Simplified Academic Research Consortium for High Bleeding Risk (ARC-HBR) Definition Predicts Bleeding Events in Patients With Heart Failure

Abstract

Background: It has recently been reported that the simplified Academic Research Consortium for High Bleeding Risk (ARC-HBR) definition, which excludes 6 rare criteria, is comparable to the original ARC-HBR definition in predicting major bleeding in patients with coronary artery disease (CAD) who undergo percutaneous coronary intervention. In this study, we investigated whether the simplified ARC-HBR definition could be applied to patients with heart failure (HF) to identify those at high bleeding risk (HBR).

Methods and Results: In all, 2,437 patients hospitalized for HF were enrolled in this study. Patients were divided into 2 groups based on the simplified ARC-HBR definition: those at HBR (n=2,026; 83.1%) and those not (non-HBR group; n=411; 16.9%). The HBR group was older (72.0 vs. 61.0 years; P<0.001) and had a lower prevalence of CAD (31.1% vs. 36.5%; P=0.034) than the non-HBR group. Kaplan-Meier analysis showed that post-discharge bleeding events defined as hemorrhagic stroke or gastrointestinal bleeding were more frequent in the HBR than non-HBR group (log-rank P<0.001). The simplified ARC-HBR definition accurately predicted bleeding events (Fine-Gray model; hazard ratio 2.777, 95% confidence interval 1.464–5.270, P=0.001).

Conclusions: The simplified ARC-HBR definition predicts a high risk of bleeding events in patients with HF.

Dual antiplatelet therapy (DAPT), consisting of a combination of aspirin and thienopyridine, reduces thrombotic events in patients with coronary artery disease (CAD) who undergo percutaneous coronary intervention (PCI).^1,2 Conversely, DAPT increases bleeding risk at the same time,^1,2 which consequently results in mortality and increased morbidity.^3,4 The current guidelines recommend optimization of the duration of DAPT after PCI on the basis of CAD status (acute coronary syndrome or stable CAD), the device used, and the presence or absence of high bleeding risk (HBR).^5–7 Although previous studies have investigated how to identify patients at HBR, the definitions they provided varied, which limited interpretation and generalization.^8–13 Based on a review of the available findings, the Academic Research Consortium for High Bleeding Risk (ARC-HBR) first launched the ARC-HBR definition of HBR in 2019, providing consistency in the definition of patients at HBR and helping with both clinical decision making and regulatory review.^14,15 Approximately 40–50% of CAD patients who undergo PCI are considered to be at HBR, and the predictive value of the ARC-HBR definition has been validated.^16–19 However, application of the ARC-HBR definition is sometimes difficult in retrospective reviews due to some of the ARC-HBR definition criteria not being regularly recorded in clinical trials.^16,20,21 To overcome this limitation, Miura et al determined the prevalence of each criterion in CAD patients who had undergone PCI, developing a simplified ARC-HBR definition that excluded 6 rare criteria (prevalence <1%).²² Miura et al found that their simplified ARC-HBR definition has comparable predictive value for long-term major bleeding events to the original ARC-HBR definition.²²

Editorial p ????

CAD can lead to the deterioration of cardiac function and consequently induce heart failure (HF).^23–25 HF itself is an independent predictor for hemorrhagic stroke.²⁶ Moreover, HF alters gastrointestinal function and may cause gastrointestinal bleeding.^27,28 However, bleeding risk in patients with HF has not yet been fully investigated. In the present study, we investigated whether the simplified ARC-HBR definition could be applied to patients with HF to identify those at HBR.

Methods

Subjects and Protocol

This was an observational study. Figure 1 shows the patient flowchart. We collected the data of 2,759 patients who were hospitalized in Fukushima Medical University Hospital due to decompensated HF and discharged alive between January 2010 and December 2019. Decompensated HF was diagnosed on the basis of the current guidelines.^23–25 Among the patients, 322 were excluded due to a lack of data on estimated glomerular filtration rate, hemoglobin, platelet count, the presence or absence of prior stroke, and/or the use of oral non-steroidal anti-inflammatory drugs or steroids. Finally, data for 2,437 patients were entered used in the analyses.

