Applying the UTAUT2 framework to patients' attitudes toward healthcare task shifting with artificial intelligence.

BACKGROUND: Increasing patient loads, healthcare inflation and ageing population have put pressure on the healthcare system. Artificial intelligence and machine learning innovations can aid in task shifting to help healthcare systems remain efficient and cost effective. To gain an understanding of patients' acceptance toward such task shifting with the aid of AI, this study adapted the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), looking at performance and effort expectancy, facilitating conditions, social influence, hedonic motivation and behavioural intention. METHODS: This was a cross-sectional study which took place between September 2021 to June 2022 at the National Heart Centre, Singapore. One hundred patients, aged ≥ 21 years with at least one heart failure symptom (pedal oedema, New York Heart Association II-III effort limitation, orthopnoea, breathlessness), who presented to the cardiac imaging laboratory for physician-ordered clinical echocardiogram, underwent both echocardiogram by skilled sonographers and the experience of echocardiogram by a novice guided by AI technologies. They were then given a survey which looked at the above-mentioned constructs using the UTAUT2 framework. RESULTS: Significant, direct, and positive effects of all constructs on the behavioral intention of accepting the AI-novice combination were found. Facilitating conditions, hedonic motivation and performance expectancy were the top 3 constructs. The analysis of the moderating variables, age, gender and education levels, found no impact on behavioral intention. CONCLUSIONS: These results are important for stakeholders and changemakers such as policymakers, governments, physicians, and insurance companies, as they design adoption strategies to ensure successful patient engagement by focusing on factors affecting the facilitating conditions, hedonic motivation and performance expectancy for AI technologies used in healthcare task shifting.

Regional Differences in the Epidemiology of Heart Failure.

Heart failure (HF) epidemiology, patient characteristics, and clinical outcomes exhibit substantial regional variations, reflecting diverse etiologies and health system capacities. This review comprehensively analyses these variations, drawing on data from recent global registries and clinical trials. Our review indicates that ischemic and hypertensive heart diseases are prevalent globally but differ in dominance depending on the region. Notably, regions such as Africa and Latin America show higher instances of HF from hypertensive heart disease and Chagas cardiomyopathy, respectively. Moreover, disparities in age and comorbidity profiles across regions highlight younger populations with HF in lower-income countries compared to older populations in high-income regions. This review also highlights the global disparity in guideline-directed medical and device therapy, underscoring significant underuse in lower-income regions. These insights emphasize the need for targeted HF management strategies considering regional clinical and demographic characteristics to enhance global HF care and outcomes.

Navigating Hurdles: A Review of the Obstacles Facing the Development of the Pandemic Treaty.

INTRODUCTION: The emergence of the COVID-19 pandemic has served as a call for enhanced global cooperation and a more robust pandemic preparedness and response framework. As a result of this pressing demand, dialogues were initiated to establish a pandemic treaty designed to foster a synchronized global strategy for addressing forthcoming health emergencies. In this review, we discussed the main obstacles to this treaty. RESULTS: Among several challenges facing the pandemic treaty, we highlighted (1) global cooperation and political will, (2) equity in access to resources and treatments, (3) sustainable financing, (4) compliance and enforcement mechanisms, (5) sovereignty concerns, and (6) data sharing and transparency. CONCLUSION: Navigating the hurdles facing the development of the pandemic treaty requires concerted efforts, diplomatic finesse, and a shared commitment to global solidarity. Addressing challenges in global cooperation, equitable access, transparency, compliance, financing, and sovereignty is essential for forging a comprehensive and effective framework for pandemic preparedness and response on the global stage.

Nurse-led home-based detection of cardiac dysfunction by ultrasound: results of the CUMIN pilot study.

