Association of digoxin with mortality in patients with advanced chronic kidney disease: A population-based cohort study

Lii-Jia Yang; Shan-Min Hsu; Ping-Hsun Wu; Ming-Yen Lin; Teng-Hui Huang; Yi-Ting Lin; Hung-Tien Kuo; Yi-Wen Chiu; Shang-Jyh Hwang; Jer-Chia Tsai; Hung-Chun Chen

doi:10.1371/journal.pone.0245620

. 2021 Jan 15;16(1):e0245620. doi: 10.1371/journal.pone.0245620

Association of digoxin with mortality in patients with advanced chronic kidney disease: A population-based cohort study

Lii-Jia Yang ^1,², Shan-Min Hsu ², Ping-Hsun Wu ^2,³, Ming-Yen Lin ^2,^4,⁵, Teng-Hui Huang ², Yi-Ting Lin ^3,^6,⁷, Hung-Tien Kuo ^2,⁴, Yi-Wen Chiu ^2,⁴, Shang-Jyh Hwang ^2,⁴, Jer-Chia Tsai ^2,^4,^*, Hung-Chun Chen ^2,⁴

Editor: Hans-Peter Brunner-La Rocca⁸

PMCID: PMC7810292 PMID: 33449946

Abstract

Digoxin is commonly prescribed for heart failure and atrial fibrillation, but there is limited data on its safety in patients with chronic kidney disease (CKD). We conducted a population-based cohort study using the pre-end stage renal disease (ESRD) care program registry and the National Health Insurance Research Database in Taiwan. Of advanced CKD patient cohort (N = 31,933), we identified the digoxin user group (N = 400) matched with age and sex non-user group (N = 2,220). Multivariable Cox proportional hazards and sub-distribution hazards models were used to evaluate the association between digoxin use and the risk of death, cardiovascular events (acute coronary syndrome, ischemic stroke, or hemorrhagic stroke) and renal outcomes (ESRD, rapid decline in estimated glomerular filtration rate—eGFR, or acute kidney injury). Results showed that all-cause mortality was higher in the digoxin user group than in the non-user group, after adjusting for covariates (adjusted hazard ratio, aHR 1.63; 95% CI 1.23–2.17). The risk for acute coronary syndrome (sub-distribution hazard ratio, sHR 1.18; 95% CI 0.75–1.86), ischemic stroke (sHR 1.42; 95% CI 0.85–2.37), and rapid eGFR decline (sHR 1.00 95% CI 0.78–1.27) was not significantly different between two groups. In conclusion, our study demonstrated that digoxin use was associated with increased mortality, but not cardiovascular events or renal function decline in advanced CKD patients. This finding warns the safety of prescribing digoxin in this population. Future prospective studies are needed to overcome the limitations of cohort study design.

Introduction

Digoxin, a cardiac glycoside, decreases heart rate and increases myocardial contractility by inhibiting cellular sodium-potassium adenosine triphosphatase (N+/K+-ATPase). Digoxin has been prescribed to treat heart failure (HF) or atrial fibrillation (AF).

In high-risk subgroups of patients with HF, such as those with New York Heart Association (NYHA) class III–IV, left ventricular ejection fraction (LVEF) <25% and cardiothoracic ratio >55%, digoxin was associated with a lower risk of all-cause mortality or hospitalization [1, 2]. Current guidelines recommend digoxin be considered for symptomatic HF patients to reduce hospitalization risk, despite receiving standard therapy, including beta-blockers, angiotensin-converting enzyme inhibitors, angiotensin-receptor blockers, or mineralocorticoid receptor antagonists [3].

By contrast, in patients with AF, treatment with digoxin could be associated with increased mortality [4, 5]. However, in a recent meta-analysis of randomized control trials, the clinical effects of digoxin on all-cause mortality, serious adverse events, quality of life, heart failure, and stroke in patients with AF remains unclear [6]. Current guidelines recommend digoxin as a rate control agent in patients with AF, particularly in those with concomitant HF [7, 8].

Patients with chronic kidney disease (CKD) have multiple comorbidities, including HF and AF [9, 10], making CKD patients possible candidates for using digoxin. However, digoxin is predominantly excreted by the kidneys, so impaired renal function can significantly influence its pharmacokinetics [11]. In addition, digoxin has a narrow therapeutic-toxicity range [12], possesses multiple drug-drug interactions [13], and the manifestation of its toxicity, nausea and vomiting, could mimic the uremic symptoms of late CKD. Notably, it was reported that CKD did not directly affect all-cause and cardiovascular mortality in patients with AF taking digoxin [14]. However, there is still a concern in prescribing digoxin for CKD patients because of its safety. Currently, limited data is addressing its safety in CKD patients from the population-based approach. This study aimed to investigate the effect of digoxin on all-cause mortality, cardiovascular events, and renal outcomes in a nationwide CKD cohort in Taiwan.

Materials and methods

A brief overview of Taiwan pre-end-stage renal disease (ESRD) pay-for-performance program

National Health Insurance (NHI) is a mandatory, universal, and single-payer insurance system in Taiwan, covering over 99% of the population. The Pre- ESRD care program, implemented by NHI in 2006, is a pay-for-performance healthcare model designed to prevent or delay dialysis, avoid uremic complications, and reduce health care costs through patient-centered case management by a multidisciplinary team. Eligibility criteria were individuals with CKD stages 3b-5 (eGFR<45 mL/min/1.73 m²), or those with proteinuria (urine protein to creatinine ratio, UPCR >1000 mg/g). Participating patients were required to attend a hospital at least quarterly for clinical and laboratory evaluation by a nephrologist, CKD education provided by a renal nurse, and a diet consultation by a dietitian. Participating health care providers received additional payment for patient enrollment and each follow-up visit, and they were also rewarded if they achieved predetermined targets, such as a reduced estimated glomerular filtration rate (eGFR) progression (<6 ml/min/1.73m²), complete remission of proteinuria (UPCR <200 mg/g), and vascular access before dialysis.

Study population and cohort

We conducted a retrospective cohort study using the Pre-ESRD care program registry linked with the National Health Insurance Research Database (NHIRD), containing detailed information on inpatient and outpatient services. To protect patients’ privacy, NHIRD had made all data fully anonymized by replacing all personal identification with surrogate numbers before researchers accessed them and further analyzed them. We included patients diagnosed with CKD, based on the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes 585 and 581.9, on at least two outpatient visits or one hospitalization between January 1, 2007 and December 31, 2011. Individuals younger than 18 years of age, with early-stage CKD (stages 1-3a), with eGFR recorded <3 times, or without baseline laboratory data were excluded. Individuals who died within three months of enrollment, underwent dialysis within three months of being enrolled, or received a renal transplant were also excluded. The index date was the date the patients were enrolled in the pre-ESRD program. CKD patients receiving digoxin treatment were defined as digoxin users, then each user was matched with five untreated control patients selected from the same registry, according to age and sex. Patients were followed until death, ESRD, or until 2012, whichever occurred first. This study was approved by the Institutional Review Board (IRB) of Kaohsiung Medical University Hospital (KMUHIRB-EXEMPT(I)-20180035), and the requirement for informed consent was waived.

Measurement of outcomes

Outcomes were the occurrence of all-cause mortality, major cardiovascular events (composite endpoints of acute coronary syndrome [ACS], ischemic stroke and hemorrhagic stroke), and renal outcomes (ESRD, rapid eGFR decline and acute kidney injury—AKI) during the study period. Death was ascertained based on the evidence of patient withdrawal from the NHI claim database. ACS, ischemic stroke, and hemorrhagic stroke were defined as hospitalization for these vascular events, which were validated in previous studies [15, 16]. For example, ICD-9-CM codes 433 (occlusion of cerebral arteries) and 434 (stenosis of precerebral arteries) were used to extract from NHIRD study subjects with ischemic stroke and were admitted for the specific diagnosis.

Patients with a subsequent diagnosis of ESRD were identified from the Registry for Catastrophic Illness Patient Database. The accuracy of ESRD diagnosis was ascertained because all ESRD patients in Taiwan were reviewed and issued a catastrophic illness registration card from the NHI Administration for waiving the co-payments for long-term dialysis. Moreover, the rapid eGFR decline was defined as a one-year eGFR slope >5 mL/min per 1.73 m² after the index date. AKI events were identified according to ICD-9-CM codes 584 [17].