Figure 1.

Patient flowchart. HBR, high bleeding risk.

The 2,437 patients were evaluated using the simplified ARC-HBR definition and divided into 2 groups: those who were at HBR (n=2,026; 83.1%) and those who were not (non-HBR group; n=411; 16.9%). Patients characteristics and post-discharge prognoses were compared between the 2 groups. The primary endpoint was bleeding events, defined as hemorrhagic stroke or gastrointestinal bleeding. The secondary endpoints were rehospitalization for HF, major adverse cardiac and cerebrovascular events, cardiac death, and all-cause death. If 1 patient experienced ≥2 endpoints, the first event was entered into the analysis. Hemorrhagic stroke was defined as a focal collection of blood within the brain parenchyma, ventricular system, or subarachnoid space that is not caused by trauma.²⁹ Gastrointestinal bleeding was defined as bleeding from the gastrointestinal tract with Bleeding Academic Research Consortium (BARC) definition Type 2–5.^27,28,30 Major adverse cardiac and cerebrovascular events were defined as non-fatal and fatal cardio- and cerebrovascular events, such as myocardial infarction and stroke.³¹ The definition of cardiac death was in accordance with our previous studies.^32,33 The patient characteristics consisted of demographic data obtained at hospital discharge, past medical history, medication, laboratory data, and echocardiographic data. Laboratory data and echocardiographic data recorded within 1 week prior to discharge in a stable condition were entered used in the analyses. CAD included acute coronary syndrome and chronic coronary heart disease.^34,35 Estimated glomerular filtration rate was calculated using a 3-variable Japanese equation.³⁶ Post-discharge follow-up methods were in accordance with our previous studies.^27,28,32,33

This study complied with the Declaration of Helsinki and the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) statement.^37,38 All patients enrolled in the study provided written informed consent.

Simplified ARC-HBR Definition

Patients were assessed as to whether they were at HBR or not using the simplified ARC-HBR definition reported by Miura et al,²² in which 6 rare criteria are excluded from the original ARC-HBR definition. The 6 rare criteria excluded were prior bleeding and transfusion, defined as both major and minor criteria, chronic bleeding diathesis, liver cirrhosis with portal hypertension, non-deferrable major surgery on DAPT, and prior trauma or surgery within the past 30 days.²² The remaining criteria in the simplified ARC-HBR definition are consistent with those in the original ARC-HBR definition.^14,15,22 Patients were considered to be at HBR if they met at least 1 major criterion or 2 minor criteria.²²

Statistical Analysis

All continuous variables recorded in this study were evaluated by the Shapiro-Wilk test and were considered to be non-normally distributed. Continuous variables are presented as the median with interquartile range (IQR), whereas categorical variables are presented as numbers and percentages. Continuous and categorical variables were compared using the Mann-Whitney U test and Chi-squared test, respectively. The occurrence of primary and secondary endpoints was compared between groups using Kaplan-Meier analysis with the log-rank test. HBR was assessed as a predictor of the primary endpoint using the Fine-Gray model to account for the competing risk of all-cause death.³⁹ The univariate Fine-Gray model was further subdivided by subgroups other than criteria included in the simplified ARC-HBR criteria based on the median of continuous variables and the presence or absence of categorical factors. Interaction P values were calculated using the multivariate model including HBR, subgroup factors, and interactions between HBR and subgroup factors. Two-sided P<0.05 was considered statistically significant in all analyses.

The Fine-Gray model was run using EZR version 1.40 (Saitama Medical Center, Jichi Medical University, Saitama, Japan), a graphical user interface for R (R Foundation for Statistical Computing, Vienna, Austria).⁴⁰ All other analyses were conducted using IBM SPSS Statistics version 27 (IBM, Armonk, NY, USA).

Results

Figure 2 summarizes the proportion of HF patients who met each criterion in the simplified ARC-HBR definition, which exceeded 1% for all criteria. More than half the patients met the criterion for the use of oral anticoagulants. There were 2,026 (83.1%) patients assigned to the HBR group.

