The difference between cystatin C‐ and creatinine‐based assessment of kidney function in acute heart failure

Abstract

Aims

Acute heart failure (HF) is associated with muscle mass loss, potentially leading to overestimation of kidney function using serum creatinine‐based estimated glomerular filtration rate (eGFR_sCr). Cystatin C‐based eGFR (eGFR_CysC) is less muscle mass dependent. Changes in the difference between eGFR_CysC and eGFR_sCr may reflect muscle mass loss. We investigated the difference between eGFR_CysC and eGFR_sCr and its association with clinical outcomes in acute HF patients.

Methods and results

A post hoc analysis was performed in 841 patients enrolled in three trials: Diuretic Optimization Strategy Evaluation (DOSE), Renal Optimization Strategies Evaluation (ROSE), and Cardiorenal Rescue Study in Acute Decompensated Heart Failure (CARRESS‐HF). Intra‐individual differences between eGFRs (eGFR_diff) were calculated as eGFR_CysC–eGFR_sCr at serial time points during HF admission. We investigated associations of (i) change in eGFR_diff between baseline and day 3 or 4 with readmission‐free survival up to day 60; (ii) index hospitalization length of stay (LOS) and readmission with eGFR_diff at day 60. eGFR_CysC reclassified 40% of samples to more advanced kidney dysfunction. Median eGFR_diff was −4 [−11 to 1.5] mL/min/1.73 m² at baseline, became more negative during admission and remained significantly different at day 60. The change in eGFR_diff between baseline and day 3 or 4 was associated with readmission‐free survival (adjusted hazard ratio per standard deviation decrease in eGFR_diff: 1.14, P = 0.035). Longer index hospitalization LOS and readmission were associated with more negative eGFR_diff at day 60 (both P ≤ 0.026 in adjusted models).

Conclusions

In acute HF, a marked difference between eGFR_CysC and eGFR_sCr is present at baseline, becomes more pronounced during hospitalization, and is sustained at 60 day follow‐up. The change in eGFR_diff during HF admission and eGFR_diff at day 60 are associated with clinical outcomes.

Introduction

Kidney dysfunction is a well-known predictor of adverse prognosis among hospitalized heart failure (HF) patients.¹ Serial evaluation of kidney function in this patient population is commonly carried out using serum creatinine (sCr)-based estimates. However, the stability of sCr-based estimated glomerular filtration rate (eGFR) in acute care setting has been challenged, particularly in patients with prolonged hospitalizations that lead to inactivity and progressive muscle mass loss.²

Cystatin C (CysC) is an endogenous protease inhibitor that is produced by all nucleated cells and, in contrast to sCr, is less influenced by changes in muscle mass. Among HF patients, CysC may more accurately reflect directly measured kidney function³ and has demonstrated superior prognostic value when compared with sCr.^4,5 However, the value of serial CysC measurements for the assessment of kidney function during and following HF hospitalization has not been well described.

Given the different relation of CysC and sCr levels with muscle mass, discrepant estimates of kidney function between these two filtration markers have been investigated as surrogate indicators of sarcopenia. The ratio of sCr to CysC (sCr/CysC) directly correlates with muscle mass in several patient populations.^6–8 Recently, the difference between CysC- and sCr-eGFR (eGFR_diff) has been investigated in the hypertensive⁹ and geriatric populations.¹⁰ Specifically, in both cohorts, a more negative eGFR_diff (sCr-eGFR exceeding CysC-eGFR) was associated with prevalent frailty and increased risk for adverse events. Estimated GFR_diff may dynamically change as a result of progressive muscle mass loss occurring in the setting of critical illness and prolonged hospitalization. Marked discrepancies between CysC- and sCr-eGFR have been recently reported among critically ill patients with COVID-19.¹¹ In a prior intensive care unit study, CysC- and sCr-eGFR were similar at the time of discharge for patients with short length of stay (LOS), while sCr-eGFR far exceeded CysC-eGFR among patients with longer LOS.² Whether eGFR_diff on admission, or longitudinal changes in eGFR_diff, carry prognostic value in hospitalized HF patients remains to be established.

The objective of this post hoc analysis conducted within the National Heart, Lung, and Blood Institute (NHLBI)-sponsored Heart Failure Network is to systematically assess kidney function utilizing CysC- and sCr-eGFR among acute HF patients during and after hospitalization. We hypothesized that the difference between the two estimates of kidney function would widen over the course of the hospitalization, and eGFR_diff would be associated with clinical events, such as mortality, readmissions, and index hospitalization LOS.

