Characteristics and long-term prognosis of patients with reduced, mid-range, and preserved ejection fraction: A systemic review and meta-analysis

Machine Learning-Based Risk Factor Analysis and Prediction Model Construction for the Occurrence of Chronic Heart Failure: Health Ecologic Study

BACKGROUND

Chronic heart failure (CHF) is a serious threat to human health, with high morbidity and mortality rates, imposing a heavy burden on the health care system and society. With the abundance of medical data and the rapid development of machine learning (ML) technologies, new opportunities are provided for in-depth investigation of the mechanisms of CHF and the construction of predictive models. The introduction of health ecology research methodology enables a comprehensive dissection of CHF risk factors from a wider range of environmental, social, and individual factors. This not only helps to identify high-risk groups at an early stage but also provides a scientific basis for the development of precise prevention and intervention strategies.

OBJECTIVE

This study aims to use ML to construct a predictive model of the risk of occurrence of CHF and analyze the risk of CHF from a health ecology perspective.

METHODS

This study sourced data from the Jackson Heart Study database. Stringent data preprocessing procedures were implemented, which included meticulous management of missing values and the standardization of data. Principal component analysis and random forest (RF) were used as feature selection techniques. Subsequently, several ML models, namely decision tree, RF, extreme gradient boosting, adaptive boosting (AdaBoost), support vector machine, naive Bayes model, multilayer perceptron, and bootstrap forest, were constructed, and their performance was evaluated. The effectiveness of the models was validated through internal validation using a 10-fold cross-validation approach on the training and validation sets. In addition, the performance metrics of each model, including accuracy, precision, sensitivity, F1-score, and area under the curve (AUC), were compared. After selecting the best model, we used hyperparameter optimization to construct a better model.

RESULTS

RF-selected features (21 in total) had an average root mean square error of 0.30, outperforming principal component analysis. Synthetic Minority Oversampling Technique and Edited Nearest Neighbors showed better accuracy in data balancing. The AdaBoost model was most effective with an AUC of 0.86, accuracy of 75.30%, precision of 0.86, sensitivity of 0.69, and F1-score of 0.76. Validation on the training and validation sets through 10-fold cross-validation gave an AUC of 0.97, an accuracy of 91.27%, a precision of 0.94, a sensitivity of 0.92, and an F1-score of 0.94. After random search processing, the accuracy and AUC of AdaBoost improved. Its accuracy was 77.68% and its AUC was 0.86.

CONCLUSIONS

This study offered insights into CHF risk prediction. Future research should focus on prospective studies, diverse data, advanced techniques, longitudinal studies, and exploring factor interactions for better CHF prevention and management.

Diagnosis and Management of Hypertensive Heart Disease: Incorporating 2023 European Society of Hypertension and 2024 European Society of Cardiology Guideline Updates

Hypertensive heart disease (HHD) continues to be a leading cause of cardiovascular morbidity and mortality worldwide, necessitating the evolution of evidence-based management strategies. This literature review examines the most recent updates from the 2023 and 2024 hypertension guidelines issued by the European Society of Hypertension (ESH) and the European Society of Cardiology (ESC). These guidelines are compared with previous key recommendations, such as the 2017 American College of Cardiology/American Heart Association guidelines and the 2018 ESC/ESH guidelines. The updated recommendations reflect a paradigm shift in the approach to hypertension diagnosis and management, including a stricter systolic blood pressure (BP) target of 120-129 mmHg, which underscores the importance of early and precise BP control. The difference between the classification of "elevated BP" and hypertension in the ESC versus ESH guidelines, particularly, regarding their implications for early detection and prevention of HHD, are critically examined, highlighting areas of clinical and academic debate. The introduction of a new "elevated BP" category (120-139/70-89 mmHg) highlights a proactive strategy aimed at identifying at-risk individuals earlier in the disease course to prevent progression to HHD. Additionally, the divergent roles of hypertension-mediated organ damage (HMOD), including HHD, in risk stratification as recommended by the ESC and ESH are discussed, emphasising their significance in tailoring management approaches. For patients with resistant hypertension, the 2023 and 2024 updates also endorse innovative therapies, such as renal denervation, an interventional procedure that has demonstrated significant promise in managing treatment-resistant cases. This review synthesises these updates, focusing on their implications for clinical practice in diagnosing and managing HHD. By emphasising aggressive intervention and the integration of novel treatment modalities, the review aims to bridge existing gaps in earlier approaches to hypertension management. The critical evaluation of guideline discrepancies and evolving evidence seeks to provide clinicians with a nuanced understanding to optimise outcomes for patients with HHD, particularly considering emerging therapeutic possibilities and more stringent BP control targets.