Figure 2.

Proportion of patients meeting each criterion in the simplified Academic Research Consortium for High Bleeding Risk (ARC-HBR) definition. CKD, chronic kidney disease; NSAIDs, non-steroidal anti-inflammatory drugs.

Comparisons of baseline patient characteristics between the HBR and non-HBR groups are presented in Table 1. The HBR group was older (72.0 vs. 61.0 years; P<0.001), with a lower proportion of males (58.1% vs. 68.4%, P<0.001). With respect to past medical history, there were fewer patients with CAD (31.1% vs. 36.5%; P=0.034) and fewer who had undergone PCI (21.5% vs. 30.2%; P<0.001) in the HBR compared with non-HBR group. The use of aspirin was more prevalent (48.3% vs. 39.4%; P=0.001) and the use of thienopyridines was less prevalent (25.9% vs. 32.1%; P=0.013) in the HBR than non-HBR group. Laboratory data revealed higher B-type natriuretic peptide concentrations in the HBR group (294.3 vs. 100.1 pg/mL; P<0.001). Left ventricular ejection fraction was comparable between the 2 groups.

Table 1. Baseline Patient Characteristics (n=2,437)

	Non-HBR group (n=411)	HBR group (n=2,026)	P value
Demographic data
Age (years)	61.0 [47.0–69.0]	72.0 [62.0–79.0]	<0.001
Age ≥75 years	36 (8.8)	867 (42.8)	<0.001
Male sex	281 (68.4)	1,178 (58.1)	<0.001
BMI (kg/m2)	23.6 [21.2–26.7]	22.6 [20.3–25.4]	<0.001
SBP (mmHg)	126.0 [111.0–144.0]	122.0 [107.0–141.0]	0.004
DBP (mmHg)	71.0 [63.0–84.0]	68.0 [60.0–80.0]	<0.001
NYHA FC III or IV	14 (3.4)	114 (5.6)	0.066
Past medical history
Prior stroke	13 (3.2)	437 (21.6)	<0.001
Stroke (major criteria)	0 (0)	73 (3.6)	N/A
Stroke (minor criteria)	13 (3.2)	364 (18.0)	<0.001
CAD	150 (36.5)	631 (31.1)	0.034
Atrial fibrillation	34 (8.3)	932 (46.0)	<0.001
Hypertension	252 (61.3)	1,426 (70.4)	<0.001
Diabetes	146 (35.5)	815 (40.2)	0.075
Dyslipidemia	294 (71.5)	1,413 (69.7)	0.470
Active malignancy	0 (0)	114 (5.6)	N/A
PCI	124 (30.2)	435 (21.5)	<0.001
Medication
RAS inhibitors	266 (64.7)	1,412 (69.7)	0.047
β-blockers	276 (67.2)	1,476 (72.9)	0.019
Loop diuretics	170 (41.4)	1,474 (72.8)	<0.001
CCBs	123 (29.9)	759 (37.5)	0.004
Anticoagulants	0 (0)	1,378 (68.0)	N/A
Aspirin	156 (39.4)	905 (48.3)	0.001
Thienopyridines	125 (32.1)	431 (25.9)	0.013
NSAIDs or steroids	22 (5.4)	235 (11.6)	<0.001
Laboratory data
BNP (pg/mL)	100.1 [29.2–290.1]	294.3 [115.2–648.5]	<0.001
Hemoglobin (g/dL)	14.2 [13.2–15.1]	12.3 [10.7–13.9]	<0.001
Severe anemia	0 (0)	577 (28.5)	N/A
Moderate anemia	37 (9.0)	517 (25.5)	<0.001
Platelet count (×109/L)	212.0 [176.0–244.5]	183.0 [146.0–229.0]	<0.001
Thrombocytopenia	0 (0)	116 (5.7)	N/A
eGFR (mL/kg/1.73 m2)	70.8 [60.3–85.1]	52.4 [36.6–67.1]	<0.001
Severe CKD	0 (0)	384 (19.0)	N/A
Moderate CKD	95 (23.1)	909 (44.9)	<0.001
Sodium (mEq/L)	140.0 [138.0–142.0]	140.0 [137.0–141.0]	0.025
Albumin (g/dL)	4.1 [3.8–4.4]	3.7 [3.3–4.1]	<0.001
Echocardiographic data
LVEF (%)	55.3 [42.4–64.4]	54.0 [39.2–63.5]	0.162