Methods

Study population

The study design and the primary results of the Diuretic Optimization Strategy Evaluation (DOSE),¹² Renal Optimization Strategies Evaluation (ROSE),¹³ and Cardiorenal Rescue Study in Acute Decompensated HF (CARRESS-HF)¹⁴ have been previously reported. All trials were approved by the institutional review board at each participating site. The datasets used for this study are available through the NHLBI Biologic Specimen and Data Repository Information Coordinating Center.

All three trials were multicentre, randomized studies performed in acute HF patients. Each trial evaluated the efficacy and the effects on kidney function of different approaches to decongestion – loop diuretics (DOSE), low-dose dopamine or nesiritide (ROSE), and ultrafiltration (CARRESS-HF). All three trials excluded patients receiving renal replacement therapy. Additional trial-specific criteria for enrolment included sCr (DOSE and CARRESS-HF excluded patients with sCr > 3 and 3.5 mg/dL, respectively) and sCr-eGFR (inclusion criteria for ROSE: 15 to 60 mL/min/1.73 m²). Notably, patients were eligible for enrolment in CARRESS-HF only in the presence of worsening kidney function, defined as an increase in sCr > 0.3 mg/dL. Among a total of 856 subjects enrolled across the three trials, we included those with at least one concurrent CysC and sCr measurement from core laboratory during the study period (n = 841).

Estimation of glomerular filtration rate

As previously described,^12–14 CysC and sCr levels were measured at a core laboratory. Estimated GFR was calculated using CysC- (eGFR_CysC) and sCr-based (eGFR_sCr) Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations¹⁵ and classified according to the categories defined by current guidelines.¹⁶ Values of eGFR were log-transformed to achieve normal distribution. For every concurrent CysC and sCr measurement, eGFR_diff was calculated as the difference between log-transformed eGFR_CysC and eGFR_sCr, which is equivalent to the log-transformed ratio eGFR_CysC/eGFR_sCr. Negative values of eGFR_diff corresponds to eGFR_sCr > eGFR_CysC and positive values to eGFR_CysC > eGFR_sCr. Kidney function assessment was performed at baseline (corresponding to the time of randomization) for all three trials and at day: (i) 3, 7 (or discharge), 60 for DOSE; (ii) 1, 2, 3 for ROSE; (iii) 4, 7 (or discharge), 60 for CARRESS-HF. Notably, baseline kidney function assessment was performed in the first 24 h of index hospitalization for patients enrolled in DOSE and ROSE and at any point during index hospitalization (median 2 days after admission) for patients enrolled in CARRESS-HF.

Baseline characteristics and outcomes

Baseline characteristics were collected at the time of randomization in all patients. Outcomes were available up to 60 day follow-up.

We investigated the association of:

• i) eGFR_diff (at baseline and day 3 or 4) and change in eGFR_diff between baseline and day 3 or 4 with a composite of death or readmission for any cause;
• ii) index hospitalization LOS and readmission for any cause with eGFR_diff at day 60.

Statistical analysis

Baseline characteristics were compared using t test or ANOVA for continuous normal-distributed variables, Kruskal–Wallis test for continuous non-normal distributed variables, and χ² test for categorical variables. Normal distribution was assessed using histograms, skewness, and kurtosis. Among patients with eGFR_sCr ≥ 30 mL/min/1.73 m² at baseline, logistic regression was used to identify clinical characteristics associated with reclassification to severe kidney dysfunction using eGFR_CysC. Variables were selected using a stepwise forward selection procedure following the Hosmer–Lemeshow goodness of fit test. Linear mixed models regressed eGFR_CysC, eGFR_sCr, and eGFR_diff longitudinally at different time points. Patients were modelled as random effects to account for within-person correlation of repeated measurements and all models included age, sex, race, and trial. The primary analysis was performed on patients from all 3 trials considering samples collected at baseline, day 3 or 4, day 7 (or discharge), and day 60. In a sensitivity analysis, we excluded patients from CARRESS-HF who were enrolled after the initial 24 h of index hospitalization or had ultrafiltration. An additional analysis was performed on patients enrolled in ROSE considering samples collected at baseline and day 1, 2, and 3. In this model, the time variable was coded as ordinal. The association of eGFR_diff (at baseline and at day 3 or 4) and change in eGFR_diff between baseline and day 3 or 4 with a composite of death or readmission for any cause was assessed using Cox proportional hazards models. Fully adjusted models included age, sex, race, trial, systolic blood pressure, left ventricular ejection fraction, use of angiotensin-converting enzyme inhibitors or angiotensin receptor blocker, baseline eGFR_sCr, N-terminal pro-brain natriuretic peptide, blood urea nitrogen, and serum sodium.¹⁷ Scaled Schoenfeld residuals were used to evaluate the proportional hazards assumption. Linear regression models assessed the association of outcomes with eGFR_diff at day 60. We considered index hospitalization LOS as a continuous variable and readmission for any cause as a categorical variable (≥1 readmission vs. no readmission). Adjusted models included age, sex, race, trial, and baseline eGFR_diff. The assumptions of linear regression were tested and confirmed. All analyses were repeated using log-transformed sCr/CysC, which, unlike eGFR_diff, has been validated as a surrogate of muscle mass in different populations.^6–8 A P < 0.05 was considered statistically significant. All analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC) and R (R Foundation for Statistical Computing, Vienna, Austria).

