Delayed Anaerobic Threshold in Heart Failure Patients With Atrial Fibrillation

The impact of atrial fibrillation on oxygen uptake and haemodynamics in patients with heart failure: a systematic review and meta-analysis

Aims

Atrial fibrillation (AF) may exacerbate exercise intolerance and haemodynamic limitations in individuals with heart failure (HF). Therefore, we performed a systematic search and meta-analysis to quantify the impact of AF on exercise tolerance (peak oxygen uptake, VO2peak; primary outcome) and exercise haemodynamics (secondary outcomes) in patients with HF.

Methods and results

PubMed, Scopus, and Web of Science databases were systematically searched for articles from inception to June 2024. Studies were included if they: (i) examined participants with HF; (ii) compared participants with AF to those not in AF (i.e. sinus rhythm); (iii) measured VO2peak from expired gas analysis. A fixed effects meta-analysis was performed, with groups compared using the weighted average effect size, represented as the weighted mean difference (WMD) with 95% confidence intervals (95% CI). Of 573 identified studies, 16 met the full inclusion comparing VO2peak in HF-patients in AF [HF-AF; n = 1,271, 68% male, 67 years, left ventricular ejection fraction (LVEF): 41%], and HF in sinus rhythm (HF-SR; n = 4910; 62% male, 62 years, LVEF: 41%). VO2peak was significantly lower in HF-AF (WMD: -1.55mL/kg/min, 95%-CI: -1.81 to -1.28, n = 6471). This coincided with a slightly lower peak heart rate (WMD: -2.94 b/min, 95%-CI: -4.76 to -1.13 b/min, n = 5115), decreased O2pulse (WMD: -1.58 mL/beat, 95% CI: -1.90 to -1.26, n = 3049), and lower systolic blood pressure (WMD: -11.11 mmHg, 95% CI: -14.01 to -8.21, n = 2409).

Conclusion

In patients with HF, AF is associated with greater VO2peak impairment, potentially due to reduced stroke volume and/or arterio-venous oxygen difference. This highlights the importance of combined strategies to identify and manage AF in individuals with HF.

Subtypes and Mechanisms of Hypertrophic Cardiomyopathy Proposed by Machine Learning Algorithms

Hypertrophic cardiomyopathy (HCM) is a relatively common inherited cardiac disease that results in left ventricular hypertrophy. Machine learning uses algorithms to study patterns in data and develop models able to make predictions. The aim of this study is to identify HCM subtypes and examine the mechanisms of HCM using machine learning algorithms. Clinical and laboratory findings of 143 adult patients with a confirmed diagnosis of nonobstructive HCM are analyzed; HCM subtypes are determined by clustering, while the presence of different HCM features is predicted in classification machine learning tasks. Four clusters are determined as the optimal number of clusters for this dataset. Models that can predict the presence of particular HCM features from other genotypic and phenotypic information are generated, and subsets of features sufficient to predict the presence of other features of HCM are determined. This research proposes four subtypes of HCM assessed by machine learning algorithms and based on the overall phenotypic expression of the participants of the study. The identified subsets of features sufficient to determine the presence of particular HCM aspects could provide deeper insights into the mechanisms of HCM.

Role of comorbidities in heart failure prognosis Part I: Anaemia, iron deficiency, diabetes, atrial fibrillation

Cardiovascular and non-cardiovascular comorbidities are frequently observed in heart failure patients, complicating the therapeutic management and leading to poor prognosis. The prompt recognition of associated comorbid conditions is of great importance to optimize the clinical management, the follow-up, and the treatment of patients affected by chronic heart failure. Anaemia and iron deficiency are commonly reported in all heart failure forms, have a multifactorial aetiology and are responsible for reduced exercise tolerance, impaired quality of life, and poor long-term prognosis. Diabetes mellitus is highly prevalent in heart failure and a poor glycaemic control is associated with worst outcome. Two specific heart failure forms are usually observed in diabetic patients: an ischaemic cardiomyopathy or a typical diabetic cardiomyopathy. The implementation of use of sodium-glucose cotransporter-2 inhibitors will much improve in the near future the long-term prognosis of patients affected by heart failure and diabetes. Among cardiovascular comorbidities, atrial fibrillation is the most common arrhythmic disease of heart failure patients and it is still not clear whether its presence should be considered as a prognostic indicator or as a marker of advanced disease. The aim of the present review was to explore the clinical and prognostic impact of anaemia and iron deficiency, diabetes mellitus, and atrial fibrillation in patients affected by chronic heart failure.