Author + information
- Received September 6, 2018
- Accepted October 4, 2018
- Published online December 31, 2018.
- Gianluigi Savarese, MD, PhDa,∗,
- Hong Xu, MDb,∗,
- Marco Trevisan, MScb,
- Ulf Dahlström, MD, PhDc,
- Patrick Rossignol, MD, PhDd,
- Bertram Pitt, MD, PhDe,
- Lars H. Lund, MD, PhDa,†∗ ( and )
- Juan J. Carrero, PharmD, PhDb,†
- aDivision of Cardiology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
- bDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- cDepartments of Cardiology Medical and Health Sciences, Linkoping University, Linkoping, Sweden
- dUniversité de Lorraine, INSERM CIC-P 1433, Centre Hospitalier Régional Universitaire de Nancy, and INSERM U1116, FCRIN INI-Cardiovascular and Renal Clinical Trialists, Nancy, France
- eDepartment of Medicine, University of Michigan, Ann Arbor, Michigan
- ↵∗Address for correspondence:
Dr. Lars H. Lund, Department of Medicine, Karolinska Institutet, and Heart and Vascular Theme, Karolinska University Hospital, FoU Tema Hjärta Kärl, Norrbacka, S1:02, 171 76 Stockholm, Sweden.
Objectives This study investigated 1-year incidence and predictors of dyskalemia (dysK) and its outcome associations in heart failure with preserved ejection fraction (HFpEF), HF with mid-range EF (HFmrEF), and HF with reduced EF (HFrEF).
Background DysK in real-world HF is insufficiently characterized. Fear of dyskalemia may lead to underuse or underdosing of renin-angiotensin-aldosterone system inhibitors.
Methods Patients enrolled in the SwedeHF (Swedish Heart Failure) Registry from 2006 to 2011 in Stockholm, Sweden were included in the analyses. Multivariate Cox regression analysis identified independent predictors of dysK within 1 year. Time-dependent Cox models assessed outcomes associated with incident dysK (all-cause death, HF, and other cardiovascular disease [CVD] hospitalizations) within 1 year from baseline.
Results Of 5,848 patients, 24.4% experienced hyperkalemia (hyperK [K >5.0 mmol/l]) at least once, and 10.2% had moderate or severe hyperK (K >5.5 mmol/l). Adjusted risk of moderate or severe hyperK was highest in HFpEF and HFmrEF. Similarly, 20.3% of patients had at least one episode of hypokalemia (hypoK [<3.5 mmol/l]), and 3.7% had severe hypoK (<3.0 mmol/l). Adjusted risk of any hypoK was highest in HFpEF. Independent predictors of both hyperK and hypoK were sex, baseline potassium and estimated glomerular filtration rate, low hemoglobin, chronic obstructive pulmonary disease (COPD), inpatient status, and higher New York Heart Association functional class. Incident dysK was associated with increased risk of mortality. Furthermore, hypoK was associated with increased CVD hospitalizations (HF-related excluded). There was no association between dysK and HF hospitalization risk, regardless of EF.
Conclusions DysK is common in HF and is associated with increased mortality. Risk of moderate or severe hyperK was highest in HFpEF and HFmrEF, whereas risk of hypoK was highest in HFpEF. HF severity, low hemoglobin, COPD, baseline high and low potassium, and low eGFR were relevant predictors of dysK occurrence.
- heart failure
- mid-range ejection fraction
- preserved ejection fraction
- reduced ejection fraction
Heart failure (HF) is associated with comorbidities (e.g., older age, chronic kidney disease [CKD], diabetes) and is treated with pharmacological regimens (e.g., angiotensin-converting enzyme [ACE] inhibitors, angiotensin receptor blockers [ARB], mineralocorticoid receptor antagonists [MRA], angiotensin-receptor neprilysin [ARN] inhibitors, and diuretics), which may interfere with potassium homeostasis, leading to dyskalemia (dysK) (both hyperkalemia [hyperK] and hypokalemia [hypoK]) (1–4).