Aims: Access to echocardiography is a significant barrier to heart failure (HF) care in many low- and middle-income countries. In this study, we hypothesized that an artificial intelligence (AI)-enhanced point-of-care ultrasound (POCUS) device could enable the detection of cardiac dysfunction by nurses in Tunisia. Methods and results: This CUMIN study was a prospective feasibility pilot assessing the diagnostic accuracy of home-based AI-POCUS for HF conducted by novice nurses compared with conventional clinic-based transthoracic echocardiography (TTE). Seven nurses underwent a one-day training program in AI-POCUS. A total of 94 patients without a previous HF diagnosis received home-based AI-POCUS, POC N-terminal pro-B-type natriuretic peptide (NT-proBNP) testing, and clinic-based TTE. The primary outcome was the sensitivity of AI-POCUS in detecting a left ventricular ejection fraction (LVEF) <50% or left atrial volume index (LAVI) >34 mL/m2, using clinic-based TTE as the reference. Out of seven nurses, five achieved a minimum standard to participate in the study. Out of the 94 patients (60% women, median age 67), 16 (17%) had an LVEF < 50% or LAVI > 34 mL/m2. AI-POCUS provided an interpretable LVEF in 75 (80%) patients and LAVI in 64 (68%). The only significant predictor of an interpretable LVEF or LAVI proportion was the nurse operator. The sensitivity for the primary outcome was 92% [95% confidence interval (CI): 62-99] for AI-POCUS compared with 87% (95% CI: 60-98) for NT-proBNP > 125 pg/mL, with AI-POCUS having a significantly higher area under the curve (P = 0.040). Conclusion: The study demonstrated the feasibility of novice nurse-led home-based detection of cardiac dysfunction using AI-POCUS in HF patients, which could alleviate the burden on under-resourced healthcare systems.

Implementing focused echocardiography and AI-supported analysis in a population-based survey in Lesotho: implications for community-based cardiovascular disease care models.

In settings where access to expert echocardiography is limited, focused echocardiography, combined with artificial intelligence (AI)-supported analysis, may improve diagnosis and monitoring of left ventricular hypertrophy (LVH). Sixteen nurses/nurse-assistants without prior experience in echocardiography underwent a 2-day hands-on intensive training to learn how to assess parasternal long axis views (PLAX) using an inexpensive hand-held ultrasound device in Lesotho, Southern Africa. Loops were stored on a cloud-drive, analyzed using deep learning algorithms at the University Hospital Basel, and afterwards confirmed by a board-certified cardiologist. The nurses/nurse-assistants obtained 756 echocardiograms. Of the 754 uploaded image files, 628 (83.3%) were evaluable by deep learning algorithms. Of those, results of 514/628 (81.9%) were confirmed by a cardiologist. Of the 126 not evaluable by the AI algorithm, 46 (36.5%) were manually evaluable. Overall, 660 (87.5%) uploaded files were evaluable and confirmed. Following short-term training of nursing cadres, a high proportion of obtained PLAX was evaluable using AI-supported analysis. This could be a basis for AI- and telemedical support in hard-to-reach areas with minimal resources.

Treatment effects of empagliflozin in hospitalized heart failure patients across the range of left ventricular ejection fraction - Results from the EMPULSE trial.