Comorbidities and exposure to confounding medications

The following comorbidities were identified as potential confounders: diabetes mellitus (DM), hypertension, hyperlipidemia, coronary artery disease, cerebrovascular disease, AF, HF, gout, and malignancy (S1 Table). The definition of DM, hypertension, and hyperlipidemia required both the specific ICD-9-CM codes and the use of disease-defining medications for a minimum of 90 days. Comorbidities were scored based on a comorbidity index developed for Chinese ESRD patients [18].

We also retrieved details regarding medication usage during the study, including antiplatelet agents/warfarin, antihypertensive drugs (angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, beta-blockers, diuretics, and calcium channel blockers), statins, oral antidiabetic agents, insulin, and nonsteroidal anti-inflammatory drugs (both traditional agents and cyclooxygenase-2 selective inhibitors; S2 Table). Medication use was defined as drugs with an accumulated duration of more than 28 days during the study period.

Characteristics of clinical data

The abbreviated Modification of Diet in Renal Disease equation was used to calculate the eGFR [19]. Baseline eGFR was calculated from the last recorded serum creatinine level before the index date. The change of eGFR between one year of follow-up and baseline was then calculated for each subject. Clinical data included body weight, blood pressure, hematocrit, serum albumin, and UPCR. The stage of CKD was defined according to baseline eGFR.

Statistical analysis

Baseline descriptive data were described as mean ± standard deviation for continuous variables, and frequency and percentage were displayed for categorical variables. The incidence rate ratios (IRRs) and 95% confidence intervals (95% CIs) of outcomes (all-cause mortality, cardiovascular events, and renal outcomes) for digoxin users versus non-users were examined by using the Poisson regression model [20]. Regarding the all-cause mortality outcome, we employed Cox proportional hazards model. Furthermore, we applied the Fine and Gray subdistribution hazards model to clarify the competing risk of death and the effects of digoxin on the cardiovascular and renal outcomes [21]. We applied the multivariable models to adjust the confounders for urbanization, socioeconomic status, comorbid disorders, clinical characteristics, and each medication class.

Intention-to-treat (ITT) and as-treated (AT) analyses were conducted. For ITT analysis, digoxin users and non-users were followed until the end of the study according to their original treatment allocations regardless of adherence by patients, subsequent withdrawal, or any change in treatment status over time. For AT analysis, the person-time was censored on the day of digoxin discontinuation. Patients were allowed to have a grace period of up to 30 days between prescription dates when calculating continuous therapy. Both approaches are associated with different biases and might complement each other [22]. All analyses were performed using SAS statistical software (version 9.2; SAS Institute Inc., www.sas.com). All statistical tests were two-sided. P < 0.05 was considered statistically significant.

Sensitivity analysis

To assess the robustness of our results, we conducted sensitivity analyses using: (1) a logistic regression model that included age, sex, urbanization, socioeconomic status, comorbidities, clinical characteristics, and concurrent medications as covariates to compute the propensity score, and by performing ITT analysis based on patients matched by propensity score; (2) propensity score-matched Cox regression models and performing AT analysis; (3) re-defined digoxin users as cumulative use ≥28 days and analyzing the original age- and sex-matched cohort with multivariable-adjusted models; (4) re-defined digoxin users as cumulative use ≥56 days; and (5) re-defined digoxin users as cumulative use ≥84 days.

Results

Baseline characteristics

As shown in Fig 1, a total of 31,993 patients had advanced CKD diagnosed between 1 January 2007 and 31 December 2011. We identified 440 CKD patients treated with digoxin and 2,200 non-users, by age- and sex-matching process. Table 1 shows the baseline characteristics of the study population. Patients who received digoxin were more likely to have DM, coronary artery disease, cerebrovascular disease, AF, chronic HF, and gout; however, they were less likely to have hypertension. Higher comorbidities scores were found for digoxin users than for non-users. Baseline clinical data demonstrated that digoxin users had higher eGFR and hematocrit, but lower systolic and diastolic blood pressure. A higher proportion of patients in the digoxin user group received concomitant medical treatment, including antiplatelet agents/warfarin, beta-blockers, diuretics, oral antidiabetic agents, and insulin, compared to those who did not use digoxin. The mean follow-up times for digoxin users and non-users were 24.8 and 26.4 months, respectively.

Table 1. Baseline characteristics among patients with chronic kidney disease receiving digoxin or not.

Characteristics	With Digoxin (n = 440)	Without Digoxin (n = 2,200)	P value
Age, yr, mean ± SD	73.9 ± 9.9	73.9 ± 9.9	>0.999
Age group, yr, n (%)			>0.999
18–39	2 (0.5%)	10 (0.5%)
40–59	47 (10.7%)	235 (10.7%)
60–79	249 (56.6%)	1245 (56.6%)
≥80	142 (32.3%)	710 (32.3%)
Men, n (%)	288 (65.5%)	1440 (65.5%)	>0.999
Urbanization level, n (%)			0.525
City area	308 (70.0%)	1576 (71.6%)
Rural area	132 (30.0%)	624 (28.4%)
Socioeconomic status^a, n (%)			0.743
Low economics	174 (39.5%)	834 (37.9%)
Moderate economics	109 (24.8%)	579 (26.3%)
High economics	157 (35.7%)	787 (35.8%)
CKD stage, n (%)			0.126
3b	152 (34.5%)	685 (31.1%)
4	186 (42.3%)	907 (41.2%)
5	102 (23.2%)	608 (27.6%)
Comorbidities^b, n (%)
Diabetes mellitus	254 (57.7%)	1067 (48.5%)	<0.001
Hypertension	332 (75.5%)	1784 (81.1%)	0.008
Hyperlipidemia	150 (34.1%)	671 (30.5%)	0.153
Coronary artery disease	203 (46.1%)	534 (24.3%)	<0.001
Cerebrovascular disease	72 (16.4%)	244 (11.1%)	0.002
Atrial fibrillation	130 (29.5%)	46 (2.1%)	<0.001
Heart failure	227 (51.6%)	237 (10.8%)	<0.001
Gout	138 (31.4%)	572 (26.0%)	0.024
Malignancy	35 (8.0%)	214 (9.7%)	0.284
Comorbidities score^c, median (IQR)	7.0 (5.0, 10.0)	4.0 (2.0, 7.0)	<0.001
Clinical characteristics
Body weight, kg	62.8 ± 12.4	63.9 ± 11.8	0.072
eGFR, ml/min per 1.73 m²	24.8 ± 10.3	23.3 ± 10.8	0.008
SBP, mmHg	129.5 ± 19.5	135.2 ± 18.1	<0.001
DBP, mmHg	72.0 ± 13.1	74.0 ± 11.6	0.002
Hematocrit, %	34.0 ± 6.0	33.2 ± 5.6	0.011
Serum albumin, g/dl	3.9 ± 0.5	4.0 ± 0.5	0.090
UPCR, mg/g	598.5 (209.5, 2428)	775.0 (233.0, 1848)	0.884
Medications used, n (%)
Antiplatelets/Warfarin	330 (75.0%)	974 (44.3%)	<0.001
ACEI/ARB	337 (76.6%)	1636 (74.4%)	0.357
B-blocker	248 (56.4%)	1114 (50.6%)	0.032
CCB	285 (64.8%)	1663 (75.6%)	<0.001
Diuretics	329 (74.8%)	1130 (51.4%)	<0.001
Statin	183 (41.6%)	936 (42.5%)	0.751
Oral antidiabetic agents	216 (49.1%)	885 (40.2%)	<0.001
Insulin	116 (26.4%)	386 (17.5%)	<0.001
NSAIDs	135 (30.7%)	683 (31.0%)	0.925
Duration of follow-up, mo, mean ± SD	24.8 ± 14.0	26.4 ± 14.8	0.031

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Footnote: eGFR, Estimated GFR; SBP, systolic blood pressure; DBP, diastolic blood pressure; UPCR, urine protein to creatinine ratio; ACEI, angiotensin-converting enzyme inhibitors; ARB, angiotensin receptor blockers; CCB, calcium channel blockers; NSAIDs, nonsteroidal anti-inflammatory drugs

^aSocioeconomic status: low economics = Dependent; moderate economics = NT$ <20000; high economics = NT$ ≥20000

^bComorbidity defined as once inpatient or twice outpatient records one year before index date

^cComorbidities score was defined as Taiwan index for hemodialysis (Reference: Clin J Am Soc Nephrol 9: 513–519, 2014)

All-cause mortality, cardiovascular events, and renal outcomes

From ITT analysis, digoxin-treated CKD patients had a higher risk of all-cause mortality (IRR 2.25, 95% CI 1.81–2.80), ACS (IRR 1.86, 95% CI 1.30–2.67), ischemic stroke (IRR 1.91, 95% CI 1.28–2.86), and AKI (IRR 1.70, 95% CI 1.34–2.16), compared with non-digoxin-treated CKD patients (Table 2).

