheorghiade M, Investigators I-H and Coordinators. Predischarge initiation of carvedilol in patients hospitalized for decompensated heart failure: results of the Initiation Management Predischarge: Process for Assessment of Carvedilol Therapy in Heart Failure (IMPACT-HF) trial. J Am Coll Cardiol. 2004;43:1534–41. [DOI] [PubMed] [Google Scholar]
- 17.Velazquez EJ, Morrow DA, DeVore AD, Duffy CI, Ambrosy AP, McCague K, Rocha R, Braunwald E and Investigators P-H. Angiotensin-Neprilysin Inhibition in Acute Decompensated Heart Failure. N Engl J Med. 2019;380:539–548. [DOI] [PubMed] [Google Scholar]
- 18.Sanam K, Bhatia V, Bajaj NS, Gaba S, Morgan CJ, Fonarow GC, Butler J, Deedwania P, Prabhu SD, Wu WC, White M, Love TE, Aronow WS, Fletcher RD, Allman RM and Ahmed A. Renin-Angiotensin System Inhibition and Lower 30-Day All-Cause Readmission in Medicare Beneficiaries with Heart Failure. Am J Med. 2016;129:1067–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Butler J, Anstrom KJ, Felker GM, Givertz MM, Kalogeropoulos AP, Konstam MA, Mann DL, Margulies KB, McNulty SE, Mentz RJ, Redfield MM, Tang WHW, Whellan DJ, Shah M, Desvigne-Nickens P, Hernandez AF, Braunwald E, National Heart L and Blood Institute Heart Failure Clinical Research N. Efficacy and Safety of Spironolactone in Acute Heart Failure: The ATHENA-HF Randomized Clinical Trial. JAMA Cardiol. 2017;2:950–958. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Ouwerkerk W, Voors AA, Anker SD, Cleland JG, Dickstein K, Filippatos G, van der Harst P, Hillege HL, Lang CC, Ter Maaten JM, Ng LL, Ponikowski P, Samani NJ, van Veldhuisen DJ, Zannad F, Metra M and Zwinderman AH. 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;38:1883–1890. [DOI] [PubMed] [Google Scholar]
- 21.Parajuli DR, Shakib S, Eng-Frost J, McKinnon RA, Caughey GE and Whitehead D. Evaluation of the prescribing practice of guideline-directed medical therapy among ambulatory chronic heart failure patients. BMC Cardiovasc Disord. 2021;21:104. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Peri-Okonny PA, Mi X, Khariton Y, Patel KK, Thomas L, Fonarow GC, Sharma PP, Duffy CI, Albert NM, Butler J, Hernandez AF, McCague K, Williams FB, DeVore AD, Patterson JH and Spertus JA. Target Doses of Heart Failure Medical Therapy and Blood Pressure: Insights From the CHAMP-HF Registry. JACC Heart Fail. 2019;7:350–358. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Joseph J, P S S, James J, Abraham S and Abdullakutty J. Guideline-directed medical therapy in heart failure patients: impact of focused care provided by a heart failure clinic in comparison to general cardiology out-patient department. Egypt Heart J. 2020;72:53. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Indridason OS, Coffman CJ and Oddone EZ. Is specialty care associated with improved survival of patients with congestive heart failure? Am Heart J. 2003;145:300–9. [DOI] [PubMed] [Google Scholar]
- 25.Joynt KE, Orav EJ and Jha AK. Physician volume, specialty, and outcomes of care for patients with heart failure. Circ Heart Fail. 2013;6:890–7. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.