AIM: The EMPULSE (EMPagliflozin in patients hospitalised with acUte heart faiLure who have been StabilizEd) trial showed that, compared to placebo, the sodium-glucose cotransporter 2 inhibitor empagliflozin (10 mg/day) improved clinical outcomes of patients hospitalized for acute heart failure (HF). We investigated whether efficacy and safety of empagliflozin were consistent across the spectrum of left ventricular ejection fraction (LVEF). METHODS AND RESULTS: A total of 530 patients hospitalized for acute de novo or decompensated HF were included irrespective of LVEF. For the present analysis, patients were classified as HF with reduced (HFrEF, LVEF ≤40%), mildly reduced (HFmrEF, LVEF 41-49%) or preserved (HFpEF, LVEF ≥50%) ejection fraction at baseline. The primary endpoint was a hierarchical outcome of death, worsening HF events (HFE) and quality of life over 90 days, assessed by the win ratio. Secondary endpoints included individual components of the primary endpoint and safety. Out of 523 patients with baseline data, 354 (67.7%) had HFrEF, 54 (10.3%) had HFmrEF and 115 (22.0%) had HFpEF. The clinical benefit (hierarchical composite of all-cause death, HFE and Kansas City Cardiomyopathy Questionnaire total symptom score) of empagliflozin at 90 days compared to placebo was consistent across LVEF categories (≤40%: win ratio 1.35 [95% confidence interval 1.04, 1.75]; 41-49%: win ratio 1.25 [0.66, 2.37)] and ≥50%: win ratio 1.40 [0.87, 2.23], pinteraction = 0.96) with a favourable safety profile. Results were consistent across individual components of the hierarchical primary endpoint. CONCLUSION: The clinical benefit of empagliflozin proved consistent across LVEF categories in the EMPULSE trial. These results support early in-hospital initiation of empagliflozin regardless of LVEF.

Point-of-care AI-enhanced novice echocardiography for screening heart failure (PANES-HF).

The increasing prevalence of heart failure (HF) in ageing populations drives demand for echocardiography (echo). There is a worldwide shortage of trained sonographers and long waiting times for expert echo. We hypothesised that artificial intelligence (AI)-enhanced point-of-care echo can enable HF screening by novices. The primary endpoint was the accuracy of AI-enhanced novice pathway in detecting reduced LV ejection fraction (LVEF) < 50%. Symptomatic patients with suspected HF (N = 100, mean age 61 ± 15 years, 56% men) were prospectively recruited. Novices with no prior echo experience underwent 2-weeks' training to acquire echo images with AI guidance using the EchoNous Kosmos handheld echo, with AI-automated reporting by Us2.ai (AI-enhanced novice pathway). All patients also had standard echo by trained sonographers interpreted by cardiologists (reference standard). LVEF < 50% by reference standard was present in 27 patients. AI-enhanced novice pathway yielded interpretable results in 96 patients and took a mean of 12 min 51 s per study. The area under the curve (AUC) of the AI novice pathway was 0.880 (95% CI 0.802, 0.958). The sensitivity, specificity, positive predictive and negative predictive values of the AI-enhanced novice pathway in detecting LVEF < 50% were 84.6%, 91.4%, 78.5% and 94.1% respectively. The median absolute deviation of the AI-novice pathway LVEF from the reference standard LVEF was 6.03%. AI-enhanced novice pathway holds potential to task shift echo beyond tertiary centres and improve the HF diagnostic workflow.

Artificial intelligence-assisted automated heart failure detection and classification from electronic health records.

AIMS: Electronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. We hypothesized that by applying keyword searches to routinely stored EHR, in conjunction with AI-powered automated reading of DICOM echocardiography images and analysing biomarkers from routinely stored plasma samples, we were able to identify heart failure (HF) patients. METHODS AND RESULTS: We used EHR data between 1993 and 2021 from Tayside and Fife (~20% of the Scottish population). We implemented a keyword search strategy complemented by filtering based on International Classification of Diseases (ICD) codes and prescription data to EHR data set. We then applied DL for the automated interpretation of echocardiographic DICOM images. These methods were then integrated with the analysis of routinely stored plasma samples to identify and categorize patients into HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and controls without HF. The final diagnosis was verified through a manual review of medical records, measured natriuretic peptides in stored blood samples, and by comparing clinical outcomes among groups. In our study, we selected the patient cohort through an algorithmic workflow. This process started with 60 850 EHR data and resulted in a final cohort of 578 patients, divided into 186 controls, 236 with HFpEF, and 156 with HFrEF, after excluding individuals with mismatched data or significant valvular heart disease. The analysis of baseline characteristics revealed that compared with controls, patients with HFrEF and HFpEF were generally older, had higher BMI, and showed a greater prevalence of co-morbidities such as diabetes, COPD, and CKD. Echocardiographic analysis, enhanced by DL, provided high coverage, and detailed insights into cardiac function, showing significant differences in parameters such as left ventricular diameter, ejection fraction, and myocardial strain among the groups. Clinical outcomes highlighted a higher risk of hospitalization and mortality for HF patients compared with controls, with particularly elevated risk ratios for both HFrEF and HFpEF groups. The concordance between the algorithmic selection of patients and manual validation demonstrated high accuracy, supporting the effectiveness of our approach in identifying and classifying HF subtypes, which could significantly impact future HF diagnosis and management strategies. CONCLUSIONS: Our study highlights the feasibility of combining keyword searches in EHR, DL automated echocardiographic interpretation, and biobank resources to identify HF subtypes.