Table 2. Association between digoxin used or not and all-cause mortality, major cardiovascular events, and renal function decline in patients with chronic kidney disease using intention-to-treat analysis.

Variable	Overall events			Adjusted Hazard Ratio (95% CI)
Variable	With Digoxin use	Without Digoxin used	IRR (95% CI)	Model 1^a	Model 2^b	Model 3^c
All-cause mortality^§	113	268	2.25 (1.81–2.80)^***	1.73 (1.32–2.27)^***	1.86 (1.41–2.45)^***	1.63 (1.23–2.17)^***
Major cardiovascular events^¶	73	212	1.88 (1.44–2.46)^***	1.59 (1.13–2.22)^**	1.70 (1.20–2.40)^**	1.33 (0.95–1.86)
Acute coronary syndrome^¶	40	116	1.86 (1.30–2.67)^***	1.33 (0.85–2.09)	1.41 (0.89–2.24)	1.18 (0.75–1.86)
Ischemic stroke^¶	32	90	1.91 (1.28–2.86)^**	1.74 (1.04–2.91)^*	1.79 (1.07–3.00)^*	1.42 (0.85–2.37)
Hemorrhagic stroke^¶	5	23	1.16 (0.44–3.06)	1.20 (0.44–3.26)	1.35 (0.48–3.77)	1.30 (0.44–3.87)
End-stage renal disease^¶	55	370	0.79 (0.60–1.05)	0.65 (0.47–0.91)^*	0.78 (0.55–1.12)	0.80 (0.55–1.14)
Rapid eGFR decline^¶^#	117	518	1.14 (0.93–1.39)	1.08 (0.85–1.37)	1.10 (0.86–1.39)	1.00 (0.78–1.27)
Acute kidney injury^¶	88	284	1.70 (1.34–2.16)^***	1.27 (0.93–1.72)	1.39 (1.02–1.90)*	1.20 (0.87–1.64)

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Footnote: IRR, incidence rate ratio; eGFR, estimated GFR

^§All-cause mortality was assessed using a Cox proportional hazard model.

^¶Cardiovascular and renal outcomes were assessed using a Fine & Gray subdistribution hazard model for competing risk of mortality.

^#Rapid eGFR decline defined as one year eGFR slope > 5 mL/min per 1.73 m²

^aModel 1: Adjusted for urbanization, socioeconomic status, comorbidities (diabetes mellitus, hypertension, hyperlipidemia, coronary artery disease, cerebrovascular disease, heart failure, gout, and malignancy).

^bModel 2: Adjusted for urbanization, socioeconomic status, comorbid disorders, clinical characteristics (eGFR, systolic blood pressure, diastolic blood pressure, hematocrit, serum albumin, urine protein creatinine ratio).

^cModel 3: Adjusted for urbanization, socioeconomic status, comorbid disorders, clinical characteristics, medications (antiplatelets, warfarin, angiotensin-converting enzyme inhibitor/angiotensin receptor blockers, calcium channel blocker, beta blocker, calcium channel blocker, diuretics, statin, oral antidiabetic agents, insulin, and nonsteroidal anti-inflammatory drugs).

* P < 0.05;

** P < 0.01;

*** P < 0.001

After adjusting for urbanization, socioeconomic status, comorbid disorders, clinical characteristics, medications, and competing risks of mortality, digoxin use was independently associated with higher mortality (adjusted hazard ratio, aHR 1.63; 95% CI 1.23–2.17). However, digoxin users were not associated with higher major cardiovascular events compared to digoxin non-users (sHR 1.33, 95% CI 0.95–1.86; Table 2, model 3). The risk of single cardiovascular event was not significantly different between two groups after adjusting for covariates, including ACS (sHR 1.18, 95% CI 0.75–1.86), ischemic stroke (sHR 1.42, 95% CI 0.85–2.37), and hemorrhagic stroke (sHR 1.30, 95% CI 0.44–3.87). No difference in risk was found in renal outcomes, including ESRD (sHR 1.18, 95% CI 0.75–1.86), rapid eGFR decline (sHR 1.18, 95% CI 0.75–1.86), and AKI (aHR 1.20, 95% CI 0.87–1.64).

AT analysis revealed similar results, with significantly higher all-cause mortality in the digoxin user group compared to the non-user group (aHR 2.06, 95% CI 1.47–2.88) after adjusting for covariates (Table 3). There was no difference in cardiovascular events or renal outcomes on AT analysis.

Table 3. Association between digoxin used or not and all-cause mortality, major cardiovascular events, and renal function decline in patients with chronic kidney disease using as treat analysis.

Variable	Overall events			Adjusted Hazard Ratio (95% CI)
Variable	With Digoxin use	Without Digoxin used	IRR (95% CI)	Model 1^a	Model 2^b	Model 3^c
All-cause mortality^§	60	268	2.63 (1.99–3.48)^***	2.43 (1.76–3.36)^***	2.28 (1.64–3.16)^***	2.06 (1.47–2.88)^***
Major cardiovascular events^¶	65	184	4.35 (3.28–5.77)^***	1.80 (1.15–2.83)^*	1.82 (1.14–2.91)^*	1.49 (0.93–2.39)
Acute coronary syndrome^¶	34	96	4.33 (2.93–6.40)^***	1.80 (1.01–3.22)^*	1.82 (1.00–3.29)^*	1.62 (0.89–2.94)
Ischemic stroke^¶	29	79	4.32 (2.83–6.62)^***	1.59 (0.76–3.32)	1.51 (0.72–3.20)	1.23 (0.56–2.67)
Hemorrhagic stroke^¶	5	22	2.68 (1.01–7.07)^*	1.55 (0.38–6.27)	1.68 (0.40–7.06)	1.57 (0.32–7.66)
End-stage renal disease^¶	24	370	0.76 (0.50–1.15)	0.67 (0.42–1.07)	0.66 (0.40–1.09)	0.67 (0.40–1.12)
Rapid eGFR decline^¶^#	67	518	1.37 (1.06–1.76)^*	1.30 (0.98–1.72)	1.26 (0.95–1.67)	1.15 (0.87–1.54)
Acute kidney injury^¶	72	216	4.21 (3.22–5.49)^***	1.51 (0.98–2.34)	1.49 (0.96–2.32)	1.30 (0.83–2.03)

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Footnote: IRR, incidence rate ratio; eGFR, estimated GFR

^§All-cause mortality was assessed using a Cox proportional hazard model.

^¶Cardiovascular and renal outcomes were assessed using a Fine & Gray subdistribution hazard model for competing risk of mortality.

^#Rapid eGFR decline defined as one year eGFR slope > 5 mL/min per 1.73 m²

* P < 0.05;

** P < 0.01;

*** P < 0.001

Sensitivity analysis

The above results demonstrated that digoxin users were consistently associated with a higher all-cause mortality rate than digoxin non-users. Using the Cox proportional hazards model, the aHRs were 1.58 (95% CI 1.09–2.28) in the propensity score matching model with ITT analysis, 2.09 (95% CI 1.31–3.32) in the propensity score matching model with AT analysis, 1.68 (95% CI 1.24–2.28) with re-defined digoxin users as cumulative use ≥28 days, 2.53 (95% CI 1.62–3.94) with re-defined digoxin users as cumulative use ≥56 days, and 2.95 (95% CI 1.62–5.39) with re-defined digoxin users as cumulative use ≥84 days (Table 4). Similarly, cardiovascular events or renal outcomes were not significantly different between these two groups.

Table 4. Sensitivity analyses showing the outcomes among patients with chronic kidney disease receiving digoxin or not.

