Prognostic Implications of Reductions in Heart Rates in Patients With Acute Heart Failure and Atrial Fibrillation

Abstract

Background: Atrial fibrillation (AF) is a common arrhythmia in patients with acute heart failure (AHF). Heart rate (HR) also changes significantly over time. However, the association between changes in HR in AF patients and prognosis is uncertain.

Methods and Results: We investigated the association between HR reduction in AF achieved within 48 h of admission and 60-day mortality in patients with AHF from the REALITY-AHF study. The percentage HR (%HR) reduction was calculated as (baseline HR–HR at 48 h) / baseline HR × 100. The primary endpoint was 60-day all-cause mortality. In 468 patients with confirmed AF at both admission and 48 h after admission, the median HR at these time points was 105±31 and 84±18 beats/min, respectively. The median %HR reduction was 15.4% (interquartile range 2.2–31.4%). During the 60 days of admission, 39 deaths (8.3%) were recorded, and the %HR reduction within 48 h was significantly associated with 60-day mortality in the unadjusted model (hazard ratio [HR] 0.85; 95% confidence interval [CI] 0.77–0.95; P=0.005) and after adjusting for other covariates (HR 0.81; 95% CI 0.68–0.96; P=0.016). Furthermore, the %HR reduction was associated with a significant reduction in 60-day mortality in patients with higher baseline HR.

Conclusions: %HR reduction is associated with a better short-term prognosis in patients with AHF presenting with AF, particularly in those with a rapid ventricular response.

The impact of heart rate (HR) on prognosis in patients with chronic heart failure (HF) has been extensively investigated, and a lower HR is associated with lower rates of adverse events.^1,2 This is currently regarded as an association and causation in sinus rhythm because therapeutic intervention for HR has been demonstrated to improve patients’ prognosis in addition to standard HF medications.³ Similarly, high HR has been regarded as a factor associated with poor prognosis and has been incorporated into several risk models for acute HF (AHF).^4–6

Atrial fibrillation (AF) is the most common arrhythmia seen in patients with AHF, and approximately 35–40% of AHF patients have a history of AF at the time of admission.^7–10 It has also been reported that AF is the second most common precipitating factor of AHF.¹¹ Although one of the causes of AF-related deterioration of HF is rapid ventricular response as a result of high HR, it is not clear whether the association between lower HR and better prognosis can be extended to patients with AHF and AF. During the management of the acute phase of AHF, HR is supposed to change significantly, and repeated measurement of HR is recommended as routine clinical practice by the European Society of Cardiology guidelines.^8,12 However, it is difficult to interpret the change in HR over time because of the paucity of data on its clinical and prognostic impact. This is clinically relevant because some treatment options with rapid ventricular response to control HR are suggested in the guidelines for AF. Thus, this study investigated the prognostic values of changes in HR during the acute phase of AHF in patients with AF.

Methods

Study Design and Patient Population

This study was based on REALITY-AHF, a prospective multicenter registry focusing on presentation and treatment during the very early phase of acute HF. The detailed study design has been published elsewhere.^13–16 The study was conducted in 20 hospitals in Japan, and consecutive acute HF patients age ≥20 years who were hospitalized via the emergency department were enrolled. Acute HF was diagnosed based on the Framingham criteria by an attending physician at each facility.¹⁷ This study, its detailed inclusion and exclusion criteria, and other information were registered and published in the publicly available University Hospital Medical Information Network (UMIN) Clinical Trials Registry (ID: UMIN000014105) before the first patient was enrolled. Informed consent was obtained from all participants using the opt-out method because of the observational nature of the study. Notably, those diagnosed with acute coronary syndrome and acute myocarditis were excluded from this registry according to the exclusion criteria. The study was performed in accordance with the Declaration of Helsinki and was approved by the local institutional review board of each hospital.

