Effect of Polypharmacy on Clinical Outcomes in Elderly Patients With Non-Valvular Atrial Fibrillation　― A Sub-Analysis of the ANAFIE Registry ―

Takeshi Yamashita; Masaharu Akao; Hirotsugu Atarashi; Takanori Ikeda; Yukihiro Koretsune; Ken Okumura; Wataru Shimizu; Shinya Suzuki; Hiroyuki Tsutsui; Kazunori Toyoda; Atsushi Hirayama; Masahiro Yasaka; Takenori Yamaguchi; Satoshi Teramukai; Tetsuya Kimura; Yoshiyuki Morishima; Atsushi Takita; Hiroshi Inoue

doi:10.1253/circj.CJ-22-0170

Abstract

Background: This All Nippon AF in the Elderly (ANAFIE) Registry sub-analysis evaluated the impact of polypharmacy on 2-year outcomes in a large, elderly (aged ≥75 years) Japanese population with non-valvular atrial fibrillation (NVAF).

Methods and Results: The ANAFIE Registry was a multicenter, prospective, observational study with a 24-month follow-up period. Of 32,275 enrolled NVAF patients, 31,419 were grouped by the number of prescribed concomitant medications (other than oral anticoagulants [OACs]): 0–4 [38.8%], 5–8 [43.3%], and ≥9 [17.9%]). Patients receiving more concomitant medications were older, had poor renal function, and suffered more comorbidities than those receiving fewer concomitant medications. Several patient background factors, including diabetes mellitus, myocardial infarction, and chronic kidney disease, were significantly correlated with an increased number of concomitant medications. With increasing medications, OAC prescription rates decreased, but the warfarin prescription rate increased, and the cumulative incidence rates of stroke/systemic embolic events (SEE), major bleeding, gastrointestinal bleeding, fracture/falls, cardiovascular events, cardiovascular death, and all-cause death significantly increased (each, P<0.05). In multivariate analysis, increasing medications was independently associated with increases in these events, except for stroke/SEE. There were no significant interactions between the number of medications and anticoagulant treatment with direct OAC or warfarin concerning the incidence of these events.

Conclusions: Polypharmacy was frequent among elderly patients with NVAF who were older with more comorbidities, and was independently associated with a higher incidence of extracranial events.

Stroke prevention with oral anticoagulants (OACs) is a cornerstone of atrial fibrillation (AF) treatment.¹ However, elderly patients with AF tend to be burdened with multimorbidity,²^–⁴ such as hypertension, heart failure, diabetes mellitus, stroke, and myocardial infarction, and most of these conditions require pharmacological therapy. Thus, many elderly patients with AF are subject to polypharmacy (i.e., chronic use of ≥5 drugs).⁵ The reported prevalence of polypharmacy in AF patients is as high as 76.5%, and patients tend to be receiving a median of 6 drugs.⁴ In patients aged ≥75 years with AF, polypharmacy was associated with worse prognosis and adverse outcomes.² It has been reported that stroke/systemic embolic events (SEE), major bleeding events, and all-cause death increase with an increasing number of comorbidities in patients with non-valvular AF (NVAF).⁶ Moreover, a meta-analysis of the Rivaroxaban Once Daily Oral Direct Factor Xa Inhibition Compared with Vitamin K Antagonism for Prevention of Stroke and Embolism Trial in Atrial Fibrillation (ROCKET-AF) and Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation (ARISTOTLE) trial reported that a greater number of concomitant medications could increase not only bleeding events but also all-cause death in patients with NVAF.⁷

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Drug concentrations are more likely to increase in elderly patients due to decreasing volume of distribution and decreased metabolism and excretion, which may increase the risk of adverse events (AEs).⁸ Polypharmacy may also cause drug interactions, which may result in increased/decreased pharmacokinetic effects.⁹ Further, polypharmacy and the associated pill burden may lead to treatment non-adherence.¹ Naturally, it is expected that polypharmacy would increase adverse outcomes or affect treatment effectiveness in elderly patients aged ≥75 years with NVAF. However, the impact of polypharmacy on adverse outcomes and treatment effectiveness in elderly patients aged ≥75 years with NVAF remains unclear in the real world. Although some studies have been conducted,⁶^,⁷ many were sub-analyses of randomized controlled trials or insurance database registration studies, which may have limitations in terms of selection bias, data validation, and low numbers of patients.

Given the super-aged AF population in Japan, this is an area in which new evidence is urgently required. Based on this need, the All Nippon AF in the Elderly (ANAFIE) Registry was initiated to clarify the clinical status and prognosis of >30,000 Japanese elderly (aged ≥75 years) patients with NVAF. Baseline demographic and clinical characteristics¹⁰ and 2-year outcomes from the study have been published, including evidence of an independent association between polypharmacy (≥5 medications) and an increased risk of major bleeding and all-cause death.¹¹ The present sub-analysis aimed to more comprehensively evaluate the impact of polypharmacy on the status of anticoagulant management and on a broad range of outcomes, including extracranial events in elderly patients with NVAF who were enrolled in the ANAFIE Registry.

Methods

Study Design and Ethical Considerations

The study rationale and design of the ANAFIE Registry have been previously described.¹² Briefly, this was a multicenter, prospective observational study enrolling >30,000 elderly NVAF patients in Japan and conducted between October 2016 and January 2020. Follow-up data were collected at 12 and 24 months.

The ethics committee approved the study protocol and related documentation at each participating site, and all patients were required to provide written informed consent to participate in the study. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki and complied with local regulatory requirements in Japan.