	Adjusted Hazard Ratio (95% CI)^a
	Approach 1^b	Approach 2^c	Approach 3^d	Approach 4^e	Approach 5^f
All-cause mortality^§	1.58 (1.09–2.28)^*	2.09 (1.31–3.32)^**	1.68 (1.24–2.28)^***	2.53 (1.62–3.94)^***	2.95 (1.62–5.39)^***
Major cardiovascular events^¶	1.73 (1.13–2.67)^*	1.82 (0.95–3.47)	1.33 (0.93–1.91)	2.15 (1.31–3.54)^*	1.85 (0.85–4.03)
Acute coronary syndrome^¶	1.52 (0.85–2.73)	1.38 (0.61–3.15)	1.19 (0.73–1.94)	2.03 (0.98–4.19)	1.62 (0.48–5.43)
Ischemic stroke^¶	1.51 (0.77–2.95)	3.02 (0.80–11.4)	1.48 (0.86–2.54)	2.05 (0.95–4.42)	1.88 (0.55–6.38)
Hemorrhagic stroke^¶	Un-estimated	Un-estimated	1.55 (0.48–5.04)	1.37 (0.19–9.83)	2.29 (0.20–26.9)
End-stage renal disease^¶	0.85 (0.54–1.32)	0.49 (0.24–0.99)^*	0.80 (0.54–1.19)	0.75 (0.38–1.48)	1.13 (0.47–2.68)
Rapid eGFR decline^¶^#	1.19 (0.88–1.62)	0.95 (0.66–1.37)	0.97 (0.75–1.26)	0.91 (0.61–1.35)	0.77 (0.44–1.34)
Acute kidney injury^¶	1.23 (0.82–1.85)	1.14 (0.62–2.10)	1.17 (0.83–1.67)	1.40 (0.77–2.55)	1.41 (0.55–3.60)

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^§All-cause mortality was assessed using a Cox proportional hazard model.

^¶Cardiovascular and renal outcomes were assessed using a Fine & Gray subdistribution hazard model for competing risk of mortality.

^#Rapid eGFR decline defined as one year eGFR slope > 5 mL/min per 1.73 m²

^aAdjusted for urbanization, socioeconomic status, comorbidities (diabetes mellitus, hypertension, hyperlipidemia, coronary artery disease, cerebrovascular disease, heart failure, gout, and malignancy), clinical characteristics (eGFR, systolic blood pressure, diastolic blood pressure, hematocrit, serum albumin, urine protein creatinine ratio), medications (antiplatelets, warfarin, angiotensin-converting enzyme inhibitor/angiotensin receptor blockers, calcium channel blocker, beta blocker, calcium channel blocker, diuretics, statin, oral antidiabetic agents, insulin, and nonsteroidal anti-inflammatory drugs).

^bApproach 1: propensity score-matched approach as intention-to-treatment analysis

^cApproach 2: propensity score-matched approach as treated analysis

^dApproach 3: Digoxin users defined as cumulative used ≥ 28 days

^eApproach 4: Digoxin users defined as cumulative used ≥ 56 days

^fApproach 5 Digoxin users defined as cumulative used ≥ 84 days

* P < 0.05;

** P < 0.01;

*** P < 0.001

Discussion

In the observational study of advanced CKD patients (stages 3b to 5) using ITT and AT analysis, digoxin use was associated with increased mortality after adjusting for patient characteristics, comorbidities, and co-administered medications. This result remained consistent in the propensity score match and when different definitions for digoxin users were used. There was no significant difference in major cardiovascular events and renal outcomes between digoxin users and non-users.

Digoxin and mortality

Few prospective studies are investigating the effect of digoxin on mortality in CKD patients. In a retrospective study from the U.S. Department of Veterans Affairs healthcare system (TREAT-AF study—The Retrospective Evaluation and Assessment of Therapies in AF), digoxin use was associated with increased risk of death in patients with AF across all stages of CKD, except for dialysis patients [23]. Digoxin use was associated with a 28% increased risk of death in another hemodialysis cohort from North America [24]. In addition to being consistent with these two studies, our results had two noteworthy features. Firstly, all patients from our study were of Chinese ethnicity, which was different from previous reports that mainly comprised Caucasian and African American participants. Thus, the association of digoxin and mortality in CKD patients might be independent of race. Secondly, the proportion of HF in digoxin users was much higher in our study compared with the TREAT-AF study (51.6% versus 21.3%). Thus, our study suggested that the association of digoxin and mortality might apply to CKD patients with AF and extend to CKD patients with HF.

Digoxin and stroke

Digoxin has been reported to be associated with increased platelet and endothelial cell activation, which may predispose patients to thrombosis [25]. In two population-based cohort studies, digoxin was associated with an increased risk of ischemic stroke in patients with AF [26, 27]. Patients with low glomerular filtration rate (<60ml/min) has been shown to be associated with an increased risk of stroke in a meta-analysis [28]. Based on the above studies, it was anticipated that CKD patients using digoxin would have an increased risk of ischemic stroke. However, our study showed that digoxin did not affect the risk of ischemic stroke. The discrepancy might be explained by the use of antiplatelet agents or warfarin, which might offset the possible thrombogenic effect of digoxin. In our study, the proportion of antiplatelet agent or warfarin use was higher among digoxin users (75%), compared with non-users (44.3%). As shown in Table 2, the aHR for ischemic stroke was significant in models 1 and 2, but not in model 3, after medication use was taken into consideration. In the above-mentioned study conducted by Chang et al. [26], only 23.9% of digoxin users had co-administration of warfarin, much lower compared with our study. In addition, a post-hoc analysis conducted by Gjesdal et al., with all patients receiving anticoagulation treatment, revealed no increase in thromboembolic events with digoxin use [29].

Digoxin and renal function change

The toxicity of digoxin was manifested in several ways, mainly cardiac, neurological, and gastrointestinal, while direct renal toxicity has rarely been reported [30]. Consistently, in the current study, there was no significant difference in adverse renal outcomes for digoxin users and non-users; however, in post-hoc analysis conducted by Testani et al., digoxin was associated with renal function improvement in patients with HF, as compared with those taking a placebo [31].

There are several plausible explanations for the discordance. Patients from the current study were older and had worse baseline renal function, with a mean age of 73.9 ± 9.9 years and a mean eGFR of 24.8 ± 10.3ml/min per 1.73m², compared with patients in the study conducted by Testani et al. (63.4 ± 10.5 years and 70 ± 21.7ml/min per 1.73m² respectively) [31]. Old age and CKD have been associated with a lower probability of renal recovery from AKI [32, 33]. Aged kidneys have altered hemodynamics and physiological behavior in response to renal insults, which impair their ability to withstand and recover from injury [34]. In addition, the proportion of patients with HF in our study was 51.6% and 10.8% for digoxin users and non-users, respectively, while all patients in the report of Testani et al. [31] had HF. HF could cause renal dysfunction through hemodynamic changes and neurohormonal effects, termed cardiorenal syndrome [35]. Although the causes of renal dysfunction in both studies were unknown, patients with HF were more likely to have cardiorenal syndrome, which might be reversible once cardiac contractility was improved through digoxin treatment.

Limitations

Several limitations need to be considered in our cohort study design. Firstly, the Pre-ESRD program database collected the data mainly related to renal care only. Some data were not available in this cohort database. For instance, serum digoxin concentrations (SDC) were not measured. Previous studies reported that high SDC (>1.2 ng/mL) might be associated with increased mortality when compared with low SDC (0.5–0.8 ng/mL) [36, 37]. Moreover, serum potassium levels were also unavailable in this cohort database. Hyperkalemia might decrease the effectiveness of digoxin, whereas hypokalemia could potentiate its toxicity.

Secondly, digoxin users might be frailer due to concomitant HF or AF compared to digoxin non-users. The presence of HF was defined based on ICD-9-CM coding; however, the severity of HF was unavailable due to the lack of NYHA classification and ejection fraction. Thus, full matching according to HF status in both groups was not likely, which may lead to biases.

Thirdly, the final limitation that should be considered is the very small portion of patients who received digoxin. These patients are, therefore, not fully representative of the whole cohort. Our study aimed to enroll more digoxin users by defining digoxin users as any single digoxin exposure within three months before the index date, but only 440 digoxin users were enrolled in total. It might be explained by the stringent indication or safety concern on prescribing digoxin for CKD patients.

Despite these limitations, we try to reduce the influence of confounding factors on the outcomes by multivariable adjustment and propensity score matching and using different statistical analysis approaches. However, the inherent weaknesses of the population-based cohort study design may limit the generalizability of our findings, and we should interpret the results with caution.

In conclusion, our study demonstrated that digoxin use was associated with increased mortality in advanced CKD patients. Thus, digoxin should be prescribed with caution in this population. It warrants future prospective and randomized studies to determine the safety of digoxin in the advanced CKD patients.

Supporting information

S1 Table. ICD-9-CM codes used to identify clinical conditions.

(DOCX)

Click here for additional data file.^{(16.8KB, docx)}

S2 Table. Drugs prescriptions during observation period among patients with chronic kidney disease.

(DOCX)

Click here for additional data file.^{(17.2KB, docx)}

S1 Checklist. STROBE statement—checklist of items that should be included in reports of cohort studies.

