Atrial fibrillation (AF) is the most common sustained arrhythmia worldwide, currently affecting >5 million patients in the United States1,2; and the incidence continues to rise.3,4 The burden of AF is even higher among patients with CKD in whom the incidence has been estimated to be two- to three-fold higher compared with the general population, affecting <25% of all patients with CKD.5678–9 Among patients with CKD, AF is associated with a higher risk of cardiovascular events and adverse kidney outcomes.101112131415–16
Studies have shown that, despite meeting clinical indications, patients with CKD are commonly underprescribed recommended cardiovascular medications, or offered fewer cardiovascular procedures or devices.17 As such, a recent European Heart Rhythm Association position paper highlighted the need for rigorous data specifically on the management of AF in patients with CKD.18 However, despite the high burden and adverse consequences of AF, there are limited data on the use of AF therapies (e.g., rate control agents, antiarrhythmic medications, anticoagulation, and AF-related procedures) in patients with CKD. The few published studies examined select CKD populations and yielded conflicting results. A study from Germany reported lower rates of antiarrhythmic agents and procedures in patients with CKD and AF, but no differences in β blocker or anticoagulation use.19 In a study of commercial billing claims of US patients undergoing catheter ablation for AF, investigators reported lower use of antiarrhythmic drugs, but higher use of rate control agents in patients with CKD.20 Further comprehensive data on the use of AF therapies across the spectrum of CKD severity may identify opportunities for new evidence generation to optimize AF management.
To address this knowledge gap, we examined a large “real-world” contemporary population of adults with incident AF to study the association of CKD with receipt of AF medications (rate control agents, antiarrhythmic medications, anticoagulants) and AF-related procedures (catheter ablation, cardioversion, pacemakers) within 1 year of newly diagnosed AF. We hypothesized that patients with CKD would be less likely to receive AF therapies, even after accounting for possible patient-level confounders.
Methods
Source Population
The source population is derived from Kaiser Permanente Northern California (KPNC) and Southern California (KPSC), two integrated health care delivery systems that provide comprehensive care for >9 million members across California. KPNC and KPSC represent the two largest sites in the Cardiovascular Research Network, a research consortium of United States health systems established to generate large-scale, “real world” evidence to guide care.21 Each site has a Virtual Data Warehouse (VDW), which served as the primary data source for subject identification and characterization.22 The VDW is a distributed, standardized data resource comprised of electronic datasets at each site that are populated with linked demographic, administrative, outpatient pharmacy, laboratory test results, and health care utilization (ambulatory visits and network and non-network hospitalizations with diagnoses and procedures) data.
Institutional review boards at participating sites approved the study, and a waiver of consent was obtained due to the retrospective data–only nature of the study.
Study Sample
We included all eligible adults with presumed incident AF from 2010 to 2017, excluding patients with a diagnosis of AF before 2010. Patients with incident AF were identified from International Classification of Diseases, Ninth and Tenth Edition (ICD-9 and ICD-10) codes from inpatient stays, emergency department encounters, and outpatient visits. AF was determined on the basis of primary diagnosis of AF (ICD-9 427.31 and ICD-10 I48.0, I48.1, I48.2, and I48.91 in any position). This approach has been validated in KPNC and KPSC by review of electrocardiograms and medical records and shown to have a positive predictive value of 96%. The date of first AF diagnosis during the study period was assigned as the index date.
For the present analysis, we excluded those with missing demographic information, age <21 years, <12 months of continuous membership/drug benefit before cohort entry, no follow-up data available after AF diagnosis (1.6%), missing serum creatinine at cohort entry, or missing ≥2 eGFR measures separated by ≥90 days in the 2 years before cohort entry, or ESKD defined as receipt of maintenance dialysis or prior kidney transplant (Figure 1).
;)
Figure 1.: Consort diagram for study cohort.
CKD Status and Severity
Kidney function was assessed by eGFR level at cohort entry (e.g., diagnosis of incident AF) using the Chronic Kidney Disease Epidemiology Collaboration formula (calculated using serum creatinine, age, sex, and race)23 and outpatient, nonemergency department serum creatinine measures performed as part of clinical care. To ensure stable eGFR classification, ≥2 eGFR measures separated by >90 days in the 2 years before cohort entry were required. CKD was defined using the widely accepted eGFR threshold of 60 ml/min per 1.73 m2, with additional CKD categorization of 45–59, 30–44, 15–29, and <15 ml/min per 1.73 m2.24,25 Baseline eGFR was defined using the most recent result before or on the index date.
AF Therapies
For the present analyses, we studied receipt of AF medication and procedures that were initiated in the 1 year after incident AF diagnosis to focus on presumed initial management. We identified receipt of the following AF medications through the VDW Pharmacy Database: rate control agents (oral β blockers, calcium channel blockers, and digoxin); antiarrhythmic agents (i.e., amiodarone, sotalol, disopyramide, flecainide, propafenone, dronedrarone, dofetilide); nonaspirin antiplatelet agents (clopidogrel, prasugrel, ticagrelor), and anticoagulation (warfarin and direct oral anticoagulants [DOACs]). We also identified targeted AF procedures (i.e., catheter ablation, AV node ablation with pacemaker implantation, and cardioversion) using ICD and Current Procedural Terminology codes.
Covariates
We ascertained information on demographic characteristics from electronic health records and comorbidity on the basis of previously validated diagnoses/procedures using ICD-9 and ICD-10 codes, laboratory results, prescribed medications, and registries in participating sites’ VDW and electronic health records.25 Potential confounders included demographic characteristics (age and self-reported sex and race/ethnicity), tobacco use, comorbid conditions in the prior 4 years on the basis of relevant diagnosis and procedure codes (prior acute coronary syndrome, coronary revascularization, ischemic stroke/transient ischemic attack, atrial fibrillation, ventricular fibrillation/tachycardia, mitral or aortic valvular heart disease, obstructive sleep apnea, peripheral artery disease, dyslipidemia, hypertension, diabetes, dementia, depression, lung disease, liver disease, and cancer), ambulatory clinic measures of blood pressure, body mass index, LDL and HDL cholesterol, hemoglobin, and urine albumin-creatinine ratio. Baseline selected medication use (angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers, calcium channel blockers, digoxin, β blockers, diuretics, statins, other lipid-lowering agents) was on the basis of having an active prescription within 120 days before index date using data from pharmacy dispensing databases.
