Heart Failure With Mid-range Ejection Fraction: A Distinctive Subtype or a Transitional Stage?

International prevalence of transthyretin amyloid cardiomyopathy in high-risk patients with heart failure and preserved or mildly reduced ejection fraction

BACKGROUND

Transthyretin amyloid cardiomyopathy (ATTR-CM) is an underdiagnosed cause of heart failure (HF).

METHODS

This epidemiology study assessed the international prevalence of ATTR-CM among patients aged ≥60 years with a history of HF, left ventricular ejection fraction (LVEF) >40%, an end-diastolic interventricular septum thickness (IVST) ≥12 mm, but without diagnosed amyloidosis, history of LVEF ≤40%, cardiomyopathy of known cause, severe valvular, or coronary heart disease. ATTR-CM was determined using cardiac scintigraphy alongside exclusionary testing for light chain amyloidosis. The study was terminated early due to slow recruitment, without safety concerns.

RESULTS

Overall, 56/315 (18%; 95% CI: 13.7-22.5) patients with evaluable scintigraphy had ATTR-CM, with a numerically higher prevalence in: Europe (24%) vs. other regions (9% Asia; 5% North America); at specialist vs non-specialist centres (26% vs. 11%); in males vs. females (24% vs. 10%); and in older vs. younger patients (e.g. >40% among those ≥85 years). Other risk markers (p<.05) included a history of carpal tunnel syndrome, higher N-terminal pro B-type natriuretic peptide concentration, and higher end-diastolic IVST.

CONCLUSIONS

ATTR-CM was diagnosed in 18% (95% CI: 13.7-22.5) of evaluable patients with HF, LVEF >40%, and risk markers for ATTR-CM, but no previous diagnosis of amyloidosis. Recruitment bias may have contributed to regional variability. NCT04424914.

Comparative Analysis of Heart Failure with Preserved Vs Reduced Ejection Fraction: Patient Characteristics, Outcomes, Mortality Prediction, and Machine Learning Model Development in the JoHFR

Background

Heart failure (HF) is a global health challenge affecting millions, with significant variations in patient characteristics and outcomes based on ejection fraction. This study aimed to differentiate between HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF) with respect to patient characteristics, risk factors, comorbidities, and clinical outcomes, incorporating advanced machine learning models for mortality prediction.

Methodology

The study included 1861 HF patients from 21 centers in Jordan, categorized into HFrEF (EF <40%) and HFpEF (EF ≥ 50%) groups. Data were collected from 2021 to 2023, and machine learning models were employed for mortality prediction.

Results

Among the participants, 29.7% had HFpEF and 70.3% HFrEF. Significant differences were noted in demographics and comorbidities, with a higher prevalence of males, younger age, smoking, and familial history of premature ASCVD in the HFrEF group. HFpEF patients were typically older, with higher rates of diabetes, hypertension, and obesity. Machine learning analysis, mainly using the Random Forest Classifier, demonstrated significant predictive capability for mortality with an accuracy of 0.9002 and an AUC of 0.7556. Other models, including Logistic Regression, SVM, and XGBoost, also showed promising results. Length of hospital stay, need for mechanical ventilation, and number of hospital admissions were the top predictors of mortality in our study.

Conclusion

The study underscores the heterogeneity in patient profiles between HFrEF and HFpEF. Integrating machine learning models offers valuable insights into mortality risk prediction in HF patients, highlighting the potential of advanced analytics in improving patient care and outcomes.

Characteristics Associated with Ventricular Tachyarrhythmias and Their Prognostic Impact in Heart Failure with Mildly Reduced Ejection Fraction

Background: The occurrence of ventricular tachyarrhythmias represents an established risk factor of mortality in heart failure (HF). However, data concerning their prognostic impact in heart failure with mildly reduced ejection fraction (HFmrEF) is limited. Therefore, the present study aims to investigate patient characteristics associated with ventricular tachyarrhythmias and their prognostic impact in patients with HFmrEF. Methods: Consecutive patients hospitalized with HFmrEF (i.e., left ventricular ejection fraction 41-49% and signs and/or symptoms of HF) were retrospectively included at one institution from 2016 to 2022. The prognosis of patients with HFmrEF and different types of ventricular tachyarrhythmias (i.e., non-sustained ventricular tachycardia (nsVT), sustained VT (sVT), and ventricular fibrillation (VF) was investigated for the primary endpoint of long-term all-cause mortality at 30 months. Secondary endpoints included in-hospital all-cause mortality and long-term HF-related rehospitalization at 30 months. Results: From a total of 2184 patients with HFmrEF, 4.4% experienced ventricular tachyarrhythmias (i.e., 2.0% nsVT, 0.7% sVT, and 1.6% VF). The occurrence of nsVT was associated with higher New York Heart Association (NYHA) functional class, whereas the incidence of sVT/VF was associated with acute myocardial infarction and ischemic heart disease. However, nsVT (25.0%; HR = 0.760; 95% CI 0.419-1.380; p = 0.367) and sVT/VF (28.8%; HR = 0.928; 95% CI 0.556-1.549; p = 0.776) were not associated with a higher risk of long-term all-cause mortality compared to patients with HFmrEF without ventricular tachyarrhythmias (31.5%). In-hospital cardiovascular mortality was more frequently observed in patients with HFmrEF and sVT/VF compared to those with HFmrEF but without sustained ventricular tachyarrhythmias (7.7% vs. 1.5%; p = 0.004). Finally, the risk of rehospitalization for worsening HF was not affected by the presence of ventricular tachyarrhythmias. Conclusions: The occurrence of ventricular tachyarrhythmias in patients hospitalized with HFmrEF was low and not associated with long-term prognosis.