Unless indicated otherwise, data are given as the median [interquartile range] or n (%). BMI, body mass index; BNP, B-type natriuretic peptide; CAD, coronary artery disease; CCBs, calcium channel blockers; CKD, chronic kidney disease; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HBR, high bleeding risk; LVEF, left ventricular ejection fraction; N/A, not available; NSAIDs, non-steroidal anti-inflammatory drugs; NYHA FC, New York Heart Association functional class; PCI, percutaneous coronary intervention; RAS, renin-angiotensin system; SBP, systolic blood pressure.

During the median post-discharge follow-up period of 1,365 days, 146 patients reached the primary endpoint (49 patients experienced hemorrhagic stroke, 94 experienced gastrointestinal bleeding, and 3 experienced both hemorrhagic stroke and gastrointestinal bleeding). With regard to the secondary endpoints, there were 631 rehospitalizations for HF, 534 major adverse cardiac and cerebrovascular events, and 789 all-cause deaths, including 355 cardiac deaths. The cumulative 1-, 2-, and 3-year incidence of the primary endpoint was higher in the HBR than non-HBR group (Supplementary Table; 1 year, 2.5% vs. 0.7% [P=0.028]; 2 years, 3.6% vs. 1.0% [P=0.005]; 3 years, 4.5% vs. 1.7% [P=0.009]). Kaplan-Meier analysis revealed that the occurrence of bleeding events was higher in the HBR group (Figure 3; Supplementary Figure 1). The cumulative 1-, 2-, and 3-year incidence of secondary endpoints was also higher in the HBR group (Supplementary Table). Kaplan-Meier analysis demonstrated consistent results with regard to secondary endpoints (Supplementary Figure 2).

Figure 3.

Kaplan-Meier analysis: bleeding events. HBR, high bleeding risk.

HBR defined by the simplified ARC-HBR definition was a significant predictor of bleeding events regardless of its definition (Figure 4). Overall, in considering the competing risk of all-cause death using the Fine-Gray model, HBR had a hazard ratio of 2.777 for bleeding events, and there were no interactions between HBR and subgroup factors, including CAD (Table 2).

Figure 4.

Prognostic impact of the simplified Academic Research Consortium for High Bleeding Risk definition and subgroups. CI, confidence interval; HBR, high bleeding risk; HR, hazard ratio.

Table 2. Fine-Gray Model Analysis for Bleeding Events Considering the Competing Risk of All-Cause Death (Event n=146/2,437 Patients)