We also calculated the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Stroke Risk Score, which was developed and validated within KPNC and KPSC in a similar cohort of patients with incident AF and is more accurate than other stroke risk scores.26 The ATRIA Stroke Risk Score uses the following variables and laboratory values: age, prior stroke, sex, diabetes mellitus, chronic heart failure, hypertension, proteinuria, and eGFR<45 ml/min per 1.73 m2, or ESKD.26 In addition, the HAS-BLED score,27 which predicts 1-year risk of major bleeding among patients with AF, was also calculated and includes the following variables: hypertension; kidney disease (dialysis, transplant, serum creatinine >2.26 mg/dl), liver disease; stroke history, prior major bleeding; medication usage predisposing to bleeding (aspirin, clopidogrel, non-steroidal anti-inflammatory drugs), and alcohol use.
Statistical Methods
All analyses were conducted using SAS, version 9.3 (Cary, N.C.). Characteristics of patients with incident AF with and without CKD were compared using ANOVA or relevant nonparametric test for continuous variables and chi-squared tests for categorical variables. Given the large sample size, standardized differences in each variable were compared between groups by computing a difference in means of the two groups divided by the pooled SD, with D values >0.10 considered potentially meaningful.
We calculated proportions of use of AF therapies in the 1 year after AF diagnosis in patients with and without CKD who were not using the AF therapy of interest at index date. Cox proportional hazard models with Fine–Gray subdistribution hazards were used to examine the association of baseline CKD severity (eGFR 45–59, 30–44, 15–29, and <15 ml/min per 1.73 m2 versus no CKD, defined as eGFR >60 ml/min per 1.73 m2) with use of each AF therapy after accounting for the potential effect of the competing risk of death with worse kidney function. Patients were censored at health plan disenrollment or end of follow-up at 1 year after AF diagnosis for nonmortal outcomes. We performed a series of nested models that sequentially adjusted for categories of potential explanatory variables: Model 1 (age, sex, race, ethnicity, education, and income), Model 2 (Model 1 + history of stroke, transient ischemic attack, peripheral vascular disease, heart failure, myocardial infarction, coronary artery bypass grafting, percutaneous coronary intervention, valvular disease, hypertension, diabetes, systolic BP, body mass index, urine albumin-creatinine ratio), and Model 3 (Model 2 + baseline use of cardiovascular medications [warfarin, DOACs, antiplatelets, antiarrhythmics, angiotensin-converting enzyme/angiotensin II receptor blockers, β blockers, calcium channel blockers, digoxin, diuretics, statins, and other lipid-lowering medications]). We performed a test for linear trend across categories of eGFR.
In a sensitivity analysis, we repeated our models but included ESKD as a censoring event, with ESKD defined as initiation of maintenance dialysis or receipt of a kidney transplant.
Results
Cohort Assembly and Patient Characteristics
Among 115,564 eligible adults with incident AF, 39,490 (34%) had baseline CKD. Overall, those with baseline CKD were substantially older, were more likely to be women, more likely to have history of heart failure, dyslipidemia, and diabetes compared with those without CKD (Table 1). Those with CKD were also more likely to be taking calcium channel blocks, diuretics, and statins. Patients with CKD were also at higher predicted risk for stroke using the ATRIA risk score (Table 1).
Table 1. -
Baseline characteristics of adults with presumed incident
atrial fibrillation, stratified by CKD status (
n=115,564)
| Characteristic |
Overall |
Baseline CKD |
No Baseline CKD |
|
| (n=115,564) |
(n=39,490) |
(n=76,074) |
D-value |
| Mean (SD) age, yr |
74.8 (11.4) |
80.3 (9.2) |
71.9 (11.5) |
0.80 |
| Age group, yr, n (%) |
|
|
|
0.34 |
| <45 |
1354 (1.2) |
50 (0.1) |
1304 (1.7) |
|
| 45–54 |
4316 (3.7) |
288 (0.7) |
4028 (5.3) |
|
| 55–64 |
14,644 (12.7) |
1790 (4.5) |
12,854 (16.9) |
|
| 65–74 |
32,478 (28.1) |
7701 (19.5) |
24,777 (32.6) |
|
| 75–84 |
38,579 (33.4) |
15,591 (39.5) |
22,988 (30.2) |
|
| ≥85 |
24,193 (20.9) |
14,070 (35.6) |
10,123 (13.3) |
|
| Women, n (%) |
53,049 (45.9) |
20,135 (51.0) |
32,914 (43.3) |
0.07 |
| Race, n (%) |
|
|
|
0.05 |
| White/European |
89,244 (77.2) |
31,086 (78.7) |
58,158 (76.4) |
|
| Black |
9021 (7.8) |
