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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Clinical characteristics and prognosis of patients with heart failure and high concentrations of interleukin-17D.

AIMS: Heart failure (HF) is associated with cytokine activation and inflammation. Experimental evidence suggests that plasma interleukin-17 (IL-17) is associated with myocardial fibrosis and cardiac dysfunction in HF. IL-17D, a subtype of IL-17 originates from particular tissues such as the heart. However, there is very limited data on the IL-17 cytokine family in patients with HF. Therefore, we investigated the association between circulating IL-17D levels, clinical characteristics and outcome in a large cohort of patients with heart failure. METHODS AND RESULTS: Plasma IL-17D was measured in 2032 patients with HF from 11 European countries using a proximity extension assay. The primary outcome was a composite of HF hospitalization or all-cause mortality. Patients with higher plasma IL-17D concentrations were more likely to have atrial fibrillation (AF), renal dysfunction and heart failure with preserved ejection fraction (HFpEF) and had higher plasma N-terminal pro-brain natriuretic peptide (NT-proBNP) concentrations (all p < 0.001). IL-17D was not associated with interleukin-6 (IL-6) or C-reactive protein (CRP) concentrations. After adjustment for confounders in a multivariable Cox regression analysis, patients in the highest quartile of plasma IL-17D had a significantly increased risk of the composite outcome of HF hospitalization or all-cause mortality compared to patients in the lowest quartile [Hazard ratio (HR) 1.28, 95% confidence interval (CI) 1.05-1.57]. CONCLUSION: In patients with HF, elevated plasma IL-17D concentrations are associated with higher plasma NT-proBNP concentrations and a higher prevalence of AF and renal dysfunction. High IL-17D concentrations are independently associated with worse outcome.

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.

Clinical Features of Heart Failure With Normal Ejection Fraction: Insights From the ASIAN-HF Registry.

Background: Heart failure and left ventricular ejection fraction in the normal range (HFnEF) (left ventricular ejection fraction [LVEF] of ≥55% for men and ≥60% for women) is understudied. Objectives: The authors aimed to characterize patients with HFnEF compared with those with preserved (≥50%) yet below the normal LVEF. Methods: In an Asian HF registry, clinical characteristics, echocardiographic features, and outcomes were compared across: 1) HFnEF; 2) heart failure with preserved left ventricular ejection fraction (HFpEF) (LVEF of ≥50%) and below normal LVEF; and 3) community-based controls without HF. Cluster analysis of echocardiographic parameters was performed and validated in an external cohort. Results: Among 1,765 patients with HFpEF (age 68 ± 12 years; 50% women), 1,313 (74.4%) had HFnEF. Compared with patients with HFpEF and below normal LVEF, patients with HFnEF had less coronary artery disease (33.7% vs 27.9%), greater LV wall thickness, and higher stroke volume, but similar 2-year age-adjusted all-cause mortality (HR: 0.8; 95% CI: 0.6-1.2). Five echocardiographic clusters with similar 2-year mortality were identified: 1) normal LV (normal structure despite increased filling pressure; least comorbidities) in 25%; 2) restrictive (smallest stroke volume; predominantly elderly women) in 26%; 3) hypertrophic (most concentric hypertrophy; more men) in 25%; 4) high output (greatest stroke volume; predominantly obese younger men) in 10%; and 5) atrial dominant (most left atrial myopathy; mainly elderly women with multiple comorbidities) in 10%. Similar patterns were found in the validation cohort. Conclusions: The majority of patients with HFpEF had normal LVEF, which consists of patients with different patterns of cardiac features and clinical characteristics. Results may carry implications for targeted treatment approaches in HFpEF.

Multiomics Analysis Provides Novel Pathways Related to Progression of Heart Failure.

BACKGROUND: Despite major advances in pharmacological treatment for patients with heart failure, residual mortality remains high. This suggests that important pathways are not yet targeted by current heart failure therapies. OBJECTIVES: We sought integration of genetic, transcriptomic, and proteomic data in a large cohort of patients with heart failure to detect major pathways related to progression of heart failure leading to death. METHODS: We used machine learning methodology based on stacked generalization framework and gradient boosting algorithms, using 54 clinical phenotypes, 403 circulating plasma proteins, 36,046 transcript expression levels in whole blood, and 6 million genomic markers to model all-cause mortality in 2,516 patients with heart failure from the BIOSTAT-CHF (Systems BIOlogy Study to TAilored Treatment in Chronic Heart Failure) study. Results were validated in an independent cohort of 1,738 patients. RESULTS: The mean age of the patients was 70 years (Q1-Q3: 61-78 years), 27% were female, median N-terminal pro-B-type natriuretic peptide was 4,275 ng/L (Q1-Q3: 2,360-8,486 ng/L), and 7% had heart failure with preserved ejection fraction. During a median follow-up of 21 months, 657 (26%) of patients died. The 4 major pathways with a significant association to all-cause mortality were: 1) the PI3K/Akt pathway; 2) the MAPK pathway; 3) the Ras signaling pathway; and 4) epidermal growth factor receptor tyrosine kinase inhibitor resistance. Results were validated in an independent cohort of 1,738 patients. CONCLUSIONS: A systems biology approach integrating genomic, transcriptomic, and proteomic data identified 4 major pathways related to mortality. These pathways are related to decreased activation of the cardioprotective ERBB2 receptor, which can be modified by neuregulin.

