Initiation and Up-Titration of Guideline-Based Medications in Hospitalized Acute Heart Failure Patients　― A Report From the West Tokyo Heart Failure Registry ―

Abstract

Background: Despite recommendations from clinical practice guidelines to initiate and titrate guideline-directed medical therapy (GDMT) during their hospitalization, patients with acute heart failure (AHF) are frequently undertreated. In this study we aimed to clarify GDMT implementation and titration rates, as well as the long-term outcomes, in hospitalized AHF patients.

Methods and Results: Among 3,164 consecutive hospitalized AHF patients included in a Japanese multicenter registry, 1,400 (44.2%) with ejection fraction ≤40% were analyzed. We assessed GDMT dosage (β-blockers, renin-angiotensin inhibitors, and mineralocorticoid-receptor antagonists) at admission and discharge, examined the contributing factors for up-titration, and evaluated associations between drug initiation/up-titration and 1-year post-discharge all-cause death and rehospitalization for HF via propensity score matching. The mean age of the patients was 71.5 years and 30.7% were female. Overall, 1,051 patients (75.0%) were deemed eligible for GDMT, based on their baseline vital signs, renal function, and electrolyte values. At discharge, only 180 patients (17.1%) received GDMT agents up-titrated to >50% of the maximum titrated dose. Up-titration was associated with a lower risk of 1-year clinical outcomes (adjusted hazard ratio: 0.58, 95% confidence interval: 0.35–0.96). Younger age and higher body mass index were significant predictors of drug up-titration.

Conclusions: Significant evidence-practice gaps in the use and dose of GDMT remain. Considering the associated favorable outcomes, further efforts to improve its implementation seem crucial.

Heart failure (HF) is a global health concern because of its poor prognosis.¹ Guideline-directed medical therapy (GDMT), including renin-angiotensin system inhibitors (RASi), β-blockers (BB), and mineralocorticoid-receptor antagonists (MRA), has become the standard of care for HF with reduced ejection fraction (HFrEF) and reportedly improves the outcomes of patients with HFrEF.^2–8 However, GDMT implementation and up-titration has not been fully adopted in clinical practice despite being strongly recommended by guidelines.^1,2

Hospitalization due to acute decompensation is a sentinel event that carries a poor prognosis for patients with HF, and also serves as a key opportunity for in-hospital GDMT initiation and titration,¹ which have been shown to be safe in clinical trial settings and are associated with long-term improvements in outcome.^1,2 Therefore, investigating the status and influential factors regarding regional GDMT utilization may lead to worldwide acceptance and thus has significant importance.

Such investigations have been performed less in Asia than in Western countries. Regional disparities in economic power, healthcare systems with medical insurance, and drug tolerability due to differences in race and ethnicity have been identified as barriers to GDMT implementation and titration.⁹ Further, understanding the status and underlying reasoning that hinder the in-hospital GDMT initiation is important with the increasing number of therapeutic options available for these patients, and will provide scope for future quality improvement (e.g., longer hospitalization for patients with acute HF [AHF]). Herein, we aimed to investigate the proportion of GDMT implementation and titration at the time of discharge, contributing factors to in-hospital up-titration, and its association with long-term outcomes among patients with HFrEF requiring hospitalization due to acute decompensation, using a contemporary Japanese acute HF registry as the data source.

Methods

Data Source

The West Tokyo Heart Failure (WET-HF) registry was launched in 2006, and consecutively registers patients hospitalized for AHF at 8 tertiary-care hospitals in the Tokyo metropolitan area. The WET-HF registry is a national resource of clinical evidence that supplies multi-aspect outcomes of AHF in Japan,¹⁰ together with international collaborative projects.^11–13

For the present study, dedicated clinical research coordinators collected the patient data from medical records and additional required information was obtained through interviews with the treating physicians. The data were entered into an electronic system that contained a robust data query engine, and system validation was performed for data quality. For registry quality control, the principal investigators (Y.S. and S.K.) checked each institution’s data consistency at least annually and queried the local investigators and treating physicians if required. Outliers within the continuous variables or unexpected categorical variable entries were also identified through this monitoring of the dataset, and the originating institution was queried about any such anomalies. The study protocol was approved by the ethics review committee of each center, and informed consent was given by all patients.

