This study aimed to conduct a phase 2 proof-of-concept and safety study to evaluate the effect of ENIBARCIMAB (EN), a non-neutralizing humanized monoclonal antibody targeting the N-terminus of adrenomedullin (ADM), administered immediately after stabilization with standard of care (SoC) treatment, in patients hospitalized for acute heart failure (AHF).

This prospective, open-label, controlled, interventional, multicenter, dose-escalation study was conducted at two cardiology sites in Indonesia. Patients were divided into two interventional groups sequentially receiving 0.5 mg/kg (SoC + EN 0.5 mg/kg, n = 10; first cohort) and 2 mg/kg (SoC + EN 2 mg/kg, n = 10; second cohort) of EN via 1-h intravenous (IV) infusion within 48 h after admission for AHF. The control group (n = 10) was treated with SoC therapy for AHF therapy. All patients were monitored continuously within 24 h post-infusion and subsequent daily until discharge. Treatment-related serious adverse events (SAEs) were recorded during hospitalization and up to 90 days after discharge. Both EN dosages were well-tolerated, and no significant safety issues were identified during hospitalization and up to 90 days of follow up. SAEs occurred in 10% of patients in each EN group but were deemed not related to treatment. No significant differences in the occurrence of SAEs were found between the groups. Five deaths occurred: three (30%) in the control group as compared with two deaths (20%) in the SoC + EN 2 mg/kg group. EN led to a significant increase in plasma bio-ADM levels within 24 h post-infusion, with the SoC + 2 mg/kg group showing the highest increase. Within 1 h from IV EN infusion, SoC + EN 2 mg/kg compared with 0.5 mg/kg, resulted in a significant percentage reduction in systolic, diastolic blood pressure, and mean arterial pressure. However, it did not result in severe hypotension and the need for drug discontinuation.

In this pilot safety study of patients hospitalized for AHF, IV infusion of EN 0.5 and 2 mg/kg increased circulating plasma bio-ADM levels and was well-tolerated without treatment-related SAEs occurring during hospitalization and up to 90 days after discharge.

Coronary artery disease (CAD) and heart failure (HF) are leading causes of global morbidity and mortality and pose economic burden. This study aims to examine the diagnostic and prognostic significance of biomarkers in patients with CAD and HF treated with empagliflozin.

In a prospective, case-control study, 180 participants were divided into 3 groups: patients with stable angina (CAD) and patients with HF on empagliflozin 10 mg daily for 6 months compared with healthy controls. Biomarker levels were measured at baseline, with hospital readmission rates monitored over 30 and 90 days. Stepwise logistic regression was used to predict hospital readmissions with and without participant clinical features. All relevant independent variables were then included in a single model using multiple logistic regression.

Significant differences in biomarker profiles were observed across groups, indicating the differential significance of these biomarkers in diagnostic and prognostic aspects. Multinomial logistic regression identified 3 biomarkers as key diagnostic markers for CAD and HF: homocysteine (relative risk ratio [RRR] = 16.5 for CAD), fetuin A (RRR = 1.1 for HF), and visfatin (RRR = 30.4 and 23.1 for CAD and HF, respectively). Prognostic analysis by multiple logistic regression identified visfatin (odds ratio = 6.5) and high-sensitivity C-reactive protein (odds ratio = 1.9) as significant predictors of hospital readmission for patients with CAD and HF, respectively.

The study underscores the promising role of selected biomarkers in the diagnosis and prognosis of CAD and HF diseases. These findings suggest the integration of biomarker profiling into clinical protocols to enhance patient care and outcomes in cardiovascular disease, especially in those treated with empagliflozin. Even if these indicators have potential, further larger size, long-term research is required to confirm their use in clinical settings.

gov identifier: NCT05911724.

Cardiac and non-cardiac comorbidities are highly prevalent in patients with heart failure (HF). The aim of the present study was to describe their selective impact on in-hospital outcomes (length of hospitalization and mortality) of HF patients hospitalized in an Internal Medicine Unit.

Between January 2017 and December 2022, 12,435 (6146 F, 6289 M) inpatients were hospitalized in our Internal Medicine Unit. HF was defined according to the International Statistical Classification of Diseases and Related Health Problems (ICD) version 9 codes 428, 402.01, 402.11, 402.91, 404.11, 404.13, 404.91, 404.93. Patients were classified by burden of overall, cardiac, and of non-cardiac comorbidities (0, 1, 2, 3 +). Multivariable regression models were used to assess associations between comorbidity burden and length of stay (linear regression) or in-hospital mortality (logistic regression).

HF patients (1481, or 11.9% of all hospitalizations during the observation period) had on average comorbidity count of 1.6. An increasing number of comorbidities was associated with longer duration of hospitalization and mortality. Non-cardiac, but not cardiac, comorbidities were associated with significantly higher length of stay (beta coefficient 2.86 ± 0.27) and in-hospital mortality (OR 1.90, 95% confidence interval (CI) 1.60-2.23; p < 0.0001).

Cardiac and non-cardiac comorbidities differentially impact on in-hospital outcomes of older HF patients hospitalized in an Internal Medicine unit. Their more precise management will allow a reduction of avoidable hospitalization in HF patients.

Heart failure self-care can contribute to a high daily workload and treatment burden. The goal of this cohort study was to assess the characteristics and outcomes associated with burden of treatment (BoT).

Surveys comprising validated instruments to measure BoT and other constructs were mailed to patients with heart failure in Southeastern Minnesota. Participants were divided into tertiles by BoT scores. Associations of clinical variables with BoT were examined using multinomial logistic regression. Associations of BoT with mortality and hospitalizations were examined using Cox proportional hazard regression and Andersen-Gill models, respectively.

A total of 609 participants (mean age 76.3 years, 60.3% men, 55.2% urban, 64.3% preserved ejection fraction) completed surveys. Higher BoT was associated with worse health status, more depressive symptoms, lower resilience, less social support, lower medication adherence, and worse health literacy. Mean±SD follow-up was 14.4 (4.1) months. Estimated 1-year mortality (8.3% [95% CI, 4.3%-12.1%], 11.0% [95% CI, 6.5%-15.2%], 16.0% [95% CI, 10.8%-21.0%]) and 1-year mean cumulative hospitalizations (0.57 [95% CI, 0.45-0.72], 0.83 [95% CI, 0.66-1.05], 1.15 [95% CI, 0.93-1.42]) increased across patients reporting low, medium, and high BoT, respectively. Adjustment for health status eliminated any significant association of BoT with risks of death and hospitalization (adjusted hazard ratio [HR], 1.10 [95% CI, 0.58-2.07] and 1.09 [95% CI, 0.74-1.61], respectively, highest versus lowest BoT tertile).

BoT in heart failure varies by clinical and psychosocial factors. Higher BoT identifies patients at increased risk of adverse health outcomes due to their worse health status. These findings can serve as a foundation for interventions to minimize workload and improve quality of life.

Cardiovascular diseases are the leading cause of mortality in the United States, presenting significant public health challenges and financial burdens, particularly in Southeastern Virginia, where African American and Hispanic (AA&H) populations are disproportionately affected.