3074 (7.8) |
5947 (7.8) |
|
| Asian/Pacific Islander |
11,183 (9.7) |
3763 (9.5) |
7420 (9.8) |
|
| Other |
1166 (1.0) |
390 (1.0) |
776 (1.0) |
|
| Unknown |
4950 (4.3) |
1177 (3.0) |
3773 (5.0) |
|
| Hispanic ethnicity, n (%) |
16,815 (14.6) |
5470 (13.9) |
11,345 (14.9) |
0.01 |
| Low educational attainment, n (%) |
20,964 (18.1) |
7285 (18.4) |
13,679 (18.0) |
0.01 |
| Annual household income <$50,000, n (%) |
28,149 (24.4) |
10,056 (25.5) |
18,093 (23.8) |
0.02 |
| Medical history, n (%) |
| Ischemic stroke or transient ischemic attack |
8963 (7.8) |
3910 (9.9) |
5053 (6.6) |
0.06 |
| Ischemic stroke |
5208 (4.5) |
2284 (5.8) |
2924 (3.8) |
0.04 |
| Transient ischemic attack |
4767 (4.1) |
2083 (5.3) |
2684 (3.5) |
0.04 |
| Acute myocardial infarction |
6980 (6.0) |
3192 (8.1) |
3788 (5.0) |
0.06 |
| Coronary artery bypass surgery |
3811 (3.3) |
1369 (3.5) |
2442 (3.2) |
0.01 |
| Percutaneous coronary intervention |
13,707 (11.9) |
5551 (14.1) |
8156 (10.7) |
0.05 |
| Chronic heart failure |
23,324 (20.2) |
12,180 (30.8) |
11,144 (14.6) |
0.19 |
| Mitral and/or aortic valvular disease |
16,803 (14.5) |
7013 (17.8) |
9790 (12.9) |
0.07 |
| Peripheral artery disease |
9475 (8.2) |
4139 (10.5) |
5336 (7.0) |
0.06 |
| Hypertension |
94,866 (82.1) |
36,618 (92.7) |
58,248 (76.6) |
0.20 |
| Dyslipidemia |
94,237 (81.5) |
34,699 (87.9) |
59,538 (78.3) |
0.12 |
| Diabetes mellitus |
42,102 (36.4) |
17,615 (44.6) |
24,487 (32.2) |
0.12 |
| Current or former smoker |
61,402 (53.1) |
21,511 (54.5) |
39,891 (52.4) |
0.02 |
| Chronic lung disease |
40,787 (35.3) |
14,693 (37.2) |
26,094 (34.3) |
0.03 |
| Obstructive sleep apnea |
15,766 (13.6) |
6314 (16.0) |
9452 (12.4) |
0.05 |
| Cancer |
22,867 (19.8) |
8511 (21.6) |
14,356 (18.9) |
0.03 |
| Chronic liver disease |
6355 (5.5) |
1720 (4.4) |
4635 (6.1) |
0.04 |
| Diagnosed dementia |
7720 (6.7) |
3787 (9.6) |
3933 (5.2) |
0.08 |
| Diagnosed depression |
23,209 (20.1) |
8428 (21.3) |
14,781 (19.4) |
0.02 |
| Venous thromboembolism |
5632 (4.9) |
2258 (5.7) |
3374 (4.4) |
0.03 |
| Baseline medication use, n (%) |
| Warfarin |
13,002 (11.3) |
4880 (12.4) |
8122 (10.7) |
0.03 |
| Direct oral anticoagulant |
1269 (1.1) |
315 (0.8) |
954 (1.3) |
0.02 |
| Antiplatelet agent |
10,515 (9.1) |
4797 (12.1) |
5718 (7.5) |
0.08 |
| Antiarrhythmic agent |
5513 (4.8) |
1749 (4.4) |
3764 (4.9) |
0.01 |
| Angiotensin-converting enzyme inhibitor |
45,141 (39.1) |
16,313 (41.3) |
28,828 (37.9) |
0.03 |
| Angiotensin II receptor blocker |
21,531 (18.6) |
8727 (22.1) |
12,804 (16.8) |
0.06 |
|
β blocker |
59,708 (51.7) |
24,020 (60.8) |
35,688 (46.9) |
0.13 |
| Calcium channel blocker |
34,454 (29.8) |
15,190 (38.5) |
19,264 (25.3) |
0.14 |
| Digoxin |
2805 (2.4) |
1049 (2.7) |
1756 (2.3) |
0.01 |
| Diuretic |
53,666 (46.4) |
23,057 (58.4) |
30,609 (40.2) |
0.17 |
| Aldosterone receptor antagonist |
3590 (3.1) |
1681 (4.3) |
1909 (2.5) |
0.05 |
| Statin |
70,581 (61.1) |
27,436 (69.5) |
43,145 (56.7) |
0.12 |
| Other lipid-lowering drug |
4770 (4.1) |
2039 (5.2) |
2731 (3.6) |
0.04 |
| Baseline vital signs |
| Systolic blood pressure, mmHg |
|
|
|
0.05 |
| ≥180 |
572 (0.5) |
280 (0.7) |
292 (0.4) |
|
| 160–179 |
2486 (2.2) |
1076 (2.7) |
1410 (1.9) |
|
| 140–159 |
13,770 (11.9) |
5233 (13.3) |
8537 (11.2) |
|
| 130–139 |
29,079 (25.2) |
9695 (24.6) |
19,384 (25.5) |
|
| 121–129 |
24,245 (21.0) |
7844 (19.9) |
16,401 (21.6) |
|
| <121 |
45,286 (39.2) |
15,318 (38.8) |
29,968 (39.4) |
|
| Missing |
126 (0.1) |
44 (0.1) |
82 (0.1) |
|
| Diastolic blood pressure, mmHg |
|
|
|
0.07 |
| ≥110 |
192 (0.2) |
57 (0.1) |
135 (0.2) |
|
| 100–109 |
683 (0.6) |
162 (0.4) |
521 (0.7) |
|
| 90–99 |
2989 (2.6) |
761 (1.9) |
2228 (2.9) |
|
| 85–89 |
5918 (5.1) |
1484 (3.8) |
4434 (5.8) |
|
| 81–84 |
7477 (6.5) |
2004 (5.1) |
5473 (7.2) |
|
| ≤80 |
98,179 (85.0) |
34,978 (88.6) |
63,201 (83.1) |
|
| Missing |
126 (0.1) |
44 (0.1) |
82 (0.1) |
|
| Body mass index, kg/m2
|
|
|
|
0.04 |
| <18.5 |
2722 (2.4) |
966 (2.4) |
1756 (2.3) |
|
| 18.5–25.0 |
31,705 (27.4) |
11,470 (29.0) |
20,235 (26.6) |
|
| 25.0–25.9 |
38,353 (33.2) |
13,257 (33.6) |
25,096 (33.0) |
|
| 30.0–39.9 |
33,839 (29.3) |
11,110 (28.1) |
22,729 (29.9) |
|
| ≥40 |
8518 (7.4) |
2533 (6.4) |
5985 (7.9) |
|
| Unknown |
427 (0.4) |
154 (0.4) |
273 (0.4) |
|
| Baseline laboratory values, n (%) |
| eGFR, ml/min per 1.73m2
|
|
|
|
0.90 |
| ≥90 |
14,675 (12.7) |
0 (0.0) |
14,675 (19.3) |
|
| 60–89 |
54,954 (47.6) |
0 (0.0) |
54,954 (72.2) |
|
| 45–59 |
24,950 (21.6) |