Prognostic impact of prior LVEF in patients with heart failure with mildly reduced ejection fraction

AIMS

As there is limited evidence regarding the prognostic impact of prior left ventricular ejection fraction (LVEF) in patients with heart failure with mildly reduced ejection fraction (HFmrEF), this study investigates the prognostic impact of longitudinal changes in LVEF in patients with HFmrEF.

METHODS

Consecutive patients with HFmrEF (i.e. LVEF 41-49% with signs and/or symptoms of HF) were included retrospectively in a monocentric registry from 2016 to 2022. Based on prior LVEF, patients were categorized into three groups: stable LVEF, improved LVEF, and deteriorated LVEF. The primary endpoint was 30-months all-cause mortality (median follow-up). Secondary endpoints included in-hospital and 12-months all-cause mortality, as well as HF-related rehospitalization at 12 and 30 months. Kaplan-Meier and multivariable Cox proportional regression analyses were applied for statistics.

RESULTS

Six hundred eighty-nine patients with HFmrEF were included. Compared to their prior LVEF, 24%, 12%, and 64% had stable, improved, and deteriorated LVEF, respectively. None of the three LVEF groups was associated with all-cause mortality at 12 (p ≥ 0.583) and 30 months (31% vs. 37% vs. 34%; log rank p ≥ 0.376). In addition, similar rates of 12- (p ≥ 0.533) and 30-months HF-related rehospitalization (21% vs. 23% vs. 21%; log rank p ≥ 0.749) were observed. These findings were confirmed in multivariable regression analyses in the entire study cohort.

CONCLUSION

The transition from HFrEF and HFpEF towards HFmrEF is very common. However, prior LVEF was not associated with prognosis, likely due to the persistently high dynamic nature of LVEF in the follow-up period.

The correlation of left atrial diameter with preserved ejection fraction, reduced ejection fraction, and mid-range ejection fraction

BACKGROUND

In patients with heart failure, left atrial remodeling often occurs to varying degrees. Left atrial enlargement has been proved to be an important predictor of cardiovascular-related adverse events. However, the relationship between left atrial diameter (LAD) with heart failure (HF) with preserved ejection fraction (HFpEF), reduced ejection fraction (HFrEF) and mid-range ejection fraction (HFmrEF) remains unclear.

HYPOTHESIS

We want to explore the correlation between left atrial diameter and HFpEF, HFmrEF, and HFrEF.

METHODS

A total of 210 patients with heart failure who underwent hospitalization in Ningbo Medical Center Lihuili Hospital, Zhejiang, China, from January 1, 2020, to June 31, 2021, were reviewed. The basic demographic characteristics, blood test, and the related indexes of echocardiography of the subjects were collected and analyzed.

RESULTS

There is a significant difference between HFpEF and HFrEF group in LAD (p = .007), and LAD is negatively correlated with left ventricular ejection fraction (LVEF) (p = .002, r = -.209).

CONCLUSION

LAD is negatively correlated with LVEF, which may predict the prevalence of HFrEF.

Ejection fraction at hospital admission stratifies mortality risk in HFmrEF patients aged ≥ 70 years: a retrospective analysis from a tertiary university institution

BACKGROUND

During the last few years, increasing focus has been placed on heart failure with mildly reduced ejection fraction (HFmrEF), an intermediate phenotype from preserved to reduced ejection fraction (EF). However, clinical features and outcome of HFmrEF in elderly patients aged ≥ 70 yrs have been poorly investigated.

METHODS

The present study retrospectively included all consecutive patients aged ≥ 70 yrs discharged from our Institution with a first diagnosis of HFmrEF, between January 2020 and November 2020. All patients underwent transthoracic echocardiography. The primary outcome was all-cause mortality, while the secondary one was the composite of all-cause mortality + rehospitalization for all causes over a mid-term follow-up.

RESULTS

The study included 107 HFmrEF patients (84.3 ± 7.4 yrs, 61.7% females). Patients were classified as "old" (70-84 yrs, n = 55) and "oldest-old" (≥ 85 yrs, n = 52) and separately analyzed. As compared to the "oldest-old" patients, the "old" ones were more commonly males (58.2% vs 17.3%, p < 0.001), with history of coronary artery disease (CAD) (54.5% vs 15.4%, p < 0.001) and significantly lower EF (43.5 ± 2.7% vs 47.3 ± 3.6%, p < 0.001) at hospital admission. Mean follow-up was 1.8 ± 1.1 yrs. During follow-up, 29 patients died and 45 were re-hospitalized. Male sex (HR 6.71, 95% CI 1.59-28.4), history of CAD (HR 5.37, 95% CI 2.04-14.1) and EF (HR 0.48, 95% CI 0.34-0.68) were independently associated with all-cause mortality in the whole study population. EF also predicted the composite of all-cause mortality + rehospitalization for all causes. EF < 45% was the best cut-off value to predict both outcomes.

