Clinical diagnosis of heart failure in nursing home residents based on history, physical exam, BNP and ECG: Is it reliable?

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.

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.

Towards Point-of-Care Heart Failure Diagnostic Platforms: BNP and NT-proBNP Biosensors

Heart failure is a class of cardiovascular diseases that remains the number one cause of death worldwide with a substantial economic burden of around $18 billion incurred by the healthcare sector in 2017 due to heart failure hospitalization and disease management. Although several laboratory tests have been used for early detection of heart failure, these traditional diagnostic methods still fail to effectively guide clinical decisions, prognosis, and therapy in a timely and cost-effective manner. Recent advances in the design and development of biosensors coupled with the discovery of new clinically relevant cardiac biomarkers are paving the way for breakthroughs in heart failure management. Natriuretic neurohormone peptides, B-type natriuretic peptide (BNP) and N-terminal prohormone of BNP (NT-proBNP), are among the most promising biomarkers for clinical use. Remarkably, they result in an increased diagnostic accuracy of around 80% owing to the strong correlation between their circulating concentrations and different heart failure events. The latter has encouraged research towards developing and optimizing BNP biosensors for rapid and highly sensitive detection in the scope of point-of-care testing. This review sheds light on the advances in BNP and NT-proBNP sensing technologies for point-of-care (POC) applications and highlights the challenges of potential integration of these technologies in the clinic. Optical and electrochemical immunosensors are currently used for BNP sensing. The performance metrics of these biosensors-expressed in terms of sensitivity, selectivity, reproducibility, and other criteria-are compared to those of traditional diagnostic techniques, and the clinical applicability of these biosensors is assessed for their potential integration in point-of-care diagnostic platforms.