This retrospective observational study analyzed data from 30,855 hospital discharges of AA&H patients across Southeastern Virginia from 2016 to 2020, focusing on individuals aged 18 to 85 with cardiovascular diseases. Utilizing the Virginia Health Information database, we examined demographic information, clinical data, and healthcare utilization patterns through hypothesis tests and regression models to explore associations between these variables and the economic impacts of cardiovascular diseases.

Heart failure and shock (47.2% of discharges) and cardiac arrhythmia and conduction disorders (12.3%) were the most prevalent cardiovascular conditions. Female patients incurred significantly higher charges than males across conditions (7.1% higher in heart failure, p < 0.0001; 8.8% higher in chest pain, p < 0.01). Younger patients (< 65 years) faced 8.5% higher charges for cardiac arrhythmia with procedures (p < 0.0001) and 5.2% higher charges for circulatory disorders (p < 0.05). Year of discharge consistently predicted increasing costs (standardized coefficient 0.816 for acute myocardial infarction, p < 0.0001). The presence of fluid and electrolyte disorders was associated with significantly higher charges across conditions (standardized coefficient 0.042 for heart failure, p < 0.0001; 0.051 for acute myocardial infarction, p < 0.0001).

The findings highlight the complex interplay between demographic characteristics and healthcare costs among AA&H populations, underscoring the need for targeted interventions. The significant economic impact observed calls for culturally competent healthcare strategies that can mitigate high costs and improve health outcomes. However, the retrospective, administrative nature of the data limits establishing causality, with potential misclassification of some conditions.

This study provides crucial insights into cardiovascular disease management's demographic and economic dimensions among AA&H populations in Southeastern Virginia. By identifying key factors contributing to healthcare disparities, the research supports the development of tailored interventions aimed at reducing the burden of cardiovascular diseases, thereby improving overall health equity and reducing economic strains on the healthcare system.

Early identification and management of worsening heart failure (HF) is necessary to prevent disease progression and hospitalizations. The ALLEVIATE-HF (Algorithm Using LINQ Sensors for Evaluation and Treatment of Heart Failure) trial is a prospective, randomized, controlled, double-blind, multicentre trial that aims to assess the safety and efficacy of using the Reveal LINQ™ insertable cardiac monitor (ICM) in patients with HF to continuously monitor and evaluate HF risk status and guide timely interventions.

The ICM algorithm uses parameters derived from electrocardiogram (atrial fibrillation [AF], ventricular rate during AF, heart rate variability, and night heart rate), three-axis accelerometer (patient activity duration), and subcutaneous bioimpedance (fluid volume, respiration rate). The trial will enroll ~760 patients with New York Heart Association class II or III HF with recent hospitalization for HF or needing intravenous diuretics in the outpatient setting or elevated natriuretic peptide levels, who do not have an implanted cardiac implantable electronic device or haemodynamic monitor. Patients are randomized to an observation or an intervention arm, where the latter will receive an intervention pathway with remote nurses implementing individualized pro re nata (PRN or 'as needed') 4-day medication interventions for acute volume management upon high risk. After 13 months of randomized follow-up, all patients enter an unblinded prolonged follow-up phase with PRN interventions upon high risk. The primary hierarchical composite endpoint for the study includes cardiovascular death, HF events, Kansas City Cardiomyopathy Questionnaire score, and 6-min walk test distance.

ALLEVIATE-HF will evaluate how ICM-based HF management can impact the outcomes of patients with HF regardless of ejection fraction.

Telemedicine interventions (TMIs) for heart failure (HF) can reduce hospitalizations and deaths. It is unclear if low literacy and limited access to technology in low- and middle-income countries affect these benefits. We evaluated whether TMIs added to usual care could reduce HF-related rehospitalizations in patients discharged from hospitals in Brazil.

A randomized clinical trial was conducted in 6 public hospitals from September 2021 to June 2022. Patients hospitalized because of HF were randomized to usual care or a multicomponent TMIs. The TMI included weekly nurse-led structured telephone support to monitor weight, blood pressure, heart rate, decompensation signs, and treatment adherence, while promoting self-care education, including diuretic dose adjustments. The nurse was linked to a cardiologist for teleconsultations, according to predefined decision trees. An educational program via text messages was also provided. The primary outcome was HF-related rehospitalizations at 180 days, analyzed by intention-to-treat analysis.

Of 127 randomized patients (TMI, n=70; usual care, n=57), mean±SD age was 64±11 years, 48% were women, 71% were Black race, 33% had <4 years of education, 65% were New York Heart Association class III/IV, and 68% had reduced ejection fraction (≤50%). At 180 days, 26% of the TMI group had HF-related rehospitalizations versus 46% in usual care (relative risk [RR]=0.56, P<0.02). All-cause death or rehospitalizations occurred in 30% of the TMI group versus 47% in usual care (RR=0.63, P=0.04). Results were consistent in "per-protocol" and subgroup analyses. Enrollment was lower than expected because of COVID-19 disruptions.

TMI reduced HF-related rehospitalizations, demonstrating its potential to improve clinical outcomes in this population.

URL: https://www.ensaiosclinicos.gov.br/rg/RBR-10znr9xn; Unique Identifier: UTN U1111-1263-9802.

Lung ultrasound (LUS) and the ACEF score (age, creatinine, and ejection fraction) have been shown to be pivotal in predicting an unfavorable prognosis in acute myocardial infarction (AMI).

The aim of this study is to investigate the prognostic value of LUS combined with ACEF score in AMI.

The ACEF score and the total number of B-lines in eight thoracic regions of LUS were calculated. Adverse events were recorded during hospitalization and follow-up, defined as all-cause death and other cardiovascular events. Multivariate logistic regression identified predictors of adverse events during hospitalization. Multivariate Cox regression identified predictors of adverse events during follow-up.

We enrolled 204 patients. The B-lines (adjusted OR 1.08, [95% CI: 1.03-1.13], p < 0.01) and the ACEF score (adjusted OR 2.71 [95% CI: 1.07-6.81], p < 0.05) independently predicted adverse events during hospitalization. The C-index values were 0.81 (p < 0.01) for the ACEF score, 0.81 (p < 0.01) for LUS, and 0.86 (p < 0.01) for their combination. One hundred seventy-one patients were followed up for 12 months (IQR, 8.13-15.93). Both the B-lines (adjusted HR 1.06 [95% CI: 1.03-1.09], p < 0.05) and the ACEF score (adjusted HR 1.95 [95% CI: 1.10-3.43], p < 0.05) remained associated with an increased risk of adverse events during follow-up. The C-index values were 0.74 (p < 0.01) for the ACEF score, 0.73 (p < 0.01) for LUS, and 0.80 (p < 0.01) for their combined predictive ability.

The B-lines and ACEF score are associated with adverse events in AMI patients. When combined, they provide increasing value in assessing the risk of adverse events, which has significant implications for risk stratification.