Factor	Subgroup	n	HR (95% CI)	P value	Interaction P value
Total	–	2,437	2.777 (1.464–5.270)	0.001	–
Age	≥70.0	1,248	3.657 (0.910–14.690)	0.068	0.510
	<70.0	1,189	2.149 (1.020–4.530)	0.044
Sex	Male	1,459	3.330 (1.458–7.605)	0.004	0.470
	Female	978	2.045 (0.738–5.663)	0.170
BMI	≥22.8	1,196	3.522 (1.287–9.637)	0.014	0.450
	<22.8	1,198	2.151 (0.937–4.933)	0.071
SBP	≥123.0	1,223	3.289 (1.333–8.118)	0.009	0.570
	<123.0	1,214	2.294 (0.929–5.666)	0.072
DBP	≥70.0	1,221	2.290 (0.990–5.293)	0.053	0.560
	<70.0	1,216	3.347 (1.230–9.106)	0.018
NYHA FC	I or II	2,309	2.997 (1.528–5.877)	0.001	0.210
	III or IV	128	0.863 (0.108–6.852)	0.890
CAD	Yes	781	3.065 (1.116–8.422)	0.030	0.870
	No	1,656	2.683 (1.174–6.128)	0.019
Hypertension	Yes	1,678	3.220 (1.419–7.308)	0.005	0.440
	No	759	1.941 (0.689–5.466)	0.210
Diabetes	Yes	961	2.467 (0.898–6.770)	0.080	0.770
	No	1,476	3.014 (1.314–6.914)	0.009
Dyslipidemia	Yes	1,707	2.037 (1.029–4.031)	0.041	0.160
	No	730	9.308 (1.276–67.920)	0.028
PCI	Yes	559	2.134 (0.758–6.008)	0.150	0.530
	No	1,878	3.220 (1.417–7.315)	0.005
RAS inhibitors	Yes	1,678	2.345 (1.143–4.813)	0.020	0.440
	No	759	4.520 (1.105–18.500)	0.036
β-blockers	Yes	1,752	2.647 (1.230–5.700)	0.013	0.810
	No	685	3.201 (1.006–10.190)	0.049
Loop diuretics	Yes	1,644	4.001 (1.281–12.500)	0.017	0.400
	No	793	2.244 (0.997–5.045)	0.051
CCBs	Yes	882	3.359 (1.057–10.670)	0.040	0.650
	No	1,555	2.452 (1.131–5.315)	0.023
Aspirin	Yes	1,061	1.918 (0.834–4.407)	0.130	0.350
	No	1,208	3.556 (1.296–9.754)	0.014
Thienopyridines	Yes	556	1.497 (0.583–3.845)	0.400	0.200
	No	1,498	3.464 (1.406–8.535)	0.006
BNP	≥246.8	1,118	6.543 (0.905–47.270)	0.063	0.410
	<246.8	1,118	2.673 (1.218–5.864)	0.014
Sodium	≥140.0	1,282	2.351 (1.016–5.438)	0.046	0.630
	<140.0	1,151	3.208 (1.177–8.747)	0.023
Albumin	≥3.8	1,194	2.620 (1.125–6.107)	0.026	0.680
	<3.8	1,026	3.678 (0.903–14.980)	0.069
LVEF	≥54.2	906	3.720 (1.162–11.900)	0.027	0.280
	<54.2	897	1.665 (0.719–3.853)	0.230

CI, confidence interval; HR, hazard ratio. Other abbreviations as in Table 1.

Discussion

To the best of our knowledge, this study is the first to apply the simplified ARC-HBR definition to patients with HF. The main findings of this study were that: (1) >80% of HF patients were at HBR; (2) the simplified ARC-HBR definition clearly identified HF patients who experienced bleeding events; and (3) there were no interactions between the simplified ARC-HBR definition and other important factors, such as past medical history of CAD, the use of antiplatelet agents, and HF severity.

The prevalence of HBR in patients with HF seemed to be substantially higher in this study (83.1%) than that in patients with CAD in previous studies.^16,21,22,41 Regarding each criterion of the simplified ARC-HBR definition, the proportion of HF patients who met the criterion for the use of oral anticoagulants (56.5%) exceeded that of CAD patients meeting this criterion in previous studies (8.2–12.7%).^16,21,22,41 This was because atrial fibrillation is more prevalent in patients with HF than in those with CAD: the prevalence of atrial fibrillation was 39.6% in patients with HF in the present study, compared with 14.5% in those with CAD in our previous study.⁴² In addition, HF itself is an important risk factor of bleeding events in patients with CAD.^7,18,43 Thus, a high proportion of patients with HF are at HBR.

The results of the present study revealed that the simplified ARC-HBR definition clearly stratified patients with HF according to bleeding risk, as reported previously in patients with CAD.^16–18 In the present study, HBR was determined by either 1 major criterion or 2 minor criteria in the simplified ARC-HBR definition, and bleeding events were well predicted.