Incidence and Long-Term Outcomes of Acute Myocardial Infarction Among Survivors of Out-of-Hospital Cardiac Arrest.

BACKGROUND: Despite the increasing long-term survival after out-of-hospital cardiac arrest (OHCA), the risk of subsequent acute myocardial infarction (AMI) remains poorly understood. We aimed to determine the incidence, predictors, and long-term outcomes of AMI among survivors of OHCA. METHODS AND RESULTS: We assembled a retrospective cohort of 882 patients with OHCA who survived to 30 days or discharge from the hospital between 2010 and 2019. Survivors of OHCA had an increased risk of subsequent AMI, defined as AMI occurring 30 days after index OHCA or following discharge from the hospital after OHCA, compared with the general population when matched for age and sex (standardized incidence ratio, 4.64 [95% CI, 3.52-6.01]). Age-specific risks of subsequent AMI for men (standardized incidence ratio, 3.29 [95% CI, 2.39-4.42]) and women (standardized incidence ratio, 6.15 [95% CI, 3.27-10.52]) were significantly increased. A total of 7.2%, 8.3%, and 14.3% of survivors of OHCA had a subsequent AMI at 3 years, 5 years, and end of follow-up, respectively. Age at OHCA (hazard ratio [HR], 1.04 [95% CI, 1.02-1.06]) and past medical history of prior AMI, defined as any AMI preceding or during the index OHCA event (HR, 1.84 [95% CI, 1.05-3.22]), were associated with subsequent AMI, while an initial shockable rhythm was not (HR, 1.00 [95% CI, 0.52-1.94]). Survivors of OHCA with subsequent AMI had a higher risk of death (HR, 1.58 [95% CI, 1.12-2.22]) than those without. CONCLUSIONS: Survivors of OHCA are at an increased risk of subsequent AMI compared with the general population. Prior AMI, but not an initial shockable rhythm, increases this risk, while subsequent AMI predicts death. Preventive measures for AMI including cardiovascular risk factor control and revascularization may thus improve outcomes in selected patients with cardiac pathogenesis.

Prediction of the Individual Aortic Stenosis Progression Rate and its Association With Clinical Outcomes.

Background: The progression rate of aortic stenosis differs between patients, complicating clinical follow-up and management. Objectives: This study aimed to identify predictors associated with the progression rate of aortic stenosis. Methods: In this retrospective longitudinal single-center cohort study, all patients with moderate aortic stenosis who presented between December 2011 and December 2022 and had echocardiograms available were included. The individual aortic stenosis progression rate was calculated based on aortic valve area (AVA) from at least 2 echocardiograms performed at least 6 months apart. Baseline factors associated with the progression rate of AVA were determined using linear mixed-effects models, and the association of progression rate with clinical outcomes was evaluated using Cox regression. Results: The study included 540 patients (median age 69 years and 38% female) with 2,937 echocardiograms (median 5 per patient). Patients had a linear progression with a median AVA decrease of 0.09 cm2/y and a median peak jet velocity increase of 0.17 m/s/y. Rapid progression was independently associated with all-cause mortality (HR: 1.77, 95% CI: 1.26-2.48) and aortic valve replacement (HR: 3.44, 95% CI: 2.55-4.64). Older age, greater left ventricular mass index, atrial fibrillation, and chronic kidney disease were associated with a faster decline of AVA. Conclusions: AVA decreases linearly in individual patients, and faster progression is independently associated with higher mortality. Routine clinical and echocardiographic variables accurately predict the individual progression rate and may aid clinicians in determining the optimal follow-up interval for patients with aortic stenosis.

