(2022) 20:69
Matsui et al. BMC Medicine
https://doi.org/10.1186/s12916-022-02268-6

RESEARCH ARTICLE

Open Access

The impact of kidney function in patientson antithrombotic therapy: a post hoc subgroup
analysis focusing on recurrent bleeding eventsfrom the AFIRE trial
Kunihiko Matsui1*, Satoshi Yasuda2,3, Koichi Kaikita4,5, Masaharu Akao6, Junya Ako7, Tetsuya Matoba8,
Masato Nakamura9, Katsumi Miyauchi10, Nobuhisa Hagiwara11, Kazuo Kimura12, Atsushi Hirayama13 andHisao Ogawa14

Abstract
Background: The success of antithrombotic therapies is assessed based on thrombotic and bleeding events. Simultaneously assessing both kinds of events might be challenging, and recurrent bleeding events are often ignored. We
tried to confirm the effects of kidney function on outcome events in patients undergoing antithrombotic therapy.
Methods: As a post hoc subgroup analysis of the Atrial Fibrillation and Ischemic Events with Rivaroxaban in Patientswith Stable Coronary Artery Disease (AFIRE) trial, a randomized clinical trial with a median follow-up of 36 months,
patients were divided into high and low estimated glomerular filtration rate (eGFR) groups with a cutoff value of 50
mL/min. The cumulative incidence of bleeding and crude incidence of recurrent bleeding per 100 patient-years were
calculated. We used the Cox regression model with multiple failure time data for recurrent bleeding events.
Results: Among 2092 patients, 1386 (66.3%) showed high eGFR. The cumulative bleeding events per 100 patients at
1 year were 5.4 and 6.2 in the high and low eGFR groups, respectively. The difference continued to increase over time.
The hazard ratio for time to the first bleeding event in the high eGFR group was 0.875 (95% confidence interval 0.701–
1.090, p = .234) and that for the first composite event was 0.723 (95% confidence interval 0.603–0.867, p < .000). The
recurrent bleeding events per 100 person-years were 11.3 and 15.3 in the high and low eGFR groups, respectively,
with a rate ratio of 0.738 (95% confidence interval 0.615–0.886, p = .001). During the observation period, the risk of
bleeding changed with time. It peaked soon after the study enrollment in both groups. It decreased continuously inthe high eGFR group but remained high in the low eGFR group.
Conclusions: We reaffirmed that kidney function affects bleeding events in patients on antithrombotic therapy, considering recurrent events. Patients should have detailed discussions with physicians regarding the possible bleeding
events when continuing antithrombotic therapy, especially in patients with decreased kidney function.
Trial registration: UMIN Clinical Trials Registry, UMIN0​00016​612. ClinicalTrials.gov, NCT02​642419. Registered on 21
October 2015.

*Correspondence: kmatsui@kumamoto-u.ac.jp
1
Department of General Medicine and Primary Care, Kumamoto
University Hospital, 1‑1‑1 Honjo, Chuo‑ku, Kumamoto, Kumamoto
860‑8556, Japan
Full list of author information is available at the end of the article© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. The Creative Commons Public Domain Dedication waiver (http://​creat​iveco​
mmons.​org/​publi​cdoma​in/​zero/1.​0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Matsui et al. BMC Medicine(2022) 20:69

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Keywords: Kidney function, Estimated glomerular filtration rate, Creatinine clearance, Antithrombotic therapy, Nonvitamin K antagonistic oral anticoagulants, Recurrent event, Bleeding event, Thrombotic event, Atrial fibrillation

BackgroundA vast majority of antithrombotic therapies are available
in the market. They have been usually assessed based on
thrombotic events for efficacy and based on bleeding
events for safety. These events are opposite to each otherdue to the nature of antithrombotic drugs. Additionally,
the frequency and effect on prognosis are distinct. Fatal
bleeding cases are relatively rare, while cases of nuisancebleedings are common, and their effect on the prognosis
is generally limited [1]. In contrast, ischemic events are
relatively less frequent but have a significant effect on the
prognosis in the form of irreversible harm and often permanent disability.
Bleeding is common and can influence adherence totherapy or result in its disruption [2, 3]. Furthermore,even its severity might vary for each case, and the discontinuation of these drugs is known to increase therisk of thrombotic events [4]. Some patients who resume
antithrombotic therapy may also develop bleeding events
again. A consensus report recommended an individualized early resumption of antithrombotic therapy after
bleeding events [4].
In addition to expert opinion, those recommendations
were primarily based on previous studies. However, theirdata should be interpreted with caution due to concerns
regarding the study settings. For example, composite
endpoints are frequently used in clinical studies. However, they have been frequently criticized because eachoutcome event has a different effect on the overall health[5]. Furthermore, especially for antithrombotic therapy,
most previous studies included only the first event as
the endpoint [6], and the recurrent bleeding events were
often ignored.
When considering therapy for a patient, the individual
patient’s characteristics are also important. Althoughphysicians attempt to assess each patient’s risk, the heterogeneity of patient characteristics makes applying aunified quantitative assessment of the absolute benefits
and risks challenging [7]. For example, the prevalenceof atrial fibrillation (AF) increases with decreasing kidney function [8], and patients with AF and chronic kidney disease have a high risk of both thromboembolic andhemorrhagic complications [9]. Meanwhile, non-vitamin
K antagonistic oral anticoagulants (NOACs) are excreted
mainly by the kidneys, and therefore, kidney function
affects the outcome events in patients on NOACs [10].
In this study, we investigated the effects of kidney function on bleeding and thrombotic cardiovascular events,

including recurrent bleeding in patients on antithrombotic therapy with rivaroxaban who were enrolled in the
Atrial Fibrillation and Ischemic Events with Rivaroxaban
in Patients with Stable Coronary Artery Disease (AFIRE)
trial [11].