(DOCX)

Click here for additional data file.^{(22KB, docx)}

Acknowledgments

This study was based on data from the National Health Insurance Research Database (NHIRD) of Taiwan provided by the Bureau of National Health Insurance, Department of Health, and managed by the National Health Research Institutes. The interpretation and conclusions contained herein do not represent the views of the Bureau of National Health Insurance, Department of Health, or National Health Research Institutes.

Data Availability

The data used in our study were limited to research purposes only and cannot be made publicly available under regulation of the Personal Information Protection Act in Taiwan. The raw data were obtained from the following sources and can be made available to qualified researchers upon request: NHIRD datasets H_NHI_OPDTE, H_NHI_IPDTE, H_NHI_DRUGE, H_NHI_OPDTO, H_NHI_IPDTO, H_NHI_DRUGO, H_NHI_ENROL, H_NHI_CATAS, and H_OST_DEATH from the Health and Welfare Data Science Center, Department of Statistics, Ministry of Health and Welfare, Taiwan (https://dep.mohw.gov.tw/DOS/np-2497-113.html). Pre-ESRD care program dataset from the National Health Insurance Administration, Ministry of Health and Welfare (https://www.nhi.gov.tw/Content_List.aspx?n=2D2FAF5214807829&topn=787128DAD5F71B1A).

Funding Statement

This study was supported by the Kaohsiung Municipal CiJin Hospital under Grant Kmch-108-001 to LJY, and partially supported by grants from the Kaohsiung Medical University Hospital (KMUH104-4M07, KMUH104-4M08, KMUH104-4R10, KMUH105-5R18, KMUH106-6R18, and KMUH107-7R17) and Ministry of Science and Technology, Taiwan, R.O.C. (MOST104-2511-S-037-004-MY2, MOST106-2511-S-037-002, and MOST107-2511-H-037-006-MY2) to JCT. The funders had no role in the study design, data collection and analysis, decision to publish, or manuscript preparation.

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PLoS One. doi: 10.1371/journal.pone.0245620.r001

Decision Letter 0

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8 Sep 2020

PONE-D-20-21787

Association of digoxin with mortality in patients with advanced chronic kidney disease: A population-based cohort study

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Reviewer #1: Overall comments:

Yang and colleagues use a national dataset of Taiwanese patients with chronic kidney disease stage 3b or worse (eGFR <45 mL/min/1.73m2) or with at least 1g proteinuria, to assess the association between digoxin use and the risk of death, cardiovascular disease and renal outcomes. They found that those taking digoxin were at higher risk of death, compared to those not taking it, but the risk of cardiovascular event and rapid eGFR decline was not different between the groups.

Digoxin is used to treat patients with heart failure with reduced EF, typically with NYHA III-IV, and EF <25%. These patients tend to be sicker and have more comorbidities than those who do not require a treatment with digoxin (as shown in table 1 of the current study). Given the characteristics associated with the population taking digoxin, it is expected that digoxin use be associated with higher mortality risk and it is expected to remain the case in a cohort of CKD patients.

In addition, people with advanced CKD are more likely to have hyperkalemia (unfortunately data not available) than the rest of the population, which adds a potential risk to digoxin use.

Major comments:

- It would be helpful if the authors could explain in more details their statistical plan, and the choice of the various tests they use.

- My understanding is that the all cause mortality outcome was assessed using a Cox proportional hazard model while the other outcomes (cardiovascular and renal) were examined using a Fine&Gray subdistribution hazard model, taking into account the competing risk of death. This should be made more clear in the tables. The tables are currently presented as if the statistical test looking at “all cause mortality” as the outcome, is also being adjusted for the competing risk of death, which is very confusing.

- How did the authors adjust for medications ? Was there a code Y/N for each medication class, or were all the medications considered as one variable ?

- “Poisson regression model was used to analyze the incidence rate ratios (IRRs) and 95% confidence intervals (95% CIs) of outcomes (all-cause mortality, cardiovascular events, and renal outcomes) were examined for digoxin users and non-users.” This sentence is not clear. Could the authors please explain why they chose to use a Poisson regression?

Minor comments:

- Using ICD codes to define CKD and AKI events is disputable, and using an average serum creatinine value, and the KIDGO definition of AKI may have been more accurate.

- “Baseline eGFR was calculated from the last recorded serum creatinine level before the index date” how long before the index date could that serum creatinine be measured ? Could an average of 2 or 3 eGFRs be used instead ? One single value for a serum creatinine (or eGFR) can be highly misleading.

Reviewer #2: This study investigates the potential impact of digoxin use in patients with CKD. The authors identified 2640 patients in their program and found that patients taking digoxin were at higher risk of dying whereas the risk of cv events and renal function decline did not differ. The potential impact of digoxin in CKD patients is of clinical relevance.

Some comments:

The most important shortcoming of this analysis is the fact that matching was based on age and sex only. Although they adjusted for multiple comorbidities, matching for the most relevant factors would be a better option. In addition and most importantly, severity of HF is not properly defined. It is VERY LIKELY that more advanced HF patients were more likely to receive digoxin as recommended by the guidelines. This could explain the lower blood pressure in those taking digoxin. The authors must be very clear on this. The way how they discuss this issues is not sufficient. Also, the authors do not provide any evidence for their discussion about potential survivors of digoxin being included in the study. They do not have any information on this (in fact if anything can be taken from their data, it would be the opposite as longer-term treatment was numerically associated with higher risk). The authors provide a reference that new digoxin users might have a higher mortality as compared to chronic users, but this was in AF only.

The authors should stress even more that SDC were not available. This is crucial as effects on mortality highly depends on SDC (as cited by the authors).A substitute could be the dosage of digoxin in relation to renal function and body weight. If dosage is available the authors should provide such calculation. If not, they need to report this as additional shortcoming.

They authors identified almost 32,000 patients but only less than 10% were actually included in the analysis. The authors report that this reduction happened after the matching process, which considered age and sex only. Sex and age is presumably known for all 32,000 patients. Why were only so few included? Was the reason that only 440 patients received digoxin (i.e. 1.4% of the total population)? The authors should include more patients or must report as a limitation that only a very small minority received digoxin which may limit generalisability of the findings. If it is possible to include more patients, it would be interesting to see results separately for patients with HF and AF.

Where there also patients without HF and AF but receiving digoxin? If yes, what was the indication in those? Generally speaking, the authors should provide information on the indication for the use of digoxin in their cohort.

The authors refer to previous studies how ACS, ischemic stroke and haemorrhagic stroke were identified. However, the authors should briefly describe this as not all readers may have access to the referred studies. In addition, they do not report on how death was identified and verified.

The authors completed the follow-up already in 2012. What is the reason for this?

The authors should report in their conclusion that full adjustment was not possible, making bias of their findings likely.

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2021 Jan 15;16(1):e0245620. doi: 10.1371/journal.pone.0245620.r002

Author response to Decision Letter 0

2 Nov 2020

Response to editor　

Thank the editor for the important comments. We have revised our manuscript and responded the questions as requested. Please see the response for eache question.

1. Please ensure that your manuscript meets PLOS ONE’s style requirements, including those for file naming.

Response to Q1:

Thank you for your comment. We have revised manuscript and file naming to meet PLOS ONE’s style requirements.

Response to Q2:

We have provided this information on the methods section and online submission form as “This study was approved by the Institutional Review Board (IRB) of Kaohsiung Medical University Hospital (KMUHIRB-EXEMPT(I)-20180035), and the requirement for informed consent was waived.” (page 8, line 112-115).

Response to Q3:

We provided this information about the patient records in the revised manuscript (page 7, line 96-101) and online submission as “We conducted a retrospective cohort study using the Pre-ESRD care program registry linked with the National Health Insurance Research Database (NHIRD), containing detailed information on inpatient and outpatient services. To protect patients’ privacy, NHIRD had made all data fully anonymized by replacing all personal identification with surrogate numbers before researchers accessed them and further analyzed them.”

Moreover, our local IRB ethics committee has waived the requirement for informed consent. This statement is stated in the revised manuscript (page 8, line 112-115) as “This study was approved by the Institutional Review Board (IRB) of Kaohsiung Medical University Hospital (KMUHIRB-EXEMPT(I)-20180035), and the requirement for informed consent was waived.”

4. Thank you for stating the following in the Competing Interests section:

“I have read the journal’s policy and the authors of this manuscript have the following competing interests: Co-author Ping-Hsun Wu is a fellow of PLOS ONE Editorial Board Members. This does not alter the authors’ adherence to PLOS ONE editorial policies and criteria.” Please confirm that this does not alter your adherence to all PLOS ONE policies on sharing data and materials, by including the following statement: “This does not alter our adherence to PLOS ONE policies on sharing data and materials.”