Distinct Comorbidity Clusters in Patients With Acute Heart Failure: Data From RELAX-AHF-2.

BACKGROUND: Multimorbidity frequently occurs in patients with acute heart failure (AHF). The co-occurrence of comorbidities often follows specific patterns. OBJECTIVES: This study investigated multimorbidity subtypes and their associations with clinical outcomes. METHODS: From the prospective RELAX-AHF-2 (Relaxin for the Treatment of Acute Heart Failure-2) trial, 6,545 patients (26% with HF with preserved ejection fraction, defined as LVEF ≥50%) were classified into multimorbidity groups using latent class analysis. The association between subgroups and clinical outcomes was examined. Validation of these findings was conducted in the RELAX-AHF trial, which comprised 1,161 patients. RESULTS: Five distinct multimorbidity groups emerged: 1) diabetes and chronic kidney disease (CKD) (often male, high prevalence of CKD and diabetes mellitus); 2) ischemic (ischemic HF); 3) elderly/atrial fibrillation (AF) (oldest, high prevalence of AF); 4) metabolic (obese, hypertensive, more often HF with preserved ejection fraction); and 5) young (fewest comorbidities). After adjusting for confounders, patients in the diabetes and CKD (HR: 1.80; 95% CI: 1.50-2.20), elderly/AF (HR: 1.42; 95% CI: 1.20-1.70), and metabolic (HR: 1.40; 95% CI: 1.20-1.80) groups had higher rates of the composite outcome than patients in the young group, primarily driven by differences in rehospitalization. Treatment allocation (placebo or serelaxin) modified these associations (Pinteraction <0.001). Serelaxin-treated patients in the young group were associated with a lower risk for all-cause mortality (HR: 0.59; 95% CI: 0.40-0.90). Similarly, patients from the RELAX-AHF trial clustered in 5 multimorbidity groups. The clinical characteristics and associations with outcomes could also be validated. CONCLUSIONS: Comorbidities naturally clustered into 5 mutually exclusive groups in RELAX-AHF-2, showing variations in clinical outcomes. These data emphasize that the specific combination of comorbidities can influence adverse outcomes and treatment responses in patients with AHF.

Longitudinal NT-proBNP: Associations With Echocardiographic Changes and Outcomes in Heart Failure.

BACKGROUND: The relationship of serial NT-proBNP (N-terminal pro-B-type natriuretic peptide) measurements with changes in cardiac features and outcomes in heart failure (HF) remains incompletely understood. We determined whether common clinical covariates impact these relationships. METHODS AND RESULTS: In 2 nationwide observational populations with HF, the relationship of serial NT-proBNP measurements with serial echocardiographic parameters and outcomes was analyzed, further stratified by HF with reduced versus preserved left ventricular ejection fraction, inpatient versus outpatient enrollment, age, obesity, chronic kidney disease, atrial fibrillation, and attainment of ≥50% guideline-recommended doses of renin-angiotensin system inhibitors and ß-blockers. Among 1911 patients (mean±SD age, 65.1±13.4 years; 26.6% women; 62% inpatient and 38% outpatient), NT-proBNP declined overall, with more rapid declines among inpatients, those with obesity, those with atrial fibrillation, and those attaining ≥50% guideline-recommended doses. Each doubling of NT-proBNP was associated with increases in left ventricular volume (by 6.1 mL), E/e' (transmitral to mitral annular early diastolic velocity ratio) (by 1.4 points), left atrial volume (by 3.6 mL), and reduced left ventricular ejection fraction (by -2.1%). The effect sizes of these associations were lower among patients with HF with preserved ejection fraction, atrial fibrillation, or advanced age (Pinteraction<0.001). A landmark analysis identified that an SD increase in NT-proBNP over 6 months was associated with a 27% increase in the risk of the composite event of HF hospitalization or all-cause death between 6 months and 2 years (adjusted hazard ratio, 1.27 [95% CI, 1.15-1.40]; P<0.001). CONCLUSIONS: The relationships between NT-proBNP and structural/functional remodeling differed by age, presence of atrial fibrillation, and HF phenotypes. The association of increased NT-proBNP with increased risk of adverse outcomes was consistent in all subgroups.