19,385 (49.1) |
5565 (7.3) |
|
| 30–44 |
14,398 (12.5) |
13,723 (34.8) |
675 (0.9) |
|
| 15–29 |
5376 (4.7) |
5221 (13.2) |
155 (0.2) |
|
| <15 |
1211 (1.0) |
1161 (2.9) |
50 (0.1) |
|
| Urine albumin-to-creatinine ratio, µg/mg |
|
|
|
0.25 |
| ≤30 |
38,949 (33.7) |
11,717 (29.7) |
27,232 (35.8) |
|
| 30–100 |
34,183 (29.6) |
12,974 (32.9) |
21,209 (27.9) |
|
| 100–300 |
5988 (5.2) |
3430 (8.7) |
2558 (3.4) |
|
| >300 |
5996 (5.2) |
4267 (10.8) |
1729 (2.3) |
|
| Missing |
30,448 (26.3) |
7102 (18.0) |
23,346 (30.7) |
|
| Hemoglobin, g/dl |
|
|
|
0.24 |
| ≥13.0 |
63,333 (54.8) |
16,147 (40.9) |
47,186 (62.0) |
|
| 12.0–12.9 |
19,953 (17.3) |
7965 (20.2) |
11,988 (15.8) |
|
| 11.0–11.9 |
12,896 (11.2) |
6395 (16.2) |
6501 (8.5) |
|
| 10.0–10.9 |
7844 (6.8) |
4257 (10.8) |
3587 (4.7) |
|
| 9.0–9.9 |
4169 (3.6) |
2302 (5.8) |
1867 (2.5) |
|
| <9.0 |
2460 (2.1) |
1307 (3.3) |
1153 (1.5) |
|
| Missing |
4909 (4.2) |
1117 (2.8) |
3792 (5.0) |
|
| Potassium, meq/L |
|
|
|
0.24 |
| Mean (SD) |
4.3 (0.5) |
4.4 (0.5) |
4.3 (0.4) |
|
| Median (interquartile range) |
4.3 (4.0–4.6) |
4.4 (4.1–4.7) |
4.3 (4.0–4.5) |
|
| Missing, n (%) |
1724 (1.5) |
188 (0.5) |
1536 (2.0) |
|
| High density lipoprotein, mg/dl |
|
|
|
0.08 |
| ≥60 |
26,631 (23.0) |
7942 (20.1) |
18,689 (24.6) |
|
| 50–59 |
24,689 (21.4) |
8037 (20.4) |
16,652 (21.9) |
|
| 40–59 |
32,121 (27.8) |
11,008 (27.9) |
21,113 (27.8) |
|
| 35–49 |
13,350 (11.6) |
5028 (12.7) |
8322 (10.9) |
|
| <35 |
11,949 (10.3) |
5042 (12.8) |
6907 (9.1) |
|
| Missing |
6824 (5.9) |
2433 (6.2) |
4391 (5.8) |
|
| Low density lipoprotein, mg/dl |
|
|
|
0.12 |
| ≥200 |
606 (0.5) |
220 (0.6) |
386 (0.5) |
|
| 160–199 |
3213 (2.8) |
887 (2.2) |
2326 (3.1) |
|
| 130–159 |
10,117 (8.8) |
2605 (6.6) |
7512 (9.9) |
|
| 100–129 |
24,350 (21.1) |
6635 (16.8) |
17,715 (23.3) |
|
| 70–99 |
42,080 (36.4) |
15,134 (38.3) |
26,946 (35.4) |
|
| <70 |
27,253 (23.6) |
11,251 (28.5) |
16,002 (21.0) |
|
| Missing |
7945 (6.9) |
2758 (7.0) |
5187 (6.8) |
|
| ATRIA stroke risk |
|
|
|
0.40 |
| Low (0–5) |
39,975 (34.6) |
4296 (10.9) |
35,679 (46.9) |
|
| Medium (6) |
13,879 (12.0) |
3399 (8.6) |
10,480 (13.8) |
|
| High (7–15) |
61,710 (53.4) |
31,795 (80.5) |
29,915 (39.3) |
|
| HAS-BLED bleeding risk |
|
|
|
0.11 |
| Low (0–3) |
108,868 (94.2) |
35,760 (90.6) |
73,108 (96.1) |
|
| High (4–7) |
6696 (5.8) |
3730 (9.4) |
2966 (3.9) |
|
ATRIA, AnTicoagulation and Risk Factors in Atrial Fibrillation Study.
Use of AF Medications and Procedures by CKD Status
Overall, rate control agents and anticoagulation were the most common therapies initiated in this population of patients with incident AF, whereas initiation of antiarrhythmic agents or procedures were less common. The proportion of patients with CKD who initiated rate control agents, DOACs, antiarrhythmic agents, or underwent catheter ablations or cardioversion was lower compared with those without CKD (Figure 2 and Supplemental Table 1). Median time to initiation of any AF therapy was 3 days (interquartile range [IQR], 1–14) overall, and was similar in patients with (4, IQR 2–18 days) and without (3, IQR 1–13 days) CKD (Supplemental Table 2).
Figure 2.: Initiation of atrial fibrillation therapies within 1 year of diagnosis in those with or without CKD.
CKD Severity and Receipt of AF Medications and Procedures
In both unadjusted and multivariable models, compared with patients with eGFR >60 ml/min per 1.73 m2, there was a graded association between CKD severity and lower rates of receipt of AF therapies (Table 2). In adjusted models, patients with eGFR 30–44 (adjusted hazard ratio [aHR], 0.91; 95% confidence interval [95% CI], 0.88 to 0.93), 15–20 (aHR, 0.78; 95% CI, 0.75 to 0.82), and <15 ml/min per 1.73 m2 (aHR, 0.64, 95% CI, 0.58 to 0.70) had significantly lower receipt of any AF therapy compared with those with eGFR ≥60 ml/min per 1.73 m2. Specifically, patients with eGFR 15–29 ml/min per 1.73 m2 had lower adjusted receipt of rate control agents (aHR, 0.61; 95% CI, 0.56 to 0.67), warfarin (aHR, 0.89; 95% CI, 0.84 to 0.94), and DOACs (aHR, 0.23; 95% CI, 0.19 to 0.27) compared with patients with eGFR ≥60 ml/min per 1.73 m2. These associations were even stronger in patients with eGFR <15 ml/min per 1.73 m2 (Table 2 and Figure 3). A statistically significant trend was observed across CKD stages.
Figure 3.: Association of estimated glomerular filtration rate with receipt of atrial fibrillation therapies and procedures among adults with incident atrial fibrillation.