Patient Selection

Among the 3,164 patients registered in the WET-HF between 2018 and 2019, 1,400 (44%) with a left ventricular EF (LVEF) ≤40% were selected for the present analysis. All patients admitted with a clinical diagnosis of AHF at each institution were screened, and consecutive patients who met the HF clinical criteria (based on the Framingham definition¹⁴) with B-type natriuretic peptide level ≥100 pg/mL (or N-terminal pro-B-type natriuretic peptide level ≥300 pg/mL) at the time of hospitalization were registered. The modified Simpson’s method was applied to measure the LVEF, which was performed during the index hospitalization. Patients presenting with acute coronary syndrome were excluded from this registry.

GDMT

We assessed the individual GDMT doses at the times of admission and discharge. GDMT included the use of BB, angiotensin-converting enzyme inhibitor (ACEi), angiotensin-receptor blocker (ARB), angiotensin receptor-neprilysin inhibitor (ARNI), and MRA. The eligibility for GDMT was evaluated using the following criteria: (1) estimated glomerular filtration rate (eGFR) ≥30 mL/min/1.73 m², (2) serum potassium ≤5.2 mEq/L, and (3) heart rate at the time of discharge ≥40 beats/min.

For the present analysis, we categorized the patients into those who fulfilled all the criteria (GDMT-eligible group) and those who did not fulfill ≥1 criteria (GDMT borderline group). GDMT-eligible patients were further divided into up-titration and non-titration groups (Figure 1). The up-titration group was defined as those treated with a maintenance dose for all GDMT components (BB, RASi [ACEi and ARB], and MRA). The maintenance dose was determined based on the approved individual GDMT doses in Japan (Supplementary Table 1). The terms associated with doses of drugs for GDMT were defined as follow: Target dose: the general target dose commonly used in Western guidelines;^1,2 maximum titrated dose: the target dose based on approved maximum dose in Japan; maintenance dose: the dose of the drug used as the criterion for achieving up-titration in this study.

Figure 1.

Study flowchart. Patients with HFrEF were categorized into groups according to their renal function, serum potassium level, and heart rate: GDMT-eligible and -borderline groups. Furthermore, the GDMT-eligible patients were divided into up-titration and non-titration groups, after which a PS-matching (1 : 2) protocol was performed, generating 122 pairs. AHF, acute heart failure; HFrEF, heart failure with reduced ejection fraction; GDMT, guideline-directed medical therapy; LVEF, left ventricular ejection fraction; PS, propensity score; WET-HF, West Tokyo Heart Failure.

Outcomes

The primary outcome was a composite of 1-year all-cause death and HF rehospitalization rates after discharge. A central study committee adjudicated the outcomes to ensure their accuracy. The investigators initially reviewed the deaths and central committee members (Y.S., S.K., and T.Y.) reviewed the abstracted records. Moreover, the treating physicians at each participating hospital identified the HF rehospitalizations according to standard definitions.¹⁵

Statistical Analysis

We compared the patient characteristics between the (1) GDMT-eligible and borderline groups and (2) the up-titration and non-titration groups among the GDMT-eligible group. Normally distributed continuous and non-normally distributed variables are expressed as the mean with standard deviation (SD) and median (interquartile range), respectively. Continuous variables were compared using the Student’s t-test or Mann-Whitney U-test. Categorical variables are expressed as frequencies with percentage, and compared using the Pearson’s chi-square test. Logistic regression analysis was performed to assess the factors independently associated with up-titration in each GDMT.