Methods
Study design and patients

This study was a post hoc subgroup analysis of the dataacquired from the AFIRE trial; the study design and primary outcomes of the AFIRE trial have been publishedpreviously [11]. Briefly, the AFIRE trial enrolled 2215
patients from 294 centers with stable coronary arterydisease with AF, and patients were randomly prescribedrivaroxaban monotherapy or rivaroxaban and antiplatelet
agent combination therapy. The primary finding was thatrivaroxaban monotherapy was non-inferior to combination therapy for a composite of stroke, systemic embolism, myocardial infarction, unstable angina requiringrevascularization, or death. Furthermore, monotherapy
was superior to combination therapy in terms of major
bleeding.
In addition to the primary analysis stated above, othersubgroup analyses have also been described previously[12]. The study protocol of the AFIRE trial itself wasapproved by the institutional review board (IRB) at eachparticipating institution, and the study was performed inline with the Declaration of Helsinki. Of the 294 study
centers, 61 did not have their own IRB, and they used
the approval from the central ethics review board. All
enrolled patients provided written informed consent.
Outcomes

In this report, we focused on any bleeding events andischemic cardiovascular events, including myocardial
infarction, unstable angina requiring revascularization,ischemic stroke, systemic embolism, and death due toany cause. We also included recurrent bleeding events
observed in each patient during the observation period.
However, in terms of ischemic cardiovascular events, weonly included the first event in each patient. We evaluated these outcomes according to the kidney function as
estimated glomerular filtration rate (eGFR), represented
by creatinine clearance (CrCl) as estimated using the
Cockcroft–Gault (CG) equation. We focused on patients
whose eGFR data were available at enrollment in the
AFIRE trial (n = 2092). The overall median follow-up was
36 months.

Matsui et al. BMC Medicine(2022) 20:69

Statistical analysis

All baseline characteristics were assessed at the timeof study enrollment. We divided the patients into highand low eGFR groups (eGFR ≥ 50 and < 50 mL/min,
respectively) at the time of study enrollment, the cutoff point was determined according to the criteria indicated in the Japanese drug package insert. Continuous
variables were compared using unpaired t-test, andthe categorical variables were compared using the chisquare test or Fisher’s exact test, as appropriate.
Cumulative incidence of bleeding over time was calculated for the two groups. In this analysis, we considered the first bleeding event and death from any cause
as competing risks [13–15]. Additionally, we performeda similar analysis with ischemic cardiovascular events,
and the first event between bleeding and the ischemic
cardiovascular event was considered for analysis.
We performed time-to-first event analysis forbleeding events as well as the first event analysis forcomposite endpoints of bleeding events, ischemic cardiovascular events, and death. We also adjusted for thedifferences in the patients’ baseline characteristics.
The crude incidence of recurrent bleeding per 100
patient-years of the follow-up period was calculatedby dividing the total number of bleeding events in each
eGFR group. The confidence intervals (CIs) were calculated using the quadratic approximation to the Poisson log-likelihood for the log-rate parameter [16]. Fromthese results, the consequent incidence ratios wereobtained [17]. Additionally, we used the negative binomial distribution model to modify an estimated rate
ratio by assessing the wide variations in patients withrespect to their risks of recurrent bleeding. This model
used the backward selection method to select the independent covariates, including personal characteristics.
Furthermore, we extended the composite of recurrent
events to include all bleeding and ischemic cardiovascular events as well as death, i.e., we counted death asan additional event. The consequent rate ratio was theeffect of eGFR on the composite of these events.
Since bleeding events might have occurred two or
more times in a patient during the study period, weused the Cox regression model with multiple failure
time data for bleeding events. We considered not only
the first bleeding event but also the subsequent events.
In this analysis, we assume that the hazard ratio maychange with time; however, time was measured fromstudy enrollment and was independent of the time thelast event occurred. For the measured time, we used
the time to each event from the time of the previous event using the conditional risk set model [18, 19]
and estimated the course with the backward selection
method to select independent covariates from the same

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variables in the previous negative binomial distribution
model.
Sensitivity analysis

In this study, we have employed eGFR, as estimated by
the CG equation, and used the cutoff point of 50 mL/
min. However, other methods such as the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI)
equation are commonly used to estimate GFR, and theiraccuracy is validated well in Western countries. Furthermore, the CG equation is said to overestimate GFR andexhibit large variability compared with the CKD-EPI
equation [20]. As a sensitivity analysis, we also employed
the CKD-EPI equation to estimate GFR, and two groups
were distinguished by the cutoff at 50 mL/min/1.73 ­m2
to compare the estimated risk of bleeding events during
the observation period. Moreover, we have also employedan additional cutoff point at 45 mL/min/1.73 m­ 2 and 60
2
mL/min/1.73 ­m as estimated by the CKD-EPI equation
since these points are used to define the CKD stage, andthey are the closest lower and higher points to 50 mL/
min/1.73 ­m2. We compared these results with the cutoff
points at 45 mL/min and 60 mL/min estimated by theCG equation. From these sensitivity analyses, we haveattempted to determine the robustness of our results, bydifferent GFR estimation methods and through different
cutoff points.
All tests were two-sided, and p values < .05 were considered to indicate a statistically significant difference. Allanalyses were performed using Stata SE v15.1 (Stata Corp
LLC, College Station, TX, USA).