Table 2. -
Association of CKD stages with AF therapy initiation within 1 year (HR with 95% CI)
| Therapy |
Unadjusted |
Model 1 |
Model 2 |
Model 3 |
| Any AF therapy |
| eGFR>60 |
ref |
ref |
ref |
ref |
| eGFR 45–59 |
0.90 (0.88 to 0.91) |
0.96 (0.94 to 0.98) |
0.99 (0.97 to 1.00) |
1.01 (0.99 to 1.02) |
| eGFR 30–44 |
0.74 (0.72 to 0.76) |
0.82 (0.80 to 0.84) |
0.87 (0.85 to 0.89) |
0.91 (0.88 to 0.93) |
| eGFR 15–29 |
0.59 (0.56 to 0.61) |
0.65 (0.62 to 0.67) |
0.72 (0.69 to 0.75) |
0.78 (0.75 to 0.82) |
| eGFR<15 |
0.50 (0.46 to 0.54) |
0.52 (0.48 to 0.57) |
0.60 (0.55 to 0.65) |
0.64 (0.58 to 0.70) |
| Rate control agent |
| eGFR>60 |
ref |
ref |
ref |
ref |
| eGFR 45–59 |
0.66 (0.64 to 0.68) |
0.73 (0.71 to 0.76) |
0.86 (0.84 to 0.89) |
0.90 (0.87 to 0.93) |
| eGFR 30–44 |
0.48 (0.46 to 0.50) |
0.54 (0.52 to 0.57) |
0.74 (0.71 to 0.78) |
0.78 (0.75 to 0.82) |
| eGFR 15–29 |
0.31 (0.28 to 0.34) |
0.35 (0.32 to 0.38) |
0.57 (0.52 to 0.62) |
0.61 (0.56 to 0.67) |
| eGFR<15 |
0.21 (0.17 to 0.26) |
0.23 (0.18 to 0.28) |
0.41 (0.33 to 0.51) |
0.41 (0.33 to 0.51) |
| Warfarin |
| eGFR>60 |
ref |
ref |
ref |
ref |
| eGFR 45–59 |
1.11 (1.08 to 1.14) |
1.13 (1.10 to 1.16) |
1.08 (1.05 to 1.11) |
1.07 (1.05 to 1.10) |
| eGFR 30–44 |
1.01 (0.98 to 1.04) |
1.07 (1.04 to 1.11) |
1.01 (0.98 to 1.05) |
1.02 (0.98 to 1.05) |
| eGFR 15–29 |
0.85 (0.81 to 0.90) |
0.90 (0.85 to 0.94) |
0.86 (0.81 to 0.91) |
0.89 (0.84 to 0.94) |
| eGFR<15 |
0.78 (0.70 to 0.87) |
0.78 (0.70 to 0.86) |
0.76 (0.68 to 0.85) |
0.81 (0.73 to 0.90) |
| DOAC |
| eGFR>60 |
ref |
ref |
ref |
ref |
| eGFR 45–59 |
0.80 (0.77 to 0.84) |
0.84 (0.80 to 0.88) |
0.86 (0.82 to 0.90) |
0.87 (0.83 to 0.91) |
| eGFR 30–44 |
0.45 (0.42 to 0.48) |
0.50 (0.46 to 0.53) |
0.53 (0.50 to 0.57) |
0.55 (0.51 to 0.59) |
| eGFR 15–29 |
0.16 (0.13 to 0.19) |
0.18 (0.15 to 0.21) |
0.20 (0.17 to 0.25) |
0.23 (0.19 to 0.27) |
| eGFR<15 |
0.04 (0.02 to 0.08) |
0.04 (0.02 to 0.08) |
0.04 (0.02 to 0.10) |
0.05 (0.02 to 0.11) |
| Any anticoagulant (warfarin or DOAC) |
| eGFR>60 |
ref |
ref |
ref |
ref |
| eGFR 45–59 |
1.02 (1.00 to 1.04) |
1.05 (1.02 to 1.07) |
1.02 (0.99 to 1.04) |
1.02 (0.99 to 1.04) |
| eGFR 30–44 |
0.83 (0.81 to 0.86) |
0.89 (0.87 to 0.92) |
0.87 (0.84 to 0.90) |
0.88 (0.85 to 0.91) |
| eGFR 15–29 |
0.64 (0.61 to 0.67) |
0.68 (0.65 to 0.71) |
0.68 (0.65 to 0.72) |
0.72 (0.68 to 0.76) |
| eGFR<15 |
0.55 (0.50 to 0.61) |
0.56 (0.50 to 0.62) |
0.58 (0.52 to 0.64) |
0.62 (0.56 to 0.70) |
| Antiarrhythmic |
| eGFR>60 |
ref |
ref |
ref |
ref |
| eGFR 45–59 |
0.79 (0.76 to 0.83) |
0.97 (0.93 to 1.02) |
0.96 (0.92 to 1.01) |
0.97 (0.92 to 1.01) |
| eGFR 30–44 |
0.67 (0.63 to 0.71) |
0.90 (0.84 to 0.95) |
0.89 (0.83 to 0.94) |
0.90 (0.85 to 0.96) |
| eGFR 15–29 |
0.66 (0.60 to 0.73) |
0.88 (0.80 to 0.97) |
0.87 (0.79 to 0.96) |
0.93 (0.84 to 1.03) |
| eGFR<15 |
0.72 (0.60 to 0.87) |
0.87 (0.72 to 1.05) |
0.93 (0.76 to 1.12) |
0.99 (0.82 to 1.20) |
| Antiplatelet |
| eGFR>60 |
ref |
ref |
ref |
ref |
| eGFR 45–59 |
1.26 (1.17 to 1.36) |
1.22 (1.13 to 1.32) |
1.12 (1.04 to 1.22) |
1.14 (1.05 to 1.24) |
| eGFR 30–44 |
1.27 (1.16 to 1.40) |
1.24 (1.13 to 1.37) |
1.05 (0.95 to 1.16) |
1.09 (0.98 to 1.21) |
| eGFR 15–29 |
1.46 (1.28 to 1.67) |
1.41 (1.23 to 1.62) |
1.03 (0.89 to 1.19) |
1.12 (0.97 to 1.30) |
| eGFR<15 |
1.62 (1.26 to 2.09) |
1.54 (1.19 to 1.98) |
1.04 (0.79 to 1.36) |
1.12 (0.86 to 1.47) |
| Catheter ablation/Pacemaker with nodal ablation/Cardioversion |
| eGFR>60 |
ref |
ref |
ref |
ref |
| eGFR 45–59 |
0.67 (0.63 to 0.72) |
0.97 (0.90 to 1.04) |
0.98 (0.91 to 1.05) |
0.99 (0.93 to 1.06) |
| eGFR 30–44 |
0.42 (0.38 to 0.46) |
0.71 (0.64 to 0.79) |
0.74 (0.67 to 0.83) |
0.78 (0.70 to 0.87) |
| eGFR 15–29 |
0.35 (0.29 to 0.42) |
0.58 (0.49 to 0.69) |
0.65 (0.54 to 0.78) |
0.73 (0.61 to 0.88) |
| eGFR<15 |
0.25 (0.16 to 0.38) |
0.34 (0.22 to 0.53) |
0.42 (0.27 to 0.65) |
0.48 (0.31 to 0.74) |
Model 1: Adjusts for age, sex, race, ethnicity, education, income.
Model 2: Model 1+ history of stroke, transient ischemic attack, peripheral vascular disease, heart failure, myocardial infarction, coronary artery bypass grafting, percutaneous coronary intervention, valvular disease, hypertension, diabetes, systolic BP, body mass index, urine albumin-creatinine ratio.
Model 3: Model 2+ baseline use of cardiovascular medications.