Patients

Included patients were aged ≥75 years at the time of informed consent who were definitively diagnosed with NVAF and could attend the study hospital visits. Excluded patients were those participating or planning to participate in other clinical studies or who had the following comorbidities: mitral stenosis; artificial heart valve replacement; history of cardiovascular events including stroke, myocardial infarction, cardiac intervention, heart failure requiring hospitalization, or any bleeding leading to hospitalization within 1 month before enrollment; and ≤1 year of life expectancy.

Study Measures and Endpoints

For this subgroup analysis, patients were categorized into 3 groups by the number of prescribed concomitant medications other than OACs: 0–4, 5–8, and ≥9 concomitant medications. The rationale for the number of concomitant medications in each group was set so that the number of subjects would be approximately the same in each group, and this grouping standard was similar to that used in a recent report.¹³

The primary outcome was the 2-year cumulative incidence rate of stroke/SEE. The secondary outcomes were the 2-year cumulative incidence rates of major bleeding, stroke, SEE, intracranial hemorrhage (ICH), gastrointestinal bleeding, fracture/falls, cardiovascular events (i.e., stroke, myocardial infarction, cardiac intervention for cardiac events other than myocardial infarction, heart failure requiring hospitalization, or cardiovascular death), cardiovascular death, and all-cause death.

Statistical Analysis

Details of the statistical analysis have been previously reported.¹¹^,¹² Baseline variables were compared among the 3 medication groups using a tendency test (trend-P), the chi-squared test for categorical variables, and analysis of variance for continuous variables. For continuous variable analysis, a general linear model was used to calculate the P value. For categorical variables, the Cochran-Mantel-Haenszel test was used to calculate the P value of the correlation statistic. The cumulative incidence rates and 95% confidence intervals (CIs) of clinical outcomes at 2 years of follow up were evaluated using the Kaplan-Meier method. The log-rank test was used for the comparison of the outcomes between groups. Tests were 2-sided, and a P value of <0.05 was considered statistically significant.

Multiple regression analysis was performed with the number of drugs administered at the time of consent as the response variable and patient background factors as explanatory variables. However, cases in which some of the explanatory factors were unknown were not included in the analysis.

Multivariate analyses to evaluate the associations between each clinical outcome and polypharmacy were conducted using the Cox proportional hazards model. The explanatory variables included as adjustment factors in the model were: sex, age, body mass index, history of bleeding, type of AF, systolic blood pressure, severe liver function disorder, diabetes mellitus, hyperuricemia, heart failure and/or reduced left ventricular ejection fraction, myocardial infarction, cerebrovascular disease, other thromboembolic diseases, active cancer, dementia, fall within 1 year, anticoagulant use, history of catheter ablation, dyslipidemia, creatinine clearance, and gastrointestinal diseases. The interaction between polypharmacy and anticoagulant treatment with direct OACs (DOACs) vs. warfarin in relation to clinical outcomes was evaluated after adjustment for these variables and the use of antiarrhythmic agents and proton pump inhibitors, P-glycoprotein inhibitors, and antiplatelet agents. All statistical analyses were conducted using SAS version 9.4 (SAS Institute, Tokyo, Japan).

Results

Patients

Of the 32,275 patients enrolled in the ANAFIE Registry, 856 patients with no concomitant drug information were excluded, and 31,419 patients with available concomitant medication information were included in the present analysis. The mean follow-up duration was 1.88 years.

Baseline characteristics of 31,419 patients are shown in Table 1. At baseline, 38.8% of patients were prescribed 0–4 medications, 43.3% used 5–8 medications, and 17.9% used ≥9 medications. The mean±standard deviation number of medications used was 6.6±3.2, and the median (Q1, Q3) was 6.0 (4.0, 9.0) medications. As the number of concomitant medications increased, patients were significantly older, were more commonly women, had significantly higher CHA₂DS₂-VASc and HAS-BLED scores, a history of major bleeding, higher proportions of comorbidities (i.e., hypertension, diabetes mellitus, chronic kidney disease, myocardial infarction, heart failure, cerebrovascular disease, gastrointestinal disease, active cancer, and dementia), higher proportions of falls within 1 year of enrollment, higher use of warfarin, and lower use of DOACs (trend P<0.001, each) (Table 1). Among patients receiving warfarin, the time in the therapeutic range significantly decreased with an increased number of concomitant medications. The most common concomitant medications, other than OACs, were antihypertensive drugs, antiarrhythmic drugs, antihyperlipidemic drugs, proton pump inhibitors, antiplatelet drugs, and antidiabetic drugs.