Triglyceride-glucose index is associated with heart failure with preserved ejection fraction in different metabolic states in patients with coronary heart disease

Background

The triglyceride-glucose (TyG) index is a surrogate indicator of insulin resistance. Therefore, we aimed to determine the association between TyG index and heart failure (HF) with preserved ejection fraction (HFpEF) in patients with coronary heart disease (CHD) and to explore whether such associations would be modified by different metabolic states.

Methods

Among 107,301 CHD patients, 62,794 were included to analyze the relationship between the TyG index and HF. Among them, 8,606 patients who had undergone echocardiography were included to identify different types of HF, including HF with reduced ejection fraction (HFrEF), HF with intermediate-range ejection fraction (HFmrEF), and HFpEF. Among them, 1896 patients were diagnosed with HFpEF. Logistic regression was used to analyze the relationship between the TyG index and HFpEF in CHD patients. In addition, the association between TyG index and HFpEF according to sex, age, blood lipids, and blood pressure was assessed.

Results

A baseline analysis of CHD patients divided into four groups according to the tertile level of the TyG index showed significant differences in the related parameters between the groups. In the multi-adjusted models, the TyG index was significantly associated with the risk of HFpEF (odds ratio [OR]: 1.17; 95% confidence interval [CI]: 1.09-1.25). After adjustment for multivariates, TyG index levels for T2 (OR: 1.33; 95% CI: 1.16-1.52) and T3 (OR: 1.52; 95% CI: 1.32-1.74) were associated with increased OR in HFpEF. In addition, the TyG index of CHD patients was significantly associated with HFpEF in older adults aged > 60 years (OR: 1.20; 95% CI: 1.11-1.29), hypertension (OR: 1.27; 95% CI: 1.17-1.37), and dyslipidemia (OR: 1.15; 95% CI: 1.08-1.24). Moreover, the OR (OR: 1.23; 95% CI: 1.11-1.36) in women is higher than in men (OR: 1.17; 95% CI: 1.02-1.22, indicating a stronger association between TyG index and HFpEF in women.

Conclusions

Our findings demonstrated a significant association between TyG index and HFpEF in CHD patients. Furthermore, TyG index was independently associated with HFpEF in hypertension, dyslipidemia, and older patients (aged > 60 years). In addition, the association between the TyG index and HFpEF in CHD patients differed according to sex.

Heart failure with preserved ejection fraction: The role of inflammation

Heart failure (HF) is a debilitating clinical syndrome affecting 64.3 million patients worldwide. More than 50% of HF cases are attributed to HF with preserved ejection fraction (HFpEF), an entity growing in prevalence and mortality. Although recent breakthroughs reveal the prognostic benefits of sodium-glucose co-transporter 2 inhibitors (SGLT2i) in HFpEF, there is still a lack of effective pharmacological therapy available. This highlights a major gap in medical knowledge that must be addressed. Current evidence attributes HFpEF pathogenesis to an interplay between cardiometabolic comorbidities, inflammation, and renin-angiotensin-aldosterone-system (RAAS) activation, leading to cardiac remodelling and diastolic dysfunction. However, conventional RAAS blockade has demonstrated limited benefits in HFpEF, which emphasises that alternative therapeutic targets should be explored. Presently, there is limited literature examining the use of anti-inflammatory HFpEF therapies despite growing evidence supporting its importance in disease progression. Hence, this review aims to explore current perspectives on HFpEF pathogenesis, including the importance of inflammation-driven cardiac remodelling and the therapeutic potential of anti-inflammatory therapies.