HR Measurement and Outcomes

In this registry, patients’ data (including HR and rhythm) were routinely collected on admission and 1.5, 6, 24, and 48 h after admission. Whether a patient was under sinus rhythm or AF, their HR was evaluated using a 12-lead electrocardiogram (ECG) at admission. REALITY-AHF included only patients admitted through the emergency department, and baseline electrograms were obtained in the emergency department. HR 1.5, 6, and 24 h after admission was recorded using a 12-lead ECG with the patient in the supine position. In cases where a 12-lead ECG was not available, a monitor ECG strip recorded in the supine position was used to define HR and cardiac rhythm. In the REALITY-AHF registry, the ECG rhythm was recorded as “Sinus rhythm”, “Atrial fibrillation”, “Pacemaker rhythm”, and “Others”. Patients showing rhythms other than AF at either baseline or 48 h after admission were excluded because the HR could be altered by changing the cardiac rhythm. The percentage HR (%HR) reduction was calculated as (baseline HR–HR at 48 h) / baseline HR × 100. A positive %HR signified a decrease in HR, and a negative value signified an increase in HR during the 48 h of admission. The primary outcome was 60-day all-cause mortality.

Statistical Analysis

Data are expressed as the mean±SD for normally distributed variables and as the median with interquartile range (IQR) for non-normally distributed data. Categorical data are expressed as numbers and percentages. When necessary, variables were transformed for further analyses. Group differences were evaluated using Student’s t-test or the Mann-Whitney U test for continuous variables and Chi-squared or Fisher exact tests for categorical variables. The longitudinal trajectory of HR over time (baseline and 1.5, 6, 24, and 48 h after admission) was assessed using linear mixed-effects models to account for within-individual correlation of a repeatedly measured HR. Patient was included as a random effect, and time was modeled linearly.

The Outcomes of a Prospective Trial of Intravenous Milrinone for Exacerbations of Chronic HF (OPTIME-CHF) score was calculated for each patient, as described previously.¹⁸ The OPIME-CHF risk score was based on age, New York Heart Association (NYHA) functional class, systolic blood pressure, blood urea nitrogen, and serum sodium concentrations. We used this score as an adjustment variable in the multivariable Cox model. Furthermore, it has recently been shown that B-type natriuretic peptide (BNP) concentrations at admission are associated with prognosis. Therefore, we planned to include the BNP concentration as an adjustment variable. Univariate and multivariate Cox regressions were performed to evaluate the association between %HR reduction and 60-day mortality. The presence of a non-linear association between the %HR reduction from baseline (i.e., at admission) to 48 h of admission and 60-day mortality was evaluated using a regular Cox regression model and Cox regression with restricted cubic splines of 3-, 4-, and 5-knots. The goodness-of-fit was compared between the models using the Akaike information criterion. Two-tailed P<0.05 was considered statistically significant. Statistical analyses were performed using R version 3.5.2 (R Foundation for Statistical Computing, Vienna, Austria; http://www.R-project.org).

Results

Of the 1,682 patients enrolled in the REALITY-AHF registry, 69, including 8 patients who died during the first 48 h of admission, were excluded because of missing HR data on admission or after 48 h of admission. In addition, 1,048 patients without AF at baseline and 97 patients who showed sinus rhythm at baseline were excluded, even if they showed AF at 48 h. Finally, 468 patients were included in this study.

The mean age of the study group was 78±12 years, with 51.2% of the group being male. The mean left ventricular ejection fraction (LVEF) measured before discharge was 48±16%, and 44.2% of patients had LVEF >50%.

Across all cohorts, the mean HR at admission and after 48 h of admission was 105±31 and 84±18 beats/min, respectively. The median of the %HR reduction was 15.4% (IQR 2.2–31.5%), with 105 patients (22.4%) showing an increase in HR from admission to 48 h after admission. The baseline characteristics of patients stratified according to tertiles of %HR reduction are provided in Table 1.

