Modifiable risk factors predict incident atrial fibrillation and heart failure

Heart failure in older patients with atrial fibrillation: incidence and risk factors

INTRODUCTION AND OBJECTIVES

Atrial fibrillation (AF) is linked to heart failure (HF). However, little has been published on the factors that may precipitate the onset of HF in AF patients. We aimed to determine the incidence, predictors, and prognosis of incident HF in older patients with AF with no prior history of HF.

METHODS

Patients with AF older than 80 years and without prior HF were identified between 2014 and 2018.

RESULTS

A total of 5794 patients (mean age, 85.2±3.8 years; 63.2% women) were followed up for 3.7 years. Incident HF, predominantly with preserved left ventricular ejection fraction, developed in 33.3% (incidence rate, 11.5-100 people-year). Multivariate analysis identified 11 clinical risk factors for incident HF, irrespective of HF subtype: significant valvular heart disease (HR, 1.99; 95%CI, 1.73-2.28), reduced baseline left ventricular ejection fraction (HR, 1.92; 95%CI, 1.68-2.19), chronic pulmonary obstructive disease (HR, 1.59; 95%CI, 1.40-1.82), enlarged left atrium (HR 1.47, 95%CI 1.33-1.62), renal dysfunction (HR 1.36, 95%CI 1.24-1.49), malnutrition (HR, 1.33; 95%CI, 1.21-1.46), anemia (HR, 1.30; 95%CI, 1.17-1.44), permanent AF (HR, 1.15; 95%CI, 1.03-1.28), diabetes mellitus (HR, 1.13; 95%CI, 1.01-1.27), age per year (HR, 1.04; 95%CI, 1.02-1.05), and high body mass index for each kg/m2 (HR, 1.03; 95%CI, 1.02-1.04). The presence of incident HF nearly doubled the mortality risk (HR, 1.67; 95%CI, 1.53-1.81).

CONCLUSIONS

The presence of HF in this cohort was relatively frequent and nearly doubled the mortality risk. Eleven risk factors for HF were identified, expanding the scope for primary prevention among elderly patients with AF.

Blood pressure, hypertension and the risk of atrial fibrillation: a systematic review and meta-analysis of cohort studies

Elevated blood pressure and hypertension have been associated with increased risk of atrial fibrillation in a number of epidemiological studies, however, the strength of the association has differed between studies. We conducted a systematic review and meta-analysis of the association between blood pressure and hypertension and atrial fibrillation. PubMed and Embase databases were searched for studies of hypertension and blood pressure and atrial fibrillation up to June 6th 2022. Cohort studies reporting adjusted relative risk (RR) estimates and 95% confidence intervals (CIs) of atrial fibrillation associated with hypertension or blood pressure were included. A random effects model was used to estimate summary RRs. Sixty eight cohort studies were included in the meta-analysis. The summary RR was 1.50 (95% CI: 1.42-1.58, I2 = 98.1%, n = 56 studies) for people with hypertension compared to those without hypertension (1,080,611 cases, 30,539,230 participants), 1.18 (95% CI: 1.16-1.21, I2 = 65.9%, n = 37 studies) per 20 mmHg increase in systolic blood pressure (346,471 cases, 14,569,396 participants), and 1.07 (95% CI: 1.03-1.11, I2 = 91.5%, n = 22 studies) per 10 mmHg increase in diastolic blood pressure (332,867 cases, 14,354,980 participants). There was evidence of a nonlinear association between diastolic blood pressure and atrial fibrillation with a steeper increase in risk at lower levels of diastolic blood pressure, but for systolic blood pressure the association appeared to be linear. For both systolic and diastolic blood pressure, the risk increased even within the normal range of blood pressure and persons at the high end of systolic and diastolic blood pressure around 180/110 mmHg had a 1.8-2.3 fold higher risk of atrial fibrillation compared to those with a blood pressure of 90/60 mmHg. These results suggest that elevated blood pressure and hypertension increases the risk of atrial fibrillation and there is some increase in risk even within the normal range of systolic and diastolic blood pressure.

Atrial fibrillation prediction by combining ECG markers and CMR radiomics

Atrial fibrillation (AF) is the most common cardiac arrhythmia. It is associated with a higher risk of important adverse health outcomes such as stroke and death. AF is linked to distinct electro-anatomic alterations. The main tool for AF diagnosis is the Electrocardiogram (ECG). However, an ECG recorded at a single time point may not detect individuals with paroxysmal AF. In this study, we developed machine learning models for discrimination of prevalent AF using a combination of image-derived radiomics phenotypes and ECG features. Thus, we characterize the phenotypes of prevalent AF in terms of ECG and imaging alterations. Moreover, we explore sex-differential remodelling by building sex-specific models. Our integrative model including radiomics and ECG together resulted in a better performance than ECG alone, particularly in women. ECG had a lower performance in women than men (AUC: 0.77 vs 0.88, p < 0.05) but adding radiomics features, the accuracy of the model was able to improve significantly. The sensitivity also increased considerably in women by adding the radiomics (0.68 vs 0.79, p < 0.05) having a higher detection of AF events. Our findings provide novel insights into AF-related electro-anatomic remodelling and its variations by sex. The integrative radiomics-ECG model also presents a potential novel approach for earlier detection of AF.

