Optimizing Guideline-Directed Medical Therapy During Hospitalization Improves Prognosis in Patients With Worsening Heart Failure Requiring Readmissions

Abstract

Background: We previously demonstrated that higher simple guideline-directed medical therapy (GDMT) scores (comprising renin-angiotensin system inhibitors, β-blockers, mineralocorticoid antagonists, and sodium-glucose cotransporter 2 inhibitors) at discharge were correlated with improved prognosis in heart failure (HF) patients. HF readmissions are linked to adverse outcomes, emphasizing the need for enhanced optimization of GDMT.

Methods and Results: Using the simple GDMT score, we evaluated the effect of revising and modifying in-hospital GDMT on the prognosis of patients with HF readmissions. In this retrospective analysis of 2,100 HF patients, we concentrated on 1,222 patients with HF with reduced ejection/moderately reduced ejection fraction, excluding patients with HF with preserved ejection fraction, on dialysis, or who died in hospital. A higher current GDMT score was associated with better HF prognosis. Of the 1,222 patients in the study, we analyzed 372 cases of rehospitalization, calculating the simple GDMT scores at admission and discharge. Patients were divided into groups according to score improvement. Multivariate analysis showed a significant association between improved in-hospital simple GDMT score and the composite outcome (HF readmission+all-cause mortality; hazard ratio 0.459; 95% confidence interval 0.257–0.820; P=0.008). Even after propensity score matching to adjust for background, among rehospitalized patients, those with an improved in-hospital simple GDMT score had a better prognosis.

Conclusions: Our results highlight the potential of robust interventions and score elevation during hospitalization leading to improved outcomes.

In recent years, the effectiveness of guideline-directed medical therapy (GDMT) for heart failure (HF), particularly for HF with reduced ejection fraction (HFrEF), has been established, and combination therapy, such as the “Fantastic Four” or quadruple therapy, involving multiple drugs, is recommended.^1–5 There are several reports on the usefulness of GDMT scoring systems,^6,7 which are potential tools to increase the implementation rate of GDMT for HF patients. We have recently reported that a simple GDMT score above 5 points is associated with a good prognosis.⁸ However, in real-world clinical practice, the implementation rate of GDMT is not sufficient due to various reasons.^9–12

Conversely, HF is known for its repeated readmissions, and patients who are readmitted because of HF have a poorer prognosis than those admitted for the first time.^13,14 In addition, the current situation is that readmission rates for HF have not decreased significantly in Japan.¹⁵ A treatment strategy to prevent further HF readmissions in such cases is needed. In clinical practice, patients with HF readmissions were supposed to have some level of GDMT introduced during their first admission. However, it is often the case that only treatments aimed at symptom relief, such as diuretics, are administered, and the adequacy of GDMT during hospitalization is not reviewed. Re-evaluating and optimizing GDMT during rehospitalization may improve a patient’s prognosis thereafter.

Therefore, the aim of this study was to investigate whether optimization of GDMT using a simple GDMT score during hospitalization affects prognosis improvement in patients readmitted for HF.

Methods

Simple GDMT Score

We used a simple GDMT score that we reported previously.⁸ In the original study, a high simple GDMT score at discharge (≥5 points) was associated with better prognosis in HF patients.⁸ The simple GDMT score is calculated on the basis of the use of renin-angiotensin system (RAS) inhibitors, β-blockers, mineralocorticoid antagonists (MRAs), and sodium-glucose cotransporter 2 (SGLT2) inhibitors. RAS inhibitors are scored as 0 if not initiated, 1 if initiated at <50% of the target dose, and 2 if initiated at 50–100% of the target dose; angiotensin receptor-neprilysin inhibitor (ARNI) are scored as 3 regardless of dose. Beta-blockers are scored as 0 if not initiated, 1 if initiated at <50% of the target dose, and 2 if initiated at 50–100% of the target dose. MRAs and SGLT2 inhibitors are scored as 0 if not initiated and 2 if initiated, regardless of dose. Thus, the possible total simple GDMT score ranges from 0 to 9 (Table 1). The original scoring system were developed based on evidence accumulated by the Heart Failure Academic Research Consortium meeting.¹⁶ In addition, the doses of each drug were calculated based on the maximum allowable doses in Japan (Supplementary Table 1).

Table 1.