Table 1. Baseline Demographic and Clinical Characteristics by Medication Group

	Number of medications			Trend P value
	0–4 (n=12,186)	5–8 (n=13,597)	≥9 (n=5,636)	Trend P value
Age, years	80.8±4.6	81.8±4.9	82.2±4.9	<0.001
≥85	2,593 (21.3)	3,863 (28.4)	1,766 (31.3)	<0.001
Sex, male	7,386 (60.6)	7,564 (55.6)	3,059 (54.3)	<0.001
BMI, kg/m²	23.1±3.3	23.4±3.6	23.7±3.9	<0.001
SBP, mmHg	128.7±16.5	127.0±16.9	125.07±18.12	<0.001
DBP, mmHg	72.4±11.3	70.2±11.5	67.8±11.9	<0.001
CHA₂DS₂-VASc score	3.9±1.3	4.6±1.3	5.1±1.4	<0.001
HAS-BLED score	1.7±0.8	1.9±0.9	2.2±0.9	<0.001
History of major bleeding	401 (3.3)	663 (4.9)	343 (6.1)	<0.001
AF type				0.002
PAF	5,195 (42.6)	5,572 (41.0)	2,406 (42.7)
Non-PAF	6,991 (57.4)	8,025 (59.0)	3,230 (57.3)
History of non-pharmacological therapy for AF	1,884 (15.5)	2,374 (17.5)	1,146 (20.3)	<0.001
Catheter ablation	1,198 (9.8)	1,177 (8.7)	462 (8.2)	<0.001
Comorbidities
Hypertension	8,028 (65.9)	10,940 (80.5)	4,730 (83.9)	<0.001
Diabetes mellitus	1,921 (15.8)	4,009 (29.5)	2,527 (44.8)	<0.001
Chronic kidney disease	1,594 (13.1)	3,158 (23.2)	1,813 (32.2)	<0.001
Myocardial infarction	205 (1.7)	883 (6.5)	724 (12.8)	<0.001
Heart failure	3,187 (26.2)	5,714 (42.0)	2,928 (52.0)	<0.001
Cerebrovascular disease	2,203 (18.1)	3,300 (24.3)	1,597 (28.3)	<0.001
Gastrointestinal diseases	2,670 (21.9)	4,325 (31.8)	2,243 (39.8)	<0.001
Active cancer	1,193 (9.8)	1,496 (11.0)	768 (13.6)	<0.001
Dementia	670 (5.5)	1,180 (8.7)	617 (10.9)	<0.001
Fall within 1 year	646 (5.3)	1,046 (7.7)	604 (10.7)	<0.001
Anticoagulant treatment
OACs	11,313 (92.8)	12,598 (92.7)	5,176 (91.8)	0.030
DOACs	8,659 (71.1)	8,978 (66.0)	3,401 (60.3)	<0.001
Warfarin	2,649 (21.7)	3,615 (26.6)	1,774 (31.5)	<0.001
TTR, %	76.9±29.3	75.9±29.5	72.3±31.2	<0.001
Medications other than OACs
Antihypertensive drugs	7,209 (59.2)	10,598 (77.9)	4,669 (82.8)	<0.001
Antiarrhythmic drugs	5,815 (47.7)	8,305 (61.1)	3,702 (65.7)	<0.001
Antihyperlipidemic drugs	2,712 (22.3)	5,912 (43.5)	3,161 (56.1)	<0.001
Proton pump inhibitors	2,534 (20.8)	5,820 (42.8)	3,291 (58.4)	<0.001
Antiplatelet drugs	791 (6.5)	2,811 (20.7)	2,009 (35.6)	<0.001
Diabetes drugs	722 (5.9)	2,440 (17.9)	1,913 (33.9)	<0.001
Psychotropic drugs	414 (3.8)	1,122 (8.4)	897 (16.0)	<0.001
Antidementia drugs	279 (2.3)	548 (4.0)	334 (5.9)	<0.001
COPD drugs	160 (1.3)	382 (2.8)	275 (4.9)	<0.001
P-gp inhibitors	119 (1.0)	255 (1.9)	172 (3.1)	<0.001
Anti-cancer agents	64 (0.5)	134 (1.0)	77 (1.4)	<0.001

Data are presented as n (%) or mean±standard deviation. AF, atrial fibrillation; BMI, body mass index; COPD, chronic obstructive pulmonary disease; DBP, diastolic blood pressure; DOAC, direct oral anticoagulant; OAC, oral anticoagulant; P-gp, P-glycoprotein; PAF, paroxysmal atrial fibrillation; SBP, systolic blood pressure; TTR, time in the therapeutic range.

Several clinical background factors and comorbidities were significantly associated with the number of medications (Supplementary Table 1). Among them, diabetes mellitus (partial regression coefficient [95% CI]: 1.35 [1.27–1.44]), myocardial infarction (1.35 [1.19–1.51]), and chronic kidney disease (creatinine clearance <30 vs. ≥50 mL/min; 1.59 [1.45–1.72]) were identified as particularly important factors that significantly increased the number of medications (partial regression coefficient >1, P<0.001).

We assessed the risk of events by use of concomitant medications, which is shown in Table 2. Concomitant use of antihypertensive drugs (adjusted hazard ratio [HR]: 0.79, 95% CI: 0.65–0.96; P=0.016) and diabetes drugs (adjusted HR: 0.80, 95% CI: 0.67–0.96; P=0.018) were associated with a reduced risk of all-cause death. Concomitant use of antihyperlipidemic drugs was associated with a reduced risk of major bleeding (adjusted HR: 0.72, 95% CI: 0.53–0.98; P=0.035); cardiovascular events (adjusted HR: 0.83, 95% CI: 0.73–0.93; P=0.002), cardiovascular death (adjusted HR: 0.72, 95% CI: 0.54–0.95; P=0.020), and all-cause death (adjusted HR: 0.71, 95% CI 0.60–0.83; P<0.001). Concomitant proton pump inhibitors were associated with an increased risk of cardiovascular events (adjusted HR: 1.22, 95% CI 1.08–1.39; P=0.002), and antidementia drugs were associated with an increased risk of cardiovascular death (adjusted HR: 1.65, 95% CI: 1.02–2.67; P=0.040) and all-cause death (adjusted HR: 1.71, 95% CI: 1.30–2.25; P<0.001). Psychotropic drugs were associated with an increased risk of major bleeding (adjusted HR: 1.59, 95% CI: 1.11–2.28; P=0.012).