In addition to the criteria included in the simplified ARC-HBR definition, there were differences several patient characteristics between the HBR and non-HBR groups. For example, lower body mass index in the HBR group was also an independent predictor of bleeding in events.¹¹ In obese patients, levels of circulating coagulation factors, such as factor VII, factor VIII, fibrinogen, and plasminogen activator inhibitor-1, are increased.^44,45 Conversely, patients with a lower body weight may experience potential overdosing of antithrombotic drugs.⁴⁶ Regarding antiplatelet agents in the present study, the 2 groups showed opposing trends: higher use of aspirin in the HBR group and higher use of thienopyridines in the non-HBR group. The choice of aspirin, not thienopyridines, could increase the risks of hemorrhagic stroke and gastrointestinal bleeding.^47,48 Although hypertension, one of the major risk factors of hemorrhagic stroke,⁴⁹ was more prevalent in the HBR group, blood pressure was lower in the HBR group. However, the clinical significance of blood pressure in patients with HF is quite different from that in the general population, the so-called “systolic blood pressure paradox”.^50,51 HF patients with lower blood pressure have advanced or end-stage HF.⁵² Other characteristics of the HBR group, such as higher age, the presence of more comorbidities, the greater use of loop diuretics, and higher B-type natriuretic peptide concentrations, also indicated more severe HF in the HBR group. As HF progresses, organ hypoperfusion and congestion worsen.^53–55 Low cardiac output and increased central venous pressure in patients with HF alter gastrointestinal function and cause gastrointestinal bleeding.^27,56 Moreover, once gastrointestinal bleeding occurs, it is associated with higher mortality in both patients with CAD and those with HF.^27,57–59 Conversely, thrombotic events, considered as a secondary endpoint in this study, were also more frequent in the HBR group. A similar trend has been reported in patients with CAD.¹⁶ Evidence of thrombotic risk in patients with HF has been accumulated but is not yet sufficient.^16,60,61 Clinicians should modulate antithrombotic therapy on the basis of balancing both thrombotic and hemorrhagic risk (i.e., simplified ARC-HBR definition) in each patient with HF. More evidence is needed in this regard.

The original ARC-HBR definition was developed to identify patients at HBR among CAD patients who undergo PCI in reference to clinical trials enrolling these populations.^8–15 The present study first applied the simplified ARC-HBR definition to patients with HF, of whom 781 (of 2,437; 32.0%) had a previous history of CAD. Of note, the predictive value of the simplified ARC-HBR definition was consistent regardless of the presence or absence of CAD. The prevalence of CAD as a leading etiology of HF has been increasing, reaching approximately 50% all over the world.^62,63 Moreover, the ARC-HBR definition predicts not only early (<1 year) bleeding events, but also late (1–4 years) and very late (>4 years) bleeding events.⁴¹ Thus, the simplified ARC-HBR definition is clinically useful in both CAD patients from the time of PCI to long-term follow-up and all HF patients.

Study Limitations

This study has several limitations. First, the study was performed in a single center and included a single race. The utility of the simplified ARC-HBR criteria should be validated in a larger HF population and in populations of different ethnicities. Second, this study did not intend to clarify how to treat patients with HF at HBR. To date, there has been only one clinical trial that proved the efficacy and safety of oral anticoagulant monotherapy in CAD patients with concomitant atrial fibrillation.^64,65 Antithrombotic therapy in patients who are at high risk of thrombotic events, such as those who have a contraindication to oral anticoagulants and those with a past history of stent thrombosis, needs further investigation to take advantage of the concept of HBR. Third, data on other bleeding risk factors, such as frailty, status of CAD (acute coronary syndrome or stable CAD), and adherence to medication, were not recorded in this study.^14,15

Conclusions

The simplified ARC-HBR definition predicts a high risk of bleeding events in patients with HF.

Acknowledgments

The authors thank Kumiko Watanabe, Yumi Yoshihisa, and Tomiko Miura for their technical assistance.

Sources of Funding

This study was supported, in part, by a Grant-in-Aid for Scientific Research (No. 20K07828) from the Japan Society for the Promotion of Science.

Disclosures

Y.T. is a member of Circulation Journal’s Editorial Team. The other authors have no conflicts of interest to declare.

IRB Information

The study protocol was approved by the Ethics Committee of Fukushima Medical University (Reference no. 823).

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-21-0686

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