Abstract
Background
Significant gap remains in the implementation of guideline‐directed medical therapy (GDMT) in patients with heart failure after a hospitalization. We aimed to evaluate the use and titration of GDMT at discharge and over a 12‐month period after hospital discharge and to identify factors associated with GDMT use and titration.
Methods and Results
The CONNECT‐HF (Care Optimization Through Patient and Hospital Engagement Clinical Trial for Heart Failure) trial evaluated the effect of a hospital and postdischarge quality improvement intervention in participants with heart failure with reduced ejection fraction. In this secondary analysis, we examined use and titration to at least 50% of the target dose of GDMTs at hospital discharge and over time. Among 4646 participants (mean age 63 years, 34% women), GDMT use did not numerically improve from discharge to 12 months: beta blockers (84%–78%), angiotensin‐converting enzyme inhibitors/angiotensin II receptor blockers/angiotensin receptor‐neprilysin inhibitors (73%–65%), mineralocorticoid receptor antagonists (39%–36%), and sodium‐glucose cotransporter 2 inhibitors (1.5%–2.1%). Achieving ≥50% of the target dose also showed little change over 12 months: beta blockers (35%–32%), angiotensin‐converting enzyme inhibitors/angiotensin II receptor blockers/angiotensin receptor‐neprilysin inhibitors (28%–25%). For all medications, use of GDMT at discharge was associated with the use and achieving ≥50% of the target dose at 12 months.
Conclusions
Following a hospitalization for heart failure, GDMT use remained low and did not numerically improve over 12 months. Use of GDMT at discharge was significantly associated with the use of GDMT over time, highlighting the importance of initiating GDMT during hospitalization.
Keywords: guideline‐directed medical therapy, heart failure, hospitalization, implementation, titration
Subject Categories: Heart Failure, Quality and Outcomes
Nonstandard Abbreviations and Acronyms
- ARNI
angiotensin receptor‐neprilysin inhibitor
- CONNECT‐HF
Care Optimization Through Patient and Hospital Engagement Clinical Trial for Heart Failure
- GDMT
guideline‐directed medical therapy
- HFrEF
heart failure with reduced ejection fraction
- MRA
mineralocorticoid receptor antagonist
- SGLT2i
sodium‐glucose cotransporter 2 inhibitor
Clinical Perspective.
What Is New?
Following a hospitalization for heart failure, the use and achievement of at least 50% of the target dose of guideline‐directed medical therapy remained low and showed no improvement over the first 12 months postdischarge.
Use of guideline‐directed medical therapy at discharge was significantly associated with its continued use and titration to at least 50% of the target dose over time.
What Are the Clinical Implications?
Efforts should be focused on initiating guideline‐directed medical therapy during a hospitalization to enhance the use and uptitration of guideline‐directed medical therapy over time, thereby improving outcomes for patients with heart failure.
A significant gap remains in the initiation and uptitration of guideline‐directed medical therapy (GDMT) in patients with heart failure with reduced ejection fraction (HFrEF). This implementation gap exists despite strong clinical guideline recommendations to use GDMT to reduce morbidity and improve clinical outcomes, including mortality, for patients and families affected by HFrEF. 1 , 2 , 3 A prior analysis of registry participants in the United States demonstrated that the use of GDMT for chronic HFrEF remained consistently low and unaltered between baseline and 12‐month follow‐up in a stable outpatient setting. 4 However, there is a scarcity of data regarding patterns of medication changes and barriers to GDMT implementation for patients after hospitalization for decompensated HF, particular for patients with HF with a range of insurers. Importantly, the clinical benefits of GDMT become apparent within days to weeks after initiation. 5 , 6 , 7 , 8 , 9 Considering that ≈25% of patients hospitalized for decompensated HF die or are readmitted within 30 days, 10 , 11 HF hospitalizations may represent an important opportunity to initiate and titrate GDMT. Therefore, analyzing data that includes the hospitalization period is crucial to optimize postdischarge management.
The CONNECT‐HF (Care Optimization Through Patient and Hospital Engagement Clinical Trial for Heart Failure) trial was a large, pragmatic trial designed to assess the effect of a hospital and postdischarge quality improvement intervention, involving monthly audit and feedback reports on GDMT performance to hospitals, compared with usual care. 12 The high‐intensity intervention did not improve the implementation of GDMT at discharge or over time. To gain further insights into the factors associated with GDMT implementation beyond the effect of the quality improvement intervention, our objective was to identify factors associated with implementing GDMT in patients with HFrEF.
METHODS
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Study Design
This current study involves a secondary analysis of the CONNECT‐HF trial, which has been described previously. 12 , 13 In brief, the CONNECT‐HF trial was a large, cluster‐randomized, pragmatic trial of 161 US hospitals to evaluate whether a hospital and post‐ischarge quality improvement intervention compared with usual care improved HF‐related quality of care and HF outcomes. The study was conducted between 2017 and 2020. The hospital and postdischarge intervention included audit and feedback on HF clinical process and outcomes for patients with HFrEF, paired with education to sites and clinicians. Participants in the study received usual HF care with no specific in‐person study visits or protocols. The co‐primary outcomes were a composite of first HF rehospitalization or all‐cause death and change in an opportunity‐based composite score for HF quality. There was no significant difference in time to first HF rehospitalization or death (adjusted hazard ratio, 0.92 [95% CI, 0.81–1.05]), or in change in a composite HF quality‐of‐care score (adjusted odds ratio [OR], 1.06 [95% CI, 0.93–1.21]) during follow‐up over 1 year postdischarge.
All 5746 patients enrolled in the CONNECT‐HF study were initially included in this analysis. Patients who died, left against medical advice before discharge, or underwent heart transplantation or left ventricular assist device implantation during the index hospitalization were excluded (N=99). We also excluded patients without any postdischarge medication data (N=1001). The final study population comprised 4646 patients.