Table 2. Risk of Events by Use of Concomitant Medications (Multivariate Analyses)

Medications other than OACs	Major bleeding		CV events		CV death		All-cause death
Medications other than OACs	Adjusted HR (95% CI)	P value	Adjusted HR (95% CI)	P value	Adjusted HR (95% CI)	P value	Adjusted HR (95% CI)	P value
Antihypertensive drugs	1.25 (0.82–1.92)	0.298	0.92 (0.79–1.08)	0.311	0.80 (0.57–1.12)	0.189	0.79 (0.65–0.96)	0.016
Antiarrhythmic drugs	0.84 (0.62–1.14)	0.264	0.94 (0.83–1.07)	0.361	1.20 (0.89–1.61)	0.229	0.93 (0.79–1.10)	0.383
Antihyperlipidemic drugs	0.72 (0.53–0.98)	0.035	0.83 (0.73–0.93)	0.002	0.72 (0.54–0.95)	0.020	0.71 (0.60–0.83)	<0.001
Proton pump inhibitors	0.95 (0.70–1.29)	0.739	1.22 (1.08–1.39)	0.002	1.03 (0.78–1.36)	0.833	1.14 (0.96–1.34)	0.125
Antiplatelet drugs	0.83 (0.60–1.16)	0.272	1.08 (0.95–1.23)	0.222	1.12 (0.84–1.49)	0.442	0.94 (0.80–1.12)	0.505
Diabetes drugs	0.87 (0.62–1.21)	0.408	1.00 (0.88–1.14)	1.000	0.76 (0.55–1.05)	0.092	0.80 (0.67–0.96)	0.018
Psychotropic drugs	1.59 (1.11–2.28)	0.012	1.00 (0.84–1.18)	0.965	1.08 (0.74–1.58)	0.674	0.92 (0.73–1.16)	0.471
Antidementia drugs	1.02 (0.53–1.93)	0.963	1.05 (0.81–1.37)	0.691	1.65 (1.02–2.67)	0.040	1.71 (1.30–2.25)	<0.001
COPD drugs	1.52 (0.84–2.75)	0.164	1.05 (0.80–1.39)	0.720	1.25 (0.71–2.19)	0.446	1.37 (1.00–1.88)	0.051
P-gp inhibitors	0.38 (0.09–1.53)	0.174	0.83 (0.56–1.23)	0.349	0.67 (0.25–1.80)	0.428	1.09 (0.69–1.72)	0.718
Anti-cancer agents	2.35 (0.96–5.75)	0.060	1.05 (0.63–1.74)	0.864	0.98 (0.31–3.08)	0.974	1.23 (0.68–2.25)	0.490

Reference: no use of the drug. Sex, age, BMI, and anticoagulants were included in the model. CI, confidence interval; CV, cardiovascular; HR, hazard ratio. Other abbreviations as in Table 1.

Study Endpoints

Figure 1 shows the cumulative incidence rates at the 2-year follow-up for each clinical outcome in the 3 groups. The cumulative incidence rates of stroke/SEE in the 0–4, 5–8, and ≥9 medication groups were 2.9%, 3.2%, and 3.6%, respectively (P=0.049). The cumulative incidence rates were 1.6%, 2.1%, and 3.3% for major bleeding, and were 1.2%, 2.3%, and 3.9% for cardiovascular death, respectively (P<0.001). In the ≥9 medications group, the cumulative incidence of all-cause death was markedly and significantly higher (11.1%) compared with the 0–4 medications group (4.8%) and the 5–8 medications group (7.7%) (P<0.001).

Figure 1.

Kaplan-Meier curves for key clinical outcomes by medication group. (A) Stroke/SEE, (B) major bleeding, (C) CV death, (D) all-cause death. CV, cardiovascular; SEE, systemic embolic event.

Figure 2 shows the cumulative incidence rates of other clinical outcomes, including ischemic stroke, hemorrhagic stroke, SEE, gastrointestinal bleeding, cardiovascular events, ICH, and fractures/falls at the 2-year follow up in the 3 groups. The cumulative incidence rates of ischemic stroke in the 0–4, 5–8, and ≥9 medications groups (2.3%, 2.5% and 2.6%; P=0.210), and of hemorrhagic stroke (0.7%, 0.6%, and 0.8%; P=0.339, respectively) were similar among the 3 medication groups. The cumulative incidence rates of SEE (0%, 0.1% and 0.2%; P=0.013) were slightly but significantly increased with increasing number of medications. The cumulative incidence rates for GI bleeding (2.5%, 3.9%, and 5.9%; P<0.001), cardiovascular events (7.0%, 11.3%, and 19.3%; P<0.001), and fractures and falls (8.6%, 12.3% and 16.7%; P<0.001) increased markedly and significantly with increasing number of medications. The cumulative incidence rates of ICH were slightly higher in the ≥9 medications group (1.9%) compared with the 0–4 medications group (1.4%) and the 5–8 medications group (1.4%) (P=0.016).

Figure 2.

Kaplan-Meier curves for additional clinical outcomes by medication group. (A) Ischemic stroke, (B) hemorrhagic stroke, (C) SEE, (D) ICH, (E) GI bleeding, (F) CV events, (G) fractures and falls. CV, cardiovascular; GI, gastrointestinal; ICH, intracranial hemorrhage; SEE, systemic embolic event.