Table 1. Baseline Characteristics of Patients Stratified by Tertile of %HR Reduction

Variables	%HR reduction tertiles			P value
	T1 (most reduced; n=156)	T2 (n=156)	T3 (least reduced; n=156)
Age (years)	77±11	79±12	80±11	0.045
Male sex	80 (51.3)	79 (50.6)	83 (53.2)	0.895
SBP (mmHg)	152±39	141±28	133±28	<0.001
DBP (mmHg)	94±27	85±21	77±19	<0.001
HR (beats/min)	132±27	105±24	81±18	<0.001
LVEF				0.164
<40%	48 (33)	45 (32)	40 (32)
40–50%	33 (23)	19 (14)	18 (14)
>50%	63 (44)	75 (54)	69 (54)
Cardiac implantable electronic devices	11 (7.1)	9 (5.8)	16 (10.3)	0.300
Comorbidities
History of HF	83 (53.2)	90 (57.7)	110 (70.5)	0.005
Hypertension	103 (66.9)	96 (61.5)	87 (55.8)	0.133
Diabetes	48 (31.0)	37 (23.9)	47 (30.1)	0.317
COPD	22 (14.4)	15 (9.7)	16 (10.3)	0.368
Coronary artery disease	33 (21.3)	28 (18.1)	37 (23.7)	0.471
Medication at admission
Loop diuretics	76 (49.7)	93 (60.0)	109 (69.9)	0.001
ACEI	27 (17.5)	30 (19.2)	25 (16.1)	0.772
ARB	36 (23.4)	57 (36.5)	42 (27.1)	0.031
β-blocker	64 (41.6)	80 (51.3)	74 (47.7)	0.222
Aldosterone blocker	28 (18.1)	42 (26.9)	53 (34.0)	0.006
Laboratory data
WBC (/μL)	7,600 [6,350–9,950]	6,500 [5,100–8,650]	6,200 [4,875–7,955]	<0.001
Albumin (g/dL)	3.6±0.5	3.4±0.5	3.4±0.6	0.037
Hemoglobin (g/dL)	12.6±2.3	11.7±2.1	11.5±2.2	<0.001
Creatinine (mg/dL)	1.02 [0.77–1.34]	1.04 [0.80–1.43]	1.14 [0.85–1.69]	0.019
BUN (mg/dL)	23 [17–30]	24 [17–32]	27 [21–39]	0.001
Sodium (mEq/L)	138.9±4.3	140.0±4.8	138.9±5.0	0.959
Potassium (mEq/L)	4.2±0.7	4.2±0.6	4.2±0.6	0.67
Glucose (mg/dL)	143 [112–209]	126 [106–146]	127 [106–156]	0.001
CRP (mg/dL)	0.59 [0.21–1.67]	0.77 [0.27–2.40]	0.59 [0.21–2.07]	0.583
BNP (pg/mL)	625 [402–1,006]	587 [349–1,026]	643 [350–1,114]	0.595
OPTIME-CHF risk score	159 [134–189]	160 [135–191]	170 [144–206]	0.053

Unless indicated otherwise, data are given as the mean±SD, median [interquartile range], or n (%). Patients were divided into 3 groups according to the tertile of %HR reduction: T1, 25.5–65.8% reduction; T2, 9.4–25.4% reduction; and T3, −97.6% to 9.0% reduction. ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; BNP, B-type natriuretic peptide; BUN, blood urea nitrogen; COPD, chronic obstructive pulmonary disease; CRP, C-reactive protein; DBP, diastolic blood pressure; HF, heart failure; LVEF, left ventricular ejection fraction; %HR, percentage heart rate; SBP, systolic blood pressure; WBC, white blood cell.

Patients were divided into 3 groups according to the tertile of %HR reduction (T1, 25.5–65.8% reduction; T2, 9.4–25.4% reduction; and T3, −97.6% to 9.0% reduction). The T3 group (least %HR reduction) was associated with older age, lower systolic and diastolic blood pressures, lower HR at admission, and a history of HF. In addition, less %HR reductions were associated with a lower white blood cell count, lower albumin and hemoglobin concentrations, and poorer renal function. Among all patients, 36 (7.7%) had cardiac implantable electronic devices (26 had pacemakers, 2 had implantable cardioverter-defibrillators, and 8 had cardiac resynchronization therapy pacemakers or defibrillators). There was no significant difference in the proportion of patients with cardiac implantable electronic devices among the %HR reduction tertiles (P=0.300). There was no significant difference in BNP, and the difference in OPTIME-CHF scores did not reach statistical significance (P=0.053; Table 1).