External validation of a deep learning algorithm for automated echocardiographic strain measurements.

Aims: Echocardiographic strain imaging reflects myocardial deformation and is a sensitive measure of cardiac function and wall-motion abnormalities. Deep learning (DL) algorithms could automate the interpretation of echocardiographic strain imaging. Methods and results: We developed and trained an automated DL-based algorithm for left ventricular (LV) strain measurements in an internal dataset. Global longitudinal strain (GLS) was validated externally in (i) a real-world Taiwanese cohort of participants with and without heart failure (HF), (ii) a core-lab measured dataset from the multinational prevalence of microvascular dysfunction-HF and preserved ejection fraction (PROMIS-HFpEF) study, and regional strain in (iii) the HMC-QU-MI study of patients with suspected myocardial infarction. Outcomes included measures of agreement [bias, mean absolute difference (MAD), root-mean-squared-error (RMSE), and Pearson's correlation (R)] and area under the curve (AUC) to identify HF and regional wall-motion abnormalities. The DL workflow successfully analysed 3741 (89%) studies in the Taiwanese cohort, 176 (96%) in PROMIS-HFpEF, and 158 (98%) in HMC-QU-MI. Automated GLS showed good agreement with manual measurements (mean ± SD): -18.9 ± 4.5% vs. -18.2 ± 4.4%, respectively, bias 0.68 ± 2.52%, MAD 2.0 ± 1.67, RMSE = 2.61, R = 0.84 in the Taiwanese cohort; and -15.4 ± 4.1% vs. -15.9 ± 3.6%, respectively, bias -0.65 ± 2.71%, MAD 2.19 ± 1.71, RMSE = 2.78, R = 0.76 in PROMIS-HFpEF. In the Taiwanese cohort, automated GLS accurately identified patients with HF (AUC = 0.89 for total HF and AUC = 0.98 for HF with reduced ejection fraction). In HMC-QU-MI, automated regional strain identified regional wall-motion abnormalities with an average AUC = 0.80. Conclusion: DL algorithms can interpret echocardiographic strain images with similar accuracy as conventional measurements. These results highlight the potential of DL algorithms to democratize the use of cardiac strain measurements and reduce time-spent and costs for echo labs globally.

Time from admission to randomization and the effect of empagliflozin in acute heart failure: A post-hoc analysis from EMPULSE.