We examined the associations between up-titration and the 1-year primary outcomes, which were estimated using the Kaplan-Meier survival function. Pairwise survival rate comparisons among the up-titration and non-titration groups were assessed using the log-rank test. A multivariable Cox proportional hazards model was used to identify the risk factors for the primary outcome. The covariates included in the multivariable model were up-titration, previous HF hospitalization history, and the Get With the Guidelines–Heart Failure (GTWG-HF) risk score. The GWTG-HF risk score has previously shown acceptable discrimination in WET-HF registry patients; the c statistic for in-hospital death in this cohort was 0.763 (95% confidence interval (CI), 0.700–0.826), and the calibration plot showed good conformance.¹⁶ Finally, we conducted a propensity score (PS)-matching analysis to evaluate the association between up-titration and the clinical outcomes in a matched cohort based on the PS calculated from all potentially unbalanced and clinically important covariates. The PS was calculated for each patient using a logistic regression model to predict the achieved up-titration using the variables that were considered important, including age, sex, body mass index (BMI), LVEF, New York Heart association (NYHA) functional classification, comorbidities, and eGFR. Matching was performed with a 1 : 2 matching protocol without replacement using a caliper width equal to 0.2 of the SD of the PS. Further, we extracted patients aged ≥65 years from the PS-matched cohort and compared their characteristics and primary outcomes between the titration and non-titration groups.

For the subgroup analysis, we categorized the 1,400 analyzed patients into 3 groups based on clinical scenario (CS): CS1 (systolic blood pressure [SBP] >140 mmHg), CS2 (SBP 100–140 mmHg), and CS3 (SBP <100 mmHg).^17,18 Among these groups, we compared the patient characteristics and GDMT descriptions (on admission, in-hospital, and at discharge), and assessed the variables that were independently associated with GDMT up-titration.

The statistical analyses were performed at the Department of Cardiology in Keio University. Novartis Pharma K.K., Tokyo did not have access to the original data. All of the analysis was conducted by investigators from the WET-HF registry. Statistical significance was set at P<0.05. Analyses were performed using the Statistical Package for the Social Sciences version 26.0 (IBM Corporation, Armonk, NY, USA) and R software (version 4.2.0; R Foundation for Statistical Computing, Vienna, Austria).

Results

Patients’ Characteristics

Of the 1,400 registered hospitalized HF patients with LVEF ≤40%, their mean age was 71.5 years, 30.7% were female, and median duration of hospitalization was 18 days. Among them, 1,051 (75.1%) and 349 (24.9%) patients were classified into the GDMT-eligible and borderline groups, respectively (Figure 1). The comparisons between groups regarding the patient characteristics at discharge are summarized in Table 1. Compared with the patients in the eligible group, those in the borderline group were older with more severe NYHA functional class, lower levels of BP and eGFR, and a history of previous HF hospitalization. GDMT was up-titrated for 180 (17.1%) of the GDMT-eligible and 24 (6.9%) of the borderline groups in this cohort.

Table 1.