In adjusted models, patients with CKD (versus those with eGFR ≥60 ml/min per 1.73 m2) also had lower receipt of AF-related procedures: patients with eGFR 30–44 ml/min per 1.73m2 (aHR, 0.78; 95% CI, 0.70 to 0.87), eGFR 15–29 ml/min per 1.73 m2 (aHR, 0.73; 95% CI, 0.61 to 0.88), and eGFR <15 ml/min per 1.73 m2 (aHR, 0.48; 95% CI, 0.31 to 0.74) (Table 2 and Figure 3). A statistically significant trend was observed across CKD stages.
Sensitivity Analysis
When we repeated our analyses that included ESKD as a censoring event, our results were very similar to the main analysis (Supplemental Table 3).
Discussion
In this community-based, demographically diverse population of patients with newly diagnosed AF, we found a strong graded association between CKD severity and receipt of selected AF medications (rate control agents, warfarin, DOACs) and AF procedures (catheter ablations, AV nodal ablation and pacemakers, and cardioversions). These data highlight opportunities to re-evaluate the level of evidence supporting the use of these therapies across the spectrum of CKD severity and to potentially improve management of AF in the CKD population, with the goal of improved long-term cardiovascular and kidney outcomes.
In this cohort of adults with newly diagnosed AF receiving care within integrated health care delivery systems in California, we found that patients with CKD had a significantly lower rate of receiving rate control agents (e.g., β blockers, calcium channel blockers, and digoxin) compared with those without CKD, even when adjusting for comorbidity and baseline use of targeted cardioprotective medications. Furthermore, these associations were stronger in those with advanced stages of CKD. Our data differ from a German study, which reported no significant difference in use of rate control agents in patients with CKD,19 and a study of patients undergoing catheter ablation, which reported a higher use of rate control agents in patients with CKD.20 There are several possible explanations for our findings. There remains a paucity of definitive randomized controlled trial data on efficacy and safety of these medications for the treatment of AF, specifically in patients across the entire range of CKD severity, which may appropriately affect prescribing practices given uncertainly about net benefit versus harm with various available pharmacological and device therapies. Data from other observational studies have suggested that patients with CKD may have different responses to standard cardiovascular therapies. Specifically, use of rate control agents may lead to hypotension (particularly in patients with CKD who are often treated with multiple antihypertensive agents) and bradycardia, particularly with reduced kidney clearance, which may increase the half-life of β blockers by as much as two-fold.28 However, further data are needed on the efficacy and potential adverse effects of rate control agents, which may guide prescription of these medications in patients with CKD and AF.
We observed that patients with CKD also were significantly less likely to receive DOACs and warfarin; particularly those with eGFR <30 ml/min per 1.73 m2. Use of anticoagulation remains controversial in patients with CKD,15,29 who, despite having a higher risk of ischemic stroke, also have a higher risk of bleeding. There are no published randomized controlled trials of anticoagulation specifically in patients with CKD and AF. One post-hoc analysis of the Stroke Prevention in Atrial Fibrillation III, which enrolled 516 patients with stage 3 CKD (eGFR 30–59 ml/min per 1.73 m2), reported reduced risk in ischemic stroke and systemic thromboembolism with dose-adjusted warfarin (mean INR, 2.4) compared with fixed dose warfarin (mean INR, 1.3) plus aspirin, with no difference in major hemorrhage.30 Similar to other studies,31,32 we found differences in warfarin use in those with CKD versus no CKD, despite studying a population receiving comprehensive care in an integrated health care delivery system. We did note a significant difference in rates of receiving a DOAC among those with versus without CKD, which was more prominent at the more advanced stages of CKD. There are limited data on the uptake of these newer anticoagulation agents in the CKD population. It is plausible that DOACs are less likely to be prescribed in patients with CKD due to concerns about safety and efficacy resulting from diminished renal excretion. In our study sites, dabigatran was the predominant DOAC used during the study period and was approved by the US Food and Drug Administration in 2010, which may explain the relatively low rates of use in our study population. Dabigatran is also the most renally cleared of available DOACs, so it is not unexpected that use of DOACs was low among the patients CKD in our study. Information on rates of patient refusal of anticoagulation was also unavailable, and this may vary by CKD status and severity. To date, there are no published randomized controlled trials that have tested the use of DOACs in patients with a range of CKD severity (although some trials in the ESKD population are ongoing, including RENal hemodialysis patients ALlocated apixaban versus warfarin in Atrial Fibrillation [ClinicalTrials.gov identifier NCT02942407], Strategies for the Management of Atrial Fibrillation in patiEnts Receiving Dialysis [ClinicalTrials.gov identifier NCT03987711], and Compare Apixaban and Vitamin-K Antagonists in Patients With Atrial Fibrillation and End-Stage Kidney Disease [ClinicalTrials.gov identifier NCT02933697]). However, post-hoc analyses of randomized trials in the general population (which included a subset of selected participants with mild or moderate CKD) and observational studies have suggested that selected DOACs appear to be safe and effective across all levels of CKD.31,33 Yet, more definitive clinical trials may be necessary to generate the need evidence to guide clinical decision making particularly in patients with more advanced CKD.
After adjustment for potential confounders, we found strong graded associations with CKD severity and receipt of an AF procedure. The few prior studies in this area have yielded conflicting findings. In the German study noted above, in unadjusted analyses, among patients who are hospitalized and have AF, those with CKD were less likely to receive AF cardioversion or catheter ablation.19 Our study adds to the findings of this previous study in several ways.19 We studied a much larger population of patients with CKD with moderate to advanced CKD (the previous study only included 780 patients with stage 3 CKD and 59 patients with stage 4 or 5 CKD). Our study population was based in the United States with greater racial and ethnic diversity and is more generalizable to a US population. Finally, the German study enrolled patients from 2004 to 2006, so our population is more contemporary and reflects more current AF treatment practices.
After adjustment for potential confounders and other cardiovascular medications, we did not find consistent differences in receipt of antiarrhythmic medications between patients with and without CKD. This conflicts with the German study that reported in unadjusted analyses that patients hospitalized with AF with CKD were less likely to receive class I or III antiarrhythmic drugs; however, a greater proportion of patients with CKD were prescribed amiodarone.19
Certain studies have shown that despite meeting clinical indications, patients with CKD are commonly underprescribed recommended cardiovascular medications or offered fewer cardiovascular procedures or devices.17,34 Therefore, the findings from our study highlight the need for several key next steps to optimize treatment of AF in patients with CKD. First, more rigorous randomized controlled trial and complementary observational data are needed in this population to assess the net efficacy and safety of a broad range of AF therapies, including procedures, across the range of CKD severity. Once those data are known, it may help focus efforts to improve evidence-based use of certain AF therapies in patients with CKD. Second, to inform implementation efforts, studies needed to explore the physician and patient reasons underlying differences in use of AF therapies in patients with CKD.