BackgroundHospitalized patients with blood cancer face an elevated risk for cardiovascular diseases caused by cardiotoxic cancer therapies, which can lead to cardiovascular-related unplanned readmissions.ObjectiveWe aimed to develop a machine learning (ML) model to predict 90-day unplanned readmissions for major adverse cardiovascular events (MACE) in hospitalized patients with blood cancers.DesignA retrospective population-based cohort study.MethodsWe analyzed patients aged ≥18 with blood cancers (leukemia, lymphoma, myeloma) using the Nationwide Readmissions Database. MACE included acute myocardial infarction, ischemic heart disease, stroke, heart failure, revascularization, malignant arrhythmias, and cardiovascular-related death. Six ML algorithms (L2-Logistic regression, Support Vector Machine, Complement Naïve Bayes, Random Forest, XGBoost, and CatBoost) were trained on 2017-2018 data and tested on 2019 data. The SuperLearner algorithm was used for stacking models. Cost-sensitive learning addressed data imbalance, and hyperparameters were tuned using 5-fold cross-validation with Optuna framework. Performance metrics included the Area Under the Receiver Operating Characteristics Curve (ROCAUC), Precision-Recall AUC (PRAUC), balanced Brier score, and F2 score. SHapley Additive exPlanations (SHAP) values assessed feature importance, and clustering analysis identified high-risk subpopulations.ResultsAmong 76 957 patients, 1031 (1.34%) experienced unplanned 90-day MACE-related readmissions. CatBoost achieved the highest ROCAUC (0.737, 95% CI: 0.712-0.763) and PRAUC (0.040, 95% CI: 0.033-0.050). The SuperLearner algorithm achieved slight improvements in most performance metrics. Four leading predictive features were consistently identified across algorithms, including older age, heart failure, coronary atherosclerosis, and cardiac dysrhythmias. Twenty-three clusters were determined with the highest-risk cluster (mean log odds of 1.41) identified by nonrheumatic/unspecified valve disorders, coronary atherosclerosis, and heart failure.ConclusionsOur ML model effectively predicts MACE-related readmissions in hospitalized patients with blood cancers, highlighting key predictors. Targeted discharge strategies may help reduce readmissions and alleviate the associated healthcare burden.

Patients with heart failure (HF) perform a variety of self-care activities to control symptoms and minimise the risk of HF decompensations. The objective of this study was to understand how patients build capacity and manage the work of living with HF.

A qualitative study using semi-structured telephone interviews. The interview guide was informed by the Cumulative Complexity Model, a conceptual framework that focuses on a patient's workload and their capacity to manage that work. Interview transcripts were analysed using a mixed inductive and deductive coding approach with organisation into larger thematic categories.

Southeastern Minnesota USA (11 counties) with capture of data from local community healthcare providers under the auspices of the Rochester Epidemiology Project.

Intentional sampling of local patients with HF (n=24, median age 69.5 years, 54% women, 63% rural, 54% preserved ejection fraction) who reported high treatment burden and/ or poor health status on a questionnaire.

Three major themes emerged: using capacity to manage workload, disruptions resulting in workload exceeding capacity and regaining workload-capacity balance. Participants described routinising the daily tasks associated with living with HF to minimise the associated burden and identified disruptions to their routines, including hospitalisations, emergency room visits, worsening health status and changes in healthcare access. To accommodate disruptions and regain workload-capacity balance, participants decreased workload and/or transferred tasks to others to maximise capacity.

Participants with HF described managing patient workload in times of stable health, but they sometimes struggled to accommodate disruptions and worsening health status. These findings can inform the design of interventions to minimise workload, maximise capacity and improve quality of life for patients with HF.

There is an unmet need for early detection of heart failure decompensation, allowing patients to be managed remotely and avoid hospitalization.

The purpose of this study was to compare a strategy utilizing data from a wearable HF sensor for management following a HF hospitalization to usual care.

Eligible subjects were discharged from the hospital within the previous 10 days and had a HF event in the previous 6 months. The concurrent control study was divided into 2 arms; a control arm, BMAD-HF and an open-label intervention arm, BMAD-TX. The HFMS (Heart Failure Monitoring System) was worn by subjects for up to 90 days. Device data was blinded to investigators and subjects in the BMAD-HF control arm but provided proactively in the BMAD-TX intervention arm. The impact of HF management with the HFMS was evaluated by Kaplan-Meier analysis of time to first HF hospitalization.

A total of 522 subjects were enrolled in the study at 93 sites. A total of 245 subjects in BMAD-HF and 249 in BMAD-TX were eligible for intention-to-treat analysis. There were 276 hospitalizations in 189 subjects at 90 days, of which 108 events were determined to be heart failure related in 82 subjects. The subjects in the arm managed using HFMS data to direct HF therapy had a 38% lower HF hospitalization rate during the 90 days following a HF hospitalization compared to subjects in the control arm (HR: 0.62; P = 0.03).

In patients with a recent HF hospitalization, a strategy of using HFMS data for HF management is associated with a 38% relative risk reduction in 90-day HF rehospitalization. (Benefits of Microcor in Ambulatory Decompensated Heart Failure [BMAD-TX]; NCT04096040; Benefits of Microcor in Ambulatory Decompensated Heart Failure [BMAD-HF]; NCT03476187).

The prognostic value of the uric acid to albumin ratio (UAR) in heart failure (HF) remains underexplored. The objective of this research was to investigate the link between UAR and short-term outcomes in Chinese HF patients.

We analyzed data from 1893 HF patients, out of an initial cohort of 2008, who had available UAR measurements. The skewed distribution of UAR data was addressed by applying a Log-10 (lg) transformation and stratifying patients into three groups accordingly (low to high). The final outcome was identified as mortality or hospital readmission within 28 days. We employed restricted cubic spline analysis (RCS), Kaplan-Meier survival curves, and Cox proportional hazards models to evaluate the link between UAR and short-term outcomes.

Among 1893 patients with HF [≥ 70 years, 1,382 (73.0%); female, 1,100 (58.1%)], the incidence of 28-day outcome was 8.6%. The RCS analysis suggested a positive relationship between lg(UAR) and 28-day outcomes, with no evidence of nonlinearity (p = 0.008). The cumulative incidence of 28-day readmission/death indicated that patients in the tertile 3 faced a significantly elevated risk of adverse outcomes (p < 0.001). Cox proportional hazards models showed that an elevated UAR was associated with a greater likelihood of 28-day mortality or hospital readmission (HR = 2.433, 95% CI: 1.638-3.615, p < 0.001). Even after accounting for possible confounding variables, the result still existed (HR = 1.594, 95% CI: 1.032-2.462, p = 0.036). Moreover, the associations were consistent in various subgroups, and sensitivity analysis (all p > 0.05).

Increased UAR correlates with a heightened risk of short-term death or hospital readmission in Chinese individuals suffering from HF. Maintaining a relatively lower UAR could potentially improve the clinical prognosis for these patients.

Trajectories of mortality after primary prevention implantable cardioverter-defibrillator (ICD) placement for older patients with heart failure during or soon after acute hospitalization have not been assessed.

The purpose of this study was to compare trajectories of mortality after primary prevention ICD placement during or soon after acute cardiac or non-cardiac hospitalization.