Results
Patient background data

Among the 2092 patients, the high eGFR group included
1386 patients (66.3%; mean eGFR 74.3 ± 21.8 mL/min),whereas the low eGFR group included 706 patients(33.7%; mean eGFR 38.7 ± 7.9 mL/min) (Table 1). There
were several differences between the two groups. For
example, the high eGFR group had younger patients(71.4 ± 7.6 vs. 80.3 ± 5.8 years, respectively, p < .001),
more male patients (85.6% vs. 66.0%, respectively, p <.001), and higher hemoglobin level (13.9 ± 1.6 vs. 12.6 ±1.7 g%, respectively, p < .001) than the low eGFR group.
In contrast, the low eGFR group showed higher rates ofheart failure (30.3% vs. 47.6%, respectively, p < .001) andkidney dysfunction (0.3% vs. 1.8%, respectively, p < .001)
than the high eGFR group. Additionally, the CHADS2
score was higher in the low eGFR group than in the higheGFR group (median, 2.0 vs. 3.0, respectively, p < .001).
There were no differences in terms of the details of theprescribed drugs at study enrollment between the twoMatsui et al. BMC Medicine(2022) 20:69

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Table 1 Clinical characteristics of the patients at study enrollment
Estimated glomerular filtration rate (mL/min)≥ 50, N = 1386 (66.3%)

Total, N = 2092

P value< .001< 50, N = 706 (33.7%)

Age, years
Mean (SD)71.4 (7.6)

80.3 (5.8)

74.4 (8.2)

Median (Q1, Q3)

72.0 (67.0, 77.0)

81.0 (77.0, 84.0)

75.0 (69.0, 80.0)1186 (85.6%)

466 (66.0%)

1652 (79.0%)< .001< .001

Male
BMI, kg/m2
Mean (SD)

25.4 (3.6)

22.7 (3.2)

24.5 (3.7)

Median (Q1, Q3)

24.9 (23.1, 27.3)

22.5 (20.5, 24.7)

24.2 (22.0, 26.6)

BMI > 24.5 kg/m2

791 (57.1%)

207 (29.3%)

998 (47.7%)< .001.004

SBP
Mean (SD)

126.9 (15.4)

124.8 (16.9)

126.2 (15.9)

Median (Q1, Q3)

127.0 (117.0, 136.0)

125.0 (113.0, 135.0)

126.0 (116.0, 136.0)

SBP > 140 mmHg

308 (22.2%)

143 (20.3%)451 (21.6%).301< .001DBP
Mean (SD)72.8 (11.5)

68.5 (11.8)

71.4 (11.8)

Median (Q1, Q3)

72.0 (65.0, 80.0)

69.0 (60.0, 77.0)

70.0 (63.0, 80.0)

Estimated glomerular filtration rate
Mean (SD)74.3 (21.8)

38.7 (7.9)

62.3 (24.9)

Median (Q1, Q3)

68.6 (59.1, 82.8)

39.7 (33.7, 45.2)

58.9 (45.0, 74.6)< .001

Hemoglobin
Mean (SD)13.9 (1.6)

12.6 (1.7)

13.5 (1.7)

Median (Q1, Q3)

14.0 (13.0, 15.0)

12.6 (11.4, 13.8)

13.6 (12.4, 14.7)

207 (14.9%)

70 (9.9%)277 (13.2%)

.001
.206

Current smoker< .001

Type of AF
Permanent

456 (32.9%)

217 (30.7%)673 (32.2%)

Paroxysmal

728 (52.5%)366 (51.8%)1094 (52.3%)

Persistent202 (14.6%)

123 (17.4%)

325 (15.5%)1201 (86.7%)

593 (84.0%)

1794 (85.8%)

Comorbid conditions
Hypertension.100

Diabetes

625 (45.1%)

253 (35.8%)

878 (42.0%)< .001

Dyslipidemia

985 (71.1%)

465 (65.9%)1450 (69.3%).015

Angina857 (61.8%)457 (64.7%)

1314 (62.8%).195Heart failure

420 (30.3%)336 (47.6%)

756 (36.1%)< .001

Liver dysfunction

29 (2.1%)

8 (1.1%)37 (1.8%).116

Kidney dysfunction

4 (0.3%)13 (1.8%)

17 (0.8%)< .001

Bleeding dysfunction

16 (1.2%)

14 (2.0%)30 (1.4%).132< .001CHADS2 score0

5 (0.4%)0 (0.0%)5 (0.2%)

1376 (27.1%)

58 (8.2%)

434 (20.7%)

2497 (35.9%)240 (34.0%)

737 (35.2%)

3311 (22.4%)

218 (30.9%)529 (25.3%)

4135 (9.7%)

124 (17.6%)

259 (12.4%)

