Management of RAASi-associated hyperkalemia in patients with cardiovascular disease.
José Carlos CardosoDulce BritoJoão Miguel FrazãoAníbal FerreiraPaulo BettencourtPatrícia BrancoCândida FonsecaPublished in: Heart failure reviews (2021)
Renin-angiotensin-aldosterone system inhibitors (RAASi) reduce morbidity and mortality in heart failure (HF) with reduced ejection fraction in a dose-dependent manner. They also have a positive impact in other cardiovascular diseases (CVDs). However, RAASi may induce hyperkalemia, a potentially life-threatening disorder. This risk is further increased in those with concomitant chronic kidney disease, diabetes mellitus, and/or in patients with hypertension. Current treatment guidelines recommend maximal RAASi dosing to improve clinical outcomes; however, this is often limited by the development of hyperkalemia. When this occurs, current guidelines recommend RAASi down-titration/interruption, which, while improving short-term prognosis, is associated with a negative long-term prognostic impact. At present, the European Society of Cardiology suggests the consideration of novel potassium binders (patiromer and sodium zirconium cyclosilicate) for the management of RAASi-associated hyperkalemia. Both drugs can reduce serum potassium levels and prevent recurrent hyperkalemia. Additionally, patiromer showed enabling of RAASi optimization in high-risk patients. Nevertheless, precise recommendations on the use of these drugs are lacking. Building upon current HF guideline recommendations, a multidisciplinary expert panel convened to design an algorithm providing practical guidance on the use of novel potassium binders/patiromer in patients with HF and/or other CVD. As a result of that effort, we present an evidence-based treatment algorithm for the management of hyperkalemia with novel potassium binders/patiromer in patients with HF and/or other CVD receiving RAASi, including the necessary monitoring to avoid induction of hypokalemia. This algorithm aims to maintain or up-titrate RAASi to optimized doses, while maintaining normokalemia, improved clinical outcomes, and long-term prognosis.
Keyphrases
- cardiovascular disease
- chronic kidney disease
- end stage renal disease
- heart failure
- clinical practice
- machine learning
- acute heart failure
- deep learning
- peritoneal dialysis
- type diabetes
- left ventricular
- cardiac surgery
- coronary artery disease
- neural network
- atrial fibrillation
- acute kidney injury
- weight loss
- prognostic factors
- body composition
- high intensity