We identified older patients with heart failure undergoing primary prevention ICD placement using 20% Medicare data (2008-2018). Placement settings were as follows: (1) Current-H-during current hospitalization, (2) Recent-H-within 90 days of hospitalization, or (3) Chronic stable. Hospitalization was categorized as cardiac vs non-cardiac. Interval mortality rates and hazard ratios (HRs) using Cox regression were estimated at 0-30, 31-90, and 91-365 days after ICD placement.

Of the 61,710 patients (mean age 76 years; 35% female; 85% white), 19% (11,947), 25% (15,147), and 56% (34,616) had ICDs in Current-H, Recent-H, and Chronic stable settings. Mortality rates (per 100 person-years) were highest during 0-30 days, with 38 (34-42) and 22 (19-24) for Current-H and Recent-H, which declined to 21 (20-22) and 16 (15-17) during 91-365 days, respectively. Compared to Chronic stable, HRs were highest during 0-30 days post-ICD placement (5.5 [4.5-6.8] for Current-H and 3.4 [2.8-4.2] for Recent-H) and decreased during 91-365 days (2.0 [1.8-2.1] for Current-H and 1.6 [1.5-1.7] for Recent-H). HR pattens were similar for cardiac and non-cardiac hospitalizations.

Primary prevention ICD placement during or soon after hospitalization for any reason was associated with worse mortality with diminishing risks after 90 days. Hospitalization likely identifies a sicker population in whom early mortality with or without ICD may be higher. Our results support careful consideration regarding ICD placement during the 90 days after hospitalization.

Outpatient follow-up after a hospital discharge may reduce the risk of readmissions, but existing evidence has methodological limitations.

To assess effect of outpatient follow-up within 7, 14, 21 and 30 days of a hospital discharge on 30-day unplanned readmissions or mortality among heart failure (HF) patients; and whether this varies for patients with different clinical complexities.

We analyzed medical records between January 2016 and December 2021 from a prospective cohort study. Using time varying mixed effects parametric survival models, we examined the association between not having an outpatient follow-up and risk of adverse events. We used interaction models to assess if the effect of outpatient follow-up visit on outcomes varies with patients' clinical complexity (comorbidities, grip strength, cognitive impairment and length of inpatient stay).

Two hundred and forty-one patients with advanced HF.

30-day all-cause (or cardiac) adverse event defined as all cause (or cardiac) unplanned readmissions or death within 30 days of an unplanned all-cause (or cardiac) admission or emergency department visit.

We analyzed 1595 all-cause admissions, inclusive of 1266 cardiac admissions. Not having an outpatient follow-up (vs having an outpatient follow-up) significantly increased the risk of 30-day all-cause adverse event. (risk [95% CI] - 14 days: 35.1 [84.5,-1.1]; 21 days: 43.9 [48.2,6.7]; 30 days: 31.1 [48.5, 7.9]) The risk (at 21 days) was higher for those with one co-morbidity (0.25 [0.11,0.58]), mild (0.67 [0.45, 1.00]) and moderate cognitive impairment (0.38 [0.17, 0.84]), normal grip strength (0.57 [0.34, 0.96]) and length of inpatient stay 7-13 days (0.45 [0.23, 0.89]).

Outpatient follow-up within 30 days after a hospital discharge reduced risk of 30-day adverse events among HF patients, the benefit varying according to clinical complexity. Results suggest the need to prioritize patients who benefit from outpatient follow-up for these visits.

Heart failure is a leading cause of death in the USA, contributing to high expenditures near the end of life. Evidence remains lacking on whether billed advance care planning changes patterns of end-of-life healthcare utilization among patients with heart failure. Large-scale claims evaluation assessing billed advance care planning and end-of-life hospitalizations among patients with heart failure can fill evidence gaps to inform health policy and clinical practice.

Assess the association between billed advance care planning delivered and Medicare beneficiaries with heart failure upon the type and quantity of healthcare utilization in the last 30 days of life.

This retrospective cross-sectional cohort study used Medicare fee-for-service claims from 2016 to 2020.

A total of 48,466 deceased patients diagnosed with heart failure on Medicare.

Billed advance care planning services between the last 12 months and last 30 days of life will serve as the exposure. The outcomes are end-of-life healthcare utilization and total expenditure in inpatient, outpatient, hospice, skilled nursing facility, and home healthcare services.

In the final cohort of 48,466 patients (median [IQR] age, 83 [76-89] years; 24,838 [51.2%] women; median [IQR] Charlson Comorbidity Index score, 4 [2-5]), 4406 patients had an advance care planning encounter. Total end-of-life expenditure among patients with billed advance care planning encounters was 19% lower (95% CI, 0.77-0.84) compared to patients without. Patients with billed advance care planning encounters had 2.65 times higher odds (95% CI, 2.47-2.83) of end-of-life outpatient utilization with a 33% higher expected total outpatient expenditure (95% CI, 1.24-1.42) compared with patients without a billed advance care planning encounter.

Billed advance care planning delivery to individuals with heart failure occurs infrequently. Prioritizing billed advance care planning delivery to these individuals may reduce total end-of-life expenditures and end-of-life inpatient expenditures through promoting use of outpatient end-of-life services, including home healthcare and hospice.

Acute heart failure (AHF) episodes are marked by high rates of morbidity and mortality during the episode and minimal advancements in its care. Multiple biomarker monitoring is now a crucial supplementary technique in the therapy of AHF. A scientific literature search was conducted by assessing and evaluating the most pertinent research that has been published, including original papers and review papers with the use of PubMed, Medline, and Cochrane databases. Established biomarkers like natriuretic peptides (BNP, NT-proBNP) and cardiac troponins play crucial roles in diagnostic and prognostic evaluation. Emerging biomarkers such as microRNAs, osteopontin, galectin-3, ST2, and GDF-15 show promise in enhancing risk stratification and predicting adverse outcomes in HF. However, while these biomarkers offer valuable insights, their clinical utility requires further validation and integration into practice. Continued research into novel biomarkers holds promise for early HF detection and risk assessment, potentially mitigating the global burden of HF. Understanding the nuances of biomarker utilization is crucial for their effective incorporation into clinical practice, ultimately improving HF management and patient care.

We sought to examine outcomes of ultrafiltration in real world community-based hospital settings.

Ultrafiltration (UF) is an accepted therapeutic option for advanced decompensated heart failure (ADHF). the feasibility of UF in a community hospital setting, by general cardiologists in a start-up program had not been objectively evaluated.

We retrospectively analyzed the first-year cohort of ADHF patients treated with UF from 10/1/2019 to 10/1/2020, which totaled 30 patients, utilizing the CHF Solutions Aquadex FlexFlow™ System with active UF rate titration.

Baseline patient characteristics were similar to RCTs: mean age 63, 73 % male; 27 % female; 53 % Caucasian; 47 % African American; 77 % had LVEF ≤ 40. The baseline mean serum creatinine (Cr) was 1.84 ±0.62 mg/dL, mean GFR of 36.95 ±9.60 ml/min. HF re-admission rates were not significantly different than prior studies (17.2 % at 30 d, 23.3 % at 60 d, but in our cohort, per patient HF re-admission rates were reduced significantly by 60 d (0.30 p = 0.017).