AIMS: Patients hospitalized for acute heart failure (HF) could be enrolled in EMPULSE (NCT04157751) upon haemodynamic stabilization and between 24 h and 5 days after hospital admission. The timing of treatment initiation may influence the efficacy and safety of drugs such as empagliflozin. The aim of this study was to evaluate patient characteristics, clinical events, and treatment effects according to time from admission to randomization. METHODS AND RESULTS: The EMPULSE population was dichotomized by median time from hospital admission to randomization (1-2 days vs. 3-5 days). The primary outcome was a hierarchical composite endpoint of time to all-cause death, number of HF events, time to first HF event, and a ≥5-point difference in Kansas City Cardiomyopathy Questionnaire total symptom score change from baseline after 90 days, analysed using the win ratio (WR) method. Patients randomized later (3-5 days, average time 3.9 days; n = 312) had a higher risk of experiencing clinical events than patients randomized earlier (1-2 days, average time 1.7 days; n = 215). The treatment effect favoured empagliflozin versus placebo in patients randomized later (3-5 days: WR 1.69, 95% confidence interval [CI] 1.26-2.25) but was attenuated in patients randomized earlier (1-2 days: WR 1.04, 95% CI 0.74-1.44) (interaction p = 0.029). A similar pattern was observed for the composite of HF hospitalization or cardiovascular death and all-cause hospitalizations (interaction p < 0.1 for both). The reduction of N-terminal pro-B-type natriuretic peptide levels was more pronounced with empagliflozin among patients randomized later than in patients randomized earlier (interaction p = 0.004). CONCLUSIONS: Among patients hospitalized for acute HF enrolled in EMPULSE, those randomized later after hospital admission (3-5 days) experienced greater clinical benefit with empagliflozin than those randomized earlier (1-2 days). These findings should be confirmed in future studies before clinical application.

Biomarker profiles associated with reverse ventricular remodelling in patients with heart failure and a reduced ejection fraction: Insights from the echocardiographic substudy of the VICTORIA trial.

AIMS: Reverse ventricular remodelling, defined as a decrease in left ventricular end-systolic volume indexed to body surface area (LVESVI) or an increase in left ventricular ejection fraction (LVEF), is associated with improved clinical outcomes in patients with heart failure with reduced ejection fraction (HFrEF). However, the underlying pathophysiological mechanisms remain unclear. METHODS AND RESULTS: We evaluated paired core-lab assessed echocardiograms and measurements of 92 biomarkers at baseline and 8 months thereafter in 419 participants with HFrEF. Reverse ventricular remodelling was defined as a >5% LVEF increase or >15% LVESVI relative decrease between baseline and 8 months. We evaluated the association between baseline biomarkers and their changes with reverse ventricular remodelling in the prospectively randomized controlled VICTORIA (Vericiguat Global Study in Subjects With Heart Failure With Reduced Ejection Fraction) trial. Of 419 patients (median age 66 [interquartile range 57-74] years, 27.4% women), 206 (49.2%) had reverse ventricular remodelling (either a 5% LVEF increase or a 15% LVESVI decrease). There were no differences in baseline biomarker concentrations between patients with versus those without reverse ventricular remodelling on follow-up. However, in patients with reverse ventricular remodelling there were significant decreases in biomarkers relating to inflammation and cardiac metabolism; particularly the tumour necrosis factor superfamily member 13B (ratio 0.82, 95% confidence interval [CI] 0.77-0.88), growth differentiation factor-15 (ratio 0.74, 95% CI 0.66-0.84), and insulin-like growth factor binding protein 7 (ratio 0.80, 95% CI 0.73-0.88). CONCLUSIONS: Reverse ventricular remodelling in patients with HFrEF is associated with a decrease of biomarkers related to inflammation and cardiac metabolism.

Distinguishing heart failure with reduced ejection fraction from heart failure with preserved ejection fraction: A phenomics approach.