Patients with HF with preserved ejection fraction (HFpEF), HF with mid-range EF (HFmrEF), or HF with reduced EF (HFrEF) have different demographics, exhibit different clinical characteristics, and are treated differently, possibly leading to different rates of dysK (5). However, current evidence for the incidence of dysK comes mainly from selective, randomized clinical trials in HFrEF, and no detailed dysK quantification exists to date in HFpEF and HFmrEF (6,7). Furthermore, data from randomized clinical trials may not represent real-world clinical practice (8). Thus, a deeper understanding of risk of dysK in these settings is needed, given that fear of dysK may lead to underuse or underdosing of renin-angiotensin-aldosterone system (RAAS) inhibitors (9,10) and that the incidence of dysK is relevant in the design of future trials in different HF phenotypes.
This study assessed potential differences and similarities in the incidence of dysK, its predictors, and outcome associations in a large and unselected populations of real-world HF patients with different EF.
Data sources and study population
The SwedeHF (Swedish Heart Failure) Registry and its linkage through the unique personal identification of Swedish citizens with general government registries, the dispensed drug registry, and the SCREAM (Stockholm CREAtinine Measurements) project has been previously described (11). More details are reported in the Online Methods.
The present study enrolled HF patients who were registered for the first time in SwedeHF and resided in the region of Stockholm between January 1, 2006, and December 31, 2010. These participants underwent plasma potassium tests (recorded in SCREAM) on the day of entry in the SwedeHF trial (index date). From a total of 8,896 HF patients registered in Stockholm during that period, we excluded those who did not have measurements of serum creatinine and plasma potassium (n = 1,638), or who showed abnormal plasma potassium concentrations (i.e., K <3.5 [n = 412] and >5.0 mmol/l [n = 141]) at registration, or had not had EF assessed (n = 849), or were undergoing chronic dialysis (n = 8). This resulted in 5,848 patients included in the analysis (see patient selection flow chart in Online Figure 1).
Exposure and baseline covariates
Demographic and clinical variables are reported in Table 1 (see definitions in Online Table 1). Ongoing medications were defined by the presence of a pharmacy dispensation within 120 days from the index date (see definitions in Online Table 2). We defined HFpEF as EF ≥50%, HFmrEF as EF 40% to 49%, and HFrEF as EF <40% (12). Plasma potassium concentration was measured by using potentiometric titration. We categorized baseline potassium levels as 3.5 to 3.9, 4.0 to 4.4, and 4.5 to 5.0 mmol/l, with the stratum 4.0 to 4.4 mmol/l chosen as reference. Plasma creatinine concentration was measured by using either the enzymatic or corrected Jaffe method (alkaline picrate reaction) and was used to estimate glomerular filtration rate (eGFR). Creatinine values <25 and >1,500 μmol/l were considered implausible and discarded. eGFR was calculated by using the CKD-Epidemiology Collaboration (CKD-EPI) equation, and eGFR strata were categorized as ≥90, 60 to 89, 45 to 59, 30 to 44, and <30 ml/min/1.73 m2. eGFR ≥ 90 ml/min/1.73 m2 was considered the reference.
The first study outcome was the 1-year incidence of hyperK and hypoK, separately; the second outcome was the 1-year risk of all-cause death, HF hospitalization, or cardiovascular disease (CVD) hospitalization other than HF after an incidence of hyperK or hypoK. Outcome analysis was also separately performed in the different EF categories, as risk of these outcomes in HFpEF versus HFmrEF versus HFmrEF has not been previously investigated.
The 1-year incidence of hyperK and hypoK was calculated from all plasma potassium measurements recorded in health care within 1 year from the index registration. We defined incident hyperK and hypoK as the first measurements of potassium, >5.0 and <3.5 mmol/l, respectively. However, because patients with K <3.0 and >5.5 mmol/l are at the highest risk of mortality (13), we also considered the categories of moderate or severe hyperkalemia (>5.5 mmol/l) and severe hypokalemia (<3.0 mmol/l). In order to exclude incident dysK that might have been the consequence of abnormal potassium baseline values, we disregarded events occurring within the first 2 weeks from the index date.
