Short Registry of Terminal Forms of Chronic Heart Failure in the Samara Region.

AIM: To study the clinical characteristics and prognosis of patients with functional class (FC) III-IV chronic heart failure (CHF) who meet the criteria for inclusion in the palliative care program. MATERIAL AND METHODS: A short registry of severe CHF forms was conducted at 60 outpatient and inpatient clinics in the Samara region for one month (16.05.2022-15.06.2022). The registry included patients with FC III-IV CHF who sought medical help during that period. Lethal outcomes were assessed at 90 days after the inclusion in the registry using the Mortality Information and Analytics system. RESULTS: 591 patients (median age, 71.0 [64.0; 80.0] years were enrolled, including 339 (57.4%) men, of which 149 (24.1%) were of working age (under 65 years). The main cause of CHF was ischemic heart disease (64.5%). 229 (38.7%) patients had left ventricular ejection fraction <40%. During the past year, 513 (86.8%) patients had at least one hospitalization for decompensated CHF. 45.7% of patients had hydrothorax, and 11.3% of patients had ascites. Low systolic blood pressure was observed in more than 25% of patients; 14.2% required in-hospital inotropic support; and 9.1% received it on the outpatient basis. 4.2% of patients received outpatient oxygen support and 0.8% required the administration of narcotic analgesics. 12 (1.9%) patients were on the waiting list for heart transplantation. In this study, there was an inconsistency in the number of patients with ventricular tachycardia and/or left bundle branch block (LBBB) who were implanted with cardiac resynchronization therapy devices (CRTD) or an implantable cardioverter defibrillator (ICD), a total of 19 patients (11 patients with CRTD and 8 patients with ICD), while 58 (9.8%) patients had indications for CRTD/ICD implantation. Within 90 days from inclusion in the registry, 59 (10.0%) patients died. According to binary logistic regression analysis, the presence of LBBB, hydrothorax, the requirement for outpatient oxygen support, and a history of cardiac surgery were associated with a high risk of death. CONCLUSION: Patients with severe forms of CHF require not only adequate drug therapy, but also dynamic clinical observation supplemented with palliative care aimed at improving the quality of life, including the ethical principles of shared decision-making and advance care planning to identify the priorities and goals of patients in relation to their care.

Determining The Risk of Atrial Fibrillation Paroxysm in Patients With Chronic Heart Failure With Intact and Reduced Ejection Fraction.

AIM: To determine predictors for the development of atrial fibrillation (AF) in patients with chronic heart failure (CHF) with preserved and reduced ejection fraction by echocardiography (EchoCG) according to an extended protocol with determination of diastolic function and left atrial global strain. MATERIAL AND METHODS: Data of 168 patients with stage I-III CHF without a history of AF were analyzed. All patients underwent echocardiography according to an extended protocol with the determination of diastolic dysfunction (DD), left atrial ejection fraction (LA EF), and left atrial global strain (LA GS). Tissue Doppler imaging (TDI) was used to evaluate the early (E) and late (A) LV filling velocity and the early (E') and late (A') diastolic mitral annular velocity. In all patients, Holter ECG monitoring (HM ECG) of heart rhythm was performed for 3 days, and ECG monitoring with telemedicine technologies was performed for 7 days, 3 times a day for 3 minutes. The follow-up period was 3 months or until an AF episode. RESULTS: During the study, paroxysmal AF (pAF) was detected in 41 (24.4%) patients using various methods of heart rhythm monitoring. Complaints of palpitations were noted for 10 (24.4%) patients during pAF, which was recorded using a CardioQVARK® device, HM ECG or a 12-lead ECG. In 5 (12.2%) patients, daily ECG monitoring revealed pAF without associated complaints. HM ECG detected 8, 2, 4 (19.5%, 4.8%, and 9.7%) cases during 24, 48 and 72 hours, respectively; a single-channel CardioQVARK® detected 30 (73.2%) cases when used 3 times a day for 7 days. These results showed that AF frequently develops in CHF without accompanying symptoms. The method for detecting pAF with CardioQVARK® showed good results: it was twice more effective than HM ECG and three times more effective than 12-lead ECG. Also, according to ultrasound data, significant changes in the following parameters were noted in patients with AF: LA EF <36% (OR 1.04, 95% CI: 1.02-1.08), p=0.003; LA GS <9.9% (OR 1.16, 95% CI: 1.02-1.38), p<0.001; TDI E med <5.7 cm/s (OR 0.97, 95% CI: 0.94-1.00), p=0.026. Grade 2 DD did not show statistically significant results (OR 1.1, 95% CI: 0.7-1.5, p=0.54). However, it was detected more frequently in patients with AF, in 34% of cases, compared to 29% of cases in patients without AF, which requires further study on a larger patient sample. CONCLUSION: Patients with CHF have a high risk of developing pAF (24.4%). 75% of patients with AF do not feel the development of paroxysm. All CHF patients should undergo EchoCG with assessment of LA EF, TDI E med and LA GS to identify a group at risk for the development of AF. Heart rhythm remote monitoring with CardioQVARK® devices can be considered a reliable method for early detection of pAF and timely initiation of anticoagulant therapy in patients with CHF.