Plasma myo-inositol elevation in heart failure: clinical implications and prognostic significance. Results from the BElgian and CAnadian MEtabolomics in HFpEF (BECAME-HF) research project

BACKGROUND

The metabolic environment plays a crucial role in the development of heart failure (HF). Our prior research demonstrated that myo-inositol, a metabolite transported by the sodium-myo-inositol co-transporter 1 (SMIT-1), can induce oxidative stress and may be detrimental to heart function. However, plasmatic myo-inositol concentration has not been comprehensively assessed in large cohorts of patients with heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection fraction (HFpEF).

METHODS

Plasmatic myo-inositol levels were measured using mass spectrometry and correlated with clinical characteristics in no HF subjects and patients with HFrEF and HFpEF from Belgian (male, no HF, 53%; HFrEF, 84% and HFpEF, 40%) and Canadian cohorts (male, no HF, 51%; HFrEF, 92% and HFpEF, 62%).

FINDINGS

Myo-inositol levels were significantly elevated in patients with HF, with a more pronounced increase observed in the HFpEF population of both cohorts. After adjusting for age, sex, body mass index, hypertension, diabetes, and atrial fibrillation, we observed that both HFpEF status and impaired kidney function were associated with elevated plasma myo-inositol. Unlike HFrEF, abnormally high myo-inositol (≥69.8 μM) was linked to unfavourable clinical outcomes (hazard ratio, 1.62; 95% confidence interval, [1.05-2.5]) in patients with HFpEF. These elevated levels were correlated with NTproBNP, troponin, and cardiac fibrosis in this subset of patients.

INTERPRETATION

Myo-inositol is a metabolite elevated in patients with HF and strongly correlated to kidney failure. In patients with HFpEF, high myo-inositol levels predict poor clinical outcomes and are linked to markers of cardiac adverse remodelling. This suggests that myo-inositol and its transporter SMIT1 may have a role in the pathophysiology of HFpEF.

FUNDING

BECAME-HF was supported by Collaborative Bilateral Research Program Québec - Wallonie-Brussels Federation.

Long-Term Prognostic Impact of Three-Dimensional Speckle-Tracking Echocardiography-Derived Left Ventricular Global Longitudinal Strain in Healthy Adults-Insights from the MAGYAR-Healthy Study

INTRODUCTION

Three-dimensional (3D) speckle-tracking echocardiography (STE) combines the advantages of STE and volumetric 3D echocardiography, which shows the left ventricle (LV) in 3D during the cardiac cycle and is also suitable for accurate strain measurements in addition to volumetric assessments using the same virtual 3D LV cast. The present study aimed to confirm the prognostic impact of 3DSTE-derived LV global longitudinal strain (GLS) in healthy adults during a 12-year follow-up period.

PATIENTS AND METHODS

The current study comprised 124 healthy individuals with a mean age of 31.0 ± 11.7 years (64 males) at the time of complete two-dimensional Doppler echocardiography (2DE) and 3DSTE.

RESULTS

During a mean follow-up of 8.01 ± 4.12 years, 10 healthy individuals suffered cardiovascular events, including 2 cardiac deaths. Using ROC analysis, 3DSTE-derived LV-GLS ≥ 14.77% was found to be a significant predictor for cardiovascular event-free survival (sensitivity 70%, specificity 71%, area under the curve 76%, p = 0.007). Using 2DE, higher LV end-diastolic and end-systolic volumes, a larger LV end-systolic diameter and a lower LV ejection fraction could be detected in subjects with LV-GLS < 14.77% as compared to cases with LV-GLS ≥ 14.77%. Subjects with events had thicker interventricular septa, a larger LV mass and lower 3DSTE-derived LV-GLS and a higher ratio of cases had LV-GLS < 14.77%. From subjects with LV-GLS < 14.77%, seven individuals (18%) had events. Multivariate regression analysis identified age and LV-GLS as independent predictors of event-free survival.

CONCLUSIONS

3DSTE-derived LV-GLS is a strong independent predictor of cardiovascular survival in healthy adults.

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.

