Electrocardiographic characteristics in patients with heart failure and normal ejection fraction: A systematic review and meta-analysis

Preload dependence in an animal model of mild heart failure with preserved ejection fraction (HFpEF)

AIM

Heart Failure with preserved Ejection Fraction (HFpEF) is characterized by diastolic dysfunction and reduced cardiac output, but its pathophysiology remains poorly understood. Animal models of HFpEF are challenging due to difficulties in assessing the degree of heart failure in small animals. This study aimed at inducing HFpEF in a mouse model to probe preload-dependency.

METHODS

Increased body mass and arterial hypertension were induced in mice using a Western diet and NO synthase inhibition. Preload dependence was tested ex vivo.

RESULTS

Mice with obesity and hypertension exhibited reduced cardiac output, indicating a failing heart. Increased left ventricular filling pressure during diastole suggested reduced compliance. Notably, the ejection fraction was preserved, suggesting the development of HFpEF. Spontaneous physical activity at night was reduced in HFpEF mice, indicating exercise intolerance; however, the cardiac connective tissue content was comparable between HFpEF and control mice. The HFpEF mice showed increased vulnerability to reduced preload ex vivo, indicating that elevated left ventricular filling pressure compensated for the rigid left ventricle, preventing a critical decrease in cardiac output.

CONCLUSION

This animal model successfully developed mild HFpEF with a reduced pump function that was dependent on a high preload. A model of mild HFpEF may serve as a valuable tool for studying disease progression and interventions aimed at delaying or reversing symptom advancement, considering the slow development of HFpEF in patients.

Integrative review of electrocardiographic characteristics in patients with reduced, mildly reduced, and preserved heart failure

INTRODUCTION

Electrocardiographic (ECG) changes in heart failure with reduced, mildly reduced, and preserved ejection fractions can be critical in clinical assessment while waiting to perform echocardiograms or when it is unavailable. This integrative review aimed to identify ECG characteristics among hospitalized patients demonstrating three types of heart failure during acute decompensation.

METHODS

We searched an electronic database of PubMed, Web of Science, EMBASE, Scopus, Google Scholar, and ClinicalTrials.gov using medical subject headings (MeSH) terms and keywords. Sixteen studies were synthesized and reported.

RESULTS

Heart failure with reduced ejection fraction (HFrEF) was more common in men, comorbid with coronary artery diseases and diabetes mellitus, higher BNP/Pro-BNP, wide QRS, and left bundle branch block on ECG. On average, clients with heart failure with preserved ejection fraction (HFpEF) were older and more likely to have a history of atrial fibrillation, valvular heart diseases, hypertension, chronic obstructive pulmonary, and atrial fibrillation (AF) on ECG. Patients with mildly reduced (HFmrEF) were more similar to HFpEF in older patients, comorbid with hypertension, AF and valvular diseases, and AF on ECG.

CONCLUSIONS

ECG characteristics might be related to left ventricular ejection fraction. Demographics, BNP/Pro-BNP, and ECG changes might help differentiate different heart failure types. Therefore, ECG might be a prognostic tool while caring for heart failure patients when highly skilled resources are unavailable. These identified ECG characteristics help generate research hypotheses and warrant validation in future research.

Comparison of Electrocardiographic Parameters by Gender in Heart Failure Patients with Preserved Ejection Fraction via Artificial Intelligence

BACKGROUND

Heart failure (HF) causes high morbidity and mortality worldwide. The prevalence of HF with preserved ejection fraction (HFpEF) is increasing compared with HF with reduced ejection fraction (HFrEF). Patients with HFpEF are a patient group with a high rate of hospitalization despite medical treatment. Early diagnosis is very important in this group of patients, and early treatment can improve their prognosis. Although electrocardiographic (ECG) findings have been adequately studied in patients with HFrEF, there are not enough studies on these parameters in patients with HFpEF. There are very few studies in the literature, especially on gender-specific changes. The current research aims to compare gender-specific ECG parameters in patients with HFpEF based on the implications of artificial intelligence (AI).

