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Meta-Analysis
. 2025 Aug;85(8):1013-1031.
doi: 10.1007/s40265-025-02198-6. Epub 2025 Jun 21.

Novel Potassium Binders in Reduction of Hyperkalemia and Optimization of RAAS Inhibitors Treatment in Patients with Chronic Kidney Disease or Heart Failure: A Systematic Review and Meta-analysis

Affiliations
Meta-Analysis

Novel Potassium Binders in Reduction of Hyperkalemia and Optimization of RAAS Inhibitors Treatment in Patients with Chronic Kidney Disease or Heart Failure: A Systematic Review and Meta-analysis

Naya Huang et al. Drugs. 2025 Aug.

Abstract

Background: Renin-angiotensin-aldosterone system inhibitors (RAASi) and its combination therapy are essential supportive treatments for patients with chronic kidney disease (CKD) and heart failure (HF). Hyperkalemia (HK) is the major safety concern associated with the treatments, which often leads to RAASi dose reduction or discontinuation, thereby compromising cardiovascular protective effects. Although novel potassium binders (NPBs) are recommended by current guidelines for the treatment of HK, systematic evidence is needed to guide their use in RAASi optimization and HK management. A systematic review and meta-analysis was conducted to evaluate the incidence of HK in patients with CKD or HF who received RAASi and the efficacy of NPBs in RAASi optimization.

Methods: PubMed, Medline, Embase, and the Cochrane Library were searched from January 1, 2011 to December 31, 2023. Any studies of adult patients with CKD or HF who received RAASi were included in systematic review of HK incidence and risk factors (part 1). Randomized controlled trials (RCTs) of NPBs in patients with CKD or HF were included in meta-analysis on the efficacy of novel potassium binders (NPBs) (part 2). The primary outcome was optimization of RAASi therapy with NPBs. A pooled analysis was conducted in part 1. Network meta-analyses using a random-effects model were performed in part 2.

Results: A total of 83 studies (24 with CKD, 54 with HF, and 5 with CKD and HF) were included in part 1 and 8 RCTs (2 with CKD, 4 with HF, and 2 with CKD and HF) were included in part 2. The pooled HK incidence was 10.7% overall at any criteria and in all patients who received RAASi. The highest incidence of HK was observed with the combination of angiotensin converting enzyme inhibitor (ACEi)/angiotensin ii receptor blockers (ARBs)/angiotensin receptor-neprilysin inhibitor (ARNi)+ mineralocorticoid receptor antagonists (MRAs) (24.4%), followed by the triple therapy ACEi/ARB/ARNi+ sodium glucose cotransporter-2 inhibitor (SGLT2i)+MRA (10.9%), and ACEi/ARB/ARNi + SGLT2i (2.2%). Novel potassium binders improved RAASi optimization by 38% compared with placebo (risk ratio, 1.38; 95% CI, 1.16-1.65). Additionally, NPBs decreased the incidence of HK by 28% and reduced the level of potassium by 0.71 mEq/L. The CKD population showed a higher optimization rate than the HF population (84% vs 29%).

Conclusion: The RAASi treatment was associated with high prevalence of HK, especially in bigeminal and triple therapy. The NPBs were effective in RAASi optimization and HK management, especially among the CKD population.

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Conflict of interest statement

Declarations. Funding: This work was supported by grants from the National Natural Science Foundation of China (Nos. 82170737, 82200820, and 82370707), NHC Key Laboratory of Clinical Nephrology (Sun Yat-Sen University), Guangzhou Science and Technology Project (No. 2025A04J5292, 202206080010), Guangdong Provincial Key Laboratory of Nephrology, Guangdong International Science and Technology Cooperation Institute of Immune Kidney Disease and Precision Medicine, Guangdong Basic and Applied Basic Research Foundation (No. 2023B0303000013), and 2024 Guangzhou Science and Technology Fund for Agriculture and Social Development Special Topic (No. 2024B03J1337). This work also received funding from AstraZeneca. Conflict of Interest: The authors (NH, YX, CL, YL, YF, ZL, DZ, HM, WC) declare no conflicts of interest. Ethics Approval: Not applicable. Consent to Participate: Not applicable. Code Availability: Not applicable. Consent for Publication: Not applicable. Availability of Data and Materials: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Author Contributions: NH, YX, CL, DZ, HM, and WC conceived and designed the study. NH, YX, CL, YL, YF, ZL, and DZ screened and selected the articles. NH, YX, CL, YF, and ZL extracted the data. NH, YX, CL, YF, and ZL assessed the risk of bias, NH, CL, YF, and ZL analyzed the data, HM, and WC supervised the data analyses, NH, and CL rated the certainty of evidence, NH, YX, and WC interpreted the data, NH, CL, YF, and ZL drafted the manuscript. All authors had full access to all the data in the study, had reviewed and approved the final version of the manuscript, and agree to be accountable for the work.

Figures

Fig. 1
Fig. 1
Flow diagram. NMA network meta-analysis
Fig. 2
Fig. 2
Forest plots for optimization of RAASi therapy. A Optimization of RAASi therapy, B HK events, C potassium level reduction. CI confidence interval, HK hyperkalemia, RAASi renin-angiotensin-aldosterone system inhibitors, RR risk ratio, SD standard deviation
Fig. 3
Fig. 3
Forest plots for any AEs and hypokalemia events. A Any AEs, B hypokalemia events. AEs adverse events, CD standard deviation, RR risk ratio
Fig. 4
Fig. 4
Subgroup analysis according to the population. A Optimization of RAASi therapy, B HK events, C potassium level reduction, D any AEs, E hypokalemia events. AEs adverse events, CI confidence interval, CKD chronic kidney disease, HF heart failure, HK hyperkalemia, RAASi renin–angiotensin–aldosterone system inhibitors, RR risk ratio, SD standard deviation
Fig. 4
Fig. 4
Subgroup analysis according to the population. A Optimization of RAASi therapy, B HK events, C potassium level reduction, D any AEs, E hypokalemia events. AEs adverse events, CI confidence interval, CKD chronic kidney disease, HF heart failure, HK hyperkalemia, RAASi renin–angiotensin–aldosterone system inhibitors, RR risk ratio, SD standard deviation
Fig. 4
Fig. 4
Subgroup analysis according to the population. A Optimization of RAASi therapy, B HK events, C potassium level reduction, D any AEs, E hypokalemia events. AEs adverse events, CI confidence interval, CKD chronic kidney disease, HF heart failure, HK hyperkalemia, RAASi renin–angiotensin–aldosterone system inhibitors, RR risk ratio, SD standard deviation
Fig. 5
Fig. 5
Subgroup analysis according to the type of NPBs. A Optimization of RAASi therapy, B HK events, C potassium level reduction, D any AEs, E hypokalemia events. AEs adverse events, CI confidence interval, HK hyperkalemia, NPBs novel potassium binders, RAASi renin–angiotensin–aldosterone system inhibitors, RR risk ratio, SD standard deviation, SZC sodium zirconium cyclosilicate
Fig. 5
Fig. 5
Subgroup analysis according to the type of NPBs. A Optimization of RAASi therapy, B HK events, C potassium level reduction, D any AEs, E hypokalemia events. AEs adverse events, CI confidence interval, HK hyperkalemia, NPBs novel potassium binders, RAASi renin–angiotensin–aldosterone system inhibitors, RR risk ratio, SD standard deviation, SZC sodium zirconium cyclosilicate

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