Number of deaths were obtained by linkage with the patient register, as well as HF hospitalizations or CVD hospitalization other than HF, which were identified by relevant International Classification of Diseases 10th revision at discharge (Online Table 1).
Continuous variables were reported as mean ± SD, and categorical variables as counts and proportions (%). Baseline characteristics were compared across the different EF categories (HFpEF vs. HFmrEF vs. HFrEF) using Pearson chi-square for proportions and analysis of variance (ANOVA) for continuous variables.
The crude 1-year incidence rates of hyperK and hypoK were assessed by using the Kaplan-Meier method. Multivariate Cox regression models with dysK as dependent variable were performed in the overall population and in the different EF categories to identify the baseline factors associated with dysK occurrence, reporting hazard ratios (HRs) and 95% confidence intervals (CIs). We also tested multiplicative interactions between the a priori-selected eGFR strata (eGFR ≥60 vs. <60 ml/min/1.73 m2); comorbidities including hypertension, diabetes, myocardial infarction, atrial fibrillation, and chronic obstructive pulmonary disease (COPD); and EF categories. Finally, the weighted contribution of selected risk factors to dysK incidence was quantified using the population-attributable fraction (PAF) adjusted for potential confounders by Cox proportional hazards model (14).
Multivariate time-dependent Cox regression models were performed to investigate the association between incident dysKs and study outcomes within the 1-year follow-up (from baseline) in the overall HF population and separately in patients with HFpEF, HFmrEF, and HFrEF. All covariates were defined at baseline, except for dysK status, which was considered time-dependent. Patients experiencing dysK, thus, contributed to the reference group during their person-time free from dysK and to the dysK risk group during the remaining person-time. A p value of <0.05 was considered statistically significant for all analyses. All analyses were performed using STATA version 14.0 software (Stata Corp., College Station, Texas).
Baseline characteristics are reported in Table 1. Of 5,848 HF patients, 37% were women, 24% had HFpEF, 22% had HFmrEF, and 54% had HFrEF. Mean age was 73 ± 13 years, mean plasma potassium at inclusion was 4.1 ± 0.4 mmol/l, and mean eGFR 61 ± 24 ml/min/1.73 m2. Older age, female sex, worse kidney function, lower hemoglobin levels, and higher user of diuretics were more common with higher EF. In contrast, the proportions of diabetes, history of myocardial infarction, coronary revascularization, peripheral artery disease, worse New York Heart Association (NYHA) functional class, and the use of HF treatments renin-angiotensin-system inhibitors, MRA, and beta-blockers increased with lower EF.
Incidence of HyperK and HypoK
Overall, 24.4% of patients experienced at least 1 hyperK occurrence within 1 year, and 10.2% reported moderate or severe hyperK (Figure 1, note that events are nonexclusive). Crude and adjusted analyses showed no statistically significant differences in risk of any hyperK across the EF spectrum (Figure 2, Table 2). Crude 1-year risk of moderate or severe hyperK was comparable across the EF strata but, after adjustments, it was higher in HFpEF and HFmrEF than in HFrEF (Online Table 3, Online Figure 2).
Overall, 20.3% of patients experienced at least 1 occurrence of hypoK within 1 year, and 3.7% experienced severe hypoK (Figure 1, nonexclusive events). Crude and adjusted risks of any hypoK were highest in HFpEF (Figure 2, Table 2). Crude and adjusted risks of severe hypoK were similar across the EF spectrum (Online Table 3, Online Figure 2).
Risk markers for HyperK and HypoK
Table 2 shows the baseline risk factors associated with the risk of dysK. Variables independently associated with increased hyperK risk were male sex, baseline potassium 4.5 to 5.0 mmol/l, lower eGFR, hemoglobin <120 g/dl, history of diabetes, COPD, alcoholism, cancer, inpatient status, NYHA functional class ≥II, use of MRA, and nonuse of beta-blockers. Conversely, baseline potassium 3.5 to 3.9 mmol/l was associated with reduced hyperK risk. Neither ACE inhibitors/ARBs nor diuretics were associated with incident hyperK. PAF was calculated to estimate the relative importance of selected risk predictors. We observed that 37% of events were attributable to use of MRA, 33% of events to hemoglobin concentration <120 g/l, 26% to eGFR <60 ml/min/1.73 m2, 25% to diabetes, and 16% to COPD and caregivers (inpatient vs. outpatient) (Online Table 4).