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Please include your updated Competing Interests statement in your cover letter; we will change the online submission form on your behalf.

Response to Q4:

We have confirmed the competing interests in the following statement in the revived manuscript (page 21, line 359-360) as:

“Co-author Ping-Hsun Wu is a fellow of PLOS ONE Editorial Board Members. This does not alter our adherence to PLOS ONE policies on sharing data and materials.”

In your revised cover letter, please address the following prompts:

Response to Q5:

The legal and ethical restrictions in Taiwan are the main reasons for not allowing to share data publicly. We stated this explanation in the revised manuscript (page 20, line 344-357) as below. “The raw data used in our study were obtained from NHIRD and "pre-ESRD care program dataset" through formal application. The datasets we used from NHIRD included "H_NHI_OPDTE, H_NHI_IPDTE, H_NHI_DRUGE, H_NHI_OPDTO, H_NHI_IPDTO, H_NHI_DRUGO, H_NHI_ENROL, H_NHI_CATAS, and H_OST_DEATH". Data holder for NHIRD was the Health and Welfare Data Science Center, Department of Statistics, Ministry of Health and Welfare, Taiwan (https://dep.mohw.gov.tw/DOS/np-2497-113.html). Data holder for "Pre-ESRD care program dataset" was Information Integration and Application Center, National Health Insurance Administration, Ministry of Health and Welfare (https://www.nhi.gov.tw/Content_List.aspx?n=2D2FAF5214807829&topn=787128DAD5F71B1A). These raw data were limited to research purposes only and cannot be made publicly available under regulation of the "Personal Information Protection Act" in Taiwan. We, as authors, did not have any special access privileges to the data that other researchers would not have.”

Response to Reviewer 1

Major comments:

Q1: It would be helpful if the authors could explain in more details their statistical

plan, and the choice of the various tests they use. My understanding is that the all cause mortality outcome was assessed using a Cox proportional hazard model while the other outcomes (cardiovascular and renal) were examined using a Fine&Gray subdistribution hazard model, taking into account the competing risk of death. This should be made more clear in the tables. The tables are currently presented as if the statistical test looking at “all cause mortality” as the outcome, is also being adjusted for the competing risk of death, which is very confusing.

Response to Q1:

Thank you for the comment. Regarding the statistical plan and the choice of the various tests, we provide this information in the revised manuscript (page 11, line 161-164 ) as “Regarding the all-cause mortality outcome, we employed Cox proportional hazards model. Furthermore, we applied the Fine and Gray subdistribution hazards model to clarify the competing risk of death and the effects of digoxin on the cardiovascular and renal outcomes [21].”

We also add this information in the footnote of Table 2 to 4 as “All-cause mortality was assessed using a Cox proportional hazard model. Effects of digoxin on the cardiovascular and renal outcomes were adjusted for competing risk of mortality using a Fine & Gray subdistribution hazard model.”

Q2: How did the authors adjust for medications? Was there a code Y/N for each medication class, or were all the medications considered as one variable?

Response to Q2:

We defined each medication class as one variable with a Y/N code. We revised the sentence in page11, line 164-166, as “We applied the multivariable models to adjust the confounders for urbanization, socioeconomic status, comorbid disorders, clinical characteristics, and each medication class.”

Q3: “Poisson regression model was used to analyze the incidence rate ratios (IRRs) and 95% confidence intervals (95% CIs) of outcomes (all-cause mortality, cardiovascular events, and renal outcomes) were examined for digoxin users and non-users.” This sentence is not clear. Could the authors please explain why they chose to use a Poisson regression?

Response to Q3:

Poisson regression model can be applied to evaluate relative risk of outcomes between groups and complex interactions with covariates [1]. Compared with Cox proportional hazards model, Poisson regression model was more appropriate to evaluate relative risk of outcomes between our groups across all periods [2]. Thus, we used the Poisson regression model to estimate the effect of digoxin on the incidence rates of outcomes in this study. We revised the sentence in the revised manuscript (page 10, line 158-160) as “The incidence rate ratios (IRRs) and 95% confidence intervals (95% CIs) of outcomes (all-cause mortality, cardiovascular events, and renal outcomes) for digoxin users versus non-users were examined by using the Poisson regression model [20].”

Minor comments:

Q4: Using ICD codes to define CKD and AKI events is disputable, and using an average serum creatinine value, and the KIDGO definition of AKI may have been more accurate.

Response to Q4:

For the question about using ICD codes to define CKD and AKI events, we agree that the KIDGO definition of AKI may have been more accurate. However, previous reports also supported the rationale of using ICD codes to define AKI events. According to the study conducted by Waikar et al., ICD-9-CM codes 584 for AKI diagnosis had a sensitivity of 35.4%, specificity of 97.7%, the positive predictive value of 47.9%, and negative predictive value of 96.1% [3]. Based on this study, using the ICD-9-CM code 584 is reliable for AKI events because of high specificity although relatively low sensitivity may miss some potential candidates.

Next, for the question about using ICD codes to define CKD, we selected the CKD population for this study from pre-ESRD program registry database in addition to ICD-CM codes 585 and 581.9. The eligibility criteria for pre-ESRD program were individuals with CKD stages 3b-5 (eGFR<45 mL/min/1.73 m2), or those with proteinuria (urine protein to creatinine ratio, UPCR >1000 mg/g). The duration of the illness must be more than 3 months before enrollment to the pre-ESRD program. Thus, patients identified through these processes fit the definition of CKD by KDIGO.

Q5: “Baseline eGFR was calculated from the last recorded serum creatinine level before the index date” how long before the index date could that serum creatinine be measured ? Could an average of 2 or 3 eGFRs be used instead ? One single value for a serum creatinine (or eGFR) can be highly misleading.

Response to Q5:

For this question, we employed the enrollment criteria for entering the pre-ESRD program to explain that baseline eGFR was calculated by last recorded serum creatinine levels. The Enrollment criteria for entering the pre-ESRD program included patients with CKD stage 3b-5 or those with proteinuria >1g/day. All patients were followed up at least quarterly. The consensus for enrollment criteria was stringent for the nephrologists to select the candidate patients under NHI regulation. Thus, we considered that a single value of serum creatinine could be used as the measurement for baseline eGFR data.

Response to Reviewer 2

Q1: The most important shortcoming of this analysis is the fact that matching was based on age and sex only. Although they adjusted for multiple comorbidities, matching for the most relevant factors would be a better option.

Response to Q1:

We agree that adding more relevant factors in matching would make two groups more comparable in addition to age and sex matching. However, this way might reduce the sample sizes and overall statistical power. To ensure having enough patients for analysis and maintain sufficient statistical power, we aimed to match patients by age and sex for the primary analysis. Furthermore, we applied propensity scores matching to consider all covariates to increase comparability and reduce potential confounding effects between two groups. Both matching approaches consistently showed that digoxin could exert a higher risk for mortality of the study population.

Q2: In addition and most importantly, severity of HF is not properly defined. It is VERY LIKELY that more advanced HF patients were more likely to receive digoxin as recommended by the guidelines. This could explain the lower blood pressure in those taking digoxin. The authors must be very clear on this. The way how they discuss this issues is not sufficient.

Response to Q2:

In our cohort study design, the Pre-ESRD program database collected the data mainly related to renal care. Severity of HF was not available in this database. However, previous study showed that diuretics use could be a proxy for reflecting the severity of HF [4]. Thus, we adjusted confounding effect of severity of HF by the variable of diuretics use. In this way, the influence of the severity of HF on the outcomes could be attenuated after adjusting diuretics use. We admit that we cannot collect and well control all confounders due to inherent limitations of cohort study design. We have addressed these limitations in the Discussion.

The revision paragraph was shown in the limitations section of Discussion (page

18, line 306-308) as “Furthermore, cardiac functional status by NYHA classification and structural parameters by ejection fraction were unavailable for us to evaluate their impacts on the outcomes.”

Q3: Also, the authors do not provide any evidence for their Discussion about potential survivors of digoxin being included in the study. They do not have any information on this (in fact if anything can be taken from their data, it would be the opposite as longer-term treatment was numerically associated with higher risk). The authors provide a reference that new digoxin users might have a higher mortality as compared to chronic users, but this was in AF only.

