Heart failure with reduced, mildly reduced, or preserved left ventricular ejection fraction: Has reasoning been lost?

Machine learning in heart failure diagnosis, prediction, and prognosis: review

Globally, cardiovascular diseases take the lives of over 17 million people each year, mostly through myocardial infarction, or MI, and heart failure (HF). This comprehensive literature review examines various aspects related to the diagnosis, prediction, and prognosis of HF in the context of machine learning (ML). The review covers an array of topics, including the diagnosis of HF with preserved ejection fraction (HFpEF) and the identification of high-risk patients with HF with reduced ejection fraction (HFrEF). The prediction of mortality in different HF populations using different ML approaches is explored, encompassing patients in the ICU, and HFpEF patients using biomarkers and gene expression. The review also delves into the prediction of mortality and hospitalization rates in HF patients with mid-range ejection fraction (HFmrEF) using ML methods. The findings highlight the significance of a multidimensional approach that encompasses clinical evaluation, laboratory assessments, and comprehensive research to improve our understanding and management of HF. Promising predictive models incorporating biomarkers, gene expression, and consideration of epigenetics demonstrate potential in estimating mortality and identifying high-risk HFpEF patients. This literature review serves as a valuable resource for researchers, clinicians, and healthcare professionals seeking a comprehensive and updated understanding of the role of ML diagnosis, prediction, and prognosis of HF across different subtypes and patient populations.

Heart failure outcomes in low-risk patients with atrial fibrillation: a case-control study of 680 523 Swedish individuals

AIMS

Knowledge of long-term outcomes in patients with atrial fibrillation (AF) remains limited. We sought to evaluate the risk of new-onset heart failure (HF) in patients with AF and a low cardiovascular risk profile.

METHODS AND RESULTS

Data from the Swedish National Patient Register were used to identify all patients with a first-time diagnosis of AF without underlying cardiovascular disease at baseline between 1987 and 2018. Each patient was compared with two controls without AF from the National Total Population Register. In total, 227 811 patients and 452 712 controls were included. During a mean follow-up of 9.1 (standard deviation 7.0) years, the hazard ratio (HR) for new-onset HF was 3.55 [95% confidence interval (CI) 3.51-3.60] in patients compared with controls. Women with AF (18-34 years) had HR for HF onset 24.6 (95% CI 7.59-80.0) and men HR 9.86 (95% CI 6.81-14.27). The highest risk was within 1 year in patients 18-34 years, HR 103.9 (95% CI 46.3-233.1). The incidence rate within 1 year increased from 6.2 (95% CI 4.5-8.6) per 1000 person-years in young patients (18-34 years) to 142.8 (95% CI 139.4-146.3) per 1000 person-years among older patients (>80 years).

CONCLUSIONS

Patients studied had a three-fold higher risk of developing HF compared with controls. Young patients, particularly women, carry up to 100-fold increased risk to develop HF within 1 year after AF. Further studies in patients with AF and low cardiovascular risk profile are needed to prevent serious complications such as HF.

The Neurohormonal Overactivity Syndrome in Heart Failure

Heart failure (HF) is categorized arbitrarily based on the left ventricular ejection fraction (LVEF) in HF with reduced (HFrEF; LVEF < 40%), mildly reduced (HFmrEF; LVEF 40−49%), or preserved ejection fraction (HFpEF; LVEF ≥ 50%). In this opinion paper, based on (patho)physiological considerations, we contend that the neurohormonal overactivity syndrome (NOHS), which is present in all symptomatic HF patients irrespective of their LVEF, not only contributes to the development of signs and symptoms but it is also a major determinant of patients’ outcomes. In this regard, NHOS is the only currently available treatment target in HF and should be combatted in most patients with the combined use of diuretics and neurohormonal inhibitors (β-blockers, angiotensin receptor-neprilysin inhibitor/angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, mineralocorticoid antagonists, and sodium-glucose co-transporter 2 inhibitors). Unfortunately, despite the advances in therapeutics, HF mortality remains high. Probably machine learning approaches could better assess the multiple and higher-dimension interactions leading to the HF syndrome and define clusters of HF treatment efficacy.

Heart failure with preserved ejection fraction: A distinct heart failure phenotype?

The present work discusses the serious confusion resulting from the arbitrary nomenclature of heart failure with preserved ejection fraction (HFpEF), the presumed underlying pathophysiology, and the supposed features. A consequence of this misconception is that HFpEF trials have recruited patients with entirely different characteristics rendering the extrapolation of the results of one study to the other infeasible and dramatically affecting diagnosis and treatment.