AIM: Pathophysiological differences between patients with heart failure with preserved (HFpEF) and reduced (HFrEF) ejection fraction (EF) remain unclear. Therefore we used a phenomics approach, integrating selected proteomics data with patient characteristics and cardiac structural and functional parameters, to get insight into differential pathophysiological mechanisms and identify potential treatment targets. METHODS AND RESULTS: We report data from a representative subcohort of the prospective Singapore Heart Failure Outcomes and Phenotypes (SHOP), including patients with HFrEF (EF <40%, n = 217), HFpEF (EF ≥50%, n = 213), and age- and sex-matched controls without HF (n = 216). We measured 92 biomarkers using a proximity extension assay and assessed cardiac structure and function in all participants using echocardiography. We used multi-block projection to latent structure analysis to integrate clinical, echocardiographic, and biomarker variables. Candidate biomarker targets were cross-referenced with small-molecule and drug databases. The total cohort had a median age of 65 years (interquartile range 60-71), and 50% were women. Protein profiles strongly discriminated patients with HFrEF (area under the curve [AUC] = 0.89) and HFpEF (AUC = 0.94) from controls. Phenomics analyses identified unique druggable inflammatory markers in HFpEF from the tumour necrosis factor receptor superfamily (TNFRSF), which were positively associated with hypertension, diabetes, and increased posterior and relative wall thickness. In HFrEF, interleukin (IL)-8 and IL-6 were possible targets related to lower EF and worsening renal function. CONCLUSION: We identified pathophysiological mechanisms related to increased cardiac wall thickness parameters and potentially druggable inflammatory markers from the TNFRSF in HFpEF.

Heart failure with improved versus persistently reduced left ventricular ejection fraction: A comparison of the BIOSTAT-CHF (European) study with the ASIAN-HF registry.

AIMS: We investigated the prevalence, clinical characteristics, and prognosis of patients with heart failure (HF) with improved ejection fraction (HFimpEF). METHODS AND RESULTS: We used data from BIOSTAT-CHF including patients with a left ventricular ejection fraction (LVEF) ≤40% at baseline who had LVEF re-assessed at 9 months. HFimpEF was defined as a LVEF >40% and a LVEF ≥10% increase from baseline at 9 months. We validated findings in the ASIAN-HF registry. The primary outcome was a composite of time to HF rehospitalization or all-cause mortality. In BIOSTAT-CHF, about 20% of patients developed HFimpEF, that was associated with a lower primary event rate of all-cause mortality (hazard ratio [HR] 0.52, 95% confidence interval [CI] 0.28-0.97, p = 0.040) and the composite endpoint (HR 0.46, 95% CI 0.30-0.70, p < 0.001) compared with patients who remained in persistent HF with reduced ejection fraction (HFrEF). The findings were similar in the ASIAN-HF (HR 0.40, 95% CI 0.18-0.89, p = 0.024, and HR 0.29, 95% CI 0.17-0.48, p < 0.001). Five independently common predictors for HFimpEF in both BIOSTAT-CHF and ASIAN-HF were female sex, absence of ischaemic heart disease, higher LVEF, smaller left ventricular end-diastolic and end-systolic diameter at baseline. A predictive model combining only five predictors (absence of ischaemic heart disease and left bundle branch block, smaller left ventricular end-systolic and left atrial diameter, and higher platelet count) for HFimpEF in the BIOSTAT-CHF achieved an area under the curve of 0.772 and 0.688 in the ASIAN-HF (due to missing left atrial diameter and platelet count). CONCLUSIONS: Approximately 20-30% of patients with HFrEF improved to HFimpEF within 1 year with better clinical outcomes. In addition, the predictive model with clinical predictors could more accurately predict HFimpEF in patients with HFrEF.

Toward a Universal Definition of Etiologies in Heart Failure: Categorizing Causes and Advancing Registry Science.

Heart failure (HF) is a well-described final common pathway for a broad range of diseases however substantial confusion exists regarding how to describe, study, and track these underlying etiologic conditions. We describe (1) the overlap in HF etiologies, comorbidities, and case definitions as currently used in HF registries led or managed by members of the global HF roundtable; (2) strategies to improve the quality of evidence on etiologies and modifiable risk factors of HF in registries; and (3) opportunities to use clinical HF registries as a platform for public health surveillance, implementation research, and randomized registry trials to reduce the global burden of noncommunicable diseases. Investment and collaboration among countries to improve the quality of evidence in global HF registries could contribute to achieving global health targets to reduce noncommunicable diseases and overall improvements in population health.