Institutional review board approval was secured from all participating hospitals and informed consent was obtained. The trial organization and data analysis were conducted by the Duke Clinical Research Institute (Durham, NC).
Medication Data
The change in an opportunity‐based composite score for HF quality was a co‐primary outcome measure. The composite quality score evaluated the guideline‐based recommendations for quality of care provided, including the use and titration of GDMT. In this analysis, evidence‐based beta blockers, angiotensin‐converting enzyme inhibitors (ACEi)/angiotensin II receptor blockers (ARB)/ angiotensin receptor‐neprilysin inhibitors (ARNI), mineralocorticoid receptor antagonists (MRA), and sodium‐glucose cotransporter 2 inhibitors (SGLT2i) were investigated. Hydralazine and isosorbide dinitrate were also investigated only for patients who were Black. Although SGLT2i were not included as standard GDMT during the trial period (ie, 2017–2020), we included SGLT2i in this analysis, given the current recommendations in the HF clinical practice guidelines. 1 , 2 Patients who were eligible for each medication at baseline were assessed for the analysis of these medications. Consequently, those who had contraindications or intolerance to each medication at baseline were excluded from the analysis of that specific medication.
Additionally, we categorized medication change patterns into 4 categories for each medication: (1) no changes and never taking, (2) no changes and remaining on medication, (3) initiation or dose increases (escalation), and (4) discontinuation or dose decreases (deescalation). Escalation and deescalation were defined based on changes in medication when comparing doses at discharge and at 12 months. Escalation of ACEi/ARB/ARNI includes both the initiation or dose increase of ACEi/ARB/ARNI and conversion from ACEi/ARB to ARNI. Conversely, deescalation of ACEi/ARB/ARNI includes any discontinuation or dose decrease of ACEi/ARB/ARNI and conversion from ARNI to ACEi/ARB. Given that very few participants were on full target doses at baseline, we did not separate them from the “no changes and remaining on medication” group to maintain simplicity. Switching within a class was not considered a change in medications for the purposes of this analysis.
Statistical Analysis
Demographic characteristics with 4 medication change patterns were analyzed for each medication class. Continuous variables were presented as means±SD. Categorical variables were expressed as counts with percentages.
For each medication class, first, we outlined the proportions of participants who (1) used and (2) achieved ≥50% of the target dose for GDMT at discharge and 12 months. The choice of reaching at least 50% of the target dose was determined by the expected improvement relative to national performance. 13 , 14 , 15 Table S1 lists the evidence‐based therapy target dosing definitions. Second, we presented the proportions of four medication change patterns: (1) no change and never taking, (2) no changes and remaining on medication, (3) escalation, and (4) deescalation for each medication. Finally, we compared the use of GDMT at 12 months in patients who were discharged on GDMT with those not discharged on GDMT.
To assess the factors associated with (1) GDMT use at 12 months, and (2) achievement of ≥50% of the target dose of GDMT at 12 months for each medication class, hierarchical logistic regression models were constructed, with random effects for site. Models included prespecified factors selected based on clinical judgment as follows: baseline medication use at discharge, quality improvement intervention, age, sex, race, region, diabetes, atrial fibrillation or flutter, chronic obstructive pulmonary disease, cerebrovascular accident/transient ischemic attack, chronic kidney disease, most recent left ventricular ejection fraction (LVEF), systolic blood pressure at discharge, creatinine at discharge, chronic HF versus new HF diagnosis, ischemic cause, ≥2 HF admissions in past 12 months, calendar time (months) at discharge from start of trial enrollment, site excess readmission rate ≥1 versus <1, site bed size, site region, and socioeconomic status factors determined by linking patient ZIP codes to 2018 Census Bureau American Community Survey data divided by college graduation rate, median household income, median home value, and unemployment rate. We did not develop models for hydralazine and isosorbide dinitrate because the number of eligible patients (Black) was expected to be insufficient to construct a reliable model.
We also assessed the factors associated with GDMT escalation and deescalation during follow‐up for each medication class. For the escalation models, we excluded participants already on the full target dose at baseline and compared those with escalation with others (reference). For the deescalation models, we excluded participants who were not taking medication at baseline and compared those with deescalation with others (reference). Variables included in the models of escalation and deescalation were same except for baseline medication use at discharge. We did not perform escalation and deescalation models for SGLT2i due to the anticipated low prescription, escalation, and deescalation rates of SGLT2i during the trial period.
Statistical significance was established based on a 2‐sided P value of <0.05, without adjusting for multiple comparisons. All statistical analyses were performed using SAS, version 9.4 (SAS Institute, Inc., Cary, NC), or R (CORE Team, 2019) software.
RESULTS
Baseline Characteristics
We analyzed 4646 participants (mean age 63 years, 34% women, 39% Black race) from 161 sites in the United States between 2017 and 2020. Baseline characteristics, stratified according to four medication change patterns for each medication, were presented in Table 1 and Table S2–S4. These tables showed a diverse population with a high burden of comorbid conditions: 86% of participants had hypertension, 47% had diabetes, and 41% had atrial fibrillation or flutter.
Table 1.