Response to Q3:

We admit that this study lacked the information about digoxin chronic users or new users due to limitations of the cohort database. We agree with your concern that our previous discussion about this issue might not be convincing. Thus, we decide to delete the following paragraph in the discussion as “On the other hand, chronic digoxin users might not be as vulnerable because they might have already survived the potential harm imposed by this drug. Otherwise, they would have died, or digoxin would have been discontinued. It has been shown that in AF patients, new digoxin users were associated with higher mortality compared with non-users, while chronic digoxin users were not” (p17 in the previous manuscript).

Q4: The authors should stress even more that SDC were not available. This is crucial as effects on mortality highly depends on SDC (as cited by the authors).A substitute could be the dosage of digoxin in relation to renal function and body weight. If dosage is available the authors should provide such calculation. If not, they need to report this as additional shortcoming.

Response to Q4:

It was reported that serum digoxin concentration (SDC) could be affected by many factors, such as renal function, the bioavailability of the digoxin formulation used, the volume of distribution, the amount of extrarenal clearance, body weight, and serum albumin concentration [5]. Although some equations have been proposed to predict the SDC, their validity in Asians or advanced CKD patients was still limited [6] [7]. Thus, there might be concerns to calculate SDC by these equations in our study population. We have stated the lack of SDC as a limitation in the Discussion (Page 18, line 301-304) as “For instance, serum digoxin concentrations (SDC) were noted measured. Previous studies reported that high SDC (>1.2 ng/mL) might be associated with increased mortality when compared with low SDC (0.5–0.8 ng/mL) [36,37].”

Q5: They authors identified almost 32,000 patients but only less than 10% were actually included in the analysis. The authors report that this reduction happened after the matching process, which considered age and sex only. Sex and age is presumably known for all 32,000 patients. Why were only so few included? Was the reason that only 440 patients received digoxin (i.e. 1.4% of the total population)? The authors should include more patients or must report as a limitation that only a very small minority received digoxin which may limit generalisability of the findings. If it is possible to include more patients, it would be interesting to see results separately for patients with HF and AF.

Response to Q5:

The small sample size could be explained by that only a small portion of CKD patients met the indication for digoxin, rather than by matching process. Two reasons may help explain this phenomenon. First, guidelines do not recommend digoxin as the first-line medication for HF, and digoxin was used mainly for symptomatic HF patients despite receiving standard therapy. Second, the information about the safety of digoxin in advanced CKD patients was limited. We stated this issue in the limitation section in the Discussion (page 18, line 309-313) as below. “Another limitation was the relatively small sample sizes of digoxin users. Our study aimed to enroll more digoxin users by defining digoxin users as any single digoxin exposure within three months before the index date, but only 440 digoxin users were enrolled in total. It might be explained by the stringent indication or safety concern on prescribing digoxin for CKD patients.”

Q6: Where there also patients without HF and AF but receiving digoxin? If yes, what was the indication in those? Generally speaking, the authors should provide information on the indication for the use of digoxin in their cohort.

Response to Q6:

To our knowledge, AF and HF were the main indications for digoxin use. However, the indication for digoxin in each patient was not well recorded in the National Health Insurance (NHI) claim database. Some reasons related to the NHI claim database’s regulations may explain why patients without HF and AF might receive digoxin. First, only the first three diagnosis codes were recorded in the National Health Insurance claim database. Thus, the corresponding ICD codes for AF or HF might be missed because they were placed in the latter part of the diagnosis list in the medical record. Second, digoxin might be prescribed for the off-label condition, such as supraventricular tachycardia, not for HF or AF.

Q7: The authors refer to previous studies how ACS, ischemic stroke and haemorrhagic stroke were identified. However, the authors should briefly describe this as not all readers may have access to the referred studies.

Response to Q7:

We defined and identified the ACS, ischemic stroke, and hemorrhagic stroke based on the ICD-9-CM codes. The detailed information for this issue was stated in the Method section (page 9, line 121-126) as below. “ACS, ischemic stroke, and hemorrhagic stroke were defined as hospitalization for these vascular events, which were validated in previous studies [15,16]. For example, ICD-9-CM codes 433 (occlusion of cerebral arteries) and 434 (stenosis of precerebral arteries) were used to extract from NHIRD study subjects with ischemic stroke and were admitted for the specific diagnosis.”

Q8: In addition, they do not report on how death was identified and verified.

Response to Q8:

We identified and verified the outcome of death based on the evidence of patient withdrawal from the NHI claim database. It was reported to be valid to identify the death outcome using a similar approach [8]. We stated this issue in the Method section (page 8, line 120-121) as “Death was ascertained based on the evidence of patient withdrawal from the NHI claim database.”

Q9: The authors completed the follow-up already in 2012. What is the reason for this?

Response to Q9:

We state the following explanations for this question. First, there was a time lag between reporting the claims to the NHI database and its release for research purposes. At that time of our application for data analysis, the patient registry time was the year 2012. Second, it takes time to process and analyze the enormous cohort database and to validate their accuracy. We aimed to ensure the robustness of research design and comprehensiveness of statistical analysis for our work.

Q10: The authors should report in their conclusion that full adjustment was not possible, making bias of their findings likely.

Response to Q10:

We are aware that full adjustment was not possible, as you mentioned. We stated this limitation in the Discussion (page 19, lime 316-320) as below. “Despite these limitations, we try to reduce the influence of confounding factors on the outcomes by multivariable adjustment and propensity score matching and using different statistical analysis approaches. The inherent weaknesses of the population-based cohort study design may limit the generalizability of our findings. We should interpret the results with caution.”

References

1. Frome, E.L. and H. Checkoway, Epidemiologic programs for computers and calculators. Use of Poisson regression models in estimating incidence rates and ratios. Am J Epidemiol, 1985. 121(2): p. 309-23.

2. Vonesh EF, Schaubel DE, Hao W, Collins AJ. Statistical methods for comparing mortality among ESRD patients: Examples of regional/international variations. Kidney International, Supplement. 2000 Dec 1;57(74).

3. Waikar, S.S., et al., Validity of International Classification of Diseases, Ninth Revision, Clinical Modification Codes for Acute Renal Failure. J Am Soc Nephrol, 2006. 17(6): p. 1688-94.

4. Gislason GH, et al. Persistent use of evidence-based pharmacotherapy in heart failure is associated with improved outcomes. Circulation. 2007; 116(7):737-44.

5. Iisalo, E., Clinical pharmacokinetics of digoxin. Clin Pharmacokinet, 1977. 2(1): p. 1-16.

6. Zhao, L., et al., Efficiency of individual dosage of digoxin with calculated concentration. Clin Interv Aging, 2014. 9: p. 1205-10.

7. Muzzarelli, S., et al., Individual dosage of digoxin in patients with heart failure. Qjm, 2011. 104(4): p. 309-17.

8. Cheng C-L, Chien H-C, Lee C-H, Lin S-J, Yang Y-HK. Validity of in-hospital mortality data among patients with acute myocardial infarction or stroke in National Health Insurance Research Database in Taiwan. International Journal of Cardiology. 2015; 201:96-101.

Attachment

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PLoS One. doi: 10.1371/journal.pone.0245620.r003

Decision Letter 1

Hans-Peter Brunner-La Rocca

27 Nov 2020

PONE-D-20-21787R1

Association of digoxin with mortality in patients with advanced chronic kidney disease: A population-based cohort study

PLOS ONE

Dear Dr. Tsai,

Please note that the reviewers still have some minor issues to be resolved. In particular, reviewer #2 asks for clarity in your statement about the interpretation of the findings. The conclusion that caution is required when giving digoxin to these patients, it must be also mentioned what is lacking and not trying to circumvent clear statements. Please have a second look also at the initial comments by reviewer #2.

Please submit your revised manuscript by Jan 11 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Hans-Peter Brunner-La Rocca, M.D.

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: The authors have answered all my comments. I still believe however, that the tables lack clarity. The outcome "all-cause mortality" is analyzed with various models which all adjust for the competing risk of death (as stated in the foot note). An asterisk should be noted there, with mention that, for this outcome, models adjust for X, Y, Z,... but not for competing risk of death.

Reviewer #2: The authors have improved the manuscript. However, they still lack sufficiently clear statements that important clinical data are missing. It is not only NYHA-class and ejection fraction, but they lack information about the presence of heart failure. This must be very clear in the text. This also means that full propensity score matching is not possible. They should also state in the conclusion that full matching was not possible to make very clear that the interpretation of their data must be done with caution.