Table 2 also reports baseline risk factors for hypoK. Female sex, potassium 3.5 to 3.9 mmol/l, eGFR <30 ml/min/1.73 m2, hemoglobin <120 g/dl, history of myocardial infarction, atrial fibrillation, COPD, alcoholism, being an inpatient, NYHA functional class ≥III, use of diuretics, nonuse of ACE inhibitors/ARBs and beta-blockers independently predicted hypoK. PAF analyses showed 25% of hypoK events were attributable to COPD, 20% to hemoglobin <120 g/dl, 18% to female sex, 14% to history of myocardial infarction, 13% to caregiver (inpatient vs. outpatient), and 11% to the nonuse of ACE/ARBs (Online Table 4).
Sensitivity analyses showed overall similar predictors for moderate or severe dysK (Online Tables 5 to 7). No multiplicative interactions were observed between EF strata (HFpEF, HFmrEF, and HFrEF), eGFR strata, and presence versus absence of comorbidities (hypertension, diabetes, myocardial infarction, atrial fibrillation, and COPD) in predicting study outcomes (p > 0.10 for all).
DysK and subsequent risk of mortality and hospitalization
During the 1-year follow-up from baseline, 1.45 deaths per 1,000 patient-years occurred after hyperK compared to 0.33 deaths that occurred in periods without or preceding hyperK. After adjustments, incident hyperK was associated with increased risk of death (HR: 4.03; 95% CI: 3.42 to 4.75) (Figure 4). Similar results were observed across the EF spectrum (Table 3). In contrast, incident hyperK was not associated with the risk of hospitalization (Figure 4, Table 3).
Similarly, 1.43 deaths per 1,000 patient-years occurred after hypoK compared to 0.36 deaths during periods without or preceding hypoK (Figure 4). After adjustments, incident hypoK was significantly associated with the risk of death (HR: 3.28; 95% CI: 2.79 to 3.86) (Figure 4), with a similar magnitude across the different EF strata (Table 3). Incident hypoK was also associated with the risk of CVD hospitalization other than HF (HR: 1.83; 95% CI: 1.24 to 2.71) but not with the risk of a new HF hospitalization (Figure 4, Table 3).
This study found that the incidence of dysK (both hyperK and hypoK) was common and associated with similar predictors, regardless of EF category, in a large cohort of real-world HF patients. Incident dysK was a strong predictor of mortality regardless of EF category. Incident dysK was not associated with the risk of a subsequent HF hospitalization, whereas incident hypoK (but not hyperK) was associated with the risk of CVD hospitalization other than HF.
Incidence of hyperkalemia and hypokalemia in real-word HF
Current evidence of the incidence of hyperK and hypoK in HF patients came mainly from randomized clinical trials. Whereas most information comes from trials including HFrEF patients (15–18), 1 trial provided estimates in HFpEF (19), and the CHARM (Candesartan in Heart Failure: Assessment of Reduction in Mortality and Morbidity) program analyzed hyperK risk throughout the EF spectrum (20).