Results

Reclassification of glomerular filtration rate category by cystatin C

Overall, 2849 concurrent CysC and sCr measurements were available in 841 unique patients. Baseline characteristics stratified by trial are shown in the Supporting Information, Table S1. Compared with eGFR_sCr, eGFR_CysC reclassified 1413 (50%) samples to different GFR categories: 1144 (81%) to more advanced and 269 (19%) to less advanced kidney dysfunction. Reclassification to more advanced kidney dysfunction occurred more frequently at day 3 or 4, day 7 (or discharge), and day 60, when compared with baseline (Supporting Information, Table S2). Across all time points, severe kidney dysfunction (<30 mL/min/1.73 m²) was more prevalent when considering eGFR_CysC (1158 samples, 41%) than eGFR_sCr (685 samples, 24%, P < 0.001; Figure 1B). Among 623 patients with eGFR_sCr ≥ 30 mL/min/1.73 m² at baseline, 133 (21%) were reclassified to severe kidney dysfunction when considering eGFR_CysC. Factors independently associated with reclassification included older age (odds ratio [OR] per 10 year increase: 1.44, P < 0.001), diabetes (OR 1.79, P = 0.008), gout (OR 2.47, P < 0.001), and higher left ventricular ejection fraction (OR per 5% increase: 1.07, P = 0.024).

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Longitudinal changes in the difference between cystatin C- and creatinine-based estimated glomerular filtration rate

Overall, eGFR_CysC was significantly lower than eGFR_sCr at all time points (P < 0.001; Figure 2). Median eGFR_diff at baseline was −4 (interquartile range [IQR]: −11 to 1.5) mL/min/1.73 m². Baseline characteristics, stratified by tertiles of eGFR_diff at baseline, are shown in Table 1. Notably, body mass index progressively decreased from the 1st to the 3rd tertile, while the 2nd tertile had higher prevalence of diabetes and kidney dysfunction.

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Table 1 Baseline characteristics stratified by tertiles of eGFR_diff at baseline

	First tertile −14 [−20 to −11] mL/min/1.73 m2	Second tertile −4 [−7 to −3] mL/min/1.73 m2	Third tertile 4 [1 to 10] mL/min/1.73 m2	P-value
Number of patients	271	275	274
Demographic and clinical characteristics
Age, years	67 ± 13	68 ± 12	69 ± 13	0.11
Female	64 (24%)	79 (29%)	66 (24%)	0.32
African–American	58 (21%)	57 (21%)	66 (24%)	0.60
Diabetes mellitus	157 (58%)	176 (64%)	130 (47%)	<0.001
BMI, kg/m2	33.4 [27.5 to 39.6]	31.6 [26.3 to 38.1]	30.5 [26.3 to 35.4]	0.002
SBP, mmHg	118 ± 19	119 ± 18	117 ± 18	0.40
Ischaemic aetiology	156 (58%)	168 (61%)	153 (56%)	0.45
LVEF, %	46 ± 18	43 ± 18	42 ± 18	0.11
LVEF ≥50%	154 (57%)	144 (52%)	131 (48%)	0.11
Trial	0.011
DOSE	97 (36%)	85 (31%)	111 (41%)
ROSE	114 (42%)	112 (41%)	119 (43%)
CARRESS-HF	60 (22%)	78 (28%)	44 (16%)
Medications
ACEi/ARB	147 (54%)	135 (49%)	171 (62%)	0.007
MRA	79 (29%)	79 (29%)	64 (23%)	0.24
Laboratory values
Creatinine, mg/dL	1.42 ± 0.41	1.95 ± 0.61	1.81 ± 0.63	<0.001
eGFRsCr, mL/min/1.73 m2	51 [40 to 63]	33 [26 to 46]	38 [27 to 54]	<0.001
Cystatin C, mg/L	1.85 ± 0.54	2.09 ± 0.68	1.51 ± 0.52	<0.001
eGFRCysC, mL/min/1.73 m2	34 [26 to 45]	29 [21 to 41]	44 [33 to 62]	<0.001
BUN, mg/dL	33 [24 to 48]	47 [33 to 66]	35 [25 to 49]	<0.001
Sodium, mEq/L	138 ± 4	138 ± 4	138 ± 4	0.53
NT-proBNP, pg/mL	4353 [2089 to 9388]	5507 [2371 to 11 664]	4269 [2310 to 9699]	0.16