Relationship Between Polypharmacy and Clinical Outcomes

A Cox proportional hazards model was used to estimate the associations between clinical outcomes and polypharmacy, using the 0–4 medications group as a reference (Table 3). After adjusting by considerable possible variables, the trend-P values indicated significant differences among the 3 groups in the incidence rates for major bleeding, gastrointestinal bleeding, cardiovascular events, all-cause death, cardiovascular death, and fractures and falls, but not for stroke/SEE, ischemic and hemorrhagic stroke, and ICH. Of note, more than half of the cardiovascular events occurred in patients with heart failure requiring hospitalization, and the incidence was 4.0% for the 0–4 medications group, 8.0% for the 5–8 medications group, and 15.0% for the ≥9 medications group.

Table 3. Incidence Rates and Adjusted HRs for Each Clinical Outcome by Medication Group

	Number of medications							Trend P value
	0–4 (n=12,186)	5–8 (n=13,597)			≥9 (n=5,636)
	Events/ 100 PY	Events/ 100 PY	Adjusted HR (95% CI)*	P value	Events/ 100 PY	Adjusted HR (95% CI)*	P value
Stroke/SEE	1.5	1.7	0.98 (0.84–1.14)	0.829	1.8	0.97 (0.80–1.19)	0.789	0.780
Stroke	1.5	1.6	0.97 (0.83–1.13)	0.714	1.7	0.93 (0.76–1.14)	0.490	0.494
Ischemic stroke	1.1	1.3	0.99 (0.83–1.18)	0.931	1.3	0.90 (0.71–1.13)	0.359	0.408
Hemorrhagic stroke	0.3	0.3	0.87 (0.62–1.21)	0.405	0.4	1.05 (0.69–1.61)	0.812	0.963
SEE	0.0	0.1	1.78 (0.60–5.27)	0.299	0.1	3.09 (0.94–10.15)	0.062	0.056
Major bleeding	0.8	1.1	1.21 (0.99–1.47)	0.059	1.7	1.77 (1.40–2.24)	<0.001	<0.001
ICH	0.7	0.7	0.94 (0.75–1.18)	0.616	1.0	1.22 (0.92–1.62)	0.169	0.254
GI bleeding	1.3	2.0	1.36 (1.17–1.58)	<0.001	3.1	1.89 (1.57–2.26)	<0.001	<0.001
CV events	3.6	6.1	1.32 (1.21–1.44)	<0.001	10.8	1.90 (1.71–2.11)	<0.001	<0.001
Heart failure requiring hospitalization	2.1	4.4	1.54 (1.38–1.72)	<0.001	8.7	2.40 (2.12–2.72)	<0.001	<0.001
Myocardial infarction	0.1	0.2	1.17 (0.73–1.89)	0.508	0.3	1.24 (0.70–2.22)	0.461	0.460
All-cause death	2.4	4.0	1.34 (1.20–1.49)	<0.001	5.8	1.65 (1.45–1.87)	<0.001	<0.001
CV death	0.6	1.2	1.48 (1.20–1.83)	<0.001	2.0	2.02 (1.59–2.58)	<0.001	<0.001
Fractures and falls	4.5	6.6	1.27 (1.17–1.39)	<0.001	9.2	1.58 (1.42–1.75)	<0.001	<0.001

*Versus 0–4 medications. CI, confidence interval; CV, cardiovascular; GI, gastrointestinal; ICH, intracranial hemorrhage; HR, hazard ratio; PY, patient-years; SEE, systemic embolic event.

The effect of anticoagulant treatment with DOACs compared with warfarin in relation to the clinical outcomes by medication groups was estimated using a Cox proportional hazards model. Significant interactions were not observed between polypharmacy and anticoagulant treatment with warfarin/DOAC in the incidence of all clinical outcomes, as well as heart failure requiring hospitalization (Table 4). Similar results were observed when excluding off-label dosing (Supplementary Table 2).

Table 4. Adjusted HRs for Direct Oral Anticoagulants vs. Warfarin for Each Clinical Outcome by Medication Group

	0–4 medications (n=12,186)		5–8 medications (n=13,597)		≥9 medications (n=5,636)		Interaction P value*
	Adjusted HR (95% CI)	P value	Adjusted HR (95% CI)	P value	Adjusted HR (95% CI)	P value	Interaction P value*
Stroke/SEE	0.86 (0.67–1.11)	0.238	0.86 (0.69–1.07)	0.189	0.70 (0.51–0.96)	0.028	0.537
Stroke	0.86 (0.67–1.11)	0.236	0.86 (0.69–1.07)	0.172	0.75 (0.54–1.04)	0.085	0.644
Ischemic stroke	0.87 (0.65–1.17)	0.357	0.87 (0.68–1.11)	0.256	0.66 (0.45–0.98)	0.037	0.488
Hemorrhagic stroke	0.77 (0.46–1.30)	0.325	0.74 (0.45–1.21)	0.233	1.35 (0.66–2.76)	0.409	0.513
SEE	0.56 (0.04–7.64)	0.665	0.65 (0.18–2.35)	0.514	0.20 (0.04–0.88)	0.034	0.809
Major bleeding	0.87 (0.62–1.21)	0.406	0.60 (0.46–0.77)	<0.001	0.88 (0.63–1.24)	0.465	0.115
ICH	0.77 (0.54–1.10)	0.153	0.54 (0.39–0.75)	<0.001	0.82 (0.53–1.27)	0.377	0.146
GI bleeding	1.07 (0.80–1.42)	0.660	1.07 (0.87–1.32)	0.516	0.91 (0.71–1.16)	0.454	0.542
CV events	0.82 (0.70–0.96)	0.016	0.84 (0.75–0.94)	0.002	0.88 (0.77–1.01)	0.073	0.908
Heart failure requiring hospitalization	0.77 (0.63–0.95)	0.013	0.80 (0.70–0.91)	<0.001	0.88 (0.76–1.03)	0.103	0.485
Myocardial infarction	1.02 (0.44–2.37)	0.956	0.77 (0.42–1.41)	0.397	1.08 (0.47–2.48)	0.849	0.333
All-cause death	0.89 (0.73–1.08)	0.246	0.88 (0.76–1.01)	0.060	0.83 (0.69–0.99)	0.038	0.664
CV death	0.81 (0.56–1.20)	0.294	0.81 (0.63–1.04)	0.093	0.84 (0.62–1.15)	0.284	0.900
Fractures and falls	0.77 (0.67–0.89)	<0.001	0.88 (0.78–0.98)	0.024	0.90 (0.78–1.05)	0.169	0.446