Baseline Characteristics Stratified by Medication Change Patterns for Evidence‐Based Beta Blockers
| Characteristic | Overall N=4546 | No changes and never taking N=479 | No changes and remain on medication N=1589 | Escalation N=1591 | Deescalation N=887 |
|---|---|---|---|---|---|
| Intervention | 2148 (47.3) | 246 (51.4) | 757 (47.6) | 716 (45.0) | 429 (48.4) |
| Age, y | 62.6 (13.5) | 65.6 (13.6) | 62.7 (13.6) | 61.4 (13.3) | 62.7 (13.4) |
| Women | 1558 (34.3) | 173 (36.1) | 567 (35.7) | 522 (32.8) | 296 (33.4) |
| Race | |||||
| White | 2524 (55.2) | 299 (62.4) | 892 (56.1) | 866 (54.4) | 467 (52.7) |
| Black | 1776 (39.1) | 153 (31.9) | 607 (38.2) | 646 (40.6) | 370 (41.7) |
| Asian | 54 (1.2) | 5 (1.0) | 18 (1.1) | 24 (1.5) | 7 (0.8) |
| Other/unknown* | 192 (4.2) | 22 (4.6) | 72 (4.5) | 55 (3.5) | 43 (4.9) |
| Hispanic or Latino ethnicity | 168 (3.7) | 13 (2.7) | 56 (3.5) | 58 (3.7) | 41 (4.6) |
| Health insurance | 4104 (90.3) | 443 (92.5) | 1444 (90.9) | 1421 (89.3) | 796 (89.7) |
| Private | 1435 (31.6) | 143 (30.0) | 502 (31.6) | 516 (32.4) | 274 (30.9) |
| Medicare | 2367 (52.1) | 284 (59.2) | 827 (52.0) | 787 (49.5) | 469 (52.9) |
| Medicaid | 913 (20.0) | 99 (20.7) | 328 (20.6) | 310 (19.5) | 176 (19.8) |
| Medical history | |||||
| Atrial fibrillation or flutter | 1874 (41.2) | 273 (57.0) | 609 (38.3) | 600 (37.7) | 392 (44.2) |
| Chronic obstructive pulmonary disease | 1174 (25.8) | 120 (25.1) | 417 (26.2) | 398 (25.0) | 239 (26.9) |
| Cerebrovascular disease | 634 (14.0) | 66 (13.8) | 231 (14.5) | 198 (12.5) | 139 (15.7) |
| Hypertension | 3927 (86.4) | 401 (83.7) | 1403 (88.3) | 1367 (85.9) | 756 (85.2) |
| Diabetes | 2121 (46.7) | 214 (44.7) | 787 (49.5) | 709 (44.6) | 411 (46.3) |
| No HF hospitalization within 12 mo | 1925 (42.3) | 187 (39.0) | 689 (43.4) | 702 (44.1) | 347 (39.1) |
| Weight, kg | 99.5 (30.2) | 98.8 (28.8) | 99.6 (29.9) | 100.6 (31.8) | 98.0 (28.2) |
| Heart rate, beats/min | 91.8 (21.0) | 92.3 (23.2) | 91.8 (20.6) | 91.9 (20.9) | 91.5 (20.5) |
| Systolic blood pressure, mm Hg | 136 (26.9) | 133 (28.1) | 138 (27.4) | 136 (26.5) | 134 (25.7) |
| Left ventricular ejection fraction | 25.7 (8.2) | 26.7 (8.3) | 26.0 (8.2) | 25.4 (8.1) | 25.0 (8.2) |
| Serum creatinine, mg/dL | 1.44 (0.74) | 1.45 (0.71) | 1.46 (0.69) | 1.40 (0.80) | 1.47 (0.75) |
| Chronic HF history | 3944 (86.8) | 427 (89.1) | 1362 (85.7) | 1372 (86.2) | 783 (88.3) |
| Ischemic cause | 2017 (44.4) | 222 (46.4) | 738 (46.4) | 670 (42.1) | 387 (43.6) |
| Discharge medications | |||||
| Angiotensin‐converting enzyme inhibitor/angiotensin‐receptor blocker/angiotensin receptor neprilysin inhibitor | 3088 (67.9) | 269 (56.2) | 1087 (68.4) | 1129 (71.0) | 603 (68.0) |
| Evidence‐based beta blocker | 3815 (83.9) | 0 (0.0) | 1589 (100.0) | 1339 (84.2) | 887 (100.0) |
| Aldosterone antagonist | 1690 (37.2) | 117 (24.4) | 611 (38.5) | 602 (37.8) | 360 (40.6) |
| Hospital characteristics | |||||
| Get With The Guidelines–Heart Failure participation | 1402 (30.8) | 142 (29.7) | 497 (31.3) | 482 (30.3) | 281 (31.7) |
| Number of total hospital beds | 479 (255) | 494 (257) | 467 (251) | 479 (258) | 493 (253) |
| Teaching hospital | 1179 (25.9) | 138 (28.8) | 398 (25.1) | 404 (25.4) | 239 (26.9) |
| Urban setting | 4247 (93.4) | 448 (93.5) | 1492 (93.9) | 1473 (92.6) | 834 (94.0) |
Values are mean±SD or n (%). The data on weight, heart rate, blood pressure, and creatinine are from the time of admission. HF indicates heart failure.
Other/unknown includes American Indian/Alaskan Native, Native Hawaiian/Pacific Islander, and Unknown.
Use, Titration, and Medication Change Patterns of GDMT
The use of GDMT did not numerically improve from discharge to 12 months: evidence‐based beta blocker (84% to 78%), ACEi/ARB/ARNI (73% to 65%), MRA (39% to 36%), SGLT2i (1.5% to 2.1%), and hydralazine and isosorbide dinitrate (30% to 33% for patients who were Black), respectively (Figure 1A ). Similarly, achievement of ≥50% of the target dose for GDMT showed little change from discharge to 12 months: evidence‐based beta blocker (35% to 32%) and ACEi/ARB/ARNI (28% to 25%), respectively (Figure 1B ). The proportion of participants with (1) no changes and never taking, (2) no changes and remaining on medication, (3) initiation or dose increases (escalation), and (4) discontinuation or dose decreases (deescalation) are shown in the Figure 2. At 12 months, proportions of escalation for evidence‐based beta blockers, ACEi/ARB/ARNI, MRA, SGLT2i, and hydralazine and isosorbide dinitrate were 35%, 38%, 20%, 1.3%, and 18%, and corresponding proportions of deescalation were 20%, 18%, 13%, 1.0%, and 9%, respectively. Finally, the use of each class of GDMT at 12 months was substantially higher among patients discharged on the class of GDMT compared with those not discharged on it: evidence‐based beta blocker (87% versus 31%), ACEi/ARB/ARNI (79% versus 30%), MRA (69% versus 15%), and SGLT2i (67% versus 1.1%), respectively (Figure 3).