Epidemiology of heart failure in diabetes: a disease in disguise

Left ventricular diastolic dysfunction (LVDD) without symptoms, and heart failure (HF) with preserved ejection fraction (HFpEF) represent the most common phenotypes of HF in individuals with type 2 diabetes mellitus, and are more common than HF with reduced ejection fraction (HFrEF), HF with mildly reduced ejection fraction (HFmrEF) and left ventricular systolic dysfunction (LVSD) in these individuals. However, diagnostic criteria for HF have changed over the years, resulting in heterogeneity in the prevalence/incidence rates reported in different studies. We aimed to give an overview of the diagnosis and epidemiology of HF in type 2 diabetes, using both a narrative and systematic review approach; we focus narratively on diagnosing (using the 2021 European Society of Cardiology [ESC] guidelines) and screening for HF in type 2 diabetes. We performed an updated (2016-October 2022) systematic review and meta-analysis of studies reporting the prevalence and incidence of HF subtypes in adults ≥18 years with type 2 diabetes, using echocardiographic data. Embase and MEDLINE databases were searched and data were assessed using random-effects meta-analyses, with findings presented as forest plots. From the 5015 studies found, 209 were screened using the full-text article. In total, 57 studies were included, together with 29 studies that were identified in a prior meta-analysis; these studies reported on the prevalence of LVSD (n=25 studies, 24,460 individuals), LVDD (n=65 studies, 25,729 individuals), HFrEF (n=4 studies, 4090 individuals), HFmrEF (n=2 studies, 2442 individuals) and/or HFpEF (n=8 studies, 5292 individuals), and on HF incidence (n=7 studies, 17,935 individuals). Using Hoy et al's risk-of-bias tool, we found that the studies included generally had a high risk of bias. They showed a prevalence of 43% (95% CI 37%, 50%) for LVDD, 17% (95% CI 7%, 35%) for HFpEF, 6% (95% CI 3%, 10%) for LVSD, 7% (95% CI 3%, 15%) for HFrEF, and 12% (95% CI 7%, 22%) for HFmrEF. For LVDD, grade I was found to be most prevalent. Additionally, we reported a higher incidence rate of HFpEF (7% [95% CI 4%, 11%]) than HFrEF 4% [95% CI 3%, 7%]). The evidence is limited by the heterogeneity of the diagnostic criteria over the years. The systematic section of this review provides new insights on the prevalence/incidence of HF in type 2 diabetes, unveiling a large pre-clinical target group with LVDD/HFpEF in which disease progression could be halted by early recognition and treatment.Registration PROSPERO ID CRD42022368035.

Predictive Value of the Hb/RDW Ratio for the Risk of All-Cause Death in Patients with Heart Failure with Different Ejection Fractions

INTRODUCTION

The prognostic value of the ratio of haemoglobin to red cell distribution width (HRR) in different types of heart failure (HF) is not well known.

METHOD AND RESULTS

We analysed the long-term prognostic value of HRR in patients with HF using the Cox proportional risk model and Kaplan-Meier method. We reviewed consecutive 972 HF patients. The overall mortality rate was 45.68%. Mortality was 52.22% in the HFrEF group and 40.99% in the HFpEF + HFmrEF group. Cox regression showed that when HRR increased by 1 unit, the risk of all-cause death in all HF patients decreased by 22.8% (HR: 0.772, 95% CI: 0.724, 0.823, p < 0.001), in the HFpEF + HFmrEF group it decreased by 15.5% (HR: 0.845, 95% CI: 0.774, 0.923, p < 0.001), and in the HFrEF group it decreased by 36.1% (HR: 0.639, 95% CI: 0.576, 0.709, p < 0.0001). Subgroup analysis showed that there were interactions between the EF and HRR groups. The group in which HRR best predicted all-cause death from HF was group 1 (EF <40%, HRR <9.45), followed by group 2 (EF <40%, HRR ≥9.45), and group 3 (EF ≥40%, HRR <9.45). HRR had no predictive value in group 4 (EF ≥40%, HRR ≥9.45).

CONCLUSION

HRR is an important predictor of all-cause mortality in patients with HF, especially HFrEF. There is an interaction between HRR group and LVEF group.