Design of the Simple GDMT Score

		Score
RAS inhibitor	None	0
	<50% maximum daily dose ACEi/ARB	1
	≥50% maximum daily dose ACEi/ARB	2
	ARNI (any dose)	3
β-blockers	None	0
	<50% maximum daily dose	1
	≥50% maximum daily dose	2
MRA	None	0
	Any dose	2
SGLT2i	None	0
	Any dose	2
Total		0–9

ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; ARNI, angiotensin receptor-neprilysin inhibitor; GDMT, guideline-directed medical therapy; MRA, mineralocorticoid receptor antagonist; RAS, renin-angiotensin system; SGLT2i, sodium-glucose cotransporter 2 inhibitor.

Study Design

This is a single-center retrospective study that examined the effect of optimizing GDMT, using the simple GDMT score during hospitalization, on subsequent prognosis in patients readmitted for HF. We retrospectively analyzed 2,100 consecutive patients with acute decompensated HF admitted to the Japanese Red Cross Fukuoka Hospital between April 2015 and June 2023. Acute decompensated HF was diagnosed according to Framingham’s heart failure criteria.¹⁷ After excluding 76 patients who died in hospital, 76 patients on dialysis, and 726 patients with HF with preserved ejection fraction, 1,222 patients with HFrEF and HF with moderately reduced ejection fraction (HFmrEF) were included in the present study. The median follow-up period was 365 days.

Among the 1,222 patients, 372 who were readmitted for HF were retrospectively analyzed. We calculated simple GDMT scores at both admission and discharge for 372 patients who were readmitted for HF. Defining an elevation in the simple GDMT score as a ≥1-point increase between admission and discharge, we analyzed the association between an elevation in the simple GDMT score and prognosis through univariate and multivariate analyses.

Furthermore, we divided 372 patients into 2 groups: those with an elevation in the simple GDMT score during hospitalization and those without. Following propensity score matching to adjust for baseline characteristics, we compared prognoses between the 2 groups. This approach allowed a detailed examination of simple GDMT score modifications on the outcomes of HF patients readmitted for HF, emphasizing the potential benefits of targeted therapeutic interventions during the hospital stay. The study design is shown in Figure 1.

Figure 1.

Flowchart showing the study protocol. CHF, congestive heart failure; GDMT, guideline-directed medical therapy; HF, heart failure; HFmrEF, heart failure with moderately reduced ejection fraction; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction.

Statistical Analyses

All statistical analyses were performed with EZR (Saitama Medical Center, Jichi Medical University, Saitama, Japan), a graphical user interface for R (R Foundation for Statistical Computing, Vienna, Austria). More precisely, EZR is a modified version of R commander designed to add statistical functions frequently used in biostatistics.¹⁸

Continuous variables were compared between the 2 groups using unpaired t-tests or the Mann-Whitney test, as appropriate. Categorical variables were compared between the 2 groups using the Chi-squared test or Fisher’s exact test, as appropriate. Univariate analysis of factors associated with HF readmissions, all-cause death, and the composite outcome (HF readmissions or all-cause death) was performed using a Cox proportional hazards model, and hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. Then, multivariate analysis using a Cox proportional hazards model was performed. The independent variables selected for the multivariate analysis were those that had P<0.05 in the univariate analysis and were clinically likely to affect the outcomes. Kaplan-Meier curves were used to analyze freedom from the composite outcome, HF readmissions, and all-cause death in the 2 groups, with comparisons made using the log-rank test. These analyses were conducted for both the entire patient cohort and the group of 372 patients with HF readmissions.

Patients with HF readmissions were divided into 2 groups: those with (n=192) and those without (n=180) an increase in the simple GDMT score. Notable differences were observed in the background characteristics between these 2 groups. Therefore, we conducted a propensity score matching analysis. A propensity score indicating the predicted probability of having or not an increase in the simple GDMT score, conditioned on observed covariates, was calculated from the logistic regression model for each patient. Variables included in the logistic regression model for calculating the propensity score were age, male, B-type natriuretic peptide (BNP) values, ejection fraction (EF), heart rate, the number of past HF hospitalizations, modified optimal medical therapy (OMT) score at admission, atrial fibrillation, diabetes, chronic obstructive pulmonary disease, blood urea nitrogen (BUN), estimated glomerular filtration rate (eGFR), sodium, potassium, hemoglobin, and albumin concentrations, catecholamine use, and the use of non-invasive positive pressure ventilation (NPPV). We performed rigorous adjustments for significant differences in baseline characteristics of patients matched by propensity score (n=87 for each group). Subgroup analysis was performed using the Cox proportional hazards model.