A new nomogram to predict in-hospital mortality in patients with acute decompensated chronic heart failure and diabetes after 48 Hours of Intensive Care Unit.

BACKGROUND: The study set out to develop an accurate and clinically valuable prognostic nomogram to assess the risk of in-hospital death in patients with acute decompensated chronic heart failure (ADCHF) and diabetes. METHODS: We extracted clinical data of patients diagnosed with ADCHF and diabetes from the Medical Information Mart for Intensive Care III database. Risk variables were selected utilizing least absolute shrinkage and selection operator regression analysis, and were included in multivariate logistic regression and presented in nomogram. bootstrap was used for internal validation. The discriminative power and predictive accuracy of the nomogram were estimated using the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA). RESULTS: Among 867 patients with ADCHF and diabetes, In-hospital death occurred in 81 (9.3%) patients. Age, heart rate, systolic blood pressure, red blood cell distribution width, shock, ß-blockers, angiotensin converting enzyme inhibitors or angiotensin receptor blockers, assisted ventilation, and blood urea nitrogen were brought into the nomogram model. The calibration curves suggested that the nomogram was well calibrated. The AUC of the nomogram was 0.873 (95% CI: 0.834-0.911), which was higher that of the Simplified Acute Physiology Score II [0.761 (95% CI: 0.711-0.810)] and sequential organ failure assessment score [0.699 (95% CI: 0.642-0.756)], and Guidelines-Heart Failure score [0.782 (95% CI: 0.731-0.835)], indicating that the nomogram had better ability to predict in-hospital mortality. In addition, the internally validated C-index was 0.857 (95% CI: 0.825-0.891), which again verified the validity of this model. CONCLUSIONS: This study constructed a simple and accurate nomogram for predicting in-hospital mortality in patients with ADCHF and diabetes, especially in those who admitted to the intensive care unit for more than 48 hours, which contributed clinicians to assess the risk and individualize the treatment of patients, thereby reducing in-hospital mortality.

Machine learning methods, applications and economic analysis to predict heart failure hospitalisation risk: a scoping review protocol.

INTRODUCTION: Machine learning (ML) has emerged as a powerful tool for uncovering patterns and generating new information. In cardiology, it has shown promising results in predictive outcomes risk assessment of heart failure (HF) patients, a chronic condition affecting over 64 million individuals globally.This scoping review aims to synthesise the evidence on ML methods, applications and economic analysis to predict the HF hospitalisation risk. METHODS AND ANALYSIS: This scoping review will use the approach described by Arksey and O'Malley. This protocol will use the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Protocol, and the PRISMA extension for scoping reviews will be used to present the results. PubMed, Scopus and Web of Science are the databases that will be searched. Two reviewers will independently screen the full-text studies for inclusion and extract the data. All the studies focusing on ML models to predict the risk of hospitalisation from HF adult patients will be included. ETHICS AND DISSEMINATION: Ethical approval is not required for this review. The dissemination strategy includes peer-reviewed publications, conference presentations and dissemination to relevant stakeholders.