Baseline Characteristics of the Eligible vs. Borderline Group

	Eligible group (n=1,051)	Borderline group (n=349)	P value
Age, years	72 (61–82)	78 (69–84)	<0.001
Men, n (%)	733 (69.7)	237 (67.9)	0.564
BMI, kg/m2	23.3 (20.8–26.3)	22.7 (20.4–25.5)	0.014
Systolic BP, mmHg	134 (116–158)	127 (105–151)	<0.001
Diastolic BP, mmHg	85 (71–103)	77 (64–92)	<0.001
Heart rate, beats/min	100 (83–117)	93 (77–109)	<0.001
LVEF, %	30 (25–35)	30 (24–35)	0.562
NYHA class, n (%)			<0.001
II	148 (14.1)	32 (9.2)
III	477 (45.5)	125 (35.9)
IV	424 (40.4)	191 (54.9)
HF etiology, n (%)			0.001
Coronary artery disease	336 (32.0)	153 (43.8)
Dilated cardiomyopathy	280 (26.6)	68 (19.5)
Valvular disease	129 (12.3)	47 (13.5)
Other	306 (29.1)	81 (23.2)
Comorbidities, n (%)
Previous HF hospitalization	300 (28.6)	166 (47.6)	<0.001
Atrial fibrillation	348 (33.1)	129 (37.0)	0.211
Hypertension	625 (59.5)	236 (67.7)	0.008
Diabetes mellitus	325 (30.9)	152 (43.6)	<0.001
Dyslipidemia	375 (35.7)	134 (38.6)	0.363
Stroke	119 (11.3)	58 (16.6)	0.013
COPD	48 (4.6)	16 (4.6)	0.999
Dementia	67 (6.9)	28 (8.5)	0.397
Laboratory data
Hemoglobin, g/dL	13.3 (11.7–14.8)	11.5 (10.1–13.4)	<0.001
BUN, mg/dL	20.4 (16.1–26.0)	42.6 (31.2–53.3)	<0.001
eGFR, mL/min/1.73 m2	52.8 (42.1–65.0)	22.8 (16.8–28.1)	<0.001
Sodium, mEq/L	140 (138–142)	139 (136–142)	<0.001
Potassium, mEq/L	4.2 (3.9–4.5)	4.7 (4.3–5.3)	<0.001
Total bilirubin, mg/dL	1.0 (0.7–1.4)	0.9 (0.6–1.4)	0.05
Albumin, mg/dL	3.6 (3.2–3.9)	3.5 (3.1–3.8)	0.008
BNP, pg/mL	786 (483–1,365)	1,443 (838–2,002)	<0.001
NT-proBNP, pg/mL	4,812 (2,690–9,103)	11,389 (4,913–24,137)	<0.001
Baseline therapy
Loop diuretic, n (%)	434 (41.3)	208 (59.6)	<0.001
ACEi or ARB, n (%)	364 (34.6)	147 (42.1)	0.014
ARNI, n (%)	2 (0.5)	3 (2.0)
β-blocker, n (%)	411 (39.1)	192 (55.0)	<0.001
MRA, n (%)	201 (19.1)	81 (23.2)	0.116
Digoxin, n (%)	27 (2.6)	10 (2.9)	0.915
SGLT2i, n (%)	12 (16)	36 (8)	0.024
ICD, n (%)	45 (4.3)	24 (6.9)
CRT, n (%)	16 (1.5)	18 (5.2)
In-hospital therapy
Diuretics IV, n (%)	883 (84.0)	272 (77.9)	0.012
Vasodilator IV, n (%)	352 (33.5)	110 (31.5)	0.539
Inotrope IV, n (%)	154 (14.7)	111 (31.8)	<0.001
Intubation, n (%)	10 (1.0)	7 (2.0)
NPPV, n (%)	197 (18.7)	64 (18.3)
Hemodialysis, n (%)	10 (1.0)	61 (17.5)	<0.001
MCS, n (%)	47 (4.5)	18 (5.2)	0.703

Continuous variables are shown with medians and quartiles. ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin-receptor blocker; ARNI, angiotensin receptor-neprilysin inhibitor; BMI, body mass index; BNP, brain natriuretic peptide; BP, blood pressure; BUN, blood urea nitrogen; COPD, chronic obstructive pulmonary disease; CRT, cardiac resynchronization therapy; eGFR, estimated glomerular filtration ratio; HF, heart failure; ICD, implantable cardioverter defibrillator; LVEF, left ventricular ejection fraction; MCS, mechanical circulatory support; MRA, mineralocorticoid receptor antagonist; NPPV, non-invasive positive pressure ventilation; NT-proBNP, N-Terminal pro brain natriuretic peptide; NYHA, New York Heart Association; SGLT2i, sodium glucose transporter 2 inhibitor.

GDMT Up-Titration vs. Non-Titration

Of the patients in the GDMT-eligible group (N=1,051), 180 (17.1%) were in the up-titration group and 871 (82.9%) in the non-titration group. Compared with the patients in the up-titration group, those in the non-titration group were older and had a lower BMI (Table 2). The independent clinical variables associated with up-titration of each GDMT using multivariate logistic regression analyses are shown in Table 3. Across all classes of GDMT, younger age was associated with up-titration. Furthermore, a higher BMI was associated with up-titration of BB and RASi but not MRA. There was no significant difference between the groups regarding other covariates, including sex, eGFR, NYHA class, and comorbidities such as hypertension and diabetes mellitus.

Table 2.