In the present study, 24.4% of patients experienced at least one incidence of hyperK, and 10.2% experienced moderate or severe hyperK within 1 year of follow-up, most of which occurred during the first 3 months of follow-up. This is certainly a larger incidence than that reported in trials with longer follow-up (e.g., in the CHARM program, clinically relevant hyperK occurred in 3.5% of the population over a median follow-up of 3.2 years) (20). Whereas we showed that in HFrEF, 9.6% and 18.0% of patients were observed to have a potassium level of >5.5 or <3.5 mmol/l, respectively, the corresponding proportions in RALES (Randomized Aldactone Evaluation Study) were 12.2% (patients with potassium = 5.5 mmol/l were included) and 11.4% (mean follow-up at 24 months), respectively (21), 13.4% and 10.7%, respectively, in EPHESUS (Eplerenone Post-Acute Myocardial Infarction Heart Failure Efficacy and Survival Study) (mean follow-up 16 months) (22), and 8.9% and 9.3%, respectively, in EMPHASIS-HF (Eplerenone in Mild Patients Hospitalization and Survival Study in Heart Failure) (mean follow-up of 21 months) (23). Additionally, in PARADIGM-HF (Prospective Comparison of ARNI with ACEI to Determine Impact on Global Mortality and Morbidity in Heart Failure Trial), the risk of hyperK was 16.7% (median follow-up of 27 months) (18). In patients with HFpEF, potassium levels of >5.5 and <3.5 mmol/l were observed in 11.4% and 25.6%, respectively, of patients within 1 year, whereas in TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist), the corresponding proportions were 13.9% (patients with potassium = 5.5 mmol/l were included) and 17.5%, respectively, over a mean follow-up of 3.3 years (19,24). Additionally, a higher hypoK incidence was found in the present study than in trials, which has received less attention in this patient population but may be equally or more harmful.
A novel finding in the present study is that the risk of dysK across the different EF categories was able to be estimated and compared. HF phenotypes reported differences in clinical characteristics and treatment patterns. After extensive adjustments, patients with HFpEF and HFmrEF were found to have higher risk of moderate or severe hyperK, whereas those with HFpEF had the highest risk of any hypoK. The present study, thus, confirms and expands to real-world setting the trends observed comparing rates from individual trials. For example, in TOPCAT (HFpEF) versus RALES (HFrEF), the crude incidence of moderate or severe hyperK (K >5.5 mmol/l) was 13.9% versus 12.2%, respectively, whereas risk of hypoK (K <3.5 mmol/l) was 17.5% versus 11.4%, respectively, suggesting higher risk of dysK in HFpEF than in HFrEF (19,21,24), also in trial populations.
Independent predictors of HyperK and HypoK
Previous post hoc analyses from trials identified predictors of hyperK in HFrEF (21–23). Despite different definitions of hyperK in those trials, common predictors across the different studies were baseline potassium, kidney function, and use of MRA (21–23). Generally, predictors of hypoK have received less attention and have only been assessed in RALES (21) where low baseline potassium, no use of MRA and ACE-I/ARB use, higher diastolic blood pressure, female sex, and black race were independently associated with the risk of hypokalemia.
Findings from the present unselected real-world population of Swedish HF patients collectively agree with the trial evidence but also provide accurate information for dysK predictors in the less-characterized categories of patients with HFpEF and HFmrEF. These specific clinical characteristics and medications associated with the occurrence of dysK across the EF spectrum may foster the identification of high-risk populations more likely to benefit from dysK monitoring and prevention strategies, including uptitration of HF therapies. Perhaps not unexpectedly, it was also observed that higher baseline potassium levels (4.5 to 5.0 mmol/l) were associated with increased risk of hyperK, whereas lower potassium levels (3.5 to 3.9 mmol/l) at baseline were associated with increased risk of hypoK but reduced risk of hyperK. Furthermore, worse NYHA functional class, COPD, low hemoglobin concentration, and being an inpatient were associated with both hyperK and hypoK. Unmeasured confounders, such as treatments for comorbidities not considered in the current analysis, may explain COPD and low hemoglobin as independent predictors of both potassium disturbances. However, low hemoglobin concentration is also a surrogate for increased congestion (along with low serum protein, albumin, and hematocrit, all of which have been proposed as surrogate markers of (de)congestion and have been found to be associated with cardiovascular endpoints), which is 1 of the main predictors of adverse outcomes and may trigger changes in kidney function and in HF medications, with an associated risk of dysK (25). In line with the importance of kidney function in clearing circulating potassium, it was observed in the present study that there was a monotonic gradual association between hyperK risk and lower eGFR strata. However, an unexpected finding was that individuals with advanced or severe CKD were at higher risk of both hyperK and hypoK. This may perhaps reflect the more fragile fluid and electrolyte balance and the more frequent medication changes, such as increases or variations of diuretic doses, variable diuretic resistance and gastrointestinal and renal congestion over time, and/or reductions of doses or discontinuation of RAAS inhibitor agents in these patients. The observation that women are at lower risk of hyperK is consistent with that in previous studies, but it was also observed in the present study that women were at higher risk of hypoK. This may reflect differences in body composition, resulting in lower total exchangeable body potassium in women than in men, or sex differences in comorbidities and treatments that were not captured in the present analysis (unmeasured confounders). Finally, as previously reported (26), diabetes was associated with the risk of hyperK, which may be explained by its role in decreasing aldosterone production or accelerating kidney function decline.