Figure 1. Proportions of participants in (A) use and (B) achieving ≥50% of the target dose of guideline‐directed medical therapy at discharge and at 12 months.
Hydralazine and isosorbide dinitrate is only for Black patients. ACEi indicates angiotensin‐converting enzyme inhibitor, ARB, angiotensin II receptor blocker, ARNI, angiotensin receptor‐neprilysin inhibitor; BB, beta blocker; GDMT, guideline‐directed medical therapy; H‐ISDN, hydralazine and isosorbide dinitrate; MRA, mineralocorticoid receptor antagonist, and SGLT2i, sodium‐glucose cotransporter 2 inhibitor.
Figure 2. Medication changes patterns categorized into 4 categories: (1) no changes and never taking, (2) no changes and remaining on medication, (3) escalation, and (4) deescalation in guideline‐directed medical therapy.
Escalation means initiation or dose increases, and deescalation means discontinuation or dose decreases. Escalation and deescalation were defined based on changes in medication when comparing doses at discharge and at 12 months. Escalation of ACEi/ARB/ARNI includes both the initiation or dose increase of ACEi/ARB/ARNI and conversion from ACEi/ARB to ARNI. Conversely, deescalation of ACEi/ARB/ARNI includes any discontinuation or dose decrease of ACEi/ARB/ARNI and conversion from ARNI to ACEi/ARB. ACEi indicates angiotensin‐converting enzyme inhibitor, ARB, angiotensin II receptor blocker, ARNI, angiotensin receptor‐neprilysin inhibitor; BB, beta blocker; H‐ISDN, hydralazine and isosorbide dinitrate; MRA, mineralocorticoid receptor antagonist, and SGLT2i, sodium‐glucose cotransporter 2 inhibitor.
Figure 3. Guideline‐directed medical therapy use at 12 months: comparison between patients discharged on GDMT versus not on GDMT.
The OR represented the association between GDMT use at discharge and subsequent use at 12 months. The OR for SGLT2i is not shown in this figure because a meaningful analysis was difficult due to the large difference in SGLT2i use at 12 months between patients on SGLT2i at discharge and those who were not (67% vs 1.1%). This figure illustrated how critical the initiation and use of GDMT during hospitalization are in determining whether patients are treated at 12 months postdischarge. ACEi indicates angiotensin‐converting enzyme inhibitor, ARB, angiotensin II receptor blocker, ARNI, angiotensin receptor‐neprilysin inhibitor; BB, beta blocker; GDMT, guideline‐directed medical therapy; MRA, mineralocorticoid receptor antagonist, and SGLT2i, sodium‐glucose cotransporter 2 inhibitor.
Factors Associated With Use, Titration, Escalation and Deescalation of GDMT
Overall, multiple clinical characteristics (age, chronic kidney disease, LVEF, atrial fibrillation or flutter, and a history of chronic HF) were associated with GDMT use at 12 months (Table 2), achieving ≥50% of the target dose of GDMT at 12 months (Table 3), and GDMT escalation (Table S5) and deescalation (Table S6) during follow‐up. For all medications, use at discharge was significantly associated with the use and achievement of ≥50% of the target dose of GDMT at 12 months, whereas the quality improvement intervention showed no significant association (Tables 2, 3).
Table 2.
Factors Associated With Medication Use at 12 Months Postdischarge
| Evidence‐based beta blockers N=4547 | ACEi/ARB/ARNI N=4306 | MRA N=4503 | SGLT2i N=4646 | |||||
|---|---|---|---|---|---|---|---|---|
| Factors | OR (95% CI) | P value | OR (95% CI) | P value | OR (95% CI) | P‐value | OR (95% CI) | P value |
| Use at discharge | 14.7 (12.0–18.1) | <0.0001 | 6.65 (5.60, 7.90) | <0.0001 | 11.2 (9.14–13.8) | <0.0001 | 109 (52.8–225) | <0.0001 |
| Quality improvement intervention | 0.95 (0.77–1.17) | 0.64 | 1.04 (0.86, 1.25) | 0.71 | 1.18 (0.94–1.49) | 0.16 | 1.12 (0.64–1.96) | 0.70 |