Multimorbidity in patients with acute heart failure across world regions and country income levels (REPORT-HF): a prospective, multicentre, global cohort study.

BACKGROUND: Multimorbidity (two or more comorbidities) is common among patients with acute heart failure, but comprehensive global information on its prevalence and clinical consequences across different world regions and income levels is scarce. This study aimed to investigate the prevalence of multimorbidity and its effect on pharmacotherapy and prognosis in participants of the REPORT-HF study. METHODS: REPORT-HF was a prospective, multicentre, global cohort study that enrolled adults (aged ≥18 years) admitted to hospital with a primary diagnosis of acute heart failure from 358 hospitals in 44 countries on six continents. Patients who currently or recently participated in a clinical treatment trial were excluded. Follow-up data were collected at 1-year post-discharge. The primary outcome was 1-year post-discharge mortality. All patients in the REPORT-HF cohort with full data on comorbidities were eligible for the present study. We stratified patients according to the number of comorbidities, and countries by world region and country income level. We used one-way ANOVA, χ2 test, or Mann-Whitney U test for comparisons between groups, as applicable, and Cox regression to analyse the association between multimorbidity and 1-year mortality. FINDINGS: Between July 23, 2014, and March 24, 2017, 18 553 patients were included in the REPORT-HF study. Of these, 18 528 patients had full data on comorbidities, of whom 11 360 (61%) were men and 7168 (39%) were women. Prevalence rates of multimorbidity were lowest in southeast Asia (72%) and highest in North America (92%). Fewer patients from lower-middle-income countries had multimorbidity than patients from high-income countries (73% vs 85%, p<0·0001). With increasing comorbidity burden, patients received fewer guideline-directed heart failure medications, yet more drugs potentially causing or worsening heart failure. Having more comorbidities was associated with worse outcomes: 1-year mortality increased from 13% (no comorbidities) to 26% (five or more comorbidities). This finding was independent of common baseline risk factors, including age and sex. The population-attributable fraction of multimorbidity for mortality was higher in high-income countries than in upper-middle-income or lower-middle-income countries (for patients with five or more comorbidities: 61% vs 27% and 31%, respectively). INTERPRETATION: Multimorbidity is highly prevalent among patients with acute heart failure across world regions, especially in high-income countries, and is associated with higher mortality, less prescription of guideline-directed heart failure pharmacotherapy, and increased use of potentially harmful medications. FUNDING: Novartis Pharma. TRANSLATIONS: For the Arabic, French, German, Hindi, Mandarin, Russian and Spanish translations of the abstract see Supplementary Materials section.

Regional differences and coronary microvascular dysfunction in heart failure with preserved ejection fraction.

AIMS: In heart failure with preserved ejection fraction (HFpEF), regional heterogeneity of clinical phenotypes is increasingly recognized, with coronary microvascular dysfunction (CMD) potentially being a common shared feature. We sought to determine the regional differences in clinical characteristics and prevalence of CMD in HFpEF. METHODS AND RESULTS: We analysed clinical characteristics and CMD in 202 patients with stable HFpEF (left ventricular ejection fraction ≥40%) in Finland, Singapore, Sweden, and United States in the multicentre PROMIS-HFpEF study. Patients with unrevascularized macrovascular coronary artery disease were excluded. CMD was assessed using Doppler echocardiography and defined as coronary flow reserve (adenosine-induced vs. resting flow) < 2.5. Patients from Singapore had the lowest body mass index yet highest prevalence of hypertension, dyslipidaemia, and diabetes; patients from Finland and Sweden were oldest, with the most atrial fibrillation, chronic kidney disease, and high smoking rates; and those from United States were youngest and most obese. The prevalence of CMD was 88% in Finland, 80% in Singapore, 77% in Sweden, and 59% in the United States; however, non-significant after adjustment for age, sex, N-terminal pro-brain natriuretic peptide, smoking, left atrial reservoir strain, and atrial fibrillation. Associations between CMD and clinical characteristics did not differ based on region (interaction analysis). CONCLUSIONS: Despite regional differences in clinical characteristics, CMD was present in the majority of patients with HFpEF across different regions of the world with the lowest prevalence in the United States. This difference was explained by differences in patient characteristics. CMD could be a common therapeutic target across regions.

Proteomic Profiling in Patients With Peripartum Cardiomyopathy: A Biomarker Study of the ESC EORP PPCM Registry.