Unless specified otherwise, all data are expressed as the mean±SD or median (95% CI). Probabilities are 2-tailed, with P<0.05 regarded as statistically significant.

Ethical Considerations

This study was approved by the Ethics Committee of Japanese Red Cross Fukuoka Hospital (Approval no. 404) and was conducted in accordance with the ethical standards of the 1964 Declaration of Helsinki and its subsequent amendments. Informed consent for data handling was obtained at admission, and informed consent for the study was obtained via opt-out.

Results

Characteristics of Study Patients

The analysis included 1,222 patients with HFrEF and HFmrEF among 2,055 HF patients admitted to the Japanese Red Cross Fukuoka Hospital between April 2015 and June 2023. The mean patient age was 75.3±13.9 years; 64.5% of patients were male, 82.7% had New York Heart Association (NYHA) Class III–IV, mean EF was 30.9±10.2%, 74.7% had HFrEF, and 30.4% had a history of HF. At discharge, RAS inhibitors were prescribed to 83.1% of patients (ARNI in 12.5% of patients), β-blockers were prescribed to 81.0% of patients, MRAs were prescribed to 61.2% of patients, SGLT2 inhibitors were prescribed to 17.4% of patients, and ivabradine and vericiguat were prescribed to 0.2% and 0.5% of patients, respectively (Supplementary Table 2).

Prognosis of Patients Rehospitalized for HF

In this study, there were 372 patients included in the HF readmission analysis. The prognosis for patients readmitted for HF was significantly worse than that for patients admitted for HF for the first time in terms of the composite outcome (33.6% vs. 18.5%, respectively; P<0.001), HF readmissions (25.3% vs. 14.7%, respectively; P<0.001), and all-cause death (12.4% vs. 5.1%, respectively; P<0.001; Supplementary Table 3).

Factors Associated With Composite Outcome, HF Readmissions, and All-Cause Death in Patients Rehospitalized for HF

Analyses were conducted in the 372 patients readmitted for HF. Receiver operating characteristic (ROC) curve analysis revealed that the cut-off value for the association between the composite outcome and the difference in simple GDMT score was 0 points (area under the curve [AUC]=0.671, 95% CI=0.630–0.706; Supplementary Figure). We also explored factors associated with clinical outcomes. Multivariate analysis revealed that higher BUN values and an elevation in the simple GDMT score (defined as a ≥1-point increase between discharge and admission scores) were strongly associated with the composite outcome. Higher age, higher BUN values, and hypoalbuminemia were strongly associated with all-cause death. An elevation in the simple GDMT score was strongly associated with HF readmission (Supplementary Table 4).

Characteristics of Patients With and Without Elevations in the Simple GDMT Score

The 372 patients readmitted for HF were divided into 2 groups: those with an elevation in the simple GDMT score (where the score at discharge was at least 1 point higher than the score at admission) and those without an elevation in the simple GDMT score (where the score remained unchanged or decreased). Before propensity score matching, the group without an increase in the simple GDMT score had lower diastolic pressure, a higher number of previous hospitalizations for HF, a higher GDMT score at admission, higher BUN levels, worse renal function, higher potassium concentrations, less carperitide use, and more catecholamine use, and tended to have a lower heart rate and lower sodium concentrations, than the group with an increase in the simple GDMT score (Table 2).

Table 2.

Characteristics of Patients Readmitted for HF, With and Without Simple GDMT Score Elevation