| Age per 5‐y increase | 0.98 (0.94–1.02) | 0.29 | 0.93 (0.90, 0.96) | <0.0001 | 0.89 (0.86–0.93) | <0.0001 | 0.91 (0.80–1.03) | 0.14 |
| Male vs female | 1.09 (0.91–1.30) | 0.34 | 0.90 (0.74, 1.09) | 0.26 | 0.88 (0.74–1.03) | 0.11 | 1.19 (0.62–2.27) | 0.60 |
| Race: White vs other race* | 1.05 (0.82–1.33) | 0.71 | 1.16 (0.97, 1.40) | 0.11 | 0.89 (0.74–1.07) | 0.23 | 1.57 (0.83–2.97) | 0.17 |
| Diabetes | 0.96 (0.79–1.16) | 0.68 | 0.97 (0.83, 1.13) | 0.67 | 0.89 (0.73–1.08) | 0.24 | 11.7 (4.74–28.8) | <0.0001 |
| Atrial fibrillation or flutter | 0.74 (0.61–0.90) | 0.003 | 0.86 (0.73–1.02) | 0.08 | 1.14 (0.94–1.37) | 0.17 | 0.98 (0.54–1.77) | 0.95 |
| Chronic obstructive pulmonary disease | 0.95 (0.78–1.17) | 0.63 | 0.93 (0.75–1.15) | 0.51 | 0.92 (0.75–1.12) | 0.42 | 0.51 (0.23–1.12) | 0.09 |
| Cerebrovascular accident/transiter ischemic attack | 1.02 (0.82–1.28) | 0.84 | 0.82 (0.67–1.02) | 0.07 | 0.95 (0.74–1.22) | 0.69 | 1.03 (0.44–2.42) | 0.94 |
| Chronic kidney disease | 0.74 (0.52–1.05) | 0.09 | 0.67 (0.52–0.87) | 0.002 | 0.79 (0.56–1.11) | 0.18 | 0.58 (0.15–2.35) | 0.45 |
| Left ventricular ejection fraction per 5% increase | 1.05 (0.99–1.11) | 0.09 | 0.99 (0.95–1.04) | 0.77 | 0.93 (0.88–0.98) | 0.01 | 1.08 (0.91–1.29) | 0.37 |
| Discharge systolic blood pressure per 5 mm Hg increases | 1.02 (0.99–1.04) | 0.20 | 1.02 (1.00–1.04) | 0.12 | 0.99 (0.96–1.01) | 0.28 | 0.92 (0.84–1.00) | 0.06 |
| Discharge creatinine | 0.99 (0.81–1.21) | 0.91 | 0.60 (0.50–0.72) | <0.0001 | 0.52 (0.42–0.65) | <0.0001 | 0.43 (0.21–0.90) | 0.03 |
| Chronic HF history vs new HF diagnosis | 0.91 (0.66–1.25) | 0.55 | 1.17 (0.83–1.65) | 0.36 | 1.34 (1.01–1.77) | 0.04 | 0.61 (0.31–1.20) | 0.15 |
| Ischemic cause | 1.07 (0.88–1.29) | 0.52 | 0.91 (0.76–1.08) | 0.28 | 1.06 (0.87–1.29) | 0.56 | 1.57 (0.79–3.12) | 0.20 |
| ≥2 HF admissions in past 1 y | 0.90 (0.73–1.13) | 0.37 | 0.86 (0.71–1.05) | 0.14 | 0.89 (0.73–1.09) | 0.25 | 0.64 (0.30–1.38) | 0.26 |
| Calendar time (mo) at discharge from start of trial enrollment | 1.00 (0.99–1.01) | 0.57 | 1.01 (1.00–1.02) | 0.04 | 1.01 (1.00–1.02) | 0.23 | 1.06 (1.01–1.11) | 0.01 |
| Site excess readmission rate ≥1 vs <1 | 0.80 (0.65–0.99) | 0.04 | 0.97 (0.79–1.17) | 0.72 | 0.98 (0.78–1.25) | 0.89 | 0.92 (0.51–1.67) | 0.78 |
| Site bed size >437 | 0.83 (0.67–1.02) | 0.08 | 0.94 (0.79–1.13) | 0.54 | 1.05 (0.83–1.31) | 0.70 | 1.01 (0.54–1.86) | 0.98 |
| Site region: Northeast vs South | 1.10 (0.79–1.55) | 0.56 | 1.20 (0.92–1.56) | 0.18 | 1.17 (0.86–1.60) | 0.32 | 0.70 (0.26–1.89) | 0.48 |
| Site region: West vs South | 1.44 (0.93–2.23) | 0.10 | 0.93 (0.67–1.30) | 0.69 | 1.06 (0.68–1.63) | 0.81 | 0.55 (0.14–2.22) | 0.40 |
| Site region: Midwest vs South | 1.05 (0.83–1.33) | 0.71 | 1.04 (0.82–1.33) | 0.75 | 0.97 (0.73–1.28) | 0.81 | 0.83 (0.39–1.77) | 0.63 |
| SES: college graduation rate, centered and scaled | 0.97 (0.84–1.13) | 0.70 | 1.05 (0.92–1.20) | 0.46 | 0.98 (0.85–1.13) | 0.79 | 1.18 (0.77–1.83) | 0.45 |
| SES: % median household income, centered and scaled | 0.99 (0.81–1.21) | 0.95 | 0.97 (0.85–1.12) | 0.70 | 1.10 (0.95–1.28) | 0.19 | 0.60 (0.29–1.22) | 0.16 |
| SES: % Median home value, centered and scaled | 0.94 (0.80–1.11) | 0.47 | 1.04 (0.91–1.19) | 0.61 | 0.93 (0.80–1.08) | 0.33 | 1.38 (0.65–2.93) | 0.40 |
| SES: Unemployment rate, centered and scaled | 0.96 (0.85–1.09) | 0.50 | 1.03 (0.93–1.14) | 0.61 | 0.93 (0.83–1.04) | 0.20 | 1.05 (0.67–1.64) | 0.84 |
ACEi indicates angiotensin‐converting enzyme inhibitor; ARB, angiotensin II receptor blocker; ARNI, angiotensin receptor‐neprilysin inhibitor; HF, heart failure; MRA, mineralocorticoid receptor antagonist; OR, odds ratio; SES, socioeconomic status; and SGLT2i, sodium‐glucose cotransporter 2 inhibitor.
Other races includes Black, Asian, and other/unknown.
Table 3.