Baseline Characteristics in the Non-Titration vs. Titration Group

	Non-titration group (n=871)	Titration group (n=180)	P value
Age, years	74 (62–83)	67 (53–76)	<0.001
Men, n (%)	599 (68.8)	134 (74.4)	0.156
BMI, kg/m2	22.8 (20.4–25.8)	25.1 (22.1–29.3)	<0.001
NYHA class, n (%)			0.41
II	118 (13.6)	30 (16.8)
III	394 (45.3)	83 (46.4)
IV	358 (41.1)	66 (36.9)
Comorbidities, n (%)
Previous HF hospitalization	246 (28.3)	54 (30)	0.707
Atrial fibrillation	284 (32.6)	64 (35.6)	0.497
Hypertension	506 (58.1)	119 (66.1)	0.056
Diabetes mellitus	262 (30.1)	63 (35.0)	0.226
Stroke	100 (11.5)	19 (10.6)	0.82
COPD	45 (5.2)	3 (1.7)	0.066
Dementia	62 (7.1)	5 (2.8)	0.045
Laboratory data
Hemoglobin, g/dL	13.2 (11.6–14.7)	13.7 (12.3–15.2)	0.003
BUN, mg/dL	20.7 (16.3–26.4)	19.3 (15.4–24.4)	0.016
eGFR, mL/min/1.73 m2	52.5 (41.6–64.7)	55.5 (45.6–65.7)	0.062
Sodium, mEq/L	140.0 (138.0–142.0)	140.8 (139.0–142.9)	0.044
Potassium, mEq/L	4.2 (3.9–4.5)	4.1 (3.7–4.4)	0.009

Continuous variables are shown with medians and quartiles. Abbreviations as in Table 1.

Table 3.

Association With Up-Titration of Each Component of GDMT

Variable	β-blocker		RAS inhibitor		MRA
	OR [95% CI]	P value	OR [95% CI]	P value	OR [95% CI]	P value
Age (per 1-y increase)	0.98 [0.97–0.99]	0.003	0.99 [0.98–1.00]	0.049	0.99 [0.98–1.00]	0.009
Male sex	1.00 [0.74–1.35]	0.985	1.24 [0.92–1.68]	0.163	0.70 [0.52–0.93]	0.016
Hypertension	1.11 [0.84–1.48]	0.46	2.40 [1.81–3.20]	<0.001	0.86 [0.66–1.13]	0.274
Diabetes	1.08 [0.81–1.44]	0.614	0.85 [0.64–1.14]	0.282	1.16 [0.87–1.53]	0.31
COPD	1.34 [0.71–2.57]	0.373	1.06 [0.56–1.98]	0.846	0.61 [0.32–1.14]	0.128
Stroke	0.92 [0.61–1.41]	0.705	0.76 [0.50–1.16]	0.215	1.08 [0.72–1.64]	0.697
Atrial fibrillation	1.64 [1.23–2.19]	0.001	0.94 [0.71–1.24]	0.641	1.25 [0.95–1.64]	0.112
NYHA III (vs. II)	0.87 [0.58–1.31]	0.516	0.89 [0.60–1.32]	0.561	0.92 [0.63–1.36]	0.689
NYHA IV (vs. II)	0.69 [0.45–1.04]	0.076	0.89 [0.59–1.33]	0.557	0.81 [0.54–1.20]	0.289
BMI (per 1-unit increase)	1.07 [1.03–1.10]	<0.001	1.04 [1.01–1.07]	0.009	1.02 [0.99–1.05]	0.14
eGFR (per 1-unit increase)	1.00 [0.99–1.01]	0.413	1.00 [0.99–1.01]	0.884	1.01 [0.99–1.01]	0.079

CI, confidence interval; OR, odds ratio. Other abbreviations as in Table 1.

Primary Outcome

During a median follow-up 353 (299–365) days after discharge, 192 primary outcome events and 60 (5.7%) all-cause deaths were noted in the GDMT-eligible group. The incidence of the primary outcome was significantly lower in patients who underwent up-titration than in those with non-titration (13.3% vs. 22.3%, log-rank P=0.011) (Figure 2). Furthermore, GDMT up-titration was independently associated with favorable outcomes following multivariate adjustments (hazard ratio [HR], 0.59; 95% confidence interval [CI], 0.36–0.97; P=0.036) (Supplementary Table 2).

Figure 2.

Primary outcome Kaplan-Meier curve and log-rank test. CI, confidence interval; HR, hazard ratio.