5

52 (3.8%)

51 (7.2%)

103 (4.9%)

6

10 (0.7%)

15 (2.1%)

25 (1.2%)

Mean (SD)

2.3 (1.1)

2.9 (1.2)

2.5 (1.2)

Median (Q1, Q3)

2.0 (1.0, 3.0)

3.0 (2.0, 4.0)

2.0 (2.0, 3.0)

< .001

Past history
Stroke178 (12.8%)

122 (17.3%)

300 (14.3%).006

Transient ischemic attack

34 (2.5%)

11 (1.6%)45 (2.2%).182

Matsui et al. BMC Medicine(2022) 20:69

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Table 1 (continued)
Estimated glomerular filtration rate (mL/min)≥ 50, N = 1386 (66.3%)

Total, N = 2092

P value< 50, N = 706 (33.7%)

Myocardial infarction

468 (33.8%)

262 (37.1%)

730 (34.9%).129

Aortic aneurism

45 (3.2%)

26 (3.7%)

71 (3.4%).603

Systemic thrombosis

7 (0.5%)3 (0.4%)10 (0.5%).802Deep vein thrombosis

9 (0.6%)4 (0.6%)

13 (0.6%).820

Pulmonary embolism

5 (0.4%)2 (0.3%)7 (0.3%).772

Peripheral artery disease64 (4.6%)

68 (9.6%)

132 (6.3%)< .001Other ischemic diseases

127 (9.2%)

57 (8.1%)

184 (8.8%).405

Other bleeding diseases

33 (2.4%)

22 (3.1%)

55 (2.6%).320

Intervention
PCI/CABG

1047 (75.5%)555 (78.6%)

1602 (76.6%).117

Others186 (13.4%)

74 (10.5%)260 (12.4%)

.054
.827

Drugs
Monotherapy

700 (50.5%)353 (50.0%)1053 (50.3%)

Combination therapy

686 (49.5%)353 (50.0%)1039 (49.7%)

Antiplatelet drug
Aspirin

506 (36.5%)

253 (35.8%)759 (36.3%).762

Clopidogrel

171 (12.3%)

99 (14.0%)

270 (12.9%).277

Prasugrel

14 (1.0%)

4 (0.6%)18 (0.9%).299

Ticlopidine

2 (0.1%)2 (0.3%)4 (0.2%).491

Anticoagulant drug
1379 (99.5%)

697 (98.7%)2076 (99.2%).056  10 ­
mg a

Rivaroxaban

357 (25.9%)

598 (84.7%)955 (46.0%)< .001  15 ­
mga

1022 (73.7%)

97 (13.7%)

1119 (53.5%)

Warfarin

3 (0.2%)0 (0.0%)3 (0.1%).216

Dabigatran

1 (0.1%)0 (0.0%)1 (0.0%).475

Apixaban

3 (0.2%)3 (0.4%)6 (0.3%).399

Edoxaban0 (0.0%)2 (0.3%)2 (0.1%)

.047
.073

Total number of antiplatelet and/or anticoagulant drugs
00 (0.0%)3 (0.4%)3 (0.1%)1682 (49.2%)

339 (48.0%)1021 (48.8%)

2511 (36.9%)

254 (36.0%)765 (36.6%)

3

189 (13.6%)

105 (14.9%)294 (14.1%)

4
Proton pump inhibitor

4 (0.3%)5 (0.7%)9 (0.4%)848 (61.2%)

435 (61.6%)

1283 (61.3%).848

AF Atrial fibrillation, BMI Body mass index, CABG Coronary artery bypass grafting, DBP Diastolic bleed pressure, PCI Percutaneous coronary intervention, SBP Systolic
bleed pressure, SD Standard deviationa

From 2074 patients with available data regarding the initial dosage of rivaroxabangroups, except for the initial dose of rivaroxaban basedon the study protocol.
Incidence of outcome events

During the study period, up to 10 episodes of bleeding were observed per patient, and there were no differences in the bleeding incidence between the twogroups (p = .546) (Table 2). There were 232 (16.7%)
patients with at least one bleeding episode in the higheGFR group compared with 131 (18.6%) patients in the
low eGFR group (p = .300). We observed up to 3 severe
bleeding episodes, as defined by the International Society on Thrombosis and Haemostasis criteria, and theincidence of severe bleeding between the two groups(p = .096) did not differ significantly. The incidence ofthe other outcome events between the two groups did
not differ significantly, except for systemic embolism
and death from any cause, which was higher in the low
eGFR group.

Matsui et al. BMC Medicine(2022) 20:69

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Table 2 Outcome events according to estimated glomerular filtration rate
Estimated glomerular filtration rate (mL/min)≥ 50, N = 1386

Total, N = 2092

P value.546< 50, N = 706

Patients no. of bleeding
01154 (83.3%)

575 (81.4%)

1729 (82.6%)

1171 (12.3%)

90 (12.7%)

261 (12.5%)

242 (3.0%)26 (3.7%)

68 (3.3%)

3

14 (1.0%)

9 (1.3%)23 (1.1%)

4

2 (0.1%)3 (0.4%)5 (0.2%)5

1 (0.1%)1 (0.1%)2 (0.1%)60 (0.0%)1 (0.1%)1 (0.0%)7

1 (0.1%)0 (0.0%)1 (0.0%)9

1 (0.1%)0 (0.0%)1 (0.0%)10

0 (0.0%)1 (0.1%)1 (0.0%)