Comparative efficacy of five traditional Chinese medicine injections for treating heart failure with reduced and mildly reduced ejection fraction: Bayesian network meta-analysis

Background

More than half of all heart failure (HF) patients have heart failure with reduced ejection fraction (HFrEF) or mildly reduced ejection fraction (HFmrEF). The combination of traditional Chinese medicine injections (TCMIs) with Western medicine treatment (WMT) has been reported to have better efficacy than using WMT alone. However, the positive effects of TCMIs combined with WMT on HFrEF and HFmrEF require more comprehensive and systematic evidence and warrant further investigation.

Methods

The NMA searched eight databases, including four English and four Chinese, from database creation to November 10, 2022. We used the Cochrane Risk of Bias tool (ROB 2) to assess the selected studies' quality. OpenBUGS and STATA 17.0 were used for network meta-analysis.

Results

The 101 RCTs were included in the systematic review. Studies have shown that when combined with any of the five TCMIs, WMT was more efficient than WMT alone. Shenmai injection (SMI) + WMT may be the best treatment for clinical effectiveness rate (CER) improvement and b-type natriuretic peptide (BNP) reduction. Huangqi injection (HQI) + WMT was the best treatment for improving left ventricular ejection fraction (LVEF). Danhong injection (DHI) + WMT may be the best treatment for lowering left ventricular end-diastolic dimension (LVEDD). Xinmailong injection (XMLI) + WMT was likely the best treatment for increasing the 6-min walking test (6MWT). In addition, XMLI had the lowest incidence of adverse reactions (3.38%).

Conclusions

Shenfu injection (SFI), SMI, DHI, XMLI, and HQI combined with WMT have stronger efficiency in treating HFrEF and HFmrEF. However, as all studies were conducted in China, this review is limited by the inevitable selection bias, and further high-quality multicenter randomized controlled trials (RCTs) are required.

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.

Non-eligibility for pivotal HFpEF/HFmrEF outcome trials and mortality in a contemporary heart failure cohort

Pivotal outcome trials targeting heart failure with preserved (HFpEF) and mildly-reduced ejection fraction (HFmrEF) may have excluded patients at highest risk of poor outcomes. We aimed to assess eligibility for HFpEF/HFmrEF outcome trials in an unselected heart failure cohort and its association with all-cause mortality. Among 32.028 patients presenting to a tertiary care center emergency unit for any reason between August 2018 and July 2019, we identified 407 admissions with evident HFpEF and HFmrEF. Eligibility criteria for pivotal trials CHARM-Preserved, I-PRESERVE, TOPCAT, PARAGON-HF, EMPEROR-Preserved and DELIVER were assessed by chart review. The proportions of admissions fulfilling HFpEF/HFmrEF trial eligibility criteria were 88% for CHARM-Preserved, 40% for I-PRESERVE, 35% for TOPCAT, 28% for PARAGON-HF, 51% for EMPEROR-Preserved, and 49% for DELIVER. During a median follow-up of 1.9 years, death-from-any-cause occurred in 121 cases (30%). Twenty-four-month overall survival estimates for non-eligible and eligible admissions were 53% vs. 76% for CHARM-Preserved (HR=2.32, 95% CI: 1.47-3.67, p<0.001), 62% vs. 87% for I-PRESERVE (HR=2.97, 1.85-4.77, p<0.001), 67% vs. 84% for TOPCAT (HR=2.04, 1.29-3.24, p = 0.002), 68% vs. 85% for PARAGONHF (HR=2.28, 1.33-3.90, p = 0.003), 64% vs. 81% for EMPEROR-Preserved (HR=1.90, 1.27-2.84, p = 0.002), and 65% vs. 80% for DELIVER (HR=1.71, 1.14-2.57, p = 0.010). Exclusion criteria independently predicting death were eGFR <20 ml/min/1.73 m2, COPD with home oxygen therapy, and severe valvular heart disease. Conclusively, in a contemporary HFpEF/HFmrEF cohort, non-eligibility for outcome trials predicted for strongly increased mortality. HFpEF/HFmrEF patients at highest mortality risk were likely underrepresented in previous outcome trials and their treatment remains an unmet medical need.