CONCLUSIONS

EF at hospital admission is independently associated with all-cause mortality and rehospitalization for all causes in elderly HFmrEF patients over a mid-term follow-up.

Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles

Background: Left ventricular ejection fraction (LVEF) is the gold standard for evaluating heart failure (HF) in coronary artery disease (CAD) patients. It is an essential metric in categorizing HF patients as preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF) ejection fraction but differs, depending on whether the ASE/EACVI or ESC guidelines are used to classify HF. Objectives: We sought to investigate the effectiveness of using deep learning as an automated tool to predict LVEF from patient clinical profiles using regression and classification trained models. We further investigate the effect of utilizing other LVEF-based thresholds to examine the discrimination ability of deep learning between HF categories grouped with narrower ranges. Methods: Data from 303 CAD patients were obtained from American and Greek patient databases and categorized based on the American Society of Echocardiography and the European Association of Cardiovascular Imaging (ASE/EACVI) guidelines into HFpEF (EF > 55%), HFmEF (50% ≤ EF ≤ 55%), and HFrEF (EF < 50%). Clinical profiles included 13 demographical and clinical markers grouped as cardiovascular risk factors, medication, and history. The most significant and important markers were determined using linear regression fitting and Chi-squared test combined with a novel dimensionality reduction algorithm based on arc radial visualization (ArcViz). Two deep learning-based models were then developed and trained using convolutional neural networks (CNN) to estimate LVEF levels from the clinical information and for classification into one of three LVEF-based HF categories. Results: A total of seven clinical markers were found important for discriminating between the three HF categories. Using statistical analysis, diabetes, diuretics medication, and prior myocardial infarction were found statistically significant (p < 0.001). Furthermore, age, body mass index (BMI), anti-arrhythmics medication, and previous ventricular tachycardia were found important after projections on the ArcViz convex hull with an average nearest centroid (NC) accuracy of 94%. The regression model estimated LVEF levels successfully with an overall accuracy of 90%, average root mean square error (RMSE) of 4.13, and correlation coefficient of 0.85. A significant improvement was then obtained with the classification model, which predicted HF categories with an accuracy ≥93%, sensitivity ≥89%, 1-specificity <5%, and average area under the receiver operating characteristics curve (AUROC) of 0.98. Conclusions: Our study suggests the potential of implementing deep learning-based models clinically to ensure faster, yet accurate, automatic prediction of HF based on the ASE/EACVI LVEF guidelines with only clinical profiles and corresponding information as input to the models. Invasive, expensive, and time-consuming clinical testing could thus be avoided, enabling reduced stress in patients and simpler triage for further intervention.

Predictive Value of HFA-PEFF Score in Patients With Heart Failure With Preserved Ejection Fraction

Aims: HFA-PEFF score has been proposed for diagnosing heart failure with preserved ejection fraction (HFpEF). Currently, there are only a limited number of tools for predicting the prognosis. In this study, we evaluated whether the HFA-PEFF score can predict mortality in patients with HFpEF.

Methods: This single-center, retrospective observational study enrolled patients diagnosed with HFpEF at the First Affiliated Hospital of Dalian Medical University between January 1, 2015, and April 30, 2018. The subjects were divided according to their HFA-PEFF score into low (0–2 points), intermediate (3–4 points), and high (5–6 points) score groups. The primary outcome was all-cause mortality.

Results: A total of 358 patients (mean age: 70.21 ± 8.64 years, 58.1% female) were included. Of these, 63 (17.6%), 156 (43.6%), and 139 (38.8%) were classified into the low, intermediate, and high score groups, respectively. Over a mean follow-up of 26.9 months, 46 patients (12.8%) died. The percentage of patients who died in the low, intermediate, and high score groups were 1 (1.6%), 18 (11.5%), and 27 (19.4%), respectively. A multivariate Cox regression identified HFA-PEFF score as an independent predictor of all-cause mortality [hazard ratio (HR):1.314, 95% CI: 1.013–1.705, P = 0.039]. A Cox analysis demonstrated a significantly higher rate of mortality in the intermediate (HR: 4.912, 95% CI 1.154–20.907, P = 0.031) and high score groups (HR: 5.291, 95% CI: 1.239–22.593, P = 0.024) than the low score group. A receiver operating characteristic (ROC) analysis indicated that the HFA-PEFF score can effectively predict all-cause mortality after adjusting for age and New York Heart Association (NYHA) class [area under the curve (AUC) 0.726, 95% CI 0.651–0.800, P = 0.000]. With an HFA-PEFF score cut-off value of 3.5, the sensitivity and specificity were 78.3 and 54.8%, respectively. The AUC on ROC analysis for the biomarker component of the score was similar to that of the total score.

Conclusions: The HFA-PEFF score can be used both to diagnose HFpEF and predict the prognosis. The higher scores are associated with higher all-cause mortality.