Severe bleeding
01336 (96.4%)

669 (94.8%)

2005 (95.8%)

1

45 (3.2%)

30 (4.2%)

75 (3.6%)

2

4 (0.3%)7 (1.0%)11 (0.5%)

3

1 (0.1%)0 (0.0%)1 (0.0%).096

Hemorrhagic stroke

11 (0.8%)

6 (0.8%)17 (0.8%).892

Any bleeding

232 (16.7%)

131 (18.6%)

363 (17.4%).300

Ischemic stroke

26 (1.9%)21 (3.0%)47 (2.2%).109

Myocardial infarction

11 (0.8%)9 (1.3%)20 (1.0%).285

Unstable angina requiring revascularization

17 (1.2%)

12 (1.7%)

29 (1.4%).381

Systemic embolization

0 (0.0%)3 (0.4%)3 (0.1%).015

All-cause death42 (3.0%)69 (9.8%)111 (5.3%)< .001

Fig. 1 Estimated cumulative number of bleeding events per 100 patients. Time since enrollment (days)

Matsui et al. BMC Medicine(2022) 20:69

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Cumulative incidence of bleeding

Figure 1 illustrates the cumulative number of bleeding events per 100 patients in the two groups over time,
while death was considered a competing risk. By the endof the first year, the cumulative episodes of bleeding per
100 patients were 5.4 in the high eGFR group and 6.2 inthe low eGFR group (difference of 0.8 per 100 patients).
Beyond 1 year, the difference increased persistently (1.3
at 2 years and 1.7 at 3 years). Similarly, the cumulative
number of bleeding and ischemic cardiovascular events
per 100 patients was also calculated. By the end of thefirst year, the cumulative events per 100 patients were 6.4
in the high eGFR group and 7.8 in the low eGFR group(a difference of 1.4 per 100 patients). Beyond 1 year, thedifference increased persistently (2.3 at 2 years and 2.9 at
3 years).
Estimation of the effect of the difference in eGFR

Table 3 summarizes the estimated effect of eGFR difference on all event outcomes. The hazard ratio in the higheGFR group compared to that in the low eGFR group
was 0.875 [95% confidence interval (CI) 0.701–1.090,
p = .234] for the first bleeding event and 0.723 (95% CI
0.603–0.867, p = .000) for the first composite events after
adjustments. In the high and low eGFR groups, there
were 314 and 198 bleeding events over 2783.4 and 1294.7
person-years of follow-up, respectively. Therefore, thebleeding rate per 100 person-years was 11.3 in the higheGFR group and 15.3 in the low eGFR group, with a rate
ratio of 0.738 (95% CI 0.615–0.886, p = .0009) for recurrent bleeding as determined by the Poisson regression.
The negative binomial regression model yielded a rate
ratio of 0.860 (95% CI 0.680–1.088, p = .208) for the higheGFR group than that for the low eGFR group. Whenextending the composite outcomes other than bleeding events, the rate ratios remained constantly lower
than those of the previous models in the high eGFR and
low eGFR groups. The recurrent bleeding event analysis
revealed larger differences than the time-to-first event
analysis in the same composite outcome settings. Also,
we assured that the effect of the interaction term between
kidney function and the number of antithrombotic
agents for all outcome analyses above was not significant.
The curves for the risk estimate of bleeding events
over time stratified according to eGFR reflect the events’dynamics (Fig. 2). Visual inspection showed a change inthe risk of recurrent bleeding over the study period. It
peaked soon after the study enrollment in both groups
and decreased continuously in the high eGFR group;
however, it remained high in the low eGFR group. Theinteraction term between antithrombotic therapy (monotherapy vs. combination therapy) and kidney function
showed a significant effect in the models for the recurrent events (p = 0.042 for the model of recurrent bleeding events), but none for the first event model. In therecurrent event models, the therapy difference had alarge impact on the high eGFR group.
Sensitivity analysis

The time course of the estimated risk of bleeding events
for the two groups divided at 50 mL/min/1.73 m­ 2, as estimated by the CKD-EPI equation, showed similar resultswith that from the CG equation at the cutoff point of 50
mL/min (Fig. 2, Fig. S1).
At the cutoff point of 45 mL/min/1.73 ­m2, as estimatedby the CKD-EPI equation, both groups showed a peaksoon after the observation was commenced, and anotherhigher peak was observed in the low eGFR group,

Table 3 Comparison of the effect of estimated glomerular filtration rate difference among different endpoints
Effect due to estimated glomerular filtration rate,
50 mL/min

95% CI

P value

Hazard ratio for the first bleeding
Unadjusted

0.830

0.670–1.028.088

Adjusted

0.875

0.701–1.090.234

Hazard ratio for the first event among the composite of bleeding, ischemic cardiovascular event, and any cause of death
Unadjusted

0.677

0.567–0.809.000

Adjusted

0.723

0.603–0.867.000

Rate ratios for recurrent bleeding
Poisson (unadjusted)

0.738

0.615–0.886.001

Negative binomial (adjusted)

0.860

0.680–1.088.208

Rate ratios for composite of recurrent bleeding, the first ischemic cardiovascular event, and any cause of death
Poisson (unadjusted)