Our analysis showed success with UF in mainstream setting with reproducible results of significant volume loss without adverse renal effect, mitigation of recurrent Hdmissions, and remarkable subjective clinical benefit.

Heart failure (HF) is a pervasive global health concern, with acute decompensated heart failure (ADHF) contributing significantly to morbidity and mortality. Medications used in patients with HF may exacerbate HF or prolong the QT interval, posing additional risks.

The objective is to assess the prevalence and utilization patterns of medications known to cause or exacerbate HF and prolong the QT interval among patients with ADHF. Understanding these patterns is crucial for optimizing patient care and minimizing potential risks.

A retrospective chart review was conducted at Huntsville Hospital, Huntsville, USA, covering 602 patients with ADHF over a 40-month period. Inclusion criteria involved age ≥ 18 years, a history of HF, and ADHF admission. The 2016 American Heart Association Scientific Statement was used to identify drugs that may cause or exacerbate HF and those that could prolong the QT interval RESULTS: Among the 602 patients, 57.3% received medications causing or exacerbating HF, notably albuterol (34.9%) and diabetes medications (20.4%), primarily metformin, followed by urologic agents (14.3%), mostly tamsulosin, and nonsteroidal anti-inflammatory drugs (NSAIDs) (6.1%). Moreover, 82.9% were on medications prolonging the QT interval, with loop diuretics, amiodarone, ondansetron, and famotidine most prevalent. Furthermore, 42.1% of the patients received more than two concomitant medications that prolong the QT interval, which can further exacerbate the risk of torsades de pointes.

This study underscores the high prevalence of HF-causing or HF-exacerbating medications and QT-prolonging drugs in patients with ADHF. Healthcare professionals must be cognizant of these patterns, advocating for safer prescribing practices to optimize patient outcomes and reduce the burden of HF-related hospitalizations.

Nitroxyl (HNO), the single-electron reduction product of nitric oxide (NO), has attracted great interest in the treatment of congestive heart failure in clinical trials. In this paper, we describe the first coumarin-based compound N-hydroxy-2-oxo-2H-chromene-6-sulfonamide (CD1) as a dualfunctional HNO donor, which can release both an HNO signaling molecule and a fluorescent reporter. Under physiological conditions (pH 7.4 and 37 °C), the CD1 HNO donor can readily decompose with a half-life of ∼90 min. The corresponding stoichiometry HNO from the CD1 donor was confirmed using both Vitamin B12 and phosphine compound traps. In addition to HNO releasing, specifically, the degradation product 2-oxo-2H-chromene-6-sulfinate (CS1) was generated as a fluorescent marker during the decomposition. Therefore, the HNO amount released in situ can be accurately monitored through fluorescence generation. As compared to the CD1 donor, the fluorescence intensity increased by about 4.9-fold. The concentration limit of detection of HNO releasing was determined to be ∼0.13 μM according to the fluorescence generation of CS1 at physiological conditions. Moreover, the bioimaging of the CD1 donor was demonstrated in the cell culture of HeLa cells, where the intracellular fluorescence signals were observed, inferring the site of HNO release. Finally, we anticipate that this novel coumarin-based CD1 donor opens a new platform for exploring the biology of HNO.

This study aims to explore the duration and influencing factors of care-seeking delay among patients with heart failure (HF) in China.

A convergent mixed method containing a cross-sectional study and two parts of qualitative studies was designed, following the STROBE and COREQ guidelines. Convenience sampling was applied to recruit patients with HF from two general hospitals from December 2021 to December 2022. Purposive sampling was used to enrol healthcare professionals from two general hospitals and two community hospitals from June to November 2022. Among the 258 patients with HF in the cross-sectional study, the median duration of care-seeking delay was 7.5 days. The result integration indicated that the delay duration was influenced by the dyspnoea symptom burden, the oedema symptom burden, and the depression status. The lower dyspnoea symptom burden, the higher oedema symptom burden, and the higher depression score were related to the prolonged care-seeking delay duration. The duration was also affected by the COVID-19 pandemic, level of support from medical system, and the symptom management abilities of the caregivers. The COVID-19 pandemic, low level of support from medical system, and limited symptom management abilities of caregivers were related to the prolonged care-seeking delay duration.

Care-seeking delay among patients with HF needs attention in China. The duration of care-seeking delay of patients with HF was influenced by the dyspnoea symptom burden, the oedema symptom burden, and depression status, as well as the COVID-19 pandemic, level of support from medical system, and the symptom management abilities of the caregivers.

Mounting evidence indicates that even device-detected subclinical atrial fibrillation is associated with a higher risk of heart failure (HF). However, the potential impact of atrial fibrillation screening on HF remains unknown.

The LOOP Study (Atrial Fibrillation detected by Continuous ECG Monitoring using Implantable Loop Recorder to prevent Stroke in High-risk Individuals) evaluated the effects of atrial fibrillation screening on stroke prevention using an implantable loop recorder (ILR) versus usual care in older individuals with additional stroke risk factors. In this secondary analysis, we explored the following HF end points: (1) HF event or cardiovascular death; (2) HF event; (3) event with HF with reduced ejection fraction (HFrEF); and (4) HFrEF event or cardiovascular death. Outcomes were assessed in a Cox model both as time-to-first events and as total (first and recurrent) events analyzed using the Andersen-and-Gill method.

Of 6004 participants (mean age 74.7 and 52.7% men), 1501 were randomized to ILR screening and 4503 to the control group. In total, 77 (5.1%) in the ILR group versus 295 (6.6%) in the control group experienced the primary outcome of an HF event or cardiovascular death. Compared with usual care, ILR screening was associated with a nonsignificant reduction in the primary outcome for the time-to-first event analysis (hazard ratio, 0.78 [95% CI, 0.61-1.01]) and the total event analysis (hazard ratio, 0.77 [95% CI, 0.59-1.01]). Similar results were obtained for the HF event. A significant risk reduction in total events was observed in the ILR group for the composite of HFrEF event or cardiovascular death and for HFrEF event (hazard ratio, 0.74 [95% CI, 0.56-0.98] and 0.65 [95% CI, 0.44-0.97], respectively).

In an older population with additional stroke risk factors, ILR screening for atrial fibrillation tended to be associated with a lower rate of total HF events and cardiovascular death, particularly those related to HFrEF. These findings should be considered hypothesis-generating and warrant further investigation.

URL: https://www.clinicaltrials.gov; Unique identifier: NCT02036450.

Heart failure (HF) is a leading cause of hospitalization and mortality worldwide and places a great economic burden on healthcare systems. Identification of prognostic factors in HF patients is of great importance to establish optimal management strategies and to avoid unnecessary invasive and costly procedures in end-stage patients.

In the current study, we aimed to investigate the association between diastolic strain parameters including E/e' SR, and short-term outcomes in advanced HF patients.

The population study included 116 advanced HF with reduced ejection fraction (HFrEF) patients. Clinical, laboratory, and echocardiographic evaluations of the patients were performed within the first 24 hours of hospital admission. Patients were followed for one month and any re-hospitalization due to worsening of HF symptoms and any mortality was recorded. The level of significance adopted in the statistical analysis was 5%.