	Before propensity score matching			After propensity score matching
	Score up(−) (n=180)	Score up(+) (n=192)	P value	Score up(−) (n=87)	Score up(+) (n=87)	P value
Background
Age (years)	78.7±11.4	76.9±12.7	0.141	79.2±10.8	79.9±10.2	0.667
Male sex	116 (64.4)	126 (65.6)	0.828	56 (64.4)	48 (55.2)	0.279
BNP (pg/mL)	1,227.9±1,024.3	1,226.4±1,170.4	0.990	1,302.7±1,105.3	1,359.5±119.8	0.737
NYHA functional class	3.12±0.63	3.10±0.70	0.737	3.14±0.53	3.17±0.63	0.698
NYHA Class 3–4	157 (87.7)	160 (83.3)	0.243	80 (92.0)	76 (87.4)	0.456
EF (%)	32.3±10.6	31.1±10.1	0.280	32.3±10.2	32.3±10.2	0.988
HFrEF (%)	123 (68.3)	139 (72.4)	0.427	61 (70.1)	58 (66.7)	0.745
ICM (%)	69 (38.3)	79 (41.1)	0.597	42 (48.3)	40 (46.0)	0.879
SBP (mmHg)	131.8±34.1	135.2±32.7	0.331	132.4±29.4	135.9±34.6	0.485
DBP (mmHg)	75.7±22.1	80.7±22.3	0.030	75.9±19.3	78.3±22.9	0.459
HR (beats/min)	87.1±22.8	91.3±24.6	0.091	86.4±20.9	86.7±24.3	0.918
BMI (kg/m2)	22.1±4.5	22.4±4.4	0.636	21.8±4.0	21.5±4.3	0.596
No. past HF hospitalizations	1.6±1.0	1.3±0.7	0.001	1.7±0.8	1.4±0.8	0.929
Simple GDMT score at admission	4.1±1.9	2.6±1.8	<0.001	3.4±1.7	3.4±1.6	0.965
Comorbidity
AF	89 (49.4)	91 (47.4)	0.756	45 (51.7)	42 (48.3)	0.762
Diabetes	62 (34.4)	77 (40.1)	0.284	36 (41.4)	31 (35.6)	0.533
COPD	15 (8.3)	13 (6.8)	0.695	3 (3.4)	5 (5.7)	0.720
Pneumonia	17 (9.4)	24 (12.5)	0.409	7 (8.0)	9 (10.3)	0.794
Laboratory data
BUN (mg/dL)	39.7±22.5	30.7±15.3	<0.001	38.2±23.1	35.0±17.7	0.312
eGFR (mL/min/1.73 m2)	33.9±19.4	41.0±19.0	<0.001	35.1±21.7	36.1±15.5	0.736
UA (mg/dL)	7.0±2.3	6.9±2.1	0.641	7.0±2.2	6.6±2.0	0.256
Na (mmol/L)	138.2±5.4	139.0±4.5	0.091	138.1±5.3	138.4±4.9	0.707
K (mmol/L)	4.57±0.89	4.29±0.67	<0.001	4.40±0.76	4.45±0.72	0.633
Hemoglobin (g/dL)	11.7±2.5	12.0±2.3	0.239	11.5±2.1	11.5±2.3	0.873
Albumin (g/dL)	3.4±0.5	3.4±0.5	0.786	3.4±0.5	3.3±0.4	0.856
In-hospital use
Vasodilator	32 (17.8)	40 (20.8)	0.512	15 (17.2)	18 (20.7)	0.699
Carperitide	13 (7.2)	29 (15.1)	0.021	9 (10.3)	14 (16.1)	0.371
Loop diuretics	151 (83.9)	165 (85.9)	0.664	76 (87.4)	73 (83.9)	0.666
TLV	89 (49.4)	83 (43.2)	0.253	46 (52.9)	43 (49.4)	0.762
Catecholamine	55 (30.6)	40 (20.8)	0.033	21 (24.1)	21 (24.1)	1.000
NPPV	29 (16.2)	20 (10.4)	0.124	7 (8.0)	9 (10.3)	0.794
At discharge
Hospital stay (days)	23.8±14.9	23.9±21.1	0.986	24.7±14.9	26.1±22.4	0.639
No. drugs prescribed	2.0±0.9	2.7±0.9	<0.001	1.8±0.7	2.8±0.8	<0.001
Simple GDMT score (points)	3.5±1.8	5.1±2.0	<0.001	3.1±1.5	5.3±2.0	<0.001
ACEi/ARB	122 (68.2)	131 (67.9)	1.000	57 (65.5)	59 (68.8)	0.872
ARNI	8 (4.5)	40 (20.7)	<0.001	3 (3.4)	21 (24.1)	<0.001
β-blockers	145 (82.4)	154 (81.5)	0.892	69 (81.2)	75 (86.2)	0.413
MRA	72 (40.9)	135 (71.4)	<0.001	28 (32.9)	66 (75.9)	<0.001
SGLT2i	13 (8.2)	47 (30.1)	<0.001	3 (3.8)	20 (27.0)	<0.001
Vericiguat	2 (1.1)	2 (1.0)	1.000	1 (1.1)	0 (0.0)	1.000
Ivabradine	0 (0.0)	1 (0.5)	1.000	0 (0.0)	0 (0.0)	NA
Loop diuretics	130 (72.6)	144 (74.6)	0.724	66 (75.9)	63 (72.9)	0.729
Loop diuretics dose (mg)	21.1±21.0	19.3±19.4	0.401	20.9±22.0	18.5±17.3	0.413
TLV	86 (48.0)	73 (37.8)	0.059	42 (48.3)	39 (44.8)	0.761
PDE III inhibitor	35 (19.6)	35 (18.1)	0.791	18 (20.7)	16 (18.4)	0.849
CRT/ICD	16 (8.9)	15 (7.8)	0.711	4 (4.6)	9 (10.3)	0.248