Drugs that potentially affected HR and were used within 48 h are listed in Table 2. Some drugs (including digoxin, diltiazem, and landiolol) were used more in patients with a greater %HR reduction. However, the use of catecholamines did not significantly differ among the %HR reduction tertiles.

Table 2. Drugs Used Within 48 h of Admission and Potentially Affecting HR in All Patients and in Patients Stratified by Tertile of Percentage HR Reduction

Drugs	All	T1 (n=156)	T2 (n=156)	T3 (n=156)	P value
Any drug related to HR	85 (18.2)	50 (32.1)	26 (16.7)	8 (5.1)	<0.001
Amiodarone	16 (3.4)	7 (4.5)	6 (3.8)	3 (1.9)	0.529
Digoxin	30 (6.4)	21 (13.5)	7 (4.5)	2 (1.3)	<0.001
Diltiazem	21 (4.5)	12 (7.7)	7 (4.5)	2 (1.3)	0.018
Landiolol	31 (6.6)	21 (13.5)	9 (5.8)	1 (0.6)	<0.001
Verapamil	8 (1.7)	6 (3.8)	1 (0.6)	1 (0.6)	0.056
Any catecholamine	62 (13.3)	16 (10.3)	18 (11.5)	28 (17.9)	0.109
Dopamine	48 (3.0)	6 (3.8)	3 (1.9)	5 (3.2)	0.703
Dobutamine	53 (11.3)	13 (8.3)	16 (10.3)	24 (15.4)	0.135
Norepinephrine	9 (1.9)	5 (3.2)	1 (0.6)	3 (1.9)	0.313

Unless indicated otherwise, data are given as n (%). Patients were divided into 3 groups according to the tertile of percentage HR reduction: T1, 25.5–65.8% reduction; T2, 9.4–25.4% reduction; and T3, −97.6% to 9.0% reduction. HR, heart rate.

Overall, HR decreased over time, and the trajectories of actual HR and %HR reductions are shown in Figure 1.

Figure 1.

Trajectories of actual heart rate (HR) and percentage HR (%HR) reduction from baseline overtime in the entire cohort.

The trajectories of actual HR and %HR reductions over time stratified by %HR reduction tertiles are shown in Supplementary Figure 1. During the 60 days of admission, a total of 39 deaths (8.3%) was recorded, and a greater %HR reduction was associated with lower 60-day mortality in Kaplan-Meier analysis (Log-rank, P=0.028; Figure 2).

Figure 2.

Kaplan-Meier curves for 60-day mortality after admission. Patients were divided into 3 groups according to the tertile of percentage heart rate reduction: T1, 25.5–65.8% reduction; T2, 9.4–25.4% reduction; and T3, −97.6% to 9.0% reduction.

In Cox regression analysis, the %HR reduction within 48 h was significantly associated with 60-day mortality in the unadjusted model (hazard ratio [HR] 0.85, 95% confidence interval [CI] 0.77–0.95, P=0.005), and after being adjusted for OPTIME-CHF score in addition to log-transformed BNP at admission (HR 0.81, 95% CI 0.68–0.96, P=0.016). Considering interinstitutional correlations, we constructed a generalized estimating equations model using institution as the clustering factor. In this model, %HR reduction retained its association with 60-day mortality even after adjusting for log(BNP) and OPTIME-CHF score (HR 0.81, 95% CI 0.67–0.97, P=0.020). We also performed a sensitivity analysis for the Cox regression model by adding all 5 components that comprised the OPTIME-CHF score (i.e., age, systolic blood pressure, blood urea nitrogen, sodium concentration, and NYHA class) as continuous variables (not as scores); %HR reduction was consistently associated with 60-day mortality (HR 0.86, 95% CI 0.76–0.99, P=0.038). We found a regular Cox regression model that best fits the association between the %HR reduction and the adjusted HR for 60-day mortality. Figure 3 shows the association between %HR reduction and the predicted 60-day mortality rate. A receiver operating characteristics (ROC) curve was constructed, and appropriate cut-off values to predict 60-day prognosis were suggested as 23.2% for the %HR reduction and 21 beats/min for absolute HR reduction by the Youden Index.¹⁹