*The P value of the interaction between polypharmacy and the type of anticoagulant. Abbreviations as in Table 3.

Discussion

Major Findings

The main objective of this sub-analysis was to comprehensively assess the impact of polypharmacy on the variety of outcomes, of elderly patients with NVAF from the ANAFIE Registry. The key findings were as follows: (1) the prevalence of polypharmacy (≥5 medications) in elderly NVAF patients was high (59.6%), and patients with more concomitant medications were associated with higher age and more comorbidities; (2) the increasing number of medications tended to be associated with an increased incidence of clinical outcomes, including stroke/SEE, major bleeding, gastrointestinal bleeding, fracture/falls, cardiovascular events, and cardiovascular and all-cause death; (3) the number of medications was not independently associated with stroke/SEE and ICH, but was associated with an increase in all the other extracranial events; and (4) these relationships were independent of the prescription of anticoagulant therapy with DOACs or warfarin. These real-world results reinforce the critical impact of polypharmacy in the management of elderly NVAF patients and indicate that more attention should be paid to the extracranial events in elderly NVAF patients under polypharmacy.

Prevalence of Polypharmacy in Elderly NVAF Patients

The prevalence of polypharmacy in this study (59.6%) was slightly higher than that reported in a previous study on elderly (aged ≥75 years) patients with AF (52%)² and lower than the rates of 71.0% and 76.5% reported in previous meta-analyses and reviews.⁴^,⁷ Additionally, the background characteristics of our patients who received an increased number of concomitant medications were consistent with those reported in a previous study of polypharmacy in elderly AF patients aged ≥75 years.² This finding is unsurprising, as it is generally recognized that polypharmacy is associated with multimorbidity frequently occurring in NVAF patients.¹⁴^–¹⁷ Our data also align with a previous cross-sectional analysis of patients aged between 50 and 80 years in a primary care setting. In that study, polypharmacy was also common and was strongly associated with hypertension, diabetes mellitus, chronic kidney disease, and cardiovascular disease.¹⁸

Stroke/SEE

The increasing incidence of stroke/SEE with polypharmacy observed in the present study was similar to that reported in the ARISTOTLE trial,¹⁹ in which stroke/SEE increased significantly with the number of drug treatments (P<0.001), representing a surrogate marker for this endpoint. Notably, in the multivariate analysis evaluating the association between medication groups and clinical outcomes, polypharmacy was not associated with increased risk of stroke/SEE, ischemic and hemorrhagic stroke. Therefore, the apparent increase in these events likely arose from the background profiles of patients under polypharmacy. These findings were consistent with those reported in a recent study by Chen et al² and the ROCKET-AF trial,²⁰ in which patients with polypharmacy did not have an increased independent risk of stroke/SEE, compared with those without polypharmacy.

Bleeding

We found that in the ANAFIE population, polypharmacy was independently associated with an increased risk of bleeding events, including major bleeding and gastrointestinal bleeding, differently from stroke/SEE. These real-world data support the results from randomized controlled trials²^,⁶^,⁷^,¹⁹^,²⁰ that reported a higher risk of bleeding among polypharmacy users compared with non-users, although the impact of polypharmacy seemed to be somewhat variable according to the nature of the studies. In the present study, prescription of ≥9 drugs was independently associated with a 1.7- to 1.9-fold increase in major bleeding and gastrointestinal bleeding.

The increased risk of bleeding among polypharmacy users is likely to be multifactorial and may be a direct effect of the medications, an effect of drug–drug interactions, or have other indirect causes.²¹ Antiplatelet drugs and non-steroidal anti-inflammatory drugs could directly cause major bleeding, particularly gastrointestinal bleeding. Warfarin has been reported to have many drug interactions,²² and DOACs have also been reported to have drug interactions with P-glycoprotein transport inhibitors and CYP3A4 inhibitors.⁷^,²³ These interactions could occur in polypharmacy and lead to an increased anticoagulant effect. Other potential reasons for the increased risk of bleeding include traumatic bleeding due to falls/fractures,²⁴ which is frequently observed in polypharmacy, as well as increased sources of gastrointestinal bleeding due to drug-induced gastrointestinal mucosal damages.²⁵

The impact of polypharmacy on ICH has been controversial. Although the ROCKET-AF trial²⁰ demonstrated a higher risk for ICH in patients prescribed ≥10 medications, this was not shown in the ARISTOTLE trial.¹⁹ Real-world data on the relationships between polypharmacy and ICH are lacking. The present data showed no significant differences in ICH incidence among the groups by multivariate analyses. In contrast, significant differences were observed in the incidence rate of ICH across the different medication groups by Kaplan-Meier analysis (higher rates observed in the ≥9 medications group [1.9%], 5–8 medications group [1.4%] and the 0–4 medications group [1.4%; P=0.016]). Patient profiles and/or the control level of comorbidities, particularly hypertension, might contribute to this apparent discrepancy.