PS Analysis

The PS-matching analysis using a 1 : 2 matching protocol generated 122 pairs of patients, whose characteristics are described in Supplementary Table 3. A lower incidence of the primary outcome was observed in patients from the up-titration group than in those from the non-titration group in the PS-matched cohort (log-rank P=0.041) (Figure 3). The association between up-titration and the lower primary outcome risk was robust even after the PS-matching analysis (HR 0.58, 95% CI, 0.35–0.96; P=0.036). We selected 200 patients aged ≥65 years from the PS-matched cohort, of which 66 (33.3%) patients were in the titration group. The median age was 78 years in non-titration group and 75 in the titration group (Supplementary Table 4). The results were consistent with main results and the difference was not significant due to the small sample size (Supplementary Figure, log-rank P=0.064).

Figure 3.

Primary outcome Kaplan-Meier curve and log-rank test after propensity score matching. CI, confidence interval; HR, hazard ratio.

Subgroup Analysis Based on CS Classification

The GDMT-eligible patients (n=1,051) were classified into 3 CS groups: CS1 (n=435), up-titration (n=86), and non-titration (n=349); CS2 (n=498), n=84, and n=414; and CS3 (n=76), n=10, and n=66. A comparison of the characteristics of the 3 CS groups is summarized in Supplementary Table 5.

We found that a younger age significantly contributed to GDMT up-titration in the CS1 and CS2 groups, but not in the CS3 group (Supplementary Table 6). In the CS3 group, sex (male) and higher eGFR were associated with GDMT up-titration. Additionally, GDMT up-titration was not associated with the primary outcome in any of the 3 groups following multivariate adjustments (Supplementary Table 7).

Discussion

Timely GDMT initiation and titration are critical for HF patients; hospitalization remains an indispensable opportunity for GDMT adjustment.^1,2 In our study, only 17.1% of GDMT-eligible patients were up-titrated to 50% of the maximum titrated dose, even with a relatively longer hospital stay (median: 18 days). Younger age and higher BMI were independently associated with in-hospital GDMT initiation and up-titration. Additionally, GDMT up-titration was associated with improved post-discharge outcomes. Our subgroup study focused on the elderly suggested the potential for GDMT titration to improve the prognosis of elderly patients with HFrEF. Altogether, hospitalized patients with AHF represent a key target population for the optimization of existing guideline-directed therapies, although gaps still exist in actual implementation of therapy.

In the era of ever-growing prevalence of HF (i.e., a HF pandemic), GDMT remains highly relevant for patients hospitalized with HFrEF. However, consistent with previous reports, GDMT utilization has remained low. For instance, in patients registered in the Change the Management of Patients with Heart Failure registry, BB, RASi, and MRA were prescribed for 66.8%, 72.1%, and 33.1% of patients, respectively, although only 1.1% of these patients were at the target doses of the 3 GDMT components.¹⁹ A retrospective single-center study of patients hospitalized for acute decompensation of HFrEF in the USA revealed that 42% of the patients were optimized at ≥50% of the BB, RASi, and MRA target doses at the time of discharge, while 32%, 9%, and 2% of the patients were at least half-optimized on 1, 2, and all 3 classes of medications, respectively.²⁰ In our study, BB, RASi, and MRA were prescribed for 81%, 65%, and 49%, respectively, of the patients at the time of discharge, with a 14% rate of up-titration. These data highlight the need for a multidisciplinary approach to improving the care of patients with HF in this era of multiple guideline-recommended therapies, because GDMT underuse seems to be independent of patient eligibility for these medications.