Clinical outcomes by serum potassium levels for patients hospitalized for heart failure: Secondary analysis of data from the China National Heart Failure Registry

BACKGROUND

Dyskalemia is a mortality risk factor in patients with heart failure (HF).

HYPOTHESIS

We described the prevalence of dyskalemia, and clinical outcomes by serum potassium (sK) levels, in Chinese patients hospitalized for HF.

METHODS

In this secondary analysis of the prospective China National Heart Failure Registry, adult patients hospitalized between January 1, 2013 and June 30, 2015 who had at least one baseline sK measurement were followed for up to 3 years after discharge. The use of renin-angiotensin-aldosterone system inhibitors at baseline and clinical outcomes during follow-up were compared among sK groups.

RESULTS

Among 6950 patients, 5529 (79.6%) had normokalemia (sK >3.5-5.0 mmol/L), 1113 (16.0%) had hypokalemia (sK 0-3.5 mmol/L), and 308 (4.4%) had hyperkalemia (sK >5.0 mmol/L). Baseline characteristics that were most common in patients with hyperkalemia than those with hypo- and normokalemia included older age, HF with reduced ejection fraction, New York Heart Association Class III/IV status, hypertension, and chronic kidney disease. Use of angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) differed across sK groups (p = .0001); reported in 64.1%, 63.4%, and 54.5% of patients with hypo-, normo-, and hyperkalemia, respectively. Overall, 26.6%, 28.6%, and 36.0% of patients with hypo-, normo-, and hyperkalemia had rehospitalization for worsened HF, or cardiovascular mortality; p = .0057 for between-group comparison.

CONCLUSIONS

Patients with hyperkalemia received ACEIs or ARBs for HF treatment at baseline less frequently than those with hypo- or normokalemia, and had worse prognoses. This warrants further investigation into effective hyperkalemia management in HF.

Insulin-like growth factor binding protein 2: a core biomarker of left ventricular dysfunction in dilated cardiomyopathy

BACKGROUND

RNA modifications, especially N6-methyladenosine, N1-methyladenosine and 5-methylcytosine, play an important role in the progression of cardiovascular disease. However, its regulatory function in dilated cardiomyopathy (DCM) remains to be undefined.

METHODS

In the study, key RNA modification regulators (RMRs) were screened by three machine learning models. Subsequently, a risk prediction model for DCM was developed and validated based on these important genes, and the diagnostic efficiency of these genes was assessed. Meanwhile, the relevance of these genes to clinical traits was explored. In both animal models and human subjects, the gene with the strongest connection was confirmed. The expression patterns of important genes were investigated using single-cell analysis.

RESULTS

A total of 4 key RMRs were identified. The risk prediction models were constructed basing on these genes which showed a good accuracy and sensitivity in both the training and test set. Correlation analysis showed that insulin-like growth factor binding protein 2 (IGFBP2) had the highest correlation with left ventricular ejection fraction (LVEF) (R = -0.49, P = 0.00039). Further validation expression level of IGFBP2 indicated that this gene was significantly upregulated in DCM animal models and patients, and correlation analysis validation showed a significant negative correlation between IGFBP2 and LVEF (R = -0.87; P = 6*10-5). Single-cell analysis revealed that this gene was mainly expressed in endothelial cells.

CONCLUSION

In conclusion, IGFBP2 is an important biomarker of left ventricular dysfunction in DCM. Future clinical applications could possibly use it as a possible therapeutic target.

The risk of stroke recurrence in patients with atrial fibrillation and reduced ejection fraction

BACKGROUND

Atrial fibrillation (AF) and congestive heart failure often coexist due to their shared risk factors leading to potential worse outcome, particularly cerebrovascular events. The aims of this study were to calculate the rates of ischemic and severe bleeding events in ischemic stroke patients having both AF and reduced ejection fraction (rEF) (⩽40%), compared to ischemic stroke patients with AF but without rEF.

METHODS

We performed a retrospective analysis that drew data from prospective studies. The primary outcome was the composite of either ischemic (stroke or systemic embolism), or hemorrhagic events (symptomatic intracranial bleeding and severe extracranial bleeding).