Stage B Heart Failure Is Ubiquitous in Emergency Patients with Asymptomatic Hypertension.

Introduction: Hypertension is the leading risk factor for morbidity and mortality throughout the world and is pervasive in United States emergency departments (ED). This study documents the point prevalence of subclinical heart disease in emergency patients with asymptomatic hypertension. Method: This was a prospective observational study of ED patients with asymptomatic hypertension conducted at two urban academic EDs that belong to an eight-hospital healthcare organization in New York. Adult (≥18 years of age) English- or Spanish-speaking patients who had an initial blood pressure (BP) ≥160/100 millimeters of mercury (mmHg) and second BP ≥140/90 mm Hg, and pending discharge, were invited to participate in the study. We excluded patients with congestive heart failure, renal insufficiency, and atrial fibrillation, or who were pregnant, a prisoner, cognitively unable to provide informed consent, or experiencing symptoms of hypertension. We assessed echocardiographic evidence of subclinical heart disease (left ventricular hypertrophy, and diastolic and systolic dysfunction). Results: A total of 53 patients were included in the study; a majority were young (mean 49.5 years old, [SD 14-52]), self-identified as Black or Other (n = 39; 73.5%), and female (n = 30; 56.6%). Mean initial blood pressure was 172/100 mm Hg, and 24 patients (45.3%) self-reported a history of hypertension. Fifty patients completed an echocardiogram. All (100%) had evidence of subclinical heart disease, with 41 (77.4%) displaying left ventricular hypertrophy and 31 (58.5%) diastolic dysfunction. There was a significant relationship between diastolic dysfunction and female gender [x2 (1, n = 53) = 3.98; P = 0.046]; Black or other race [x2 (3, n = 53) = 9.138; P = 0.03] and Hispanic or other ethnicity [x2 (2, n = 53) = 8.03; P = 0.02]. Less than one third of patients demonstrated systolic dysfunction on echocardiogram, and this was more likely to occur in patients with diabetes mellitus [x2 (1, n = 51) = 4.84; P = 0.02]. Conclusion: There is a high probability that Black, Hispanic, and female patients with asymptomatic hypertension are on the continuum for developing overt heart failure. Emergency clinicians should provide individualized care that considers their unique health needs, cultural backgrounds, and social determinants of health.

Prediction of Mortality and Heart Failure Hospitalization After Transcatheter Tricuspid Valve Interventions: Validation of TRISCORE.

BACKGROUND: Data on the prognostic role of the TRI-SCORE in patients undergoing transcatheter tricuspid valve intervention (TTVI) are limited. OBJECTIVES: The aim of this study was to evaluate the performance of the TRI-SCORE in predicting outcomes of patients undergoing TTVI. METHODS: TriValve (Transcatheter Tricuspid Valve Therapies) is a large multicenter multinational registry including patients undergoing TTVI. The TRI-SCORE is a risk model recently proposed to predict in-hospital mortality after tricuspid valve surgery. The TriValve population was stratified based on the TRI-SCORE tertiles. The outcomes of interest were all-cause death and all-cause death or heart failure hospitalization. Procedural complications and changes in NYHA functional class were also reported. RESULTS: Among the 634 patients included, 223 patients (35.2%) had a TRI-SCORE between 0 and 5, 221 (34.8%) had 6 or 7, and 190 (30%) had ≥8 points. Postprocedural blood transfusion, acute kidney injury, new atrial fibrillation, and in-hospital mortality were more frequent in the highest TRI-SCORE tertile. Postprocedure length of stay increased with a TRI-SCORE increase. A TRI-SCORE ≥8 was associated with an increased risk of 30-day all-cause mortality and all-cause mortality and the composite endpoint assessed at a median follow-up of 186 days (OR: 3.00; 95% CI: 1.38-6.55; HR: 2.17; 95% CI: 1.78-4.13; HR: 2.08, 95% CI: 1.57-2.74, respectively) even after adjustment for procedural success and EuroSCORE II or Society of Thoracic Surgeons Predicted Risk of Mortality. The NYHA functional class improved across all TRI-SCORE values. CONCLUSIONS: In the TriValve registry, the TRI-SCORE has a suboptimal performance in predicting clinical outcomes. However, a TRISCORE ≥8 is associated with an increased risk of clinical events and a lack of prognostic benefit after successful TTVI.