When considering all three trials, eGFR_diff became more negative from baseline to day 3 or 4 and remained significantly different from baseline up to day 60 (all P < 0.001 vs. baseline; Figure 1C, Figure 2 and Supporting Information, Table S3). There was no significant change in eGFR_diff between day 3 or 4 and the subsequent time points. A sensitivity analysis excluding the patients from CARRESS-HF who were enrolled after the initial 24 h of index hospitalization or had ultrafiltration yielded similar results (Supporting Information, Figure S1). In the analysis limited to ROSE, eGFR_diff showed a progressive widening from the time of admission to day 3 (P < 0.001, Figure 3). Results were similar when the analyses were performed using sCr/CysC in lieu of eGFR_diff, with the ratio remaining significantly lower at all subsequent time points when compared with baseline (Supporting Information, Table S3).

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Association of the difference between cystatin C- and creatinine-based estimated glomerular filtration rate during index hospitalization with readmission-free survival

Among 720 patients who had samples available at baseline and at day 3 or 4, median eGFR_diff was −4 (IQR: −11 to +1) mL/min/1.73 m² at baseline and decreased to −6 (IQR: −14 to 0) mL/min/1.73 m² at day 3 or 4 (P < 0.001), resulting in a median change in eGFR_diff of −2 (IQR: −7 to 3) mL/min/1.73 m². Baseline characteristics stratified by tertiles in change of eGFR_diff are presented in Supporting Information, Table S4. Patients in the 1st tertile of change in eGFR_diff (indicating wider eGFR_diff at day 3 or 4 vs. baseline) were younger, more likely to be male and African-American and had lower prevalence of diabetes and kidney dysfunction. In this cohort, the composite outcome occurred in 303 (42%) patients (73 deaths, 263 readmissions for any cause). At baseline and at day 3 or 4, eGFR_diff had no significant association with readmission-free survival (Table 2). When we considered the change in eGFR_diff between baseline and day 3 or 4, there was a trend towards an increased risk for the composite outcome with a change in eGFR_diff towards more negative values (P = 0.065). After adjustment, this association became significant (hazard ratio per standard deviation decrease: 1.14, P = 0.035; Table 2). The change in sCr/CysC between baseline and day 3 or 4 showed a similar association with the composite outcome (Supporting Information, Table S5).

Table 2 Association of eGFR_diff and change in eGFR_diff with 60 day readmission-free survival

	HR (95% CI) per 1-SD decrease Death or readmission for any cause Events n = 303
Unadjusted	Model 1a	Model 2b
eGFRdiff Baseline	1.00	1.00	0.92
(0.89–1.13)	(0.89–1.13)	(0.81–1.04)
P-value = 0.95	P-value = 0.99	P-value = 0.19
eGFRdiff Day 3 or 4	1.09	1.09	1.02
(0.97–1.22)	(0.97–1.23)	(0.90–1.16)
P-value = 0.16	P-value = 0.15	P-value = 0.73
Change in eGFRdiff between baseline and day 3 or 4 (= eGFRdiff day 3 or 4 -eGFRdiff baseline)	1.12	1.13	1.14
(0.99–1.26)	(1.00–1.27)	(1.01–1.28)
P-value = 0.065	P-value = 0.048	P-value = 0.035