Our study had some notable strengths. We studied a large, well-characterized, diverse community-based population with incident AF from California. We ascertained use of a broad range of AF therapies through comprehensive pharmacy and procedure databases. CKD and CKD stages were defined by serial measurements of ambulatory creatinine values, making misclassification less likely. We were able to account for differences between those with and without CKD across a broad range of confounders. However, we also recognize a few study limitations. Incident AF was identified from diagnosis codes; however, these codes have been previously validated using electrocardiograms findings and/or manual review of medical records.35 This was an observational study of therapies, so although we adjusted for numerous potential confounders, residual confounding remains a possibility. Patients with CKD were more likely to be taking β blockers and calcium channel blockers at baseline; although we adjusted for this in our multivariable models, there may have been residual confounding. From the current data, we were not able to ascertain reasons why a patient may have or have not been treated with a particular AF therapy. As a study of insured patients within integrated health care delivery systems in California, our results may not be fully generalizable to uninsured patients, geographic areas, or practice settings.
In conclusion, among a large, diverse cohort of adults with newly diagnosed AF, we found a strong graded association between CKD severity and lower rates of receiving a rate control agent, warfarin or a DOAC for treatment of AF within 1 year of AF diagnosis. Further, there was also a significant association of CKD severity with lower rates of receipt of an AF-related procedure. Additional data on efficacy and safety of AF therapies in CKD populations are needed to inform evidence-based management strategies.
Disclosures
A. Go reports receiving research grants through his institution from Bristol Meyers-Squibb, iRhythm Technologies, the National Heart, Lung and Blood Institute, National Institute of Diabetes, Digestive and Kidney Diseases, National Institute on Aging, and Novartis; reports current employer KPNC; reports receiving research funding from Amarin Pharmaceuticals, CSL Behring, and Janssen Research and Development. D. Fan reports their current employer is Kaiser Permanente. D. Singer reports consultancy agreements with Bristol Myers Squibb, Fitbit, and Pfizer; and reports receiving research funding from Bristol Myers Squibb. K. Reynolds reports current employer Kaiser Permanente Southern California; reports receiving research funding from Amgen Inc., CSL Behring, and Merck & Co; reports being a scientific advisor or member of the American Journal of Hypertension Editorial Board, Associate Editor of Cardiovascular Epidemiology and Prevention (specialty section of Frontiers in Cardiovascular Medicine), Journal of Diabetes and Its Complications Editorial Board, and International Journal of Cardiology Hypertension Editorial Board. L. Zelnick reports having consultancy agreements with Veterans Medical Research Foundation; reports being a scientific advisor or member of Statistical Editor for the Clinical Journal of the American Society of Nephrology. N. Bansal reports being a scientific advisor or member as Kidney360 Associate Editor. S. Sung reports their current employer is KPNC.
Funding
This work was supported by the National Institutes of Health, National Heart, Lung and Blood Institute (R01 HL142834).
Supplemental Material
This article contains the following supplemental material online at http://jasn.asnjournals.org/lookup/suppl/doi:10.1681/ASN.2021060744/-/DCSupplemental.
Supplemental Table 1. Initiation of AF therapies within 1 year for presumed incident AF, stratified by CKD status (n, %)
Supplemental Table 2. Median time (in days) to initiation of AF therapies in the cohort.
Supplemental Table 3. Adjusted association of CKD stages with AF therapy initiation within 1 year (HR with 95% CI), censoring at ESKD.
References
1. Chugh SS, Havmoeller R, Narayanan K, Singh D, Rienstra M, Benjamin EJ, et al.: Worldwide epidemiology of
atrial fibrillation: A Global Burden of Disease 2010 Study. Circulation 129: 837–847, 2014
2. Colilla S, Crow A, Petkun W, Singer DE, Simon T, Liu X: Estimates of current and future incidence and prevalence of
atrial fibrillation in the U.S. adult population. Am J Cardiol 112: 1142–1147, 2013
3. Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Blaha MJ, et al.; American Heart Association Statistics Committee and Stroke Statistics Subcommittee: Heart disease and stroke statistics--2014 update: A report from the American Heart Association. Circulation 129: e28–e292, 2014
4. Freeman JV, Wang Y, Akar J, Desai N, Krumholz H: National trends in
atrial fibrillation hospitalization, readmission, and mortality for Medicare beneficiaries, 1999–2013. Circulation 135: 1227–1239, 2017
5. Alonso A, Lopez FL, Matsushita K, Loehr LR, Agarwal SK, Chen LY, et al.: Chronic kidney disease is associated with the incidence of
atrial fibrillation: The Atherosclerosis Risk in Communities (ARIC) study. Circulation 123: 2946–2953, 2011
6. Ananthapanyasut W, Napan S, Rudolph EH, Harindhanavudhi T, Ayash H, Guglielmi KE, et al.: Prevalence of
atrial fibrillation and its predictors in nondialysis patients with chronic kidney disease. Clin J Am Soc Nephrol 5: 173–181, 2010
7. Baber U, Howard VJ, Halperin JL, Soliman EZ, Zhang X, McClellan W, et al.: Association of chronic kidney disease with
atrial fibrillation among adults in the United States: REasons for Geographic and Racial Differences in Stroke (REGARDS) Study. Circ Arrhythm Electrophysiol 4: 26–32, 2011
8. Horio T, Iwashima Y, Kamide K, Tokudome T, Yoshihara F, Nakamura S, et al.: Chronic kidney disease as an independent risk factor for new-onset
atrial fibrillation in hypertensive patients. J Hypertens 28: 1738–1744, 2010
9. Soliman EZ, Prineas RJ, Go AS, Xie D, Lash JP, Rahman M, et al.;
Chronic Renal Insufficiency Cohort (CRIC) Study Group: Chronic kidney disease and prevalent
atrial fibrillation: The
Chronic Renal Insufficiency Cohort (CRIC). Am Heart J 159: 1102–1107, 2010