E/e' SR was significantly higher in the patient group compared to the control group (p=0.001). During one-month follow-up, 13.8% of patients died and 37.1% of patients were rehospitalized. Serum NT-ProBNP (p=0.034) and E/e' SR (p=0.033) were found to be independent predictors of mortality and ACEİ use (p=0.027) and apical 3C strain (p=0.011) were found to be independent predictors of rehospitalization in the patient group.

Findings of the current prospective study demonstrate that E/e' SR measured by speckle tracking echocardiography is an independent and sensitive predictor of short-term mortality in advanced HFrEF patients and may have a role in the identification of end-stage HFrEF patients.

Congestive heart failure (CHF) is a fatal disease with progressive severity and no cure; the heart's inability to adequately pump blood leads to fluid accumulation and frequent hospital readmissions after initial treatments. Therefore, it is imperative to continuously monitor CHF patients during its early stages to slow its progression and enable timely medical interventions for optimal treatment. An increase in interstitial fluid pressure (IFP) is indicative of acute CHF exacerbation, making IFP a viable biomarker for predicting upcoming CHF if continuously monitored. In this paper, we present an inductor-capacitor (LC) sensor for subcutaneous wireless and continuous IFP monitoring. The sensor is composed of inexpensive planar copper coils defined by a simple craft cutter, which serves as both the inductor and capacitor. Because of its sensing mechanism, the sensor does not require batteries and can wirelessly transmit pressure information. The sensor has a low-profile form factor for subcutaneous implantation and can communicate with a readout device through 4 layers of skin (12.7 mm thick in total). With a soft silicone rubber as the dielectric material between the copper coils, the sensor demonstrates an average sensitivity as high as -8.03 MHz/mmHg during in vitro simulations.

To investigate whether the timing of a previous hospital admission for acute heart failure (AHF) is a prognostic factor for AHF patients revisiting the emergency department (ED) in the subsequent 12-month follow-up. All ED AHF patients enrolled in the previously described EAHFE registry were stratified by the presence or absence of an AHF hospitalization admission in the prior 12 months. The primary outcome was 12-month all-cause mortality post ED visit. Secondary end points were hospital admission, prolonged hospitalization (> 7 days), mortality during hospitalization and a 90-day post-discharge adverse composite event (ACE) rate, defined as ED revisits due to AHF, hospitalizations due to AHF, or all-cause mortality. Outcomes were adjusted for baseline and AHF episode characteristics.Of 5,757 patients included, the median age was 84 years (IQR 77-88); 57% were women, and 3,759 (65.3%) had an AHF hospitalization in the previous 12 months. The 12-month mortality was 37% (41.7% vs. 28.3% p < 0.001), hospital admission was 76.1% (78.8% vs. 71.1% p < 0.001) ACE was 60.2% (65.1% vs. 50.5% p < 0.001). In the adjusted analysis, patients with AHF hospitalization in the prior 12 months had a higher mortality (HR = 1.41; 95% CI 1.27-1.56), 90-day ACE rate (HR = 1.45: 95% CI 1.32-1.59), and more hospital admissions (OR = 1.32; 95% CI 1.16-1.51), with shorter times since the previous hospitalization being related to the outcomes analyzed. One-year mortality, adverse events at 90 days, and readmission rates are increased in ED AHF patients previously admitted within the last 12 months.

Although previous risk models exist for advanced heart failure with reduced ejection fraction (HFrEF), few integrate invasive hemodynamics or support missing data. This study developed and validated a heart failure (HF) hemodynamic risk and phenotyping score for HFrEF, using Machine Learning (ML).

Prior to modeling, patients in training and validation HF cohorts were assigned to 1 of 5 risk categories based on the composite endpoint of death, left ventricular assist device (LVAD) implantation or transplantation (DeLvTx), and rehospitalization in 6 months of follow-up using unsupervised clustering. The goal of our novel interpretable ML modeling approach, which is robust to missing data, was to predict this risk category (1, 2, 3, 4, or 5) using either invasive hemodynamics alone or a rich and inclusive feature set that included noninvasive hemodynamics (all features). The models were trained using the ESCAPE trial and validated using 4 advanced HF patient cohorts collected from previous trials, then compared with traditional ML models. Prediction accuracy for each of these 5 categories was determined separately for each risk category to generate 5 areas under the curve (AUCs, or C-statistics) for belonging to risk category 1, 2, 3, 4, or 5, respectively.

Across all outcomes, our models performed well for predicting the risk category for each patient. Accuracies of 5 separate models predicting a patient's risk category ranged from 0.896 +/- 0.074 to 0.969 +/- 0.081 for the invasive hemodynamics feature set and 0.858 +/- 0.067 to 0.997 +/- 0.070 for the all features feature set.

Novel interpretable ML models predicted risk categories with a high degree of accuracy. This approach offers a new paradigm for risk stratification that differs from prediction of a binary outcome. Prospective clinical evaluation of this approach is indicated to determine utility for selecting the best treatment approach for patients based on risk and prognosis.

Left ventricular diastolic dysfunction (LVDD) has been observed in people with nonalcoholic fatty liver disease (NAFLD) in cross-sectional studies but the causal relationship is unclear. This study aimed to investigate the impact of NAFLD and the fibrotic progression of the disease on the development of LVDD, assessed by serial echocardiography, in a large population over a 7-year longitudinal setting.

This retrospective cohort study included the data of 3,380 subjects from a medical health check-up program. We defined subjects having NAFLD by abdominal ultrasonography and assessed significant liver fibrosis by the aspartate transaminase (AST) to platelet ratio index (APRI), the NAFLD fibrosis score (NFS), and the fibrosis-4 (FIB-4) index. LVDD was defined using serial echocardiography. A parametric Cox proportional hazards model was used.

During 11,327 person-years of follow-up, there were 560 (16.0 %) incident cases of LVDD. After adjustment for multiple risk factors, subjects with NAFLD showed an increased adjusted hazard ratio (aHR) of 1.21 (95 % confidence interval [CI]=1.02-1.43) for incident LVDD compared to those without. The risk of LV diastolic dysfunction increased progressively with increasing degree of hepatic steatosis (P< 0.001). Compared to subjects without NAFLD, the multivariable-aHR (95 % CI) for LVDD in subjects with APRI < 0.5 and APRI ≥ 0.5 were 1.20 (1.01-1.42) and 1.36 (0.90-2.06), respectively (P= 0.036), while other fibrosis prediction models (NFS and FIB-4 index) showed insignificant results.

This study demonstrated that NAFLD was associated with an increased risk of LVDD in a large cohort. More severe forms of hepatic steatosis and/or significant liver fibrosis may increase the risk of developing LVDD.

Digital health interventions (DHIs) have shown promising results in enhancing the management of heart failure (HF). Although health care interventions are increasingly being delivered digitally, with growing evidence on the potential cost-effectiveness of adopting them, there has been little effort to collate and synthesize the findings.

This study's objective was to systematically review the economic evaluations that assess the adoption of DHIs in the management and treatment of HF.