Understanding the impact of medication in real-world data is difficult, as these variables are affected by both confounding by indication and reverse causation. For instance, ACE inhibitors/ARBs did not associate with the risk of hyperkalemia in the present study, perhaps because their very high use (80%) did not offer discrimination, but also perhaps because the rich covariate adjustment covered the indications for which ACE inhibitors/ARBs were prescribed. On the other hand, use of MRA was associated with higher risk of hyperK, and use of diuretics predicted hypoK, in agreement with general understanding and use of those medications. Beta-blockers were associated with lower risk of hypoK, which is coherent with their role in promoting potassium shift out of cells by inhibiting sodium-potassium ATPase. However, beta-blockers were also associated with reduced risk of hyperK, which may be explained by reverse causation.
Clinical consequences of dyskalemia
Incident hypoK or hyperK was strongly associated with subsequent risk of death in the present study. Similar findings were recently shown in an HF Danish registry-based study (13). Furthermore, we observed a similar risk of death across the EF spectrum, which agrees with a subgroup analysis of a post-hoc study of the DIG (Digitalis Investigation Group) trial, where EF, categorized as ≤45% versus >45%, had no significant interaction with hypoK (K <4 mEq/l) for prediction of mortality (27). In addition, TOPCAT trial data showed increased CVD mortality risk in HFpEF patients reporting incident hyperK (K >5.5 mmol/l) or hypoK (K <3.5 mmol/l) (24). Associations between dysK and subsequent hospitalizations have received even less investigation. In DIG trial, hypoK (K <4 mEq/l) was not associated with increased risk of all-cause hospitalization (27). Conversely, this present study showed increased risk of CVD (excluding HF) but not HF hospitalizations in hypoK patients (K <3.5 mmol/l), particularly in patients with HFrEF. This finding may be explained by higher mortality associated with hyperK than with hypoK, and thus, death more likely to circumvent hospitalization after hyperK than hypoK.
The unselected inclusion and rich patient characterization of the present study is a strength. Additionally, plasma (and not serum) potassium levels were measured, because plasma levels are not affected by leukocytosis and thrombocytosis and therefore the likelihood of false hyperK is low. However, all observational data are subject to selection bias, and the influence of unmeasured confounders cannot be ruled out. Detection bias can also exist in outcome ascertainment, as patients at higher risk of events may be more likely to use health care and have potassium levels measured. Patients were included in SwedeHF on the basis of clinician-judged HF, thus it is possible that some patients, particularly those with HFpEF, might not have had HF. HF phenotype was defined according to the EF reported in the SwedeHF registration recorded on the same day that potassium plasma levels were assessed. The limited amount of longitudinal data available in the overall SwedeHF population prevented performing separate analyses in patients with transitioning EF. Furthermore, use of therapies was recorded at the time that plasma potassium levels were measured; thus, changes in treatments and doses affecting potassium homeostasis over time cannot be ruled out. However, these changes might have had the same chance to occur across the different HF phenotypes and not had a major impact on comparative results. Finally, analysis was limited to years 2006 to 2011, which prevented inclusion of data for ARN inhibitors, which have been shown to be associated with a lower risk of hyperK than ACE inhibitors/ARB.