Unless indicated otherwise, data are given as the mean±SD or n (%). AF, atrial fibrillation; BMI, body mass index; BNP, B-type natriuretic peptide; BUN, blood urea nitrogen; COPD, chronic obstructive pulmonary disease; CRT/ICD, cardiac resynchronization therapy/implantable cardioverter defibrillator; DBP, diastolic blood pressure; EF, ejection fraction; eGFR, estimated glomerular filtration rate; HF, heart failure; HFrEF, heart failure with reduced ejection fraction; HR, heart rate; ICM, ischemic cardiomyopathy; K, potassium; Na, sodium; NPPV, non-invasive positive pressure ventilation; NYHA, New York Heart Association; PDE III, phosphodiesterase 3; SBP, systolic blood pressure; TLV, tolvaptan; UA, uric acid. Other abbreviations as in Table 1.

All-Cause Death, HF Readmission, and Composite Outcome in Patients With and Without Simple GDMT Score Elevations After Propensity Score Matching

To equalize background characteristics, we performed propensity score matching. After propensity score matching, the group with an elevation in the simple GDMT score experienced fewer events related to the composite outcome (HR 0.413; 95% CI 0.256–0.665; P<0.001) and HF readmission (HR 0.454; 95% CI 0.260–0.793; P=0.005), and showed a tendency towards fewer all-cause death events (HR 0.516; 95% CI 0.238–1.118; P=0.093; Figure 2). Stratification analysis of simple GDMT scores into score decreased, score unchanged, score elevated 1–2 points, and score elevated ≥3 points revealed no significant differences in the occurrence of each event within the groups with and without score elevation (Figure 3).

Figure 2.

Kaplan-Meier survival curves for freedom from 1-year (A) all-cause death, (B) heart failure (HF) readmissions, and (C) composite outcome in groups with and without elevations in the simple guideline-directed medical therapy (GDMT) score. Hazard ratios (HR) and 95% confidence intervals (CIs) were from Cox proportional hazards regression analysis.

Figure 3.

Stratified analysis of Kaplan-Meier survival curves of freedom from 1-year composite outcome according to changes in the simple guideline-directed medical therapy (GDMT) score.

Subgroup Analysis

Subgroup analyses were performed for the composite outcome by age, sex, NYHA class, BNP concentration, systolic blood pressure, EF, underlying disease, presence of diabetes, eGFR, sodium concentrations, and hemoglobin level. In these subgroups, an increase in the simple GDMT score was consistently associated with a good prognosis (Figure 4).

Figure 4.

Forest plot showing hazard ratios (HRs) and 95% confidence intervals (CIs) derived from a Cox proportional hazard model for the composite outcome in different subgroups of patients with an increase in the simple guideline-directed medical therapy (GDMT) score.

Factors Influencing the Inability to Increase Simple GDMT Scores

A logistic regression analysis was conducted to evaluate factors associated with unchanged or decreased simple GDMT scores at discharge compared with admission. The results showed a strong association with older age, high simple GDMT scores at admission, and NPPV use. No associations were found with blood pressure, heart rate, renal function, or serum potassium concentrations (Table 3).

Table 3.