Advances in heart failure monitoring: Biosensors targeting molecular markers in peripheral bio-fluids.

Cardiovascular diseases (CVDs), especially chronic heart failure, threaten many patients' lives worldwide. Because of its slow course and complex causes, its clinical screening, diagnosis, and prognosis are essential challenges. Clinical biomarkers and biosensor technologies can rapidly screen and diagnose. Multiple types of biomarkers are employed for screening purposes, precise diagnosis, and treatment follow-up. This article provides an up-to-date overview of the biomarkers associated with the six main heart failure etiology pathways. Plasma natriuretic peptides (BNP and NT-proBNP) and cardiac troponins (cTnT, cTnl) are still analyzed as gold-standard markers for heart failure. Other complementary biomarkers include growth differentiation factor 15 (GDF-15), circulating Galactose Lectin 3 (Gal-3), soluble interleukin (sST2), C-reactive protein (CRP), and tumor necrosis factor-alpha (TNF-α). For these biomarkers, the electrochemical biosensors have exhibited sufficient sensitivity, detection limit, and specificity. This review systematically summarizes the latest molecular biomarkers and sensors for heart failure, which will provide comprehensive and cutting-edge authoritative scientific information for biomedical and electronic-sensing researchers in the field of heart failure, as well as patients. In addition, our proposed future outlook may provide new research ideas for researchers.

Sodium-glucose cotransporter 2 inhibitors, inflammation, and heart failure: a two-sample Mendelian randomization study.

BACKGROUND: Sodium-glucose cotransporter 2 (SGLT-2) inhibitors are increasingly recognized for their role in reducing the risk and improving the prognosis of heart failure (HF). However, the precise mechanisms involved remain to be fully delineated. Evidence points to their potential anti-inflammatory pathway in mitigating the risk of HF. METHODS: A two-sample, two-step Mendelian Randomization (MR) approach was employed to assess the correlation between SGLT-2 inhibition and HF, along with the mediating effects of inflammatory biomarkers in this relationship. MR is an analytical methodology that leverages single nucleotide polymorphisms as instrumental variables to infer potential causal inferences between exposures and outcomes within observational data frameworks. Genetic variants correlated with the expression of the SLC5A2 gene and glycated hemoglobin levels (HbA1c) were selected using datasets from the Genotype-Tissue Expression project and the eQTLGen consortium. The Genome-wide association study (GWAS) data for 92 inflammatory biomarkers were obtained from two datasets, which included 14,824 and 575,531 individuals of European ancestry, respectively. GWAS data for HF was derived from a meta-analysis that combined 26 cohorts, including 47,309 HF cases and 930,014 controls. Odds ratios (ORs) and 95% confidence interval (CI) for HF were calculated per 1 unit change of HbA1c. RESULTS: Genetically predicted SGLT-2 inhibition was associated with a reduced risk of HF (OR 0.42 [95% CI 0.30-0.59], P < 0.0001). Of the 92 inflammatory biomarkers studied, two inflammatory biomarkers (C-X-C motif chemokine ligand 10 [CXCL10] and leukemia inhibitory factor) were associated with both SGLT-2 inhibition and HF. Multivariable MR analysis revealed that CXCL10 was the primary inflammatory cytokine related to HF (MIP = 0.861, MACE = 0.224, FDR-adjusted P = 0.0844). The effect of SGLT-2 inhibition on HF was mediated by CXCL10 by 17.85% of the total effect (95% CI [3.03%-32.68%], P = 0.0183). CONCLUSIONS: This study provides genetic evidence supporting the anti-inflammatory effects of SGLT-2 inhibitors and their beneficial impact in reducing the risk of HF. CXCL10 emerged as a potential mediator, offering a novel intervention pathway for HF treatment.