Association of index hospitalization length of stay and readmission for any cause with the difference between cystatin C- and creatinine-based estimated glomerular filtration rate at day 60

The baseline characteristics of the 301 patients from DOSE and CARRESS-HF who had samples available at baseline and at day 60 are shown in Supporting Information, Table S6. Median eGFR_diff was −4 (IQR: −11 to +1) mL/min/1.73 m² at baseline and decreased to −7 (IQR: −15 to 0) mL/min/1.73 m² at day 60 (P < 0.001). In this cohort, the median index hospitalization LOS was 6 (IQR: 4 to 9) days and 117 (39%) patients had ≥1 readmission for any cause. Longer index hospitalization LOS was associated with a more negative eGFR_diff at day 60 in univariable and multivariable models (P ≤ 0.002, Table 3, Supporting Information, Figure S2A). Of note, the association remained significant in a sensitivity analysis excluding patients with LOS > 20 days (P = 0.008, Supporting Information, Figure S2B). We found a trend for a more negative eGFR_diff at day 60 among patients who had ≥1 readmission for any cause, which became significant after adjustment (Table 3). In a fully adjusted model including both LOS and readmission for any cause, we found that: (i) for every additional 5 days of LOS, there was a 2.5% decrease in eGFR_diff at day 60 (P = 0.001); (ii) readmission for any cause was associated with a 6% more negative eGFR_diff at day 60 (P = 0.02). Results did not meaningfully differ in models including sCr/CysC in lieu of eGFR_diff (Supporting Information, Table S7).

Table 3 Association of index hospitalization length of stay and readmission for any cause with eGFR_diff at day 60

eGFRdiff at day 60 dependent variable	Index hospitalization LOS (estimates for each additional day)	≥1 Readmission vs. no readmission
Model	β	P-value	β	P-value
Unadjusted	−0.0073	<0.001
Unadjusted			−0.067	0.052
Model 1	−0.0050	0.002
Model 2			−0.063	0.026
Model 3	−0.0051	0.001	−0.065	0.02

Discussion

The present study has several important findings: (i) the use of eGFR_CysC versus eGFR_sCr reclassified a large proportion of acute HF patients to more advanced kidney dysfunction; (ii) eGFR_diff widened during HF admission and this change was sustained at day 60, particularly in patients with prolonged index hospitalization or readmission; (iii) the change in eGFR_diff between baseline and day 3 or 4 was independently associated with a composite of death or readmission for any cause; and (iv) results were similar when sCr/CysC was used in lieu of eGFR_diff.

The GFR is the most important measure of kidney function and is reflective of the overall renal reserve, which may partly explain its powerful predictive capacity of HF outcomes.¹⁸ The gold standard for GFR measurement is represented by exogenous markers (e.g. iothalamate). However, these measurements are not practical for routine clinical use. Therefore, endogenous filtration markers are commonly used.Serum creatinine is a product of skeletal muscle creatine metabolism and, as such, represents an imperfect marker for kidney function assessment in conditions characterized by high prevalence of sarcopenia, such as HF. To overcome this limitation, CysC has been suggested as an alternative. CysC is produced by all nucleated cells and is less dependent on muscle mass.

Our study adds to the growing body of literature pointing to the discrepancies in kidney function assessment using the above mentioned renal biomarkers, and suggesting that the use of CysC in the HF population results in markedly lower eGFR when compared with sCr.^19,20 Recent HF guidelines preferentially recommend the use of sCr as the renal biomarker of choice (Class I, Level C).²¹ However, Kidney Disease Improving Global Outcomes guidelines¹⁶ and a position statement from the European Society of Cardiology¹⁸ favour CysC in patients with sarcopenia. Prior research assessing the accuracy of eGFR in the HF population, while limited by single-centre design and relatively small sample size, suggests the superiority of CysC- over sCr-based equations. In HF studies using gold standard methods for GFR measurement (clearance of an exogenous filtration marker), sCr-based equations markedly overestimated kidney function with a mean difference for sCr-based CKD-EPI equation ranging from 15 to 26 mL/min/1.73 m², while CysC-based CKD-EPI provided the closest approximation to the directly measured values.^3,22 However, this topic remains at the centre of an intense debate. A recent report of 296 HF patients found an association of CysC with muscle mass (although to a lesser extent than sCr), which could potentially bias CysC-based GFR estimates.²³ Indeed, both sCr- and CysC-based equations showed poor accuracy in this study, although the limited precision of creatinine clearance as the reference method to determine kidney function may, at least in part, account for this result.²⁴