The present study offers estimates on the incidence, predictors, and outcomes associated with dysK in real-world HF patients with different EF. These real-world data may be helpful to tailor RAAS inhibitor therapy, fostering identification of patients in need of stricter potassium monitoring while they are receiving therapies that affect potassium homeostasis and those who are more likely to develop dysK following changes in therapies and doses. Finally, present analyses may support the design of trials of, for example, RAAS inhibitors and K-binder agents in HF by providing important insights regarding potential populations to be enrolled.
COMPETENCY IN MEDICAL KNOWLEDGE: Patients with HFpEF, HFmrEF, and HFrEF exhibit different demographics and clinical characteristics and are treated differently, possibly leading to different rates of dysK. Fear of dysK in these patients may lead to underuse or underdosing of RAAS inhibitors. The present authors reported that dysKs are common in HF and are associated with increased mortality. Risk of moderate or severe hyperK was highest in HFpEF and HFmrEF, whereas risk of hypoK was highest in HFpEF. HF severity, low hemoglobin, COPD, high and low potassium levels at baseline, and low eGFR were predictors of dysK occurrence.
TRANSLATIONAL OUTLOOK: This study provides data for incidence, predictors, and outcomes associated with dysK in real-world HFpEF, HFmrEF, and HFrEF patients that may be helpful to tailor RAAS inhibitor therapy, fostering identification of those who are more in need of stricter potassium monitoring while receiving therapies that affect potassium homeostasis and those who are more likely to develop dysK following change in therapies and doses. These data may also support the design of trials of, for example, RAAS inhibitors and K-binder agents in HF.
↵∗ Drs. Savarese and Xu contributed equally to this work and are joint first authors.
↵† Drs. Lund and Carrero contributed equally to this work and are joint senior authors.
Supported by institutional grants from Vifor Fresenius Medical Care Renal Pharma and Relypsa to Karolinska Institutet, Swedish Heart and Lung Foundation, Swedish Research Council, Stockholm County Council (including ALF projects), AstraZeneca, and Martin Rind’s and Westman’s Foundations. Dr. Lund is supported by Swedish Research Council grants 2013-23897-104604-23 and 523-2014-2336, and Swedish Heart Lung Foundation grants 20120321 and 20150557; has received grants from Relypsa, Vifor Pharma, AstraZeneca, and Novartis through his institution; and consults for Relypsa, Vifor Pharma, AstraZeneca, Novartis, and Sanofi. Dr. Rossignol is supported by French National Research Agency public grant ANR-15-RHU-0004 as part of the second Investissements d’Avenir program FIGHT-HF and by French PIA project Lorraine Université d’Excellence grant ANR-15-IDEX-04-LUE; consults for Novartis, Relypsa, AstraZeneca, Grünenthal, Stealth Peptides, Fresenius, Idorsia, Vifor Fresenius Medical Care Renal Pharma, Vifor, and CTMA; has received lecture fees from Bayer and CVRx; and is a cofounder of CardioRenal. Dr. Savarese has received research grants from Merck and Co. and Boehringer Ingelheim; and has received honoraria from Vifor and AstraZeneca. Dr. Carrero has received research support from AstraZeneca. Dr. Pitt has received fees from Bayer, Sanofi, AstraZeneca, KDP Pharmaceuticals, Relypsa/Vifor, Cpharmaceuticals, Tricida, Stealth Peptides, Sarfez Pharmaceuticals, and G3 Pharmaceuticals; and is a patent pending holder for eplerenone delivery. Dr. Dahlström has received research support from AstraZeneca through his institution; and has received honoraria from and consults for AstraZeneca and Novartis. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Abbreviations and Acronyms
- ACE inhibitors
- angiotensin-converting enzyme inhibitors
- angiotensin receptor blockers
- angiotensin-receptor neprilysin inhibitors
- chronic obstructive pulmonary disease
- cardiovascular disease
- ejection fraction
- estimated glomerular filtration rate
- heart failure
- heart failure with reduced ejection fraction
- mineralocorticoid receptor antagonists
- New York Heart Association
- peripheral artery disease
- population-attributable fraction
- renin-angiotensin-aldosterone system
- Received September 6, 2018.
- Accepted October 4, 2018.
- 2019 American College of Cardiology Foundation
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