Factors Associated With Achieving ≥50% of the Target Dose of Medications at 12 Months Postdischarge
| Evidence‐based beta blockers ≥50% target dose N=4547 | ACEi/ARB/ARNI ≥50% target dose N=4306 | |||
|---|---|---|---|---|
| Factors | OR (95% CI) | P‐value | OR (95% CI) | P‐value |
| Use at discharge | 9.01 (7.62–10.6) | <0.0001 | 4.02 (3.07–5.26) | <0.0001 |
| Quality improvement intervention | 0.99 (0.80–1.22) | 0.92 | 0.99 (0.85–1.16) | 0.93 |
| Age per 5‐y increase | 0.95 (0.92–0.99) | 0.02 | 0.93 (0.90–0.97) | 0.0007 |
| Male vs female | 0.90 (0.76–1.07) | 0.22 | 1.13 (0.96–1.34) | 0.15 |
| Race: White vs other races* | 1.01 (0.84–1.22) | 0.89 | 0.89 (0.73–1.07) | 0.21 |
| Diabetes | 0.98 (0.82–1.17) | 0.84 | 1.07 (0.90–1.27) | 0.45 |
| Atrial fibrillation or flutter | 0.79 (0.66–0.95) | 0.01 | 0.80 (0.68–0.95) | 0.01 |
| Chronic obstructive pulmonary disease | 0.84 (0.70–1.00) | 0.054 | 1.00 (0.82–1.21) | 0.98 |
| Cerebrovascular accident/transient ischemic attack | 0.85 (0.68–1.06) | 0.14 | 0.99 (0.78–1.26) | 0.96 |
| Chronic kidney disease | 0.94 (0.68–1.30) | 0.70 | 0.62 (0.43–0.91) | 0.01 |
| Left ventricular ejection fraction per 5% increase | 1.03 (0.98–1.09) | 0.22 | 1.02 (0.97–1.08) | 0.37 |
| Discharge systolic blood pressure per 5 mm Hg increases | 1.05 (1.02–1.07) | <0.0001 | 1.06 (1.03–1.09) | <0.0001 |
| Discharge creatinine | 0.99 (0.83–1.16) | 0.86 | 0.81 (0.67–0.98) | 0.03 |
| Chronic HF history vs new HF diagnosis | 0.73 (0.56–0.96) | 0.02 | 0.94 (0.72–1.23) | 0.66 |
| Ischemic cause | 1.04 (0.87–1.25) | 0.66 | 0.84 (0.72–0.99) | 0.04 |
| ≥2 HF admissions in past 1 y | 0.90 (0.74–1.09) | 0.27 | 0.76 (0.61–0.95) | 0.01 |
| Calendar time (mo) at discharge from start of trial enrollment | 1.00 (0.99–1.01) | 0.86 | 1.00 (0.99–1.02) | 0.38 |
| Site excess readmission rate ≥1 vs <1 | 0.86 (0.70–1.06) | 0.15 | 1.05 (0.89–1.24) | 0.56 |
| Site bed size >437 | 0.97 (0.79–1.19) | 0.75 | 0.90 (0.77–1.06) | 0.22 |
| Site region: Northeast vs South | 1.08 (0.79–1.46) | 0.64 | 1.25 (0.99–1.57) | 0.06 |
| Site region: West vs South | 1.29 (0.84–1.98) | 0.24 | 0.83 (0.57–1.20) | 0.32 |
| Site region: Midwest vs South | 1.04 (0.80–1.34) | 0.78 | 0.95 (0.78–1.16) | 0.62 |
| SES: college graduation rate, centered and scaled | 0.99 (0.86–1.14) | 0.91 | 1.06 (0.92–1.23) | 0.40 |
| SES: % median household income, centered and scaled | 1.01 (0.87–1.18) | 0.85 | 0.92 (0.79–1.07) | 0.28 |
| SES: % median home value, centered and scaled | 0.98 (0.84–1.14) | 0.80 | 1.06 (0.93–1.20) | 0.39 |
| SES: Unemployment rate, centered and scaled | 1.03 (0.92–1.15) | 0.63 | 0.99 (0.88–1.10) | 0.81 |
ACEi indicates angiotensin‐converting enzyme inhibitor; ARB, angiotensin II receptor blocker; ARNI, angiotensin receptor‐neprilysin inhibitor; HF, heart failure; OR, odds ratio; and SES, socioeconomic status.
Other races includes Black, Asian, and other/unknown.
Evidence‐Based Beta Blocker
Use of beta blockers at discharge was associated with its use at 12 months (OR, 14.7 [95% CI, 12.0–18.1]), whereas atrial fibrillation or flutter and site excess readmission rate ≥ 1 were associated with nonuse at 12 months (Table 2). Use at discharge was associated with achieving ≥50% of the target dose at 12 months (OR, 9.01 [95% CI, 7.62–10.6]), although older age, atrial fibrillation or flutter, and a history of chronic HF were associated with not achieving this target at 12 months (Table 3). Atrial fibrillation or flutter was associated with a reduced rate of escalation and an increased rate of deescalation (Tables S5, S6).
Angiotensin‐Converting Enzyme Inhibitor/Angiotensin II Receptor Blocker/Angiotensin Receptor‐Neprilysin Inhibitor
Use of ACEi/ARB/ARNI at discharge (OR, 6.65 [95% CI, 5.60–7.90]) was associated with its use at 12 months, whereas older age and chronic kidney disease were associated with nonuse at 12 months (Table 2). Use at discharge (OR, 4.02 [95% CI, 3.07–5.26]) and higher systolic blood pressure were associated with achieving ≥50% of the target dose at 12 months, whereas older age, chronic kidney disease, atrial fibrillation or flutter, ischemic cause, and ≥2 HF admissions in the past 1 year were associated with not achieving this target at 12 months (Table 3). Lower LVEF was associated with an increased rate of escalation, whereas older age and chronic kidney disease were associated with a reduced rate of escalation (Tables S5, S6).
Mineralocorticoid Receptor Antagonist
Use of MRA at discharge (OR, 11.2 [95% CI, 9.14–13.8]), lower LVEF, and a history of HF were associated with its use at 12 months, whereas higher creatinine was associated with nonuse at 12 months (Table 2). Increased age, diabetes, higher LVEF, higher systolic blood pressure, and higher creatinine were associated with a reduced rate of escalation. A history of chronic HF was associated with an increased rate of escalation. Increased age and creatinine were associated with an increased risk of deescalation (Tables S5, S6).