Figure 3.

Relationship between percentage heart rate (HR) reduction within 48 h and the adjusted hazard ratio for 60-day mortality. The solid line indicates the adjusted hazard ratio for 60-day mortality and the shaded area is the 95% confidence interval.

Furthermore, the interaction between baseline HR and the %HR reduction on 60-day mortality was evaluated. We found that a higher %HR reduction was associated with a higher reduction in 60-day mortality in patients with a higher baseline HR (P_interaction=0.048; Supplementary Figure 2). Nonetheless, no significant interaction was found between the use of an HR-changing drug and the effect of the change in the %HR on 60-day mortality (P_interaction=0.399). There was no significant interaction between the change in %HR and LVEF (P_interaction=0.473).

Discussion

In this study we have shown, for the first time, that the HR of AF changed significantly and generally decreased during the acute phase of AHF. The change in the %HR of AF achieved within 48 h of admission was associated with 60-day mortality, independent of other comorbidities. This association was affected by baseline HR and was stronger in patients with a higher baseline HR. No interaction was observed between the prognostic impact of the change in %HR and the use of a drug that potentially modified HR.

Some studies have investigated the association between HR reduction and prognosis in AHF; however, a handful of these studies included patients with AF. For example, Rosa et al investigated 1,398 consecutive patients admitted because of AHF and evaluated the prognostic impact of HR reductions in both sinus rhythm and AF.²⁰ These authors found no association between HR reduction (defined as the difference in HR between admission and discharge) and 1-year all-cause mortality.²⁰ Furthermore, Takahama et al studied 421 patients with AHF (including both AF and sinus rhythm) and demonstrated that only HR reduction was achieved during the index hospital stay.²¹ Nevertheless, HR at discharge was not associated with post-discharge prognosis.²¹ Although these studies provide important information regarding the prognostic impact of HR reduction achieved during the hospitalization of patients with HF, it should be acknowledged that the period of hospital stay varied from patient to patient. This may have affected the association between HR reduction and prognosis.

Although one study has shown an association between HR at discharge and prognosis in patients with AF, no study has observed and analyzed the prognostic implications of HR changes in AF in the very acute phase of AHF. The current European Society of Cardiology’s HF guidelines do not suggest a clear target HR for AF in patients with AHF. In 2020, the Acute Cardiovascular Care Association and the European Heart Rhythm Association of the European Society of Cardiology issued a position statement focusing on AF in patients with AHF.²² Although the statement does not propose a target HR for AF in patients with AHF, it recommends rate/rhythm control for those with a baseline HR >100 beats/min. Our study apparently supports this recommendation because a significant interaction was demonstrated between baseline HR and the prognostic impact of %HR reduction. This is more significant in patients with a higher than lower baseline HR. There is a need for further studies because the optimal target HR or %HR reduction is still unknown.

Our study was not designed to investigate the mechanism behind the %HR reduction and prognosis. However, AF supposedly contributed to more impaired ventricular filling and reduced the stroke rate compared with sinus tachycardia because of the more rapid ventricular rate under normal atrioventricular node conduction. This led to a shortened filling time and a loss of atrial systole. A study showed that controlling HR at approximately 90 beats/min in patients with AF may be optimal. This was because the reduction in rapid ventricular rate resulted in a longer diastolic filling period and increased the rate of the left ventricular stroke. Nevertheless, this led to reduced cardiac contractility, resulting in fewer beats per minute.²³ Our study results regarding the interaction between baseline HR and the %HR reduction indicate that this notion is clinically applicable. However, the results of our study should be interpreted cautiously because of the observational nature of the study, and the study does not directly suggest that reducing the HR of AF in patients with AHF improves prognosis.