Other Adverse Events

The present study found a relationship between polypharmacy and increased rates of fall/fracture. Polypharmacy itself has been reported as a risk factor for falls.²⁶ Of note, the Shinken database analysis study recently reported that elderly Japanese patients aged ≥75 years with NVAF receiving ≥10 medications had a significantly higher risk of fractures/falls,¹⁶ which is consistent with our findings.

Antihypertensive drugs, antiarrhythmic drugs, antiplatelet agents, and proton pump inhibitors were among the most common concomitant medications in this study. Although the association between antihypertensive and antiarrhythmic drugs and falls was inconsistent in previous studies,²⁶^,²⁷ cardiovascular medications and proton pump inhibitors are generally considered fall risk-increasing drugs,²⁸^–³⁰ and were common among the medications taken by elderly NVAF patients.³¹ Furthermore, a recent study reported that AF treatment, specifically antiarrhythmic drug use, substantially increased the risk of falls and syncope in a real-world cohort of older patients.³² Thus, the specific concomitant medications often prescribed to NVAF patients may explain why falls/fractures were frequent in patients with polypharmacy. Moreover, polypharmacy might be observed frequently in frail patients³³ who are prone to fall, thus representing a surrogate marker for fracture/fall.

It is interesting that the prescription rate for antiarrhythmic drugs increased with an increasing number of drugs prescribed. It would be expected that patients who would normally require ≥9 medications would be older, and that the majority of the physicians would avoid the use of antiarrhythmics agents for maintaining sinus rhythm in such patients. Indeed, 57.3% of the patients had non-paroxysmal AF, which would seem to indicate that there was less opportunity to administer antiarrhythmic drugs. In addition, in the case of sodium channel blockers use, the risk of falls and trauma might be increased due to exacerbations of sinus node dysfunction, including tachycardia-bradycardia syndrome. It is possible that many cardiologists, who specialize in managing arrhythmia, participated in the ANAFIE Registry, which may explain these results. Furthermore, β-blockers are classified as antiarrhythmic agents and are among the antiarrhythmic drugs recommended for rate control in Japan³⁴ and elsewhere.³⁵^,³⁶ Rate control has been referred to as the frontline therapy for managing AF and has been associated with reduced mortality rates among Asian patients with AF and heart failure.³⁷ Beta-blocker treatment has also been associated with improved prognosis among AF patients without heart failure.³⁸

Polypharmacy was also associated with an increased risk of all-cause and cardiovascular deaths in the present sub-analysis, a finding consistent with previous studies.¹⁹^,²⁰ However, the relationship between polypharmacy and all-cause and cardiovascular deaths is more complex than that between polypharmacy and major bleeding. As polypharmacy is a marker of multimorbidity,³⁹ it is possible that adjustments in the multivariate analysis did not sufficiently correct for confounders or that unknown confounding factors were involved. Although we found that polypharmacy was an independent determinant of these outcomes, the question of whether there is a causal relationship with any of the studied factors warrants further investigation.

Relationships to Anticoagulant Drugs

The absence of interaction between polypharmacy and DOAC treatment indicates that the efficacy and safety of DOACs compared with warfarin was maintained in this population, regardless of the number of concomitant medications prescribed. This finding is consistent with prior studies in which polypharmacy did not impact DOAC effectiveness,²^,⁷ and indicates the need for careful management of NVAF patients under polypharmacy, irrespective of the type of OACs used.

Strengths and Limitations

This sub-analysis has some important features. Unlike randomized controlled trials, the ANAFIE Registry has a lower potential for patient selection bias as the selection criteria were less stringent and allowed for the inclusion of patients with multimorbidity, who are generally excluded from clinical trials. The quality of data in this study was much higher than the studies using healthcare insurance databases because of the monitoring and validation processes. Of note, the present study was based on data obtained from an extensive database, including over 30,000 cases, permitting the evaluation of the independent significance of polypharmacy in this population. Although clinical trials regarding deprescribing medications for NVAF patients have not been performed, a previous study examining the outcome by deprescribing medications in a randomized controlled fashion found no significant increase in mortality in general elderly patients.⁴⁰ Our study indicates that more caution is warranted for extracranial events in NVAF patients under polypharmacy, and highlights a greater need for assessing the effects of deprescribing in increasing elderly NVAF patients.

The limitations of the ANAFIE Registry have been previously described¹⁰ and are mainly related to the observational study design and the enrollment of only Japanese patients, which limits the generalizability of the findings. Furthermore, although the number of patients was sufficient to adjust for known confounders, unknown confounding factors cannot be completely ruled out. Finally, the numbers of medications used to classify patients into medication groups for this analysis were based on data obtained at the time of registration, and any changes, as well as adherence to the prescribed treatment during follow up, were not considered.

Conclusions

The prevalence of polypharmacy among elderly NVAF patients from the ANAFIE Registry was high at nearly 60%. Polypharmacy was associated with increased risks of major bleeding, gastrointestinal bleeding, fall/fracture, cardiovascular events, cardiovascular death, and all-cause death, but not stroke/SEE or ICH, irrespective of the type of OACs used.

The present study findings suggest that elderly NVAF patients with polypharmacy require more comprehensive management, closer follow up, and monitoring for extracranial events than patients receiving fewer medications. Interventions by clinicians, such as drug reviews aiming to reduce concomitant medications, particularly those that increase the risk of falls, could potentially reduce the risk of bleeding, fall/fracture, cardiovascular events, and death among elderly patients with NVAF and polypharmacy.