METHODS

A total of 118 patients participated in the study, of which 66 (56%) were women with HFpEF and 52 (44%) were men with HFpEF. Demographic, echocardiographic, and electrocardiographic characteristics of the patients were analyzed to compare gender-specific ECG parameters in patients with HFpEF. The AI approach combined with machine learning approaches (gradient boosting machine, k-nearest neighbors, logistic regression, random forest, and support vector machines) was applied for distinguishing male patients with HFpEF from female patients with HFpEF.

RESULTS

After determining the parameters (demographic, echocardiographic, and electrocardiographic) to distinguish male patients with HFpEF from female patients with HFpEF, machine learning methods were applied, and among these methods, the random forest model achieved an average accuracy of 84.7%. The random forest algorithm results showed that smoking, P-wave dispersion, P-wave amplitude, T-end P/(PQ*Age), Cornell product, and P-wave duration were the most influential parameters for distinguishing male patients with HFpEF from female patients with HFpEF.

CONCLUSIONS

The proposed model serves as a valuable tool for physicians, facilitating the diagnosis, treatment, and follow-up for distinguishing male patients with HFpEF from female patients with HFpEF. Analyzing readily accessible electrocardiographic parameters empowers medical professionals to make informed decisions and provide enhanced care to a wide range of individuals.

Electrocardiographic Patterns of Depolarization Abnormalities Help to Identify Reduced Left Ventricular Ejection Fraction

The aim of the study was to investigate the relationship between a decrease in the left ventricular ejection fraction (EF) and traditional ECG signs associated with structural changes of the myocardium (pathological Q wave, ventricular arrhythmias) and relatively new and poorly understood (fragmented QRS complex (fQRS), early repolarization pattern (ERP)) and evaluate their significance for identifying patients with mildly reduced EF (mrEF). The study included 148 patients who were treated and examined at the Almazov Medical Research Center. FQRS, ERP, pathological Q wave, and premature ventricular contractions (PVC) were described in the analysis of the ECG, and the results of echocardiography and statistical data were analyzed: Fisher’s test and chi-square, correlation analysis, and ROC analysis. According to the level of EF, patients were divided into three groups: group 1—patients with low EF (lEF) (less than 40%), group 2—patients with mildly reduced EF (mrEF) (40–49%); group 3—patients with preserved EF (pEF) (more than 50%). In the first group (EF), fQRS was registered in 16 (51.6%) patients, in the mrEF in 16 (18.2%). Pathological Q wave was detected in lEF in 20 (65%), in mrEF in 10 (35%), 15 (18%), in pEF in 15 (18%). The fQRS has been found to be more important in identifying patients with mrEF. In lEF in 2 (6.5%) patients, in mrEF in 2 (6.9%), in pEF in 11 (12.5%). There was no relationship between ERP, the amount of PVC, and the presence of ventricular tachycardia with EF. FQRS is significantly more common occurred with a decrease in EF and may be a marker of a mrEF. Thus, fQRS is associated with mrEF and pay close attention in routine clinical practice to identify patients at high risk of developing systolic dysfunction.

Electrocardiogram-Based Heart Age Estimation by a Deep Learning Model Provides More Information on the Incidence of Cardiovascular Disorders

Objective

The biological age progression of the heart varies from person to person. We developed a deep learning model (DLM) to predict the biological age via ECG to explore its contribution to future cardiovascular diseases (CVDs).

Methods

There were 71,741 cases ranging from 20 to 80 years old recruited from the health examination center. The development set used 32,707 cases to train the DLM for estimating the ECG-age, and 8,295 cases were used as the tuning set. The validation set included 30,469 ECGs to follow the outcomes, including all-cause mortality, cardiovascular-cause mortality, heart failure (HF), diabetes mellitus (DM), chronic kidney disease (CKD), acute myocardial infarction (AMI), stroke (STK), coronary artery disease (CAD), atrial fibrillation (AF), and hypertension (HTN). Two independent external validation sets (SaMi-Trop and CODE15) were also used to validate our DLM.