0.616

0.531–0.716.000

Negative binomial (adjusted)

0.742

0.615–0.896.002

CI Confidential intervalMatsui et al. BMC Medicine(2022) 20:69

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Fig. 2 Estimated risk of bleeding events, Cockcroft–Gault equation at 50 mL/min. Time since enrollment (days)

afterward. On the other hand, the risk of the high eGFR
group decreased continuously. The low eGFR group
had a higher bleeding risk than the high-eGFR groupthroughout the observation period. The shapes of thesecurves were similar to those estimated by the CG equation at the cutoff point of 45 mL/min (Figs. S2 and S3). At
the cutoff point of 60 mL/min/1.73 m­ 2, as estimated by
the CKD-EPI equation, the estimated bleeding risk had
decreased gradually, and increased afterwards in the low
eGFR group, while it had decreased from the peak in thehigh eGFR group. Although the high eGFR group showeda peak of bleeding risk following enrollment, the shapesof the curves were similar between the different estimation methods for GFR (Figs. S4 and S5). Between the different estimation methods for GFR at the same cutoff
point, a similar time course of the bleeding risk was also
observed.

Discussion
In this study, we demonstrated the effects of kidney function, represented by eGFR, on the outcome events inpatients from the AFIRE trial with stable coronary arterydisease who were on antithrombotic therapy. We foundthat eGFR < 50 mL/min was related to a high outcome
incidence, and the bleeding risk remained high over timein these patients.
Our findings are consistent with and extended theresults of previous studies. Adverse medication-related
outcomes in the studies for chronic kidney disease
patients could be classified as those leading to kidneydamage directly and other metabolic complications suchas hyperkalemia and bleeding events [21]. In patients
suffering from chronic kidney disease and receiving oral
anticoagulant therapy, the reduction in thrombotic risk
outweighs the bleeding risk in most cases and should beconsidered based on the balance between the benefits
and risks [22]. For each type of thrombotic and bleeding event, composite endpoints are frequently used todemonstrate the summarized effect of the intervention.
Although there are advantages to using composite outcomes, such as statistical power, we should also highlighttheir rationale. Composite outcomes are based on theassumption that a similar direction of the effect of theintervention will occur for each component of an aggregate outcome [5]. However, composite outcomes may
include heterogeneous outcome endpoints with different magnitudes or directions as well as clinical impacts.
In some studies, the newly proposed composite variables, such as net clinical benefits and net adverse clinicalevents, have also been used [23, 24]. Those variables can
include both bleeding and thrombotic events, and they
can be easily quantified as the summarized measures.
Combining endpoints with different types of endpoints
and directions can be problematic because the clinical effect of each event varies along with its severity andfrequency. Therefore, we should examine the details of acomposite event in a clinical context. Weighting methods
have also been suggested to overcome these challenges[25, 26]. Each type of outcome event is assigned a specificweight based on the clinical effect on a patient. However,
the weight for a type of outcome might not be consistent
between patients and studies [27]. These findings suggest

Matsui et al. BMC Medicine(2022) 20:69

that although these new methods are promising, they arestill under development for clinical use [28].
The results of the present study can be interpreted
as not only an extension of the original AFIRE trial but
also as complementary to that study. Our present results
further explain the effect of kidney function. We also
performed the analysis for recurrent bleeding events.
Including recurrent events has been reported to resultin greater power and more accurate estimation of therisks [29]. We added the first thrombotic cardiovascular
events to the bleeding events to confirm the change indirection and magnitude. Although they should be interpreted with caution for the abovementioned composite
outcomes, the comparable and consistent results demonstrated the effects of kidney function on the outcome
events. Without including bleeding events, these results
would have underestimated the effects of kidney function
on the outcome events. The effect on the outcomes wasquite large, as evident from the difference of incidence
between the groups. It is expected that a bleeding event
could have influenced the decision to continue the drugs
and that discontinuation of drugs would consequently be
related to the thrombotic events. However, our findings
for the effect of kidney function on the outcomes would
be consistent.
In our study, the bleeding risk increased in both groups
after enrollment. However, the risk in the low eGFR
group was higher than that in the high eGFR group. Overtime, the bleeding risk in both groups decreased, but thatin the low eGFR group remained high during this study
period. It has been reported that the risk of bleeding inpatients on anticoagulant therapy peaks immediately
after the initiation of therapy [30]. However, the resultsfrom the said study might be biased due to the enrollment of prevalent drug users, in addition to the disease
severity [31]. In our study, both groups had similar drug
usage in addition to the other background characteristics at enrollment, which would have had little effect on
our results. Therefore, we should consider resuming oral
anticoagulants soon after bleeding events, especially inpatients with preserved eGFR. In contrast, detailed discussions and considerations between patients and physicians are necessary, especially in patients with decreased
eGFR. These findings are consistent with and extend
expert consensus report, which recommended resuming
oral antithrombotic therapy in all situations with clear
indications, even in cases of major bleeding, as long as it
is not a life-threatening intracranial or extracranial bleeding [4]. Our findings could contribute to facilitating communication between physicians and patients not only toconsider the patient’s preferences but also to convey thephysicians’ recommendations to each other. For such
points, a recent clinical guideline recommended shared