A systematic review was conducted using 3 electronic databases: PubMed, EBSCOhost, and Scopus. Articles reporting full economic evaluations of DHIs for patients with HF published up to July 2023 were eligible for inclusion. Study characteristics, design (both trial based and model based), input parameters, and main results were extracted from full-text articles. Data synthesis was conducted based on the technologies used for delivering DHIs in the management of patients with HF, and the findings were analyzed narratively. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed for this systematic review. The reporting quality of the included studies was evaluated using the CHEERS (Consolidated Health Economic Evaluation Reporting Standards) guidelines.

Overall, 27 economic evaluations were included in the review. The economic evaluations were based on models (13/27, 48%), trials (13/27, 48%), or a combination approach (1/27, 4%). The devices evaluated included noninvasive remote monitoring devices (eg, home telemonitoring using digital tablets or specific medical devices that enable transmission of physiological data), telephone support, mobile apps and wearables, remote monitoring follow-up in patients with implantable medical devices, and videoconferencing systems. Most of the studies (24/27, 89%) used cost-utility analysis. The majority of the studies (25/27, 93%) were conducted in high-income countries, particularly European countries (16/27, 59%) such as the United Kingdom and the Netherlands. Mobile apps and wearables, remote monitoring follow-up in patients with implantable medical devices, and videoconferencing systems yielded cost-effective results or even emerged as dominant strategies. However, conflicting results were observed, particularly in noninvasive remote monitoring devices and telephone support. In 15% (4/27) of the studies, these DHIs were found to be less costly and more effective than the comparators (ie, dominant), while 33% (9/27) reported them to be more costly but more effective with incremental cost-effectiveness ratios below the respective willingness-to-pay thresholds (ie, cost-effective). Furthermore, in 11% (3/27) of the studies, noninvasive remote monitoring devices and telephone support were either above the willingness-to-pay thresholds or more costly than, yet as effective as, the comparators (ie, not cost-effective). In terms of reporting quality, the studies were classified as good (20/27, 74%), moderate (6/27, 22%), or excellent (1/27, 4%).

Despite the conflicting results, the main findings indicated that, overall, DHIs were more cost-effective than non-DHI alternatives.

PROSPERO CRD42023388241; https://tinyurl.com/2p9axpmc.

This study aims to propose a semi-automatic method for monitoring the waiting times of follow-up examinations within the National Health System (NHS) in Italy, which is currently not possible to due the absence of the necessary structured information in the official databases.

A Natural Language Processing (NLP) based pipeline has been developed to extract the waiting time information from the text of referrals for follow-up examinations in the Lombardy Region. A manually annotated dataset of 10 000 referrals has been used to develop the pipeline and another manually annotated dataset of 10 000 referrals has been used to test its performance. Subsequently, the pipeline has been used to analyze all 12 million referrals prescribed in 2021 and performed by May 2022 in the Lombardy Region.

The NLP-based pipeline exhibited high precision (0.999) and recall (0.973) in identifying waiting time information from referrals' texts, with high accuracy in normalization (0.948-0.998). The overall reporting of timing indications in referrals' texts for follow-up examinations was low (2%), showing notable variations across medical disciplines and types of prescribing physicians. Among the referrals reporting waiting times, 16% experienced delays (average delay = 19 days, standard deviation = 34 days), with significant differences observed across medical disciplines and geographical areas.

The use of NLP proved to be a valuable tool for assessing waiting times in follow-up examinations, which are particularly critical for the NHS due to the significant impact of chronic diseases, where follow-up exams are pivotal. Health authorities can exploit this tool to monitor the quality of NHS services and optimize resource allocation.

Cardiovascular disease (CVD) is a serious public health concern whose incidence has been on a rise and is projected by the World Health Organization to be the leading global cause of mortality by 2030. Heart failure (HF) is a complicated syndrome resulting from various CVDs of heterogeneous etiologies and exhibits varying pathophysiology, including activation of inflammatory signaling cascade, apoptosis, fibrotic pathway, and neuro-humoral system, thereby leading to compromised cardiac function. During this process, several biomolecules involved in the onset and progression of HF are released into circulation. These circulating biomolecules could serve as unique biomarkers for the detection of subclinical changes and can be utilized for monitoring disease severity. Hence, it is imperative to identify these biomarkers to devise an early predictive strategy to stop the deterioration of cardiac function caused by these complex cellular events. Furthermore, measurement of multiple biomarkers allows clinicians to divide HF patients into sub-groups for treatment and management based on early health outcomes. The present article provides a comprehensive overview of current omics platform available for discovering biomarkers for HF management. Some of the existing and novel biomarkers for the early detection of HF with special reference to endothelial biology are also discussed.

Many patients with chronic heart failure (HF) experience a reduced health status, leading to readmission after hospitalization despite receiving conventional care. Telemonitoring approaches aim to improve the early detection of HF decompensations and prevent readmissions. However, knowledge about the impact of telemonitoring on preventing readmissions and related costs remains scarce.

This study assessed the effectiveness of adding a telemonitoring solution to the standard of care (SOC) for the prevention of hospitalization and related costs in patients with HF in Finland.

We performed a nonrandomized pre-post telemonitoring study to estimate health care costs and resource use during 6 months on SOC followed by 6 months on SOC with a novel telemonitoring solution. The telemonitoring solution consisted of a digital platform for patient-reported symptoms and daily weight and blood pressure measurements, automatically generated alerts triggering phone calls with secondary care nurses, and rapid response to alerts by treating physicians. Telemonitoring solution data were linked to patient register data on primary care, secondary care, and hospitalization. The patient register of the Southern Savonia Social and Health Care Authority (Essote) was used. Eligible patients had at least 1 hospital admission within the last 12 months and self-reported New York Heart Association class II-IV from the central hospital in the Southern Savonia region.

Out of 50 recruited patients with HF, 43 completed the study and were included in the analysis. The hospitalization-related cost decreased (49%; P=.03) from €2189 (95% CI €1384-€2994; a currency exchange rate of EUR €1=US $1.10589 is applicable) during SOC to €1114 (95% CI €425-€1803) during telemonitoring. The number of patients with at least 1 hospitalization due to HF was reduced by 70% (P=.002) from 20 (47%) out of 43patients during SOC to 6 (14%) out of 43 patients in telemonitoring. The estimated mean total health care cost per patient was €3124 (95% CI €2212-€4036) during SOC and €2104 (95% CI €1313-€2895) during telemonitoring, resulting in a 33% reduction (P=.07) in costs with telemonitoring.

The results suggest that the telemonitoring solution can reduce hospital-related costs for patients with HF with a recent hospital admission.

Heart failure (HF) is a chronic and progressive syndrome associated with a poor prognosis. While it may seem intuitive that the risk of adverse outcomes varies across the different stages of HF, an overview of these risks is lacking. This study aims to determine the risk of all-cause mortality and HF hospitalizations associated with new-onset HF, chronic HF (CHF), worsening HF (WHF), and advanced HF.