10. Piccini JP, Stevens SR, Chang Y, Singer DE, Lokhnygina Y, Go AS, et al.; ROCKET AF Steering Committee and Investigators: Renal dysfunction as a predictor of stroke and systemic embolism in patients with nonvalvular
atrial fibrillation: Validation of the R(2)CHADS(2) index in the ROCKET AF (Rivaroxaban Once-daily, oral, direct factor Xa inhibition Compared with vitamin K antagonism for prevention of stroke and Embolism Trial in
Atrial Fibrillation) and ATRIA (AnTicoagulation and Risk factors In
Atrial fibrillation) study cohorts. Circulation 127: 224–232, 2013
11. Nakagawa K, Hirai T, Takashima S, Fukuda N, Ohara K, Sasahara E, et al.: Chronic kidney disease and CHADS(2) score independently predict
cardiovascular events and mortality in patients with nonvalvular
atrial fibrillation. Am J Cardiol 107: 912–916, 2011
12. Go AS, Fang MC, Udaltsova N, Chang Y, Pomernacki NK, Borowsky L, et al.; ATRIA Study Investigators: Impact of proteinuria and glomerular filtration rate on risk of thromboembolism in
atrial fibrillation: The anticoagulation and risk factors in
atrial fibrillation (ATRIA) study. Circulation 119: 1363–1369, 2009
13. Nelson SE, Shroff GR, Li S, Herzog CA: Impact of chronic kidney disease on risk of incident
atrial fibrillation and subsequent survival in Medicare patients. J Am Heart Assoc 1: e002097, 2012
14. Bansal N, Fan D, Hsu CY, Ordonez JD, Go AS: Incident
atrial fibrillation and risk of death in adults with chronic kidney disease. J Am Heart Assoc 3: e001303, 2014
15. Olesen JB, Lip GY, Kamper AL, Hommel K, Køber L, Lane DA, et al.: Stroke and bleeding in
atrial fibrillation with chronic kidney disease. N Engl J Med 367: 625–635, 2012
16. Bansal N, Fan D, Hsu CY, Ordonez JD, Marcus GM, Go AS: Incident
atrial fibrillation and risk of end-stage renal disease in adults with chronic kidney disease. Circulation 127: 569–574, 2013
17. Chertow GM, Normand SL, McNeil BJ: “Renalism”: Inappropriately low rates of coronary angiography in elderly individuals with renal insufficiency. J Am Soc Nephrol 15: 2462–2468, 2004
18. Boriani G, Savelieva I, Dan GA, Deharo JC, Ferro C, Israel CW, Lane DA, La Manna G, Morton J, Mitjans AM, Vos MA, Turakhia MP, Lip GY: Chronic kidney disease in patients with cardiac rhythm disturbances or implantable electrical devices: Clinical significance and implications for decision making-a position paper of the European Heart Rhythm Association endorsed by the Heart Rhythm Society and the Asia Pacific Heart Rhythm Society. Europace 17: 1169–1196, 2015
19. Reinecke H, Nabauer M, Gerth A, Limbourg T, Treszl A, Engelbertz C, et al.; AFNET Study Group: Morbidity and treatment in patients with
atrial fibrillation and chronic kidney disease. Kidney Int 87: 200–209, 2015
20. Ullal AJ, Kaiser DW, Fan J, Schmitt SK, Than CT, Winkelmayer WC, et al.: Safety and clinical outcomes of catheter ablation of
atrial fibrillation in patients with chronic kidney disease. J Cardiovasc Electrophysiol 28: 39–48, 2017
21. Go AS, Magid DJ, Wells B, Sung SH, Cassidy-Bushrow AE, Greenlee RT, et al.: The
Cardiovascular Research Network: A new paradigm for
cardiovascular quality and outcomes research. Circ Cardiovasc Qual Outcomes 1: 138–147, 2008
22. Magid DJ, Gurwitz JH, Rumsfeld JS, Go AS: Creating a research data network for
cardiovascular disease: The CVRN. Expert Rev Cardiovasc Ther 6: 1043–1045, 2008
23. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, et al.; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration): A new equation to estimate glomerular filtration rate. Ann Intern Med 150: 604–612, 2009
24. Levey AS, de Jong PE, Coresh J, El Nahas M, Astor BC, Matsushita K, et al.: The definition, classification, and prognosis of chronic kidney disease: A KDIGO Controversies Conference report. Kidney Int 80: 17–28, 2011
25. Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY: Chronic kidney disease and the risks of death,
cardiovascular events, and hospitalization. N Engl J Med 351: 1296–1305, 2004
26. Singer DE, Chang Y, Borowsky LH, Fang MC, Pomernacki NK, Udaltsova N, et al.: A new risk scheme to predict ischemic stroke and other thromboembolism in
atrial fibrillation: The ATRIA study stroke risk score. J Am Heart Assoc 2: e000250, 2013
27. Pisters R, Lane DA, Nieuwlaat R, de Vos CB, Crijns HJGM, Lip GYH: A novel user-friendly score (HAS-BLED) to assess 1-year risk of major bleeding in patients with
atrial fibrillation: The Euro Heart Survey. Chest 138: 1093–1100, 2010
28. Borchard U: Pharmacokinetics of beta-adrenoceptor blocking agents: Clinical significance of hepatic and/or renal clearance. Clin Physiol Biochem 8[Suppl 2]: 28–34, 1990
29. Kumar S, Lim E, Covic A, Verhamme P, Gale CP, Camm AJ, et al.: Anticoagulation in concomitant chronic kidney disease and
atrial fibrillation: JACC review topic of the week. J Am Coll Cardiol 74: 2204–2215, 2019
30. Hart RG, Pearce LA, Asinger RW, Herzog CA: Warfarin in
atrial fibrillation patients with moderate chronic kidney disease. Clin J Am Soc Nephrol 6: 2599–2604, 2011
31. Shin J-I, Secora A, Alexander GC, Inker LA, Coresh J, Chang AR, et al.: Risks and benefits of direct oral anticoagulants across the spectrum of GFR among incident and prevalent patients with
atrial fibrillation. Clin J Am Soc Nephrol 13: 1144–1152, 2018
32. Yang F, Hellyer JA, Than C, Ullal AJ, Kaiser DW, Heidenreich PA, et al.: Warfarin utilisation and anticoagulation control in patients with
atrial fibrillation and chronic kidney disease. Heart 103: 818–826, 2017
33. Makani A, Saba S, Jain SK, Bhonsale A, Sharbaugh MS, Thoma F, et al.: Safety and efficacy of direct oral anticoagulants versus warfarin in patients with chronic kidney disease and
atrial fibrillation. Am J Cardiol 125: 210–214, 2020
34. Baigent C, Landray MJ, Reith C, Emberson J, Wheeler DC, Tomson C, et al.; SHARP Investigators: The effects of lowering LDL cholesterol with simvastatin plus ezetimibe in patients with chronic kidney disease (Study of Heart and Renal Protection): A randomised placebo-controlled trial. Lancet 377: 2181–2192, 2011
35. Go AS, Hylek EM, Phillips KA, Chang Y, Henault LE, Selby JV, et al.: Prevalence of diagnosed
atrial fibrillation in adults:Nnational implications for rhythm management and stroke prevention: The AnTicoagulation and Risk Factors in
Atrial Fibrillation (ATRIA) Study. JAMA 285: 2370–2375, 2001