Page 9 of 11

decision-making to explore patients’ values, goals, andpreferences [32]. In contrast, all clinical guidelines forpatients with decreased kidney function suggest dose
adjustments for drugs including anticoagulants [33, 34].
Most of them have not suggested resuming anticoagulants after bleeding events. A recent expert consensus
report recommended the resumption of anticoagulation
after bleeding events [35]. Considering this lack of coherence between recommendations, our study results might
be considered as offering significant evidence and providing a direction for future studies on chronic kidney disease patients with anticoagulants.
This study has several limitations. First, we used different analytic methods to assess the composite outcome events, which could include inherent problems.
However, our results demonstrated that the effectsof the differences in kidney function on the outcome
events were consistent with those from previous studies. Second, this study was performed as a subanalysisof the AFIRE trial. In the original study protocol, theresumption of antithrombotic therapy after a bleeding
event was expected to be as early as possible [12]. Furthermore, we did not include the dosages of the drugs
used in this analysis. We have assumed that the subsequent decision to continue or discontinue the drugs
after bleeding episodes would be similar between thetwo groups. In each case, these decisions were left tothe physicians, which might not be consistent in all
patients. However, it is unrealistic to use a unified protocol following bleeding events in future trials. Third,
the study population included only Japanese patientswith stable coronary artery disease, limiting the generalizability of our findings only to Japanese patients.
However, most recent antithrombotic drugs called
NOACs have similar renal excretion; therefore, ourinferences might be consistent for different NOACs.
Fourth, we used CrCl to estimate GFR and the cutoff
point at 50 mL/min to succeed for the original AFIRE
study protocol. Using other measures to show thekidney function classification might have been more
relevant, such as CKD-EPI (Chronic Kidney Disease
Epidemiology Collaboration) equations and CKD staging of KDIGO (Kidney Disease Improving Global Outcomes). However, given the imprecision in measuresfor estimating kidney function, individualization of
the drug dosing based on each method is reasonable[33]. Moreover, in our sensitivity analysis, we have also
employed the CKD-EPI equation and different cutoff
points. Accordingly, we had confirmed the robustnessof our findings where different methods of estimatesultimately led to similar results. Finally, our present
study results were from prespecified subgroup analyses
in addition to being a set of post hoc analyses after aMatsui et al. BMC Medicine(2022) 20:69

randomized controlled clinical trial; it would be inevitable they are subject to inflated false-positive rates,from multiple testing [36]. Therefore, our results should
be interpretated with caution, and future researchwould be expected; despite this, our findings might notnecessarily change significantly.

Conclusions
In conclusion, our results reaffirmed that kidney function affects bleeding events in patients on antithrombotic therapy, considering recurrent events. We believethat patients on antithrombotic therapy, especiallythose with decreased kidney function, should receive
detailed explanations from their physicians regarding
possible bleeding events when continuing antithrombotic therapy.
Abbreviations
AF: Atrial fibrillation; AFIRE: Atrial Fibrillation and Ischemic Events with Rivaroxaban in Patients with Stable Coronary Artery Disease trial; CG: Cockcroft–
Gault; CI: Confidence interval; CKD-EPI: Chronic Kidney Disease Epidemiology
Collaboration; CrCl: Creatinine clearance; eGFR: Estimated glomerular filtration
rate; NOACs: Non-vitamin K antagonistic oral anticoagulants.

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​022-​02268-6.
Additional file 1: Figure S1. Estimated risk of bleeding events, CKD-EPI
equation at 50 ml/min/1.73m2. Time since enrollment (days).
Additional file 2: Figure S2. Estimated risk of bleeding events, CKD-EPI
equation at 45 ml/min/1.73m2. Time since enrollment (days).
Additional file 3: Figure S3. Estimated risk of bleeding events, CockcroftGault equation at 45 ml/min. Time since enrollment (days).
Additional file 4: Figure S4. Estimated risk of bleeding events, CKD-EPI
equation at 60 ml/min/1.73m2. Time since enrollment (days).
Additional file 5: Figure S5. Estimated risk of bleeding events, CockcroftGault equation at 60 ml/min. Time since enrollment (days).
Acknowledgements
We thank the AFIRE investigators, Mebix, Inc., and the Japan Cardiovascular
Research Foundation.
Authors’ contributions
KMa performed the statistical analyses and drafted the manuscript. SY and HO
contributed to the study conception, design, and data acquisition. All authors
contributed to the drafting and critical revision of the manuscript for important intellectual content. All authors have read and approved the final versionof the manuscript for publication.
Funding
This work was supported by the Japan Cardiovascular Research Foundation
based on a contract with Bayer Yakuhin, Ltd., who had no role in the design ofthe trial, collection or analysis of the data, interpretation of the trial results, or
writing of the manuscript.
Availability of data and materials
Data are available from the corresponding author upon reasonable request.