Results

The mean absolute errors of chronologic age and ECG-age was 6.899 years (r = 0.822). The higher difference between ECG-age and chronological age was related to more comorbidities and abnormal ECG rhythm. The cases with the difference of more than 7 years had higher risk on the all-cause mortality [hazard ratio (HR): 1.61, 95% CI: 1.23–2.12], CV-cause mortality (HR: 3.49, 95% CI: 1.74–7.01), HF (HR: 2.79, 95% CI: 2.25–3.45), DM (HR: 1.70, 95% CI: 1.53–1.89), CKD (HR: 1.67, 95% CI: 1.41–1.97), AMI (HR: 1.76, 95% CI: 1.20–2.57), STK (HR: 1.65, 95% CI: 1.42–1.92), CAD (HR: 1.24, 95% CI: 1.12–1.37), AF (HR: 2.38, 95% CI: 1.86–3.04), and HTN (HR: 1.67, 95% CI: 1.51–1.85). The external validation sets also validated that an ECG-age >7 years compare to chronologic age had 3.16-fold risk (95% CI: 1.72–5.78) and 1.59-fold risk (95% CI: 1.45–1.74) on all-cause mortality in SaMi-Trop and CODE15 cohorts. The ECG-age significantly contributed additional information on heart failure, stroke, coronary artery disease, and atrial fibrillation predictions after considering all the known risk factors.

Conclusions

The ECG-age estimated via DLM provides additional information for CVD incidence. Older ECG-age is correlated with not only on mortality but also on other CVDs compared with chronological age.

Electrocardiographic Features of Left Ventricular Diastolic Dysfunction and Heart Failure With Preserved Ejection Fraction: A Systematic Review

Background: Electrocardiographic features are well-known for heart failure with reduced ejection fraction (HFrEF), but not for left ventricular diastolic dysfunction (LVDD) and heart failure with preserved ejection fraction (HFpEF). As ECG features could help to identify high-risk individuals in primary care, we systematically reviewed the literature for ECG features diagnosing women and men suspected of LVDD and HFpEF. Methods and Results: Among the 7,127 records identified, only 10 studies reported diagnostic measures, of which 9 studied LVDD. For LVDD, the most promising features were T-end-P/(PQ*age), which is the electrocardiographic equivalent of the passive-to-active filling (AUC: 0.91-0.96), and repolarization times (QTc interval ≥ 350 ms, AUC: 0.85). For HFpEF, the Cornell product ≥ 1,800 mm*ms showed poor sensitivity of 40% (AUC: 0.62). No studies presented results stratified by sex. Conclusion: Electrocardiographic features are not widely evaluated in diagnostic studies for LVDD and HFpEF. Only for LVDD, two ECG features related to the diastolic interval, and repolarization measures showed diagnostic potential. To improve diagnosis and care for women and men suspected of heart failure, reporting of sex-specific data on ECG features is encouraged.

Electrocardiographic characteristics in patients with heart failure and normal ejection fraction: A systematic review and meta-analysis

BACKGROUND

Little is known about ECG abnormalities in patients with heart failure and normal ejection fraction (HeFNEF) and how they relate to different etiologies or outcomes.

METHODS AND RESULTS

We searched the literature for peer-reviewed studies describing ECG abnormalities in HeFNEF other than heart rhythm alone. Thirty five studies were identified and 32,006 participants. ECG abnormalities reported in patients with HeFNEF include atrial fibrillation (prevalence 12%-46%), long PR interval (11%-20%), left ventricular hypertrophy (LVH, 10%-30%), pathological Q waves (11%-18%), RBBB (6%-16%), LBBB (0%-8%), and long JTc (3%-4%). Atrial fibrillation is more common in patients with HeFNEF compared to those with heart failure and reduced ejection fraction (HeFREF). In contrast, long PR interval, LVH, Q waves, LBBB, and long JTc are more common in patients with HeFREF. A pooled effect estimate analysis showed that QRS duration ≥120 ms, although uncommon (13%-19%), is associated with worse outcomes in patients with HeFNEF.

CONCLUSIONS

There is high variability in the prevalence of ECG abnormalities in patients with HeFNEF. Atrial fibrillation is more common in patients with HeFNEF compared to those with HeFREF. QRS duration ≥120 ms is associated with worse outcomes in patients with HeFNEF. Further studies are needed to address whether ECG abnormalities correlate with different phenotypes in HeFNEF.