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Novel pharmacologic treatment options reduce mortality and morbidity in a cost-effective manner in patients with heart failure (HF). Undisputedly, the effective implementation of these agents is an essential element of good clinical practice, which is endorsed by the European Society of Cardiology (ESC) guidelines on acute and chronic HF. Yet, physicians struggle to implement these therapies as they have to balance the true and/or perceived risks versus their substantial benefits in clinical practice. Any worsening of biomarkers of renal function is often perceived as being disadvantageous and is in clinical practice one of the most common reasons for ineffective drug implementation. However, even in this context, they clearly reduce mortality and morbidity in HF with reduced ejection fraction (HFrEF) patients, even in patients with poor renal function. Furthermore these agents are also beneficial in HF with mildly reduced ejection fraction (HFmrEF) and sodium-glucose cotransporter 2 (SGLT2) inhibitors more recently demonstrated a beneficial effect in HF with preserved ejection fraction (HFpEF). The emerge of several new classes (angiotensin receptor-neprilysin inhibitor [ARNI], SGLT2 inhibitors, vericiguat, omecamtiv mecarbil) and the recommendation by the 2021 ESC guidelines for the diagnosis and treatment of acute and chronic HF of early initiation and titration of quadruple disease-modifying therapies (ARNI/angiotensin-converting enzyme inhibitor + beta-blocker + mineralocorticoid receptor antagonist and SGLT2 inhibitor) in HFrEF increases the likelihood of treatment-induced changes in renal function. This may be (incorrectly) perceived as deleterious, resulting in inertia of starting and uptitrating these lifesaving therapies. Therefore, the objective of this consensus document is to provide advice of the effect HF drugs on renal function.

Background: Left atrial (LA) myopathy is common in patients with heart failure and preserved ejection fraction and leads to the development of atrial fibrillation (AF). We investigated whether the likelihood of LA remodeling, LA dysfunction, altered hemodynamics, and risk for incident AF could be identified from a single 12-lead ECG using a novel artificial intelligence (AI)-enabled ECG analysis. Methods: Patients with heart failure and preserved ejection fraction (n=613) underwent AI-enabled ECG analysis, echocardiography, and cardiac catheterization. Individuals were grouped by AI-enabled ECG probability of contemporaneous AF, taken as an indicator of underlying LA myopathy. Results: Structural heart disease was more severe in patients with higher AI-probability of AF, with more left ventricular hypertrophy, larger LA volumes, and lower LA reservoir and booster strain. Cardiac filling pressures and pulmonary artery pressures were higher in patients with higher AI-probability, while cardiac output reserve was more impaired during exercise. Among patients with sinus rhythm and no prior AF, each 10% increase in AI-probability was associated with a 31% greater risk of developing new-onset AF (hazard ratio, 1.31 [95% CI, 1.20–1.42]; P P Conclusions: A novel AI-enabled score derived from a single 12-lead ECG identifies the presence of underlying LA myopathy in patients with heart failure and preserved ejection fraction as evidenced by structural, functional, and hemodynamic abnormalities, as well as long-term risk for incident AF. Further research is required to determine the role of the AI-enabled ECG in the evaluation and care of patients with heart failure and preserved ejection fraction.