Sodium‐Glucose Cotransporter 2 Inhibitor
Use of SGLT2i at discharge (OR, 109 [95% CI, 52.8–225]) and diabetes were significantly associated with its use at 12 months, and higher creatinine was associated with nonuse at 12 months (Table 2).
DISCUSSION
In this secondary analysis of the CONNECT‐HF trial spanning from the HF hospitalization phase to 1 year postdischarge, we examined medication change patterns over a year of follow‐up and assessed factors associated with the use, titration to at least 50% of the target dose, and the escalation and deescalation of GDMT. Unfortunately, medication initiation and dose changes remained low at all assessment periods to 12 months following discharge from a HF hospitalization. Notably, use of GDMT at discharge was strongly associated with use and achievement of ≥50% of the target dose of GDMT at 12 months. These findings underscore the crucial role of initiating each class of GDMT during hospitalization, aligning with the recommendations of current clinical practice guidelines. 1 , 2
The use and titration of GDMT remained largely unchanged, with only a few participants achieving even half of the target doses for GDMT. This observation aligns with findings from previous studies, highlighting the persistent challenges that hospitals and clinicians face when attempting to implement GDMT. 14 , 15 , 16 However, the CONNECT‐HF trial involved a diverse group of participating health systems and included a large cohort of patients with a broad spectrum of health insurance coverage, spanning from the hospitalization phase to the outpatient setting. Although the trial tested a quality improvement intervention, with a focus on posthospital discharge, it did not appear to be successful in improving GDMT use and uptitration. One possible reason is that clinicians in the outpatient setting may have deferred to therapeutic inertia rather than adopting the use of clinical decision support tools embedded in electronic health records to provide quality feedback on GDMT metrics. Additionally, patients may not have expected further GDMT initiation or incremental dose increases as part of their outpatient care over time. Emphasizing the need for patient education regarding the importance of comprehensive GDMT and uptitration as well tolerated is warranted. Moreover, the disconnection between the myriad of clinicians involved in inpatient and outpatient care may contribute to gaps in GDMT implementation. To enhance the quality of care and overcome this stagnation, especially posthospital discharge, more resources and innovative approaches are necessary, such as leveraging electronic health records, 17 polypill strategies, 18 GDMT clinics, 19 , 20 , 21 or adopting value‐based care models, 22 though these strategies require further investigation.
We examined the factors associated with the use and titration to at least 50% of the target dose of GDMT using a data set originating from a HF hospitalization. Our analysis identified that GDMT use at hospital discharge is the strongest factor associated with use and titration to at least 50% of the target dose of GDMT at 12 months, outweighing the association of the quality improvement intervention itself. This finding underscores the critical need for initiating GDMT during hospitalization for patients with HFrEF. Notably, this result aligns with the findings of OPTIMIZE‐HF (Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients With Heart Failure), Get With The Guidelines‐HF, the STRONG‐HF (Safety, Tolerability and Efficacy of Rapid Optimization, Helped by NT‐proBNP [N‐terminal pro‐B‐type natriuretic peptide] Testing, of Heart Failure Therapies) trial, and the updated clinical practice guidelines, which emphasize an intensive strategy of initiation and uptitration of GDMT before discharge and early after discharge. 1 , 23 , 24 , 25 It is essential for clinicians to acknowledge that a significant proportion of patients hospitalized for HF have a high risk of either death or rehospitalization within 30 days postdischarge. Given that the clinical benefits of quadruple therapy appear within days to weeks after initiation and incremental additive benefits of these medications, 5 , 6 , 7 , 8 , 9 , 26 commencing these medications during the hospital stay can help overcome clinical inertia and potentially prevent avoidable deaths and readmissions among these high‐risk patients.
Clinicians may have concerns about the simultaneous or rapid initiation of GDMT due to potential adverse effects or medication intolerance, as many of these therapies may affect hemodynamics, electrolytes, and kidney function. However, accumulating evidence suggests that simultaneous initiation can enhance tolerance and adherence. 27 For instance, a subanalysis of the EMPEROR‐Preserved (Empagliflozin Outcome Trial in Patients With Chronic Heart Failure With Preserved Ejection Fraction) trial demonstrated that among patients already on MRA at baseline, those initiated on SGLT2i were 22% less likely to discontinue MRA during follow‐up. 28 Similarly, initiation of ARNI was associated with fewer discontinuations of background MRA therapy compared with ACEi. 29 Delaying the initiation of SGLT2i or the switch from ACEi to ARNI may unnecessarily expose patients to the risk of hyperkalemia and MRA discontinuation. 30 Moreover, the STRONG‐HF trial demonstrated that simultaneous or rapidly sequenced GDMT initiation was associated with a reduced risk of 180‐day all‐cause death or HF hospitalization compared with usual care (risk ratio, 0.66 [95% CI, 0.50–0.86]), with no significant difference in serious adverse events. 25 The trial was terminated early based on the recommendation by data safety monitoring board due to the large benefits observed in the simultaneous or rapidly sequenced GDMT initiation group. A larger proportion of participants in the simultaneous or rapidly sequenced GDMT initiation group had successfully uptitrated to full or half‐doses of their prescribed medications. 31 This evidence underscores the importance of timely GDMT initiation and the need for clinicians to deliver best practice to their patients.