RESULTS

The cohort for this analysis comprised 3477 patients with ischemic stroke and AF, of which, 643 (18.3%) had also rEF. After a mean follow-up of 7.5 ± 9.1 months, 375 (10.8%) patients had 382 recorded outcome events, for an annual rate of 18.0%. While the number of primary outcome events in patients with rEF was 86 (13.4%), compared to 289 (10.2%) for the patients without rEF; on multivariable analysis rEF was not associated with the primary outcome (OR 1.25; 95% CI 0.84-1.88). At the end of follow-up, 321 (49.9%) patients with rEF were deceased or disabled (mRS ⩾3), compared with 1145 (40.4%) of those without rEF; on multivariable analysis, rEF was correlated with mortality or disability (OR 1.35; 95% CI 1.03-1.77).

CONCLUSIONS

In patients with ischemic stroke and AF, the presence of rEF was not associated with the composite outcome of ischemic or hemorrhagic events over short-term follow-up but was associated with increased mortality or disability.

Acute heart failure with mildly reduced ejection fraction and myocardial infarction: a multi-institutional cohort study

BACKGROUND

Little research has been done on ischemic outcomes related to left ventricular ejection fraction (EF) in acute decompensated heart failure (ADHF).

METHODS

A retrospective cohort study was conducted between 2001 and 2021 using the Chang Gung Research Database. ADHF Patients discharged from hospitals between January 1, 2005, and December 31, 2019. Cardiovascular (CV) mortality and heart failure (HF) rehospitalization are the primary outcome components, along with all-cause mortality, acute myocardial infarction (AMI) and stroke.

RESULTS

A total of 12,852 ADHF patients were identified, of whom 2,222 (17.3%) had HFmrEF, the mean (SD) age was 68.5 (14.6) years, and 1,327 (59.7%) were males. In comparison with HFrEF and HFpEF patients, HFmrEF patients had a significant phenotype comorbid with diabetes, dyslipidemia, and ischemic heart disease. Patients with HFmrEF were more likely to experience renal failure, dialysis, and replacement. Both HFmrEF and HFrEF had similar rates of cardioversion and coronary interventions. There was an intermediate clinical outcome between HFpEF and HFrEF, but HFmrEF had the highest rate of AMI (HFpEF, 9.3%; HFmrEF, 13.6%; HFrEF, 9.9%). The AMI rates in HFmrEF were higher than those in HFpEF (AHR, 1.15; 95% Confidence Interval, 0.99 to 1.32) but not in HFrEF (AHR, 0.99; 95% Confidence Interval, 0.87 to 1.13).

CONCLUSION

Acute decompression in patients with HFmrEF increases the risk of myocardial infarction. The relationship between HFmrEF and ischemic cardiomyopathy, as well as optimal anti-ischemic treatment, requires further research on a large scale.

ANALYSIS OF FACTORS ASSOCIATED WITH FRAILTY SYNDROME IN PATIENTS WITH HEART FAILURE

OBJECTIVE

Aim: Determination of factors associated with frailty syndrome (FS) in patients with heart failure (HF).

PATIENTS AND METHODS

Materials and methods: Consecutive patients hospitalized in the department were assessed for the presence of FS using L. Fried criteria, Edmonton Frail Scale (EFS) and Tilburg Frailty Indicator (TFI). Presence of arterial hypertension, diabetes, obesity, chronic obstructive pulmonary disease (COPD), and heart failure was included in the analysis based on patients' medical history and findings from current hospitalization. Patients were assessed for the presence of depression using Beck's Depression Inventory (BDI). Physical capacity was assessed using NYHA classification.