This study has some limitations. This was a retrospective study; therefore, it did not predefine the exploratory analysis of the registry. We focused on patients with AF; therefore, the number of patients and events was relatively small. Moreover, many factors were associated with HR changes in patients with AHF and AF. We evaluated the effects of some drugs that potentially affected HR; however, it is evident that this was not enough to adjust for confounders. We measured HR of AF with ECGs according to the protocols of individual institutions, which are not necessarily the same, and this may have affected the results of our study. Indeed, one study showed that estimated HRs based on ECGs significantly changed according to the length of ECG recording.²⁴ In the present study, HRs were evaluated only at 4 time points, which may have been insufficient to capture precise data. We also did not collect data on HR after discharge. Future studies in which HR is more closely and continuously monitored are warranted to confirm the findings of the present study. Given that in this study variability becomes significantly wider when HR is higher, the results should be carefully interpreted. One of the limitations of this study that should be taken seriously is the limited number of events, which consequently did not allow us to make adjustments for possible confounders. To adjust for confounders as much as possible and to avoid model overfitting, we used a well-established risk score; however, there could be other confounding factors that were not identified. Moreover, although the prognostic impact of %HR reduction in the acute phase can differ according to the etiology of HF, we could not evaluate it due to difficulties in defining the etiology of HF in each patient. These points should be clarified in future studies with adequate study designs and power. In addition, we included only patients who were hospitalized through emergency department; thus, our findings may not be generalizable to non-hospitalized patients. We demonstrated no association between HR-lowering drugs and %HR reduction on 60-day mortality. However, we were unable to determine whether the %HR reduction achieved by such drugs could have the same prognostic impact as the %HR reduction achieved without the use of drugs. The results of this should be interpreted carefully because this study is observational and cannot address the causality between %HR reduction and prognosis.

Conclusions

In conclusion, %HR reduction was associated with better short-term prognosis in patients with AHF presenting with AF, particularly those with a rapid ventricular response. Based on these hypothesis-generating results, a further randomized clinical trial is needed to test whether the acute phase rate control in patients with AHF and AF with rapid ventricular response leads to a better prognosis.

Acknowledgments

None.

Sources of Funding

The REALITY-AHF study was funded by The Cardiovascular Research Fund, Japan. This study was partly supported by a JSPS KAKENHI Grant-in-Aid for early career scientists (Grant no. 18K15862).

Disclosures

K.Kida has received honoraria from Daichi Sankyo Co., Ono Pharmaceutical Co., Ltd., AstraZeneca K.K., Otsuka Pharmaceutical Co., Ltd., and Novartis Pharmaceuticals Co., Ltd. T.Okumura has received research grants from Ono Pharmaceutical Co., Ltd., Bayer Pharmaceutical Co., Ltd., Daiichi-Sankyo Pharma Inc., and Amgen Astellas BioPharma K.K. outside the submitted work. T.Okumura has received honoraria from Novartis Pharma K.K., Ono Pharmaceutical Co., Ltd., Otsuka Pharmaceutical Co., Ltd., and Medtronic Japan Co., Ltd. Y.Matsue is affiliated with a department endowed by Philips Respironics, ResMed, Teijin Home Healthcare, and Fukuda Denshi, and has received an honorarium from Otsuka Pharmaceutical Co. The other authors have nothing to declare.

IRB Information

The study protocol was approved by the St. Marianna University School of Medicine Institutional Committee on Human Resource, Kawasaki, Japan (No. 2746).

Data Availability

The deidentified participant data will not be shared.

Supplementary Files

Please find supplementary file(s);

http://dx.doi.org/10.1253/circj.CJ-21-0269

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