Non-Invasive Heart Failure Evaluation Using Machine Learning Algorithms.

Heart failure is a prevalent cardiovascular condition with significant health implications, necessitating effective diagnostic strategies for timely intervention. This study explores the potential of continuous monitoring of non-invasive signals, specifically integrating photoplethysmogram (PPG) and electrocardiogram (ECG), for enhancing early detection and diagnosis of heart failure. Leveraging a dataset from the MIMIC-III database, encompassing 682 heart failure patients and 954 controls, our approach focuses on continuous, non-invasive monitoring. Key features, including the QRS interval, RR interval, augmentation index, heart rate, systolic pressure, diastolic pressure, and peak-to-peak amplitude, were carefully selected for their clinical relevance and ability to capture cardiovascular dynamics. This feature selection not only highlighted important physiological indicators but also helped reduce computational complexity and the risk of overfitting in machine learning models. The use of these features in training machine learning algorithms led to a model with impressive accuracy (98%), sensitivity (97.60%), specificity (96.90%), and precision (97.20%). Our integrated approach, combining PPG and ECG signals, demonstrates superior performance compared to single-signal strategies, emphasizing its potential in early and precise heart failure diagnosis. The study also highlights the importance of continuous monitoring with wearable technology, suggesting a significant stride forward in non-invasive cardiovascular health assessment. The proposed approach holds promise for implementation in hardware systems to enable continuous monitoring, aiding in early detection and prevention of critical health conditions.

Interaction of left ventricular size with the outcome of cardiac resynchronization therapy in Japanese patients.

BACKGROUND: We analyzed the influence of the QRS duration (QRSd) to LV end-diastolic volume (LVEDV) ratio on cardiac resynchronization therapy (CRT) outcomes in heart failure patients classified as III/IV per the New York Heart Association (NYHA) and with small body size. HYPOTHESIS: We proposed the hypothesis that the QRSd/LV size ratio is a better index of the CRT substrate. METHODS: We enrolled 114 patients with advanced heart failure (NYHA class III/IV, and LV ejection fraction >35%) who received a CRT device, including those with left bundle branch block (LBBB) and QRSd ≥120 milliseconds (n = 60), non-LBBB and QRSd ≥150 milliseconds (n = 30) and non-LBBB and QRSd of 120-149 milliseconds (n = 24). RESULTS: Over a mean follow-up period of 65 ± 58 months, the incidence of the primary endpoint, a composite of all-cause death and hospitalization for heart failure, showed no significant intergroup difference (43.3% vs. 50.0% vs. 37.5%, respectively, p = .72). Similarly, among 104 patients with QRSd/LVEDV ≥ 0.67 (n = 54) and QRSd/LVEDV < 0.67 (n = 52), no significant differences were observed in the incidence of the primary endpoint (35.1% vs. 51.9%, p = .49). Nevertheless, patients with QRSd/LVEDV ≥ 0.67 showed better survival than those with QRSd/LVEDV < 0.67 (14.8% vs. 34.6%, p = .0024). CONCLUSION: Advanced HF patients with a higher QRSd/LVEDV ratio showed better survival in this small-body-size population. Thus, the risk is concentrated among those with a larger QRSd, and patients with a relatively smaller left ventricular size appeared to benefit from CRT.

Identifying subgroups in heart failure patients with multimorbidity by clustering and network analysis.

This study presents a workflow for identifying and characterizing patients with Heart Failure (HF) and multimorbidity utilizing data from Electronic Health Records. Multimorbidity, the co-occurrence of two or more chronic conditions, poses a significant challenge on healthcare systems. Nonetheless, understanding of patients with multimorbidity, including the most common disease interactions, risk factors, and treatment responses, remains limited, particularly for complex and heterogeneous conditions like HF. We conducted a clustering analysis of 3745 HF patients using demographics, comorbidities, laboratory values, and drug prescriptions. Our analysis revealed four distinct clusters with significant differences in multimorbidity profiles showing differential prognostic implications regarding unplanned hospital admissions. These findings underscore the considerable disease heterogeneity within HF patients and emphasize the potential for improved characterization of patient subgroups for clinical risk stratification through the use of EHR data.