Regarding the eligibility for GDMT, in the Asian Sudden Cardiac Death in Heart Failure (ASIAN-HF) registry, only 127 (2%) of the cohort (5,276) had hypotension, 324 (6%) had bradycardia, and 442 (11%) of the 4,187 patients with serum creatinine or eGFR baseline measurements had severe renal impairment (including renal failure).²¹ In our study, 298 (21.3%) patients had severe renal dysfunction, 97 (6.9%) had hyperkalemia, and 7 (0.5%) had bradycardia. The GDMT eligibility criteria did not match those of the ASIAN-HF registry, but the rate of renal dysfunction was higher in our study. In recent years, various approaches for GDMT optimization have been attempted, including intervention by pharmacists or nurse practitioners,^22,23 telemedicine adjustment,²⁴ and virtual adjustment based on the medical records created during hospitalization for reasons other than heart disease.²⁵

To date, only a few studies have investigated the association between GDMT utilization during hospitalization and long-term outcomes after discharge. In this context, several strengths and novel features of this analysis should be mentioned. Firstly, the analysis was performed by incorporating rigorous statistical matching using prospectively collected, predefined data variables. Secondly, in addition to the medication use and dosing data, the registry collected serial information on vital signs and laboratory data. Finally, we identified the factors associated with up-titration during hospitalization (younger age and higher BMI). The clinical practice guidelines recommend that GDMT implementation and up-titration should be performed after hemodynamic stability is achieved,^1,2 and these patients may be at ease in reaching a euvolemic status. The other potential barriers to optimization include insufficient quality of care or clinical inertia,^18,20,26 and further efforts are needed to identify the potential reasons for non-titration to better reflect the realities and complexities of modern care.

Various regional differences must be considered to achieve optimal GDMT on a global scale. In the REPORT-HF study, economic disparities, including low income and no insurance, and sex (female) were independently associated with GDMT underutilization,²⁷ similar to the trends reported in the ASIAN-HF registry. However, Japan had the lowest doses of BBs, with 41% of the patients prescribed <25% of the target dose, despite being a high-income country with a high BB prescription rate (91%).²¹ In Japan, the approved maximum titrated dose of carvedilol is 10 mg twice daily for HF. In a phase 1 carvedilol study in Japan, carvedilol at 20, 40, and 60 mg/day was administered to healthy young male patients for 1day or 2 consecutive days, after which adverse symptoms, such as lightheadedness or transient dizziness were observed in the 40 and 60 mg/day groups.^28,29 Consequently, carvedilol administration at a dose ≤20 mg once daily was suggested to be safe for patients with hypertension or angina. This result was also cited in the dosing for patients with HF. In the randomized controlled MUCHA trial in Japan, 71% or 91% risk reduction of the composite of death and rehospitalization due to cardiovascular events was noted with carvedilol at 5 or 20 mg/day, respectively, compared with placebo.³⁰ The optimal titration of BB may be influenced by pharmacokinetics or pharmacodynamics owing to differences in ethnicity.³¹ Taking this into consideration, there is a potential ethnic difference in drug tolerability, in addition to regional economic and medical disparities. To provide medical care, it is necessary to develop a social medical system and insurance. The elimination of clinical inertia is most important, but individual patient limitations for GDMT, such as renal dysfunction, bradycardia, and comorbidities, usually exist. In a rapidly aging society, the difficulty in achieving target doses will increase. Although the universal importance of GDMT implementation and titration never changes, indicators other than achievement of the target dose may be needed to evaluate optimal GDMT for individual patients. Additionally, in our subgroup analysis, achieving up-titration for CS1 was significantly associated with risk reduction for the primary outcome, but not for CS2 or CS3. This inconsistency could be attributed to several factors that influence the effects of GDMT for long-term outcomes within hypotensive patients with HFrEF. Firstly, hypotension such as in CS3 could lead to a reduction or discontinuation of each GDMT component, potentially diminishing the prognostic effect of GDMT,³² because it was associated with discontinuation and non-completion of the target dose within BB.^6,33–35 Furthermore, the comorbidities of hypotensive patients also lead to discontinuous GDMT. For example, renal dysfunction increases the risk of hyperkalemia, which is linked to discontinuation of renin-angiotensin-aldosterone system inhibitors.^36,37 Therefore, the treating physicians might consider reducing or discontinuing GDMT in patients with multiple comorbidities to prevent such adverse events, potentially leading to a reduction or discontinuation of the intended effect of GDMT.³² However, given we did not assess the implementation of GDMT after discharge of our cohort, this is speculation. Secondly, multiple comorbidities complicated with hypotension could attenuate the favorable effect of GDMT. Patients with hypotension are more likely to have multiple comorbidities³⁸ that diminish the favorable effect of GDMT in patients with HFrEF.^39,40 Indeed, patients in CS2 or CS3 often had atrial fibrillation, which would attenuate the beneficial effect of BBs.⁴¹ Given that the numbers of patients with HFrEF and multiple comorbidities have increased,⁴² the best management strategy for such patients is warranted. In pivotal randomized controlled trials (RCTs) that established the importance of GDMT, the mean SBP of patients was ≈120 mmHg,^3,6,43 and such trials commonly exclude individuals with low BP due to drug tolerance concerns. Although subanalyses in RCTs of ARNI and BB have suggested favorable benefits for hypotensive patients as well as normal or hypertensive patients,^33,34 these results potentially carried selective biases due to the inclusion of patients who exhibited some degree of tolerance for GDMT, thus not accurately representing the challenges of sustaining GDMT in real-world clinical practice. Therefore, further investigation of the relationship between the prognosis of hypotensive patients and post-discharge GDMT dosages or each complicated comorbidity will be important.