They should also mention as a limitation not specifically the relatively small sample size, but the fact that only a very small portion of patients received digoxine. These patients are therefore likely not representative for the whole cohort.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

PLoS One. 2021 Jan 15;16(1):e0245620. doi: 10.1371/journal.pone.0245620.r004

Author response to Decision Letter 1

4 Dec 2020

Dear Editor and reviewers,

We appreciate your relevant comments from the Editor and reviewers. We have revised the manuscript based on reviewers’ comments and suggestions as below. Please kindly check the updated submission of the revised manuscript.

Kind regards

Jer-Chia Tsai, MD.

Response to Reviewer 1

Q1: The authors have answered all my comments. I still believe however, that the tables lack clarity. The outcome “all-cause mortality” is analyzed with various models which all adjust for the competing risk of death (as stated in the foot note). An asterisk should be noted there, with mention that, for this outcome, models adjust for X, Y, Z,... but not for competing risk of death.

Response to Q1:

Thank you for the comment. To clarify the statistical analysis for each outcome in Table 2, 3, and 4, we labeled the marks and the corresponding statistical analysis as:

“§All-cause mortality was assessed using a Cox proportional hazard model, and ¶Cardiovascular and renal outcomes were assessed using a Fine & Gray subdistribution hazard model for competing risk of mortality.”

Response to Reviewer 2

Q1: The authors have improved the manuscript. However, they still lack sufficiently clear statements that important clinical data are missing. It is not only NYHA-class and ejection fraction, but they lack information about the presence of heart failure. This must be very clear in the text. This also means that full propensity score matching is not possible. They should also state in the conclusion that full matching was not possible to make very clear that the interpretation of their data must be done with caution.

Response to Q1:

Thank you for the comment. For the information about the presence of heart failure (HF), Table 1 shows the rates of HF in the digoxin user and non-user groups were 51.6% and 10.8%, respectively. The definition of HF is based on ICD-9-CM codes 398, 402, 404, and 428. We admit that this study lacks the clinical data about NYHA-class and ejection fraction.

Following your suggestion, we state this limitation in the revision as: “Secondly, digoxin users might be frailer due to concomitant HF or AF compared to digoxin non-users. The presence of HF was defined based on ICD-9-CM coding; however, the severity of HF was unavailable due to the lack of NYHA classification and ejection fraction. Thus, full matching according to HF status in both groups was not likely, which may lead to biases.” (page 18, line 305-309)

Furthermore, we also state the limitation of this population-based cohort study design as: “However, the inherent weaknesses of the population-based cohort study design may limit the generalizability of our findings, and we should interpret the results with caution.” (page 19, line 318-320).

Q2: They should also mention as a limitation not specifically the relatively small sample size, but the fact that only a very small portion of patients received digoxine. These patients are therefore likely not representative for the whole cohort.

Response to Q2:

Thank you for the comment. Following your suggestion, we revise the paragraph as: “Thirdly, the final limitation should be considered is that a very small portion of patients received digoxin. These patients are, therefore, not fully representative of the whole cohort. This study aimed to enroll more digoxin users by defining digoxin users as any single digoxin exposure within three months before the index date, but only 440 digoxin users were enrolled in total. It might be explained by the stringent indication or safety concern on prescribing digoxin for CKD patients.” (page 18, line 310 to page 19, line 315)

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(14.5KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0245620.r005

Decision Letter 2

Hans-Peter Brunner-La Rocca

14 Dec 2020

PONE-D-20-21787R2

Association of digoxin with mortality in patients with advanced chronic kidney disease: A population-based cohort study

PLOS ONE

Dear Dr. Tsai,

As most of the remaining issues have been addressed, I do not send your manuscript back to the reviewers. However, I would like you to address the following points: As PLOS ONE does not provide proofreading, even small details need to be adequately addressed. In your revised manuscript you write: "Thirdly, the final limitation should be considered is that a very small portion of patients received digoxin." This is not best English and should be e.g. "Thirdly, the final limitation that should be considered is the very small portion of patients who received digoxin." In addition, you must temper your conclusions as addressed by reviewer #2. In the limitation section, you mention that the results must be interpreted with caution, but in the conclusion, you present your findings as "consistent evidence". This is simply not possible with the design of the study. You really need to temper this significantly and mainly say that digoxin should be given with caution, based on your results. You then can highlight the need for prospective testing.

Please submit your revised manuscript by Jan 28 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Hans-Peter Brunner-La Rocca, M.D.

Academic Editor

PLOS ONE

PLoS One. 2021 Jan 15;16(1):e0245620. doi: 10.1371/journal.pone.0245620.r006

Author response to Decision Letter 2

30 Dec 2020

Q1: As PLOS ONE does not provide proofreading, even small details need to be adequately addressed. In your revised manuscript you write: "Thirdly, the final limitation should be considered is that a very small portion of patients received digoxin." This is not best English and should be e.g. "Thirdly, the final limitation that should be considered is the very small portion of patients who received digoxin."

Response to Q1:

Thank you for the comment. We have revised the sentence as “Thirdly, the final limitation that should be considered is the very small portion of patients who received digoxin.” (Page 18, line 310-311)

Q2: In addition, you must temper your conclusions as addressed by reviewer #2. In the limitation section, you mention that the results must be interpreted with caution, but in the conclusion, you present your findings as "consistent evidence". This is simply not possible with the design of the study. You really need to temper this significantly and mainly say that digoxin should be given with caution, based on your results. You then can highlight the need for prospective testing.

Response to Q2:

Thank you for the comment. We have revised the paragraph as “In conclusion, our study demonstrated that digoxin use was associated with increased mortality in advanced CKD patients. Thus, digoxin should be prescribed with caution in this population. It warrants future prospective and randomized studies to determine the safety of digoxin in the advanced CKD patients.” (page 19, line 321-324)

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PLoS One. doi: 10.1371/journal.pone.0245620.r007

Decision Letter 3

Hans-Peter Brunner-La Rocca

5 Jan 2021

Association of digoxin with mortality in patients with advanced chronic kidney disease: A population-based cohort study

PONE-D-20-21787R3

Dear Dr. Tsai,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Hans-Peter Brunner-La Rocca, M.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0245620.r008

Acceptance letter

Hans-Peter Brunner-La Rocca

7 Jan 2021

PONE-D-20-21787R3

Association of digoxin with mortality in patients with advanced chronic kidney disease: A population-based cohort study

Dear Dr. Tsai:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Hans-Peter Brunner-La Rocca

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. ICD-9-CM codes used to identify clinical conditions.

(DOCX)

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S2 Table. Drugs prescriptions during observation period among patients with chronic kidney disease.

(DOCX)

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S1 Checklist. STROBE statement—checklist of items that should be included in reports of cohort studies.

(DOCX)

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Data Availability Statement

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PERMALINK

Association of digoxin with mortality in patients with advanced chronic kidney disease: A population-based cohort study

Lii-Jia Yang

Shan-Min Hsu

Ping-Hsun Wu

Ming-Yen Lin

Teng-Hui Huang

Yi-Ting Lin

Hung-Tien Kuo

Yi-Wen Chiu

Shang-Jyh Hwang

Jer-Chia Tsai

Hung-Chun Chen

Roles

Abstract

Introduction

Materials and methods

A brief overview of Taiwan pre-end-stage renal disease (ESRD) pay-for-performance program

Study population and cohort

Measurement of outcomes

Comorbidities and exposure to confounding medications

Characteristics of clinical data

Statistical analysis

Sensitivity analysis

Results

Baseline characteristics

Fig 1. Study flowchart.

Table 1. Baseline characteristics among patients with chronic kidney disease receiving digoxin or not.

All-cause mortality, cardiovascular events, and renal outcomes

Table 2. Association between digoxin used or not and all-cause mortality, major cardiovascular events, and renal function decline in patients with chronic kidney disease using intention-to-treat analysis.

Table 3. Association between digoxin used or not and all-cause mortality, major cardiovascular events, and renal function decline in patients with chronic kidney disease using as treat analysis.

Sensitivity analysis

Table 4. Sensitivity analyses showing the outcomes among patients with chronic kidney disease receiving digoxin or not.

Discussion

Digoxin and mortality

Digoxin and stroke

Digoxin and renal function change

Limitations

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Hans-Peter Brunner-La Rocca

Roles

Author response to Decision Letter 0

Decision Letter 1

Hans-Peter Brunner-La Rocca

Roles

Author response to Decision Letter 1

Decision Letter 2

Hans-Peter Brunner-La Rocca

Roles

Author response to Decision Letter 2

Decision Letter 3

Hans-Peter Brunner-La Rocca

Roles

Acceptance letter

Hans-Peter Brunner-La Rocca

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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