Assessment of new-onset heart failure prediction in a diabetic population using left ventricular global strain: a prospective cohort study based on UK Biobank.

Background: Impaired glucose utilization influences myocardial contractile function. However, the prognostic importance of left ventricular global radial strain (LV-GRS), left ventricular global circumferential strain (LV-GCS), and left ventricular global longitudinal strain (LV-GLS) in predicting new-onset heart failure (HF) in a population with diabetes is unclear. Methods: The study design is prospective cohort from the UK Biobank. Totally 37,899 participants had a complete data of cardiac magnetic resonance (CMR), of which 940 patients with diabetes were included, and all the participants completed follow-up. LV-GRS, LV-GCS, and LV-GLS were measured by completely automated CMR with tissue tagging. Cox proportional hazards regression analysis and C-index was performed to evaluate the association between the strain parameters and the new-onset HF in patients suffering from diabetes. Results: The average age of the 940 participants was 57.67 ± 6.97 years, with males comprising 66.4% of the overall population. With an average follow-up period of 166.82 ± 15.26 months, 35 (3.72%) patients reached the endpoint (emergence of new-onset HF). Significant associations were found for the three strain parameters and the new-onset HF (LV-GRS-hazard ratio [HR]: 0.946, 95% CI: 0.916-0.976; LV-GCS-HR: 1.162, 95% CI: 1.086-1.244; LV-GCS-HR: 1.181, 95% CI: 1.082-1.289). LV-GRS, LV-GCS, and LV-GLS were closely related to the related indicators to HF, and showed a high relationship to new-onset HF in individuals with diabetes at 5 and 10 years: LV-GRS: 0.75 (95% CI, 0.41-0.94) and 0.76 (95% CI, 0.44-0.98), respectively; LV-GCS: 0.80 (95% CI, 0.50-0.96) and 0.75 (95% CI, 0.41-0.98), respectively; LV-GLS: 0.72 (95% CI, 0.40-0.93) and 0.76 (95% CI, 0.48-0.97), respectively. In addition, age, sex, body mass index (BMI), and presence of hypertension or coronary artery disease (CAD) made no impacts on the association between the global strain parameters and the incidence of HF. Conclusion: LV-GRS, LV-GCS, and LV-GLS is significantly related to new-onset HF in patients with diabetes at 5 and 10 years.

Qualitative Analysis of Patient Decisional Needs for Medications to Treat Heart Failure.

BACKGROUND: The development of tools to support shared decision-making should be informed by patients' decisional needs and treatment preferences, which are largely unknown for heart failure (HF) with reduced ejection fraction (HFrEF) pharmacotherapy decisions. We aimed to identify patients' decisional needs when considering HFrEF medication options. METHODS: This was a qualitative study using semi-structured interviews. We recruited patients with HFrEF from 2 Canadian ambulatory HF clinics and clinicians from Canadian HF guideline panels, HF clinics, and Canadian HF Society membership. We identified themes through inductive thematic analysis. RESULTS: Participants included 15 patients and 12 clinicians. Six themes and associated subthemes emerged related to HFrEF pharmacotherapy decision-making: (1) patient decisional needs included lack of awareness of a choice or options, difficult decision timing and stage, information overload, and inadequate motivation, support and resources; (2) patients' decisional conflict varied substantially, driven by unclear trade-offs; (3) treatment attribute preferences-patients focused on both benefits and downsides of treatment, whereas clinicians centered discussion on benefits; (4) quality of life-patients' definition of quality of life depended on pre-HF activity, though most patients demonstrated adaptability in adjusting their daily activities to manage HF; (5) shared decision-making process-clinicians' described a process more akin to informed consent; (6) decision support-multimedia decision aids, virtual appointments, and primary-care comanagement emerged as potential enablers of shared decision-making. CONCLUSIONS: Patients with HFrEF have several decisional needs, which are consistent with those that may respond to decision aids. These findings can inform the development of HFrEF pharmacotherapy decision aids to address these decisional needs and facilitate shared decision-making.