Page 10 of 11

Declarations
Ethics approval and consent to participate
The trial was conducted according to the Declaration of Helsinki andapproved by the institutional review board of the National Cerebral andCardiovascular Center, Japan, along with the institutional review boards of allparticipating institutions. All participants provided written informed consent
before enrollment.
Consent for publication
Obtained.
Competing interests
KMa has no relationships relevant to the contents of this paper to disclose.
SY reports grants from Takeda Pharmaceutical, Abbott, and Boston Scientificand personal fees from Daiichi-Sankyo and Bristol-Meyers. KKa reports grants
from Grants-in-Aid for Scientific Research (20K08451) from the Ministry of
Education, Culture, Sports, Science and Technology of Japan and grants andpersonal fees from Bayer Yakuhin and Daiichi Sankyo. MA reports grants fromthe Japan Agency for Medical Research and Development (AMED), personal
fees from Bristol-Myers Squibb and Nippon Boehringer Ingelheim, and grants
and personal fees from Bayer Yakuhin and Daiichi Sankyo. JA reports personal
fees from Bayer Yakuhin and Sanofi and grants and personal fees from Daiichi
Sankyo. TM reports grants from Japan Cardiovascular Research Foundationand personal fees from Nippon Boehringer Ingelheim, Daiichi Sankyo, Astra
Zeneca, and Bayer Yakuhin. MN reports grants and personal fees from Bayer
Yakuhin, Daiichi Sankyo, and Sanofi and personal fees from Bristol-Myers
Squibb and Nippon Boehringer Ingelheim. KMi reports personal fees from
Amgen Astellas BioPharma, Astellas Pharma, MSD, Bayer Yakuhin, Sanofi,
Takeda Pharmaceutical, Daiichi-Sankyo, Nippon Boehringer Ingelheim, andBristol-Myers Squibb. NH reports grants and personal fees from Bayer Yakuhin,
grants from Nippon Boehringer Ingelheim, and personal fees from BristolMyers Squibb. KKi reports grants from the Japan Cardiovascular Research
Foundation; grants and personal fees from Bayer Yakuhin, Daiichi Sankyo,
Sanofi, MSD, and AstraZeneca; and personal fees from Bristol-Myers Squibb
and Nippon Boehringer Ingelheim. AH reports grants and personal fees from
Boston Scientific Japan, Otsuka Pharmaceutical, Sanofi, Astellas Pharma,
Bristol-Myers Squibb, Daiichi Sankyo, and Bayer Yakuhin; grants from Fukuda
Denshi, Abbott Japan, Japan Lifeline, Takeda Pharmaceutical, and Sumitomo
Dainippon Pharma; and personal fees from Toa Eiyo, Nippon Boehringer
Ingelheim, Amgen Astellas BioPharma, and AstraZeneca. HO reports personal
fees from Towa Pharmaceutical, Bristol-Meyers Squibb, Pfizer, Toa Eiyo, Bayer
Yakuhin, and Novartis Pharma.
Author details
1
Department of General Medicine and Primary Care, Kumamoto University
Hospital, 1‑1‑1 Honjo, Chuo‑ku, Kumamoto, Kumamoto 860‑8556, Japan.
2
Department of Cardiovascular Medicine, Tohoku University Graduate Schoolof Medicine, 1‑1 Seiryo‑machi, Aoba‑ku, Sendai, Miyagi 980‑8574, Japan.
3
National Cerebral and Cardiovascular Center, 6‑1 Kishibe Shimmachi, Suita,
Osaka 564‑8565, Japan. 4 Division of Cardiovascular Medicine and Nephrology,
Department of Internal Medicine, Faculty of Medicine, University of Miyazaki,
5200 Kihara, Kiyotake, Miyazaki 889‑1692, Japan. 5 Department of Cardiovascular Medicine, Graduate School of Medical Sciences, Kumamoto University,
1‑1‑1 Honjo, Chuo‑ku, Kumamoto, Kumamoto 860‑8556, Japan. 6 Department of Cardiology, National Hospital Organization Kyoto Medical Center,
1‑1 Mukaihata‑cho, Fukakusa, Fushimi‑ku, Kyoto, Kyoto 612‑8555, Japan.
7
Department of Cardiovascular Medicine, Kitasato University School of Medicine, 1‑15‑1 Kitasato, Minami‑ku, Sagamihara, Kanagawa 252‑0373, Japan.
8Department of Cardiovascular Medicine, Faculty of Medical Sciences, Kyushu
University, 3‑1‑1 Maidashi, Fukuoka, Fukuoka 812‑8582, Japan. 9 Divisionof Cardiovascular Medicine, Toho University Ohashi Medical Center, 2‑22‑36,
Ohashi, Meguro‑ku, Tokyo 153‑8515, Japan. 10 Department of Cardiovascular
Medicine, Juntendo Tokyo Koto Geriatric Medical Center, 3‑3‑20 Shinsuna,
Koto‑ku, Tokyo 136‑0075, Japan. 11 Department of Cardiology, Tokyo Women’s
Medical University, 8‑1, Kawada‑cho, Shinjuku‑ku, Tokyo 162‑8666, Japan.
12
Cardiovascular Center, Yokohama City University Medical Center, 4‑57,
Urafune‑cho, Minami‑ku, Yokohama, Kanagawa 232‑0024, Japan. 13 Department of Cardiology Osaka Police Hospital, 10‑31 Kitayama‑cho, Tennouji‑ku,

Matsui et al. BMC Medicine(2022) 20:69

Page 11 of 11

Osaka City, Osaka 543‑0035, Japan. 14 Kumamoto University, 2‑39‑1 Kurokami,
Chuo‑ku, Kumamoto 860‑8555, Japan.
Received: 4 August 2021 Accepted: 19 January 2022

21.
22.
23.

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