RESULTS

Results: 87 patients (mean age 81.4±6.7; 57 women; 11 HFrEF, mean NYHA 2.36±1.21; 11 HFmrEF, mean NYHA 2.18±1.08; 65 HFpEF mean NYHA 1.94±1.09) were included in the analysis. Multivariable analysis showed significant relationship between FS assessed with EFS and age (β=0.316, SE=0.08; p=0.0001), arterial hypertension (β=-0.194, SE=0.08; p=0.0173), COPD (β=0.176, SE=0.08; p=0.0300) and depression (β=0.565, SE=0.08; p=0.0000). FS assessed with L. Fried criteria was significantly related to age (β=0.359, SE=0.09; p= 0.0001), NYHA classification (β= 0.336, SE=0.09; p=0.0002) and depression (β=0.297, SE=0.09; p=0.0010). Age (β=0.251, SE=0.10; p=0.0114) and depression (β=0.375, SE=0.1; p=0.0002) were significantly related to FS assessed using TFI. In multivariable analysis HF phenotype was not significantly related to FS.

CONCLUSION

Conclusions: Age and depression assessed with BDI are related to FS in patients with HF. Arterial hypertension and COPD are linked to FS assessed using EFS, whereas NYHA classification is linked to FS assessed with L. Fried criteria. No statistically significant relationship was found between FS and HF phenotype.

Point-of-care heart failure platform: where are we now and where are we going to?

INTRODUCTION

Heart failure (HF) remains a leading cause of cardiovascular (CV) mortality in patients with CV disease. The point-of-care (POC) HF platform seems to be an ideal noninvasive workflow-adapted system for personally adjusted management of patients with HF.

AREAS COVERED

In the present manuscript, we reviewed the literature covering some relevant studies regarding the role of point-of care heart failure platform in the risk stratification, earlier diagnosis and prognostically beneficial treatment of patients with different phenotypes of HF.

EXPERT OPINION

POC HF platform including personal consultation, optimization of the comorbidity treatment, step-by-step HF diagnostic algorithm, single biomarker measurements, has also partially been provided in the current guidelines. Although there are several obstacles to implement POC in routine practice, such as education level, aging, affordability of health care, even partial implementation of POC can also improve clinical outcomes. POC seems to be an evolving model, more research studies are required to clearly see whether it helps to make better decisions with diagnosis and care of HF, as well helps to achieve better clinical outcomes.In summary, the POC HF platform is considered to be a more effective tool than conventional algorithm of HF management.

Depressive Symptoms and Quality of Life in Patients With Heart Failure and an Implantable Cardioverter-Defibrillator

BACKGROUND

Heart failure (HF) is associated with development of depressive symptoms and reduced quality of life (QoL). Patients with HF and an implantable cardioverter-defibrillator (ICD) were evaluated regarding depressive symptoms and QoL.

METHODS

The present study included 446 patients with HF and an ICD. Depressive symptoms were assessed using the Patient Health Questionnaire 9 (PHQ-9), QoL was evaluated using the Minnesota Living with Heart Failure Questionnaire (MLHFQ). Functional ability and exercise tolerance were assessed at inclusion and after 6 months with help of the 6-min walking test (6MWT).

RESULTS

Patients included in the study had a mean age of 65.8 years and were predominantly male (83.6%), with mostly ischemic (n = 277; 62.1%) or dilated (n = 150; 33.6%) cardiomyopathy. One hundred ninety-three (43.2%) patients had depressive symptoms, of whom 75 patients (16.8%) were classified as moderate to severe depression according to the PHQ-9 at baseline. Depressive symptoms were associated with low QoL independent of NYHA functional class. High NYHA functional class, high PHQ-9 score, age and body mass index (BMI) were associated with a lower 6MWT at enrollment, while depressive symptoms (expressed as higher PHQ-9 score) and age were associated with a lower 6MWT after 6 months. Patients with history of smoking and a higher BMI showed higher PHQ-9 scores after 6 months. Patients under antidepressant medication showed improved PHQ-9 score after 6 months, indicating controlled/treated depression. However, patients with low QoL at inclusion remained with low QoL after 6 months.

CONCLUSION

Depressive symptoms correlate with low QoL and lower long-term functional status in patients with HF and an ICD. Depressive symptoms are associated with smoking and obesity, which themselves are risk factors for a poor prognosis in HF. Only a small fraction of patients with HF and ICD showing depressive symptoms receives appropriate treatment. Assessing depressive symptoms and lifestyle factors should be part of a multimodal treatment plan in patients with HF and an ICD.