We performed a systematic review of observational studies from 2012 to 2022 using five different databases. The primary outcomes were 30-day and 1-year all-cause mortality, as well as 1-year HF hospitalization. Studies were pooled using random effects meta-analysis, and mixed-effects meta-regression was used to compare the different HF groups. Among the 15 759 studies screened, 66 were included representing 862 046 HF patients. Pooled 30-day mortality rates did not reveal a significant distinction between hospital-admitted patients, with rates of 10.13% for new-onset HF and 8.11% for WHF (p = 0.10). However, the 1-year mortality risk differed and increased stepwise from CHF to advanced HF, with a rate of 8.47% (95% confidence interval [CI] 7.24-9.89) for CHF, 21.15% (95% CI 17.78-24.95) for new-onset HF, 26.84% (95% CI 23.74-30.19) for WHF, and 29.74% (95% CI 24.15-36.10) for advanced HF. Readmission rates for HF at 1 year followed a similar trend.

Our meta-analysis of observational studies confirms the different risk for adverse outcomes across the distinct HF stages. Moreover, it emphasizes the negative prognostic value of WHF as the first progressive stage from CHF towards advanced HF.

Heart failure (HF) is a life-threatening syndrome. Timely and accurate bedside monitoring of the occurrence and progression of HF via measurements of multiple HF-related biomarkers remains a challenge. Here, we report a triple cascade quantum-strip (TCQS) sensing strategy for the rapid and selective multiplex-tracing of three clinically validated HF biomarkers (BNP/NT-proBNP/ST2) in serum. High selectivity to the three biomarkers is achieved by controlling the individual recognition ability of three target-specific quantum immunoprobes and tuning their simultaneous use to BNP/NT-proBNP/ST2 recognition without mutual interference, which allows the three biomarkers to be directly enriched from serum samples. Benefiting from the fast release-binding kinetics of target-bound immunoprobes on TCQS, recognizable fluorescent signals can be rapidly read out through combining with a self-designed smartphone-based portable reader. This rapid and simple profiling strategy results in good specificity and sensitivity with LODs of 0.097, 0.072, and 0.948 ng/mL for BNP, NT-proBNP, and ST2, respectively, which match the need of clinical applications. Real serum samples are tested with an accuracy of 92.86% for HF diagnosis, validating the capability of the smartphone-read TCQS for practical applications. In particular, the simultaneous detection of the TCQS sensing strategy for BNP/NT-proBNP/ST2 will facilitate the accurate monitoring of HF occurrence, risk stratification, progression, and prognosis as a powerful POCT tool.

Studies on readmission for pediatric heart failure (HF) patients is sparse.

This study evaluated 30- and 60-day readmission rates in pediatric HF patients from 2010 to 2019.

The authors used data from the Nationwide Readmission Database to evaluate trends in 30- and 60-day hospital readmissions among pediatric patients with HF and compare them with adults with HF. Readmissions were also stratified by sex, diagnosis, neighborhood income, and hospital volume.

There were 84,731 hospital admissions for HF. Compared with children without HF, those with HF were older, had Medicare/Medicaid insurance, and resided in micropolitan areas and low-income neighborhoods. The 30- (19.5% vs 3.1%) and 60-day (27.5% vs 4.3%) all-cause readmission rates were higher for children with HF compared with those without HF. Compared with children without HF, lengths of stay, deaths, and costs related to their readmission were higher for children readmitted with HF (P < 0.05 for all). There was no significant decline in pediatric HF-related 30- or 60- day readmissions during the study period overall, or for those with congenital heart disease (P > 0.05), unlike adult HF readmissions (P < 0.01). Infants were at highest risk, and readmission rates for teenagers are rising.

The 30- and 60-day readmission rates for pediatric patients with HF in the current era is high (∼20% and 30%, respectively). Unlike adult HF, pediatric HF readmission rates have not declined. Pediatric HF patients readmitted to the hospital have higher death rates and greater resource utilization than patients without HF. National measures to decrease readmissions for pediatric patients with HF is warranted.

Acute heart failure (AHF) carries a grave prognosis, marked by high readmission and mortality rates within 90 days post-discharge. This underscores the urgent need for enhanced care transitions, early monitoring, and precise interventions for at-risk individuals during this critical period.

Our study aims to develop and validate an interpretable machine learning (ML) model that integrates peripheral immune cell data with conventional clinical markers. Our goal is to accurately predict 90-day readmission or mortality in patients AHF.

In our study, we conducted a retrospective analysis on 1210 AHF patients, segregating them into training and external validation cohorts. Patients were categorized based on their 90-day outcomes post-discharge into groups of 'with readmission/mortality' and 'without readmission/mortality'. We developed various ML models using data from peripheral immune cells, traditional clinical indicators, or both, which were then internally validated. The feature importance of the most promising model was examined through the Shapley Additive Explanations (SHAP) method, culminating in external validation.

In our cohort of 1210 patients, 28.4% (344) faced readmission or mortality within 90 days post-discharge. Our study pinpointed 10 significant indicators-spanning peripheral immune cells and traditional clinical metrics-that predict these outcomes, with the support vector machine (SVM) model showing superior performance. SHAP analysis further distilled these predictors to five key determinants, including three clinical indicators and two immune cell types, essential for assessing 90-day readmission or mortality risks.

Our analysis identified the SVM model, which merges traditional clinical indicators and peripheral immune cells, as the most effective for predicting 90-day readmission or mortality in AHF patients. This innovative approach promises to refine risk assessment and enable more targeted interventions for at-risk individuals through continuous improvement.

Heart failure is associated with a rehospitalisation rate of up to 50% within six months. Elevated central venous pressure may serve as an early warning sign. While invasive procedures are used to measure central venous pressure for guiding treatment in hospital, this becomes impractical upon discharge. A non-invasive estimation technique exists, where the clinician visually inspects the pulsation of the jugular veins in the neck, but it is less reliable due to human limitations. Video and signal processing technologies may offer a high-fidelity alternative. This state-of-the-art review analyses existing literature on camera-based methods for jugular vein assessment. We summarize key design considerations and suggest avenues for future research. Our review highlights the neck as a rich imaging target beyond the jugular veins, capturing comprehensive cardiac signals, and outlines factors affecting signal quality and measurement accuracy. Addressing an often quoted limitation in the field, we also propose minimum reporting standards for future studies.

Difficulty in adherence to treatment and self-care behaviours is a leading cause of preventable readmission in people with chronic heart failure (CHF). Although there is evidence of benefits of health coaching for the management of this situation, few interventions have been tested in the hospital setting.

To evaluate a coaching programme (H-Coaching) designed to develop nursing capacity in health coaching for chronic heart failure inpatients.

A quasi-experimental pre-post study including all nurses in a single centre cardiology ward (N = 19). The intervention consisted of two training packages: (1) five theoretical-practical sessions on health-coaching competencies, emotional intelligence, communication and support of chronic heart failure patients in their illness in the hospital setting; and (2) training sessions seven months after the first training package to reinforce the theoretical and practical knowledge. On four occasions, the Competence Instrument of Health Education for the Nursing professional was used to measure nurses' knowledge, skills and attitudes in health coaching for chronic heart failure patients.