Epicardial adipose tissue, obesity and the occurrence of atrial fibrillation: an overview of pathophysiology and treatment methods

INTRODUCTION

Obesity is a chronic disease, which has significant health consequences and is a staggering burden to health care systems. Obesity can have harmful effects on the cardiovascular system, including heart failure, hypertension, coronary heart disease, and atrial fibrillation (AF). One of the possible substrates might be epicardial adipose tissue (EAT), which can be the link between AF and obesity. EAT is a fat deposit located between the myocardium and the visceral pericardium. Numerous studies have demonstrated that EAT plays a pivotal role in this relationship regarding atrial fibrillation.

AREAS COVERED

This review will focus on the role of obesity and the occurrence of atrial fibrillation (AF) and examine the connection between these and epicardial adipose tissue (EAT). The first part of this review will explain the pathophysiology of EAT and its association with the occurrence of AF. Secondly, we will review bariatric and metabolic surgery and its effects on EAT and AF.

EXPERT COMMENTARY

In this review, the epidemiology, pathophysiology, and treatments methods of AF are explained. Secondly the effects on EAT were elucidated. Due to the complex pathophysiological link between EAT, AF, and obesity, it is still uncertain which treatment strategy is superior.

Temporal Association of Atrial Fibrillation With Cardiac Implanted Electronic Device Detected Heart Failure Status

OBJECTIVES

This study sought to investigate the temporal association between changes in physiologic heart failure (HF) sensors, atrial fibrillation (AF) progression, and clinical HF in patients with cardiac resynchronization therapy implantable defibrillators (CRT-D) designed to monitor AF and HF daily.

BACKGROUND

AF is a common comorbidity in HF; however, it is unclear if HF triggers AF, or vice-versa. Current implantable cardiac devices have sensors capable of quantifying HF status, which permits a greater understanding of the impact of AF on HF status and may help guide treatment.

METHODS

The MultiSENSE (Multisensor Chronic Evaluation in Ambulatory Heart Failure Patients) study collected multiple sensor data indicative of HF status in patients with CRT-D followed for up to 12 months. Patients were grouped according to their longest daily AF burden: 1) at least 24 hours of AF (HIGH AF); 2) between 6 minutes and 24 hours (MID AF); and 3) <6 minutes (NO AF). Sensor data were aligned to the first qualifying AF event or a randomly selected day for patients in the NO AF group.

RESULTS

Among 869 patients with daily AF data available, 98 patients had HIGH AF, 141 patients MID AF, and 630 patients NO AF. At baseline, history of AF, N-terminal pro hormone B-type natriuretic peptide and device-measured S3 were associated with development of AF. HeartLogic index increased before AF onset (Δ HeartLogic = 9.83 ± 2.49; P < 0.001). Multivariable time-dependent Cox regression showed an increased risk for HF events following a 24-hour AF episode compared with no 24-hour AF (hazard ratio: 1.96; 95% confidence interval: 1.03-3.74).

CONCLUSIONS

Device-measured HF indicators worsened before AF onset, whereas clinical HF deterioration only became apparent after AF occurred. Thus, the sensitivity of methods to ascertain AF and HF status appear to influence the direction of perceived causality. (Multisensor Chronic Evaluation in Ambulatory Heart Failure Patients [MultiSENSE]; NCT01128166).

Incidence and Predictors of Heart Failure in Patients With Atrial Fibrillation

BACKGROUND

Heart failure (HF) is a frequent cause of hospitalization and death in patients with atrial fibrillation (AF). Identifying AF patients at risk of HF hospitalization could help select individuals for intensive follow-up and treatment.

METHODS

We pooled data from 3 randomized trials (ACTIVE-A, RE-LY, AVERROES) of AF patients, for derivation and internal validation of a risk score for first HF hospitalization. Secondary endpoints were cardiovascular death and a composite of HF hospitalizations and cardiovascular death.

RESULTS

In 23,503 patients, the mean age was 71.3 years, and 62% were male. Over a mean follow-up of 2.0 years, 875 patients (3.7%) experienced their first HF hospitalization, and 1037 patients (4.4%) died from cardiovascular causes. Incidence rates per 100 patient-years were 1.85 for HF hospitalizations, 2.15 for cardiovascular death, and 3.71 for the composite. Independent predictors for HF hospitalizations included the following: increased age, weight, heart rate and serum creatinine level, lower height and systolic blood pressure, diabetes, vascular disease, valvular disease, heart rhythm, left ventricular hypertrophy, and intraventricular conduction delay. The C-statistic (95% confidence intervals by bootstrap simulations) was 0.717 (0.705-0.732). At 2 years of follow-up, the incidence rate of the primary outcome increased across risk-score quintiles: 0.49, 0.87, 1.29, 2.44, and 4.51 per 100 patient-years, respectively. Patients in the highest quintile had an absolute risk of 6.8% for the primary endpoint at 2 years.

CONCLUSIONS

In a large AF population, new-onset HF was common. A combination of characteristics can identify high-risk patients for whom strategies to prevent HF should be considered.