Clinical and Echocardiographic Factors Associated With New-Onset Atrial Fibrillation in Heart Failure - Subanalysis of the WARCEF Trial

Machine Learning Predicting Atrial Fibrillation as an Adverse Event in the Warfarin and Aspirin in Reduced Cardiac Ejection Fraction (WARCEF) Trial

BACKGROUND

Atrial fibrillation and heart failure commonly coexist due to shared pathophysiological mechanisms. Prompt identification of patients with heart failure at risk of developing atrial fibrillation would allow clinicians the opportunity to implement appropriate monitoring strategy and timely treatment, reducing the impact of atrial fibrillation on patients' health.

METHODS

Four machine learning models combined with logistic regression and cluster analysis were applied post hoc to patient-level data from the Warfarin and Aspirin in Patients with Heart Failure and Sinus Rhythm (WARCEF) trial to identify factors that predict development of atrial fibrillation in patients with heart failure.

RESULTS

Logistic regression showed that White divorced patients have a 1.75-fold higher risk of atrial fibrillation than White patients reporting other marital statuses. By contrast, similar analysis suggests that non-White patients who live alone have a 2.58-fold higher risk than those not living alone. Machine learning analysis also identified "marital status" and "live alone" as relevant predictors of atrial fibrillation. Apart from previously well-recognized factors, the machine learning algorithms and cluster analysis identified 2 distinct clusters, namely White and non-White ethnicities. This should serve as a reminder of the impact of social factors on health.

CONCLUSION

The use of machine learning can prove useful in identifying novel cardiac risk factors. Our analysis has shown that "social factors," such as living alone, may disproportionately increase the risk of atrial fibrillation in the under-represented non-White patient group with heart failure, highlighting the need for more studies focusing on stratification of multiracial cohorts to better uncover the heterogeneity of atrial fibrillation.

Prognostic Impact and Predictors of New-Onset Atrial Fibrillation in Heart Failure

Background: The prognostic impact and predictors of NOAF in HF patients are not fully elucidated. This study aims to determine whether new-onset atrial fibrillation (NOAF) affects patient outcome and investigate predictors of atrial fibrillation (AF) in acute heart failure (HF) patients using real-world data. Methods: The factors associated with NOAF in 2894 patients with sinus rhythm (SR) enrolled in the Korean Acute Heart Failure (KorAHF) registry were investigated. Survival was analyzed using AF as a time-dependent covariate. Relevant predictors of NOAF were analyzed using multivariate proportional hazards models. Results: Over 27.4 months, 187 patients developed AF. The median overall survival time was over 48 and 9.9 months for the SR and NOAF groups, respectively. Cox regression analysis with NOAF as a time-dependent covariate showed a higher risk of death among patients with NOAF. Multivariate Cox modeling showed that age, worsening HF, valvular heart disease (VHD), loop diuretics, lower heart rate, larger left atrium (LA) diameter, and elevated creatinine levels were independently associated with NOAF. Risk score indicated the number of independent predictors. The incidence of NOAF was 2.9%, 9.4%, and 21.8% in the low-risk, moderate-risk, and high-risk groups, respectively (p < 0.001). Conditional inference tree analysis identified worsening HF, heart rate, age, LA diameter, and VHD as discriminators. Conclusions: NOAF was associated with decreased survival in acute HF patients with SR. Age, worsening HF, VHD, loop diuretics, lower heart rate, larger LA diameter, and elevated creatinine could independently predict NOAF. This may be useful to risk-stratify HF patients at risk for AF.

Imaging for Risk Stratification in Atrial Fibrillation with Heart Failure

Atrial fibrillation (AF) is the most common cardiac rhythm disorder and is associated with heart failure (HF). Cardiac imaging modalities play an important role in risk assessment and managing AF. This article reviews the use of cardiac imaging for risk assessment and to optimize treatment strategy in patients with AF and HF. First, the clinical role of echocardiography, computed tomography, and cardiac magnetic resonance for risk stratification is provided. Second, the value of imaging in catheter ablation is reviewed, including preoperative assessment, optimizing patient selection for ablation, use during the ablation procedure, and postoperative scar assessment.

Is extensive atrial fibrosis in the setting of heart failure associated with a reduced atrial fibrillation burden?

Atrial fibrillation (AF) affects 10-50% of patients with chronic heart failure (HF) and is associated with poor long-term prognosis. AF is commonly associated with atrial structural remodeling (ASR), principally characterized by atrial dilatation and fibrosis. However, the occurrence of AF in the full spectrum of ASR encountered in patients with HF is poorly defined. Experimental studies have presented evidence that extensive ASR can be accompanied with a reduced burden of AF, secondary to a prominent depression of atrial excitability. This reduction in AF burden is associated with severe atrial fibrosis rather than with dilatation. Clinical studies of patients with HF point to the possibility that advanced ASR is associated with a less frequent AF occurrence than moderate ASR. Our goal in this review is to introduce the hypothesis that AF is less likely to occur in severe versus moderate atrial ASR in the setting of HF and that it is severe atrial fibrosis-associated depression of atrial excitability that reduces AF burden.

CHA2 DS2 -VASc score and adverse outcomes in patients with heart failure with reduced ejection fraction and sinus rhythm

AIMS

The aim of this study was to determine whether the CHA₂ DS₂ -VASc score can predict adverse outcomes such as death, ischaemic stroke, and major haemorrhage, in patients with systolic heart failure in sinus rhythm.

METHODS AND RESULTS

CHA₂ DS₂ -VASc scores were calculated for 1101 patients randomized to warfarin and 1123 patients randomized to aspirin. Adverse outcomes were defined as death or ischaemic stroke, death alone, ischaemic stroke alone, and major haemorrhage. Using proportional hazards models, we found that each 1-point increase in the CHA₂ DS₂ -VASc score was associated with increased hazard of death or ischaemic stroke events [hazard ratio (HR) for the warfarin arm = 1.21, 95% confidence interval (CI) 1.13-1.30, P < 0.001; for aspirin, HR = 1.20, 95% CI 1.11-1.29, P < 0.001]. Similar increased hazards for higher CHA₂ DS₂ -VASc scores were observed for death alone, ischaemic stroke alone, and major haemorrhage. Overall performance of the CHA₂ DS₂ -VASc score was assessed using c-statistics for full models containing the risk score, treatment assignment, and score-treatment interaction, with the c-statistics for the full models ranging from 0.57 for death to 0.68 for major haemorrhage.

CONCLUSIONS

The CHA₂ DS₂ -VASc score predicted adverse outcomes in patients with systolic heart failure in sinus rhythm, with modest prediction accuracy.