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    Management of Hyperkalemia in Heart Failure
    (2021) Altay, Hakan; Cavusoglu, Yuksel; Celik, Ahmet; Demir, Serafettin; Kilicarslan, Baris; Nalbantgil, Sanem; Temizhan, Ahmet; Tokgoz, Bulent; Ural, Dilek; Yesilbursa, Dilek; Yildirimturk, Ozlem; Yilmaz, Mehmet Birhan; 34738907
    Hyperkalemia is a common electrolyte abnormality in heart failure (HF) that can cause potentially life-threatening cardiac arrhythmias and sudden cardiac death. HF patients with diabetes, chronic kidney disease and older age are at higher risk of hyperkalemia. Moreover, hyperkalemia is also often associated with the use of renin-angiotensin-aldosterone system inhibitors (RAASi) including angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, mineralocorticoid receptor antagonists and sacubitril-valsartan. In clinical practice, the occurrence of hyperkalemia is a major concern among the clinicians and often limits RAASi use and/ or lead to dose reduction or discontinuation, thereby reducing their potential benefits for HF. Furthermore, recurrent hyperkalemia is frequent in the long-term and is associated with an increase in hyperkalemia-related hospitalizations. Therefore, management of hyperkalemia has a special importance in HF patients. However, treatment options in chronic management are currently limited. Dietary restriction of potassium is usually ineffective with variable adherence. Sodium polystyrene sulfonate is commonly used, but its effectiveness is uncertain and reported to be associated with intestinal toxicity. New therapeutic options such as potassium binders have been suggested as potentially beneficial agents in the management of hyperkalemia. This document discusses prevalence, predictors and management of hyperkalemia in HF, emphasizing the importance of careful patient selection for medical treatment, uptitration of the doses of RAASi, regular surveillance of potassium and treatment options of hyperkalemia.
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    Importance of Systematic Right Ventricular Assessment in Cardiac Resynchronization Therapy Candidates: A Machine Learning Approach
    (2021) Galli, Elena; Le Rolle, Virginie; Smiseth, Otto A.; Duchenne, Jurgen; Aalen, John M.; Larsen, Camilla K.; Sade, Elif A.; Hubert, Arnaud; Anilkumar, Smitha; Penicka, Martin; Linde, Cecilia; Leclercq, Christophe; Hernandez, Alfredo; Voigt, Jens-Uwe; Donal, Erwan; 33422667
    Background: Despite all having systolic heart failure and broad QRS intervals, patients screened for cardiac resynchronization therapy (CRT) are highly heterogeneous, and it remains extremely challenging to predict the impact of CRT devices on left ventricular function and outcomes. The aim of this study was to evaluate the relative impact of clinical, electrocardiographic, and echocardiographic data on the left ventricular remodeling and prognosis of CRT candidates by the application of machine learning approaches. Methods: One hundred ninety-three patients with systolic heart failure receiving CRT according to current recommendations were prospectively included in this multicenter study. A combination of the Boruta algorithm and random forest methods was used to identify features predicting both CRT volumetric response and prognosis. Model performance was tested using the area under the receiver operating characteristic curve. The k-medoid method was also applied to identify clusters of phenotypically similar patients. Results: From 28 clinical, electrocardiographic, and echocardiographic variables, 16 features were predictive of CRT response, and 11 features were predictive of prognosis. Among the predictors of CRT response, eight variables (50%) pertained to right ventricular size or function. Tricuspid annular plane systolic excursion was the main feature associated with prognosis. The selected features were associated with particularly good prediction of both CRT response (area under the curve, 0.81; 95% CI, 0.74-0.87) and outcomes (area under the curve, 0.84; 95% CI, 0.75-0.93). An unsupervised machine learning approach allowed the identification of two phenogroups of patients who differed significantly in clinical variables and parameters of biventricular size and right ventricular function. The two phenogroups had significantly different prognosis (hazard ratio, 4.70; 95% CI, 2.1-10.0; P < .0001; log-rank P < .0001). Conclusions: Machine learning can reliably identify clinical and echocardiographic features associated with CRT response and prognosis. The evaluation of both right ventricular size and functional parameters has pivotal importance for the risk stratification of CRT candidates and should be systematically performed in patients undergoing CRT. (J Am Soc Echocardiogr 2021;34:494-502.)