Factors Influencing the Inability to Enhance GDMT

	Univariate analysis			Multivariate analysis
	HR	95% CI	P value	HR	95% CI	P value
Background
Age	1.010	0.996–1.030	0.141	1.040	1.020–1.070	<0.001
Male sex	0.949	0.620–1.450	0.811
BNP	1.000	1.000–1.000	0.990
NYHA functional class	1.050	0.776–1.430	0.736
EF	1.010	0.991–1.030	0.279
ICM	0.889	0.587–1.350	0.580
SBP	0.997	0.991–1.000	0.330
DBP	0.990	0.981–0.999	0.031	0.998	0.985–1.010	0.762
Heart rate	0.993	0.984–1.000	0.091	0.992	0.980–1.000	0.166
BMI	0.989	0.945–1.040	0.635
No. past HF hospitalizations	1.460	1.150–1.850	0.002	1.240	0.941–1.640	0.127
Pre-simple GDMT score (points)	1.500	1.330–1.700	<0.001	1.780	1.520–2.080	<0.001
Comorbidity
AF	1.090	0.723–1.630	0.693
Diabetes	0.785	0.515–1.200	0.260
COPD	0.250	0.578–2.710	0.569
Pneumonia	0.730	0.378–1.410	0.348
Laboratory data
BUN	1.030	1.010–1.040	<0.001	1.020	1.000–1.040	0.047
eGFR	0.981	0.970–0.992	<0.001	0.995	0.977–1.010	0.550
UA	1.020	0.930–1.130	0.639
Na	0.965	0.926–1.010	0.092	0.987	0.938–1.040	0.616
K	1.650	1.230–2.200	<0.001	1.280	0.889–1.840	0.185
Hemoglobin	0.951	0.875–1.030	0.239
Albumin	0.947	0.641–1.400	0.786
In-hospital use
Vasodilator	0.822	0.490–1,380	0.456
Carperitide	0.438	0.220–0.871	0.018	0.588	0.267–1.290	0.187
Loop diuretics	0.852	0.482–1.500	0.581
TLV	1.280	0.854–1.930	0.230
Catecholamine	1.670	1.040–2.680	0.032	1.080	0.604–1.940	0.792
NPPV	1.820	0.985–3.370	0.056	2.590	1.200–5.590	0.015

Abbreviations as in Tables 1,2.

Discussion

This study is the first to demonstrate that optimizing GDMT, using a simple GDMT score comprising RAS inhibitors, β-blockers, MRAs, and SGLT2 inhibitors, is associated with improved outcomes following readmission for HF. The efficacy of combination therapy, including the “Fantastic Four” for HFrEF, has become increasingly recognized.¹⁹ Although the present study included both HFrEF and HFmrEF patients, there is evidence to suggest that GDMT is beneficial up to an EF of 50%, indicating that similar treatments should be applied to both HFrEF and HFmrEF.²⁰ Because recent findings, such as those from the Safety, Tolerability and Efficacy of Rapid Optimization, Helped by NT-proBNP Testing, of Heart Failure Therapies (STRONG-HF) study,²¹ have demonstrated the effectiveness of aggressively initiating and titrating GDMT during and shortly after hospitalization for HF, the European Society of Cardiology (ESC) guidelines now endorse such a therapeutic strategy as a Class I recommendation.²² Despite these guidelines, the real-world implementation rate of GDMT remains low,^11,12 highlighting a significant evidence-practice gap. To bridge this evidence-practice gap, specific applications and other tools may be used.^23,24 Our recent work leveraging a simple GDMT score suggests that achieving a score of ≥5 points could contribute to improved prognosis, indicating that such scoring systems may be useful tools for bridging the evidence-practice gap.⁶

By focusing on patients readmitted for HF, this study sought to further use the simple GDMT score as a tool for effective intervention. Despite some level of GDMT being introduced at the time of initial hospitalization for HF, readmitted patients often have a notably poor prognosis.^13,14 The poor prognosis of patients readmitted for HF observed in the present study aligns with previous reports (Supplementary Table 3). In clinical practice, such patients are often discharged after only symptomatic treatment, without revisiting the existing GDMT plan. However, patients with HF readmissions should receive some form of interventions during hospitalization to prevent further readmission. Such interventions may include non-pharmacological treatments, like valve surgery, coronary revascularization, or cardiac resynchronization therapy; however, optimization of pharmacological therapy remains crucial. Thus, the present study hypothesized the importance of GDMT re-evaluation and intervention during hospitalization, using the simple GDMT score to investigate the utility of such interventions.

This study found that an increase in the simple GDMT score at discharge compared with admission was associated with an improved prognosis, highlighting the potential to reduce further readmissions through in-hospital GDMT interventions. In the stratification analysis, a larger increase in score was associated with a better prognosis, although the difference was not statistically significant. Interestingly, the group with unchanged scores tended to have a worse prognosis than the group with decreased scores. A decrease in the simple GDMT score indicates that an intervention was made based on the physician’s judgment, which could be due to various reasons, such as low blood pressure or worsening renal function. In such cases, reducing or discontinuing medication is unavoidable, and ignoring such adverse events would likely have a worse impact on prognosis.²⁵ Conversely, it is also possible that some patients did not undergo a reassessment of GDMT due to clinical inertia, without any particular reason. It is not possible to determine from this study what proportion of cases were due to clinical inertia. However, the results of this stratification analysis may suggest that failing to review and intervene in GDMT without a valid reason could potentially have a negative impact on prognosis.