In agreement with our a priori hypothesis, eGFR_diff widened during HF admission. We speculate that changes in eGFR_diff might be driven by a decrease in sCr generation secondary to progressive muscle mass loss occurring during HF admission. This interpretation is strengthened by the fact that (i) changes in eGFR_diff compatible with muscle wasting were associated with worse readmission-free survival; (ii) prolonged index hospitalization and a subsequent readmission were associated with more negative values of eGFR_diff at day 60; and (iii) results were similar when the analysis was performed using sCr/CysC, which has been shown to correlate with muscle mass in prior studies.^6–8 An early and progressive muscle mass loss has been described during the first week of critical illness.²⁵ Ravn et al.² showed that sCr-eGFR exceeded CysC-eGFR by 24 mL/min/1.73 m² at the time of discharge from the intensive care unit despite similar estimates at the time of admission. The present results are also in line with our prior findings demonstrating a widening difference between sCr-eGFR and CysC-eGFR during the first month following left ventricular assist device surgery.²⁶ Notably, in the same report, early post-operative changes in sCr-eGFR correlated with changes in muscle mass as assessed by computed tomography.

Cystatin C levels may rise and peak earlier than sCr in the setting of acute kidney injury.²⁷ However, the different kinetics of CysC and sCr do not appear to fully explain the change in eGFR_diff observed in this study, because eGFR_diff did not stabilize during the course of HF admission but rather progressively widened over time.

This study has several important clinical and scientific implications. In acute HF patients, accurate estimation of kidney function is vital in determining appropriate dosing of decongestive therapies and guideline-directed medical therapy.²⁸ Conventional sCr-based equations may underestimate the prevalence of severe kidney dysfunction and the degree of worsening of kidney function during HF hospitalization and the subsequent follow-up. This may prevent the identification of patients with advanced HF and cardiorenal syndrome and thus delay the initiation of appropriate therapies. Furthermore, clinical trials routinely exclude HF patients with severely reduced sCr-eGFR and incorporate renal endpoints defined by sCr-based estimates. The use of CysC in future trials may improve the selection of participants and possibly provide a more accurate assessment of renal endpoints. Finally, our findings warrant further research investigating eGFR_diff and sCr/CysC as markers of sarcopenia and frailty and thereby prognosis among HF patients.

This study has several limitations. First, in the absence of gold standard measure of GFR, we are unable to definitively conclude which marker provides the most accurate estimate of kidney function in hospitalized HF patients. Similarly, we did not have any objective measures of muscle mass and therefore the relation between changes in eGFR_diff and muscle mass loss, while plausible, remains to be demonstrated. Additionally, we acknowledge that all the eGFR equations used in the present study were derived in patients with stable kidney function and may have limited reliability in hospitalized patients. However, decrease in eGFR has been widely used to define worsening kidney function in acute HF.²⁹ Moreover, outcomes were available only up to day 60 after HF admission, thus limiting the assessment of long-term prognostic significance of eGFR_diff. Lastly, no adjustment for multiple comparisons was made. Therefore, our study findings should be viewed as hypothesis-generating.

In conclusion, in this post hoc analysis of 3 acute HF trials, CysC led to the reclassification of a large proportion of patients to more advanced kidney dysfunction when compared with sCr-based assessment. During HF admission, eGFR_diff widened, with more pronounced changes at 60 day follow-up among patients with prolonged index hospitalization or readmission. The change in eGFR_diff during HF admission was independently associated with a composite of death or readmission for any cause. Additional studies are warranted to further validate our findings and establish new standards for the assessment of kidney function in acute HF patients.

Acknowledgements

This manuscript was prepared using datasets obtained from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center and does not necessarily reflect the opinions or views of the NHLBI or the study investigators.

Conflict of interest

Y.N. serves as a consultant for Abbott. G.T.S. serves as a consultant for Abbott. N.U. has received research grant support and consultant fees from Abbott and Medtronic. P.C.C. has received research grant support from Abbott and serves as a consultant for the same company. The remaining authors have no conflict of interest to declare.

Funding

This publication was supported from the Lisa and Mark Schwartz Program to Reverse Heart Failure at New York-Presbyterian Hospital/Columbia University.

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