Study Limitations

Firstly, unobserved variables may be associated with GDMT titration and the outcomes. Furthermore, withdrawal from or intolerance to medical therapy following recent hospitalization for HF is an exceptionally high-risk scenario, for which advanced HF therapies or palliative care may be considered. Secondly, the GDMT did not include novel agents, such as SGLT-2i, because they were not approved for patients with HF in Japan during the study period. Thirdly, data regarding additional GDMT titration, withdrawal, and discontinuation over a longer duration (e.g., after discharge) were not obtained in this study. Additionally, although the WET-HF registry is a large multicenter registry, it does not include all hospitals in Japan, which could be considered a limitation in terms of generalizability.

Conclusions

In this large contemporary Japanese registry of inpatients with HFrEF, the majority of eligible patients were not prescribed target doses of medical therapy, and only a few patients had their doses titrated during their hospitalization. In the multivariate analysis across the classes of medication, some patient characteristics were repeatedly associated with a higher likelihood of dose up-titration (e.g., younger age and higher BMI). Up-titration at discharge in patients hospitalized for AHF was independently associated with a reduction in the risk of all-cause death and HF rehospitalization. Further efforts to implement and titrate GDMT are crucial, taking into consideration various influential regional factors.

Acknowledgments

The authors appreciate the contribution of all the investigators, clinical coordinators, and institutions involved in the WET-HF registry.

Sources of Funding

The West Tokyo Heart Failure Registry was supported by a grant from the Japan Agency for Medical Research and Development [S.K. 201439013C], Grants-in-Aid for Scientific Research [T.Y. JPSS KAKENHI, 23591062, 26461088, 18K08056, 21K08142; T.K. 17K09526, 20K08408; A.G. 21K08087; S.K. 20H03915], a Grant-in-Aid for Young Scientists [Y.S. JPSS KAKENHI, 18K15860], Grant-in-Aid for Clinical Research from the Japanese Circulation Society [Y.S. 2019], Grant-in-Aid from the Japanese Ministry of Health, Labor and Welfare [S.K. H29-Refractory Disease-034], Health Labour Science Research Grant [S.K. 14528506], and Sakakibara Clinical Research Grant for the Promotion of Science [T.Y. 2012-2021]. This study was funded by an unrestricted research grant from Novartis Pharma K.K.

Disclosures

S.K. received lecture fees and research grants from Bristol Myers Squibb and Novartis, respectively. Y.S. is received research grants from the Uehara Memorial Foundation and the SECOM Science and Technology Foundation and honoraria from Boehringer-Ingelheim Co., Ltd., Ono Pharmaceutical Co., Ltd., AstraZeneca Co., Ltd., Novartis Pharma Co., Ltd., and Otsuka Pharmaceutical Co., Ltd. T.Y. is a member of Circulation Journal’s Editorial Team. F.N. and I.U. are employees of Novartis Pharma K.K. All other authors have no relevant conflicts of interest to disclose.

IRB Information

The present study was approved by the IRB of the Keio University School of Medicine. Reference no. 20170292.

Supplementary Files

Please find supplementary file(s);

https://doi.org/10.1253/circj.CJ-23-0356

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