Difficulty With GDMT Up-Titration

Ultimately, the decision to intervene with OMT is at the discretion of the attending physician, and the reasons for this cannot be fully understood, which represents a selection bias and is one of the major limitations of this study. The group without an increase in simple GDMT score had lower diastolic pressure, a higher number of previous hospitalizations for HF, a higher GDMT score at admission, higher BUN levels, worse renal function, higher potassium concentrations, less carperitide use, and more catecholamine use, and tended to have a lower heart rate and lower sodium concentrations. However, multivariate analysis showed that older age, higher simple GDMT score at admission, and the use of NPPV were strongly associated with the inability to enhance GDMT. These were factors associated with difficulty intervening with GDMT in this study.

It is often more challenging to intensify GDMT in elderly patients. For instance, it may be difficult to increase the dosage of β-blockers due to bradycardia, and it has been reported that the effectiveness of GDMT diminishes in older patients.²⁶ However, recent studies have shown that MRAs are effective even in older patients,²⁷ and subanalyses of the STRONG-HF trial have reported that aggressive introduction and titration of GDMT during hospitalization are effective even in older patients.²⁸ Our study also found that intervening with GDMT during hospitalization was effective in patients aged ≥80 years in subgroup analyses. Thus, it may not be justifiable to give up on intensifying GDMT simply because of age. Regarding potassium concentrations, recent guidelines from the ESC recommend not stopping or reducing GDMT in HF patients with hyperkalemia, if possible, but rather using potassium-lowering drugs.²⁹ The era of considering hyperkalemia as an excuse for not intensifying GDMT is coming to an end. It seems that considering intensification of GDMT potassium-lowering drugs is the approach for the future.

Furthermore, in subgroup analyses of patients with systolic blood pressure <100 mmHg, the beneficial effects of increased simple GDMT score on prognosis disappeared; the HR was <1.0, but the 95% CI was very wide. This suggests that in groups with low blood pressure, increasing the dose of RAS inhibitors or β-blockers could lead to further hypotension and increase adverse events such as dizziness, potentially worsening the prognosis. These results may suggest that there may be risks in forcibly enhancing GDMT in such groups.

In this study, ivabradine and vericiguat were used in 0.2% and 0.5% of patients, respectively. The impact of these drugs in the present study is considered minimal. In this study, these 2 drugs were not included in the scoring system. Indeed, in the Japanese guidelines, ivabradine is still recommended with a Class IIa rating only for HFrEF, and vericiguat is not yet recommended at all.^30,31 However, because this study targeted cases of HF readmission, considering the addition of these 2 drugs as an intervention for such patients is crucial for the next steps. It is also important for practical clinical application to develop a new scoring system that incorporates these 2 drugs, and this remains a task for future research.

Study Limitations

The major limitation of this study is its retrospective observational nature. A second limitation is that the decision to prescribe was dependent on the attending physician’s judgment, and selection bias cannot be excluded. Prospective studies using such simple GDMT scores should be conducted.

Conclusions

In patients readmitted for HF, an increase in the simple GDMT score during hospitalization is associated with an improved prognosis. Reviewing and intervening in GDMT during hospitalization to optimize treatment play crucial roles in preventing further readmissions in HF patients. The present scoring system could potentially facilitate this process.

Acknowledgments

None.

Sources of Funding

This research received no grant from any funding agency in the public, commercial or not-for-profit sectors.

Disclosures

R.M. has received lecture fees from Otsuka Pharmaceutical, AstraZeneca, Novartis Japan, Ono Pharmaceutical, and Boehringer Ingelheim. Y.M. has received lecture fees from Daiichi Sankyo and Medtronic. The remaining authors have nothing to disclose.

IRB Information

The study protocol was approved by the Institutional Review Board of Fukuoka Red Cross Hospital (Approval no. 404), and informed consent was obtained from patients before study participation and the use of their data in this study.

Data Availability

The deidentified participant data will not be shared.

Supplementary Files

Please find supplementary file(s);

https://doi.org/10.1253/circj.CJ-24-0265

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