Limitations
Our study has several limitations that need to be considered. First, as a secondary analysis of a randomized controlled trial, there may be unknown and unmeasured confounding factors associated with the implementation of GDMT. For example, when considering improvements in the implementation of GDMT post‐HF hospitalization within outpatient setting, it is crucial to examine factors such as the time to the first visit, the frequency of follow‐up, and the specific health care providers involved (cardiologists or primary care physicians) in patient care. Unfortunately, we do not have this information available; therefore, we were unable to evaluate whether these factors are associated with the implementation of GDMT at 12 months. Furthermore, if patients are already prescribed many medications, this might present a barrier to the initiation of GDMT due to concerns about the cost burden or polypharmacy. We also do not have information on the number of concomitant medications, but it could indeed be associated with the implementation of GDMT. However, it is worth noting that our models for each medication class consistently demonstrated a strong association between GDMT use at discharge, suggesting the robustness of this finding. Second, our study included only participants with complete medication information at discharge and 12 months, leading to the exclusion of participants with insufficient medication data. However, it is important to highlight that the CONNECT‐HF trial, designed to assess GDMT performance as the primary outcome, resulted in a data set with accurate and minimal missing data. Third, because we primarily focused on medication changes at discharge and 12 months, we did not delve into the detailed medication changes throughout the study period. Fourth, the baseline characteristics of our randomized controlled trial study population may slightly differ from those in routine clinical practice, potentially limiting generalizability. Nevertheless, it is essential to note that the trial included a diverse range of participants, including those with various insurers and those from hospitals not participating in the Get With The Guidelines‐HF registry as well as nonteaching hospitals. Fifth, we find no clear explanations for the observed lower use of beta blockers in patients with atrial fibrillation or flutter at 12 months. Although we lack data on the use of antiarrhythmic medications (eg, amiodarone), the increased rate of deescalation from beta blockers in these patients suggests a possible transition to antiarrhythmic medications as a contributing factor. Finally, we did not collect detailed information on the specific reasons for medication use or changes, such as medical reasons, cost, patient requests, health care team decisions, or system‐based factors. This information could more accurately identify targets for intervention. However, to the best of our knowledge, this study represents the largest analysis using data spanning from the HF hospitalization phase to the postdischarge phase, shedding light on the most crucial strategies for GDMT implementation during this vulnerable high‐risk period.
CONCLUSIONS
In this secondary analysis of the CONNECT‐HF trial, we observed, that following hospital discharge, the rates of GDMT use and achievement of ≥50% of the target dose were low shortly after discharge and remained unchanged during the 12‐month follow‐up period. Most notably, use of GDMT at discharge was significantly associated with use and achievement of ≥50% of the target dose of GDMT at 12 months. These findings underscore the importance of initiating GDMT in patients with HFrEF at the time of HF hospitalization.
Sources of Funding
The CONNECT‐HF trial was funded by Novartis Pharmaceuticals Corporation (East Hanover, NJ) through an investigator‐initiated trial program (CLCZ696BUS05T).
Disclosures
Bradi B. Granger reports receiving research support from the National Institutes of Health, Novartis, AstraZeneca, and Alph Phi Foundation. Gregg C. Fonarow reports consulting for Abbott, Amgen, AstraZeneca, Bayer, Boehinger Ingelheim, Cytokinetics, Eli Lilly, Johnson & Johnson, Medtronic, Merck, Novartis, and Pfizer. Nancy M. Albert reports receiving research grants through her institution from AstraZeneca, Novartis and Roche and provides consulting for American Regent, AstraZeneca, Boehringer Ingelheim, Daiichi Sankyo, Eli Lilly, Lexicon, and Merck. Javed Butler has disclosures for Abbott, American Regent, Amgen, Applied Therapeutic, AskBio, Astellas, AstraZeneca, Bayer, Boehringer Ingelheim, Boston Scientific, Bristol Myers Squibb, Cardiac Dimension, Cardiocell, Cardior, Cardiorem, CSL Bearing, CVRx, Cytokinetics, Daxor, Edwards, Element Science, Faraday, Foundry, G3P, Innolife, Impulse Dynamics, Imbria, Inventiva, Ionis, Lexicon, Lilly, LivaNova, Janssen, Medtronics, Merck, Occlutech, Owkin, Novartis, Novo Nordisk, Pfizer, Pharmacosmos, Pharmain, Pfize, Prolaio, Regeneron, Renibus, Roche, Salamandra, Sanofi, SC Pharma, Secretome, Sequana, SQ Innovation, Tenex, Tricog, Ultromics, Vifor, and Zoll. Larry Allen reports receiving grant support from Patient‐Centered Outcomes Research Institute and National Institutes of Health, and consulting fees from ACI Clinical, Boston Scientific, Cytokinetics, Novartis, and UpToDate. David E. Lanfear reports consulting for Abbott, AstraZeneca, Janssen, and research with AstraZeneca, Lilly, Pfizer, Illumina; his research effort is supported in part by the National Institutes of Health (P50MD017351, R01HL132154). G. Michael Felker has received research grants from National Institutes of Health, Bayer, BMS, Novartis, Daxor, Merck, Cytokinetics, and CSL‐Behring; he has acted as a consultant to Novartis, BMS, Cytokinetics, Innolife, Cardionomic, Boehringer‐Ingelheim, Abbott, Regeneron, Reprieve, Myovant, Sequana, Windtree Therapuetics, and Whiteswell, and has served on clinical end point committees/data safety monitoring boards for Amgen, Merck, Medtronic, EBR Systems, Rocket Pharma, V‐Wave, LivaNova. Ileana L Piña reports an advisory committee for Boehringer Ingelheim and BI Lilly. Adam DeVore reports research funding through his institution from Biofourmis, Bodyport, Cytokinetics, American Regent, Inc, the National Institutes of Health and National Heart, Lung, and Blood Institute, Novartis, and Story Health. He also provides consulting services for and receives honoraria from Bodyport, Cardionomic, LivaNova, Myovant, Natera, NovoNordisk, and Zoll. Jennifer Thibodeau has no disclosures to report.
Supporting information
Tables S1–S6
This article was sent to Sula Mazimba, MD, MPH, Associate Editor, for review by expert referees, editorial decision, and final disposition.
Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/JAHA.124.036998
For Sources of Funding and Disclosures, see page 11.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Tables S1–S6