Supplementary Material

Supplementary material associated with this article can be found in the online version, at doi:10.1253/circj.CJ-22-0170.

Acknowledgments

The authors wish to thank all individuals (physicians, nurses, institutional staff, and patients) involved in the ANAFIE Registry. They also thank IQVIA Services Japan K.K. and EP-CRSU for their partial support in the conduct of this Registry, and Keyra Martinez Dunn, MD, of Edanz (www.edanz.com) for providing medical writing support, which was funded by Daiichi Sankyo Co., Ltd., in accordance with Good Publication Practice (GPP3) guidelines (http://www.ismpp.org/gpp3). In addition, the authors thank Daisuke Chiba, of Daiichi Sankyo Co., Ltd., for supporting the preparation of the manuscript.

Sources of Funding

This research was supported by Daiichi Sankyo Co., Ltd.

Disclosures

T. Yamashita received research funding from Bristol-Myers Squibb, Bayer, and Daiichi Sankyo, manuscript fees from Daiichi Sankyo and Bristol-Myers Squibb, and remuneration from Daiichi Sankyo, Bayer, Pfizer Japan, and Bristol-Myers Squibb. M.A. received research funding from Bayer and Daiichi Sankyo, and remuneration from Bristol-Myers Squibb, Nippon Boehringer Ingelheim, Bayer, and Daiichi Sankyo. H.A. received remuneration from Daiichi Sankyo. T.I. received research funding from Daiichi Sankyo and Bayer, and remuneration from Daiichi Sankyo, Bayer, Nippon Boehringer Ingelheim, and Bristol-Myers Squibb. Y.K. received remuneration from Daiichi Sankyo, Bayer, and Nippon Boehringer Ingelheim. K.O. received remuneration from Nippon Boehringer Ingelheim, Daiichi Sankyo, Johnson & Johnson, and Medtronic. W.S. received research funding from Bristol-Myers Squibb, Daiichi Sankyo, and Nippon Boehringer Ingelheim, and patent royalties/licensing fees from Daiichi Sankyo, Pfizer Japan, Bristol-Myers Squibb, Bayer, and Nippon Boehringer Ingelheim. S.S. received research funding from Daiichi Sankyo, and remuneration from Bristol-Myers Squibb and Daiichi Sankyo. H.T. received research funding from Daiichi Sankyo and Nippon Boehringer Ingelheim, remuneration from Daiichi Sankyo, Bayer, Nippon Boehringer Ingelheim, and Pfizer Japan, scholarship funding from Daiichi Sankyo, and consultancy fees from Pfizer Japan, Bayer, and Nippon Boehringer Ingelheim. K.T. received received lecture honoraria from Daiichi-Sankyo, Otsuka, Novartis, Abbott, Bayer Yakuhin, and Bristol-Myers Squibb. A.H. participated in a course endowed by Boston Scientific Japan, has received research funding from Daiichi Sankyo and Bayer, and remuneration from Bayer, Daiichi Sankyo, Bristol-Myers Squibb, and Nippon Boehringer Ingelheim. M.Y. received research funding from Nippon Boehringer Ingelheim, and remuneration from Nippon Boehringer Ingelheim, Daiichi Sankyo, Bayer, Bristol-Myers Squibb, and Pfizer Japan. T. Yamaguchi acted as an advisory board member for Daiichi Sankyo and has received remuneration from Daiichi Sankyo and Bristol-Myers Squibb. S.T. received research funding from Nippon Boehringer Ingelheim and remuneration from Daiichi Sankyo, Sanofi, Takeda, Chugai Pharmaceutical, Solasia Pharma, Bayer, Sysmex, Nipro, NapaJen Pharma, Gunze, Kaneka, Kringle Pharma, and Atworking. T.K., Y.M., and A.T. are employees of Daiichi Sankyo. H.I. received remuneration from Daiichi Sankyo, Bayer, Bristol-Myers Squibb, and Nippon Boehringer Ingelheim.

Author Contributions

T. Yamashita, M.A., H.A., T.I., K.O., Y.K., S.S., W.S., H.T., K.T., A.H., M.Y., T. Yamaguchi, and H.I. designed and conducted the study; T. Yamashita interpreted the data analysis; S.T. carried out statistical analyses; T. Yamashita, T.K., Y.M., and A.T. wrote and reviewed the manuscript; all authors revised and commented on the manuscript, and approved the final version.

IRB Information

Ethical approval was obtained from all relevant institutional review boards, and all patients provided written informed consent and were free to withdraw from the registry at any time. The name of the principal ethics committee was The Ethics Committees of The Cardiovascular Institute (Tokyo, Japan) and the approval number was 299.

Data Availability

1. Will the individual deidentified participant data (including data dictionaries) be shared?

→ Yes

2. What data in particular will be shared?

→ Individual participant data that underlie the results reported in this article, after deidentification (text, tables, figures, and appendices).

3. Will any additional, related documents be available? If so, what is it? (e.g., study protocol, statistical analysis plan, etc.)

→ Study Protocol

4. When will the data become available and for how long?

→ Ending 36 months following article publication.

5. By what access criteria will the data be shared (including with whom)?

→ The access criteria for data sharing (including requests) will be decided by a committee led by Daiichi-Sankyo.

6. For what types of analyses, and by what mechanism will the data be available?

→ Any purpose: Proposals should be directed to yamt-tky@umin.ac.jp

To gain access, data requestors will need to sign a data access agreement.

Supplementary Files

Please find supplementary file(s);

https://doi.org/10.1253/circj.CJ-22-0170

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