BACKGROUND: Peripartum cardiomyopathy (PPCM) remains an important cause of maternal morbidity and mortality globally. The pathophysiology remains incompletely understood, and the diagnosis is often missed or delayed. OBJECTIVES: This study explored the serum proteome profile of patients with newly diagnosed PPCM, as compared with matched healthy postpartum mothers, to unravel novel protein biomarkers that would further an understanding of the pathogenesis of PPCM and improve diagnostic precision. METHODS: Study investigators performed untargeted serum proteome profiling using data-independent acquisition-based label-free quantitative liquid chromatography-tandem mass spectrometry on 84 patients with PPCM, as compared with 29 postpartum healthy controls (HCs). Significant changes in protein intensities were determined with nonpaired Student's t-tests and were further classified by using the Boruta algorithm. The proteins' diagnostic performance was evaluated by area under the curve (AUC) and validated using the 10-fold cross-validation. RESULTS: Patients with PPCM presented with a mean left ventricular ejection fraction of 33.5% ± 9.3% vs 57.0% ± 8.8% in HCs (P < 0.001). Study investigators identified 15 differentially up-regulated and 14 down-regulated proteins in patients with PPCM compared with HCs. Seven of these proteins were recognized as significant by the Boruta algorithm. The combination of adiponectin, quiescin sulfhydryl oxidase 1, inter-α-trypsin inhibitor heavy chain, and N-terminal pro-B-type natriuretic peptide had the best diagnostic precision (AUC: 0.90; 95% CI: 0.84-0.96) to distinguish patients with PPCM from HCs. CONCLUSIONS: Salient biologic themes related to immune response proteins, inflammation, fibrosis, angiogenesis, apoptosis, and coagulation were predominant in patients with PPCM compared with HCs. These newly identified proteins warrant further evaluation to establish their role in the pathogenesis of PPCM and potential use as diagnostic markers.

Global Variations According to Sex in Patients Hospitalized for Heart Failure in the REPORT-HF Registry.

BACKGROUND: Previous reports suggest that risk factors, management, and outcomes of acute heart failure (AHF) may differ by sex, but they rarely extended analysis to low- and middle-income countries. OBJECTIVES: In this study, the authors sought to analyze sex differences in treatment and outcomes in patients hospitalized for AHF in 44 countries. METHODS: The authors investigated differences between men and women in treatment and outcomes in 18,553 patients hospitalized for AHF in 44 countries in the REPORT-HF (Registry to Assess Medical Practice With Longitudinal Observation for the Treatment of Heart Failure) registry stratified by country income level, income disparity, and world region. The primary outcome was 1-year all-cause mortality. RESULTS: Women (n = 7,181) were older than men (n = 11,372), were more likely to have heart failure with preserved left ventricular ejection fraction, had more comorbid conditions except for coronary artery disease, and had more severe signs and symptoms at admission. Coronary angiography, cardiac stress tests, and coronary revascularization were less frequently performed in women than in men. Women with AHF and reduced left ventricular ejection fraction were less likely to receive an implanted device, regardless of region or country income level. Women were more likely to receive treatments that could worsen HF than men (18% vs 13%; P < 0.0001). In countries with low-income disparity, women had better 1-year survival than men. This advantage was lost in countries with greater income disparity (Pinteraction < 0.001). CONCLUSIONS: Women were less likely to have diagnostic testing or receive guideline-directed care than men. A survival advantage for women was observed only in countries with low income disparity, suggesting that equity of HF care between sexes remains an unmet goal worldwide.

Personalized lifetime prediction of survival and treatment benefit in patients with heart failure with reduced ejection fraction: The LIFE-HF model.

AIMS: Although trials have proven the group-level effectiveness of various therapies for heart failure with reduced ejection fraction (HFrEF), important differences in absolute effectiveness exist between individuals. We developed and validated the LIFEtime-perspective for Heart Failure (LIFE-HF) model for the prediction of individual (lifetime) risk and treatment benefit in patients with HFrEF. METHODS AND RESULTS: Cox proportional hazards functions with age as the time scale were developed in the PARADIGM-HF and ATMOSPHERE trials (n = 15 415). Outcomes were cardiovascular death, heart failure (HF) hospitalization or cardiovascular death, and non-cardiovascular mortality. Predictors were age, sex, New York Heart Association class, prior HF hospitalization, diabetes mellitus, extracardiac vascular disease, systolic blood pressure, left ventricular ejection fraction, N-terminal pro-B-type natriuretic peptide, and glomerular filtration rate. The functions were combined in life-tables to predict individual overall and HF hospitalization-free survival. External validation was performed in the SwedeHF registry, ASIAN-HF registry, and DAPA-HF trial (n = 51 286). Calibration of 2- to 10-year risk was adequate, and c-statistics were 0.65-0.74. An interactive tool was developed combining the model with hazard ratios from trials to allow estimation of an individual's (lifetime) risk and treatment benefit in clinical practice. Applying the tool to the development cohort, combined treatment with a mineralocorticoid receptor antagonist, sodium-glucose cotransporter 2 inhibitor, and angiotensin receptor-neprilysin inhibitor was estimated to afford a median of 2.5 (interquartile range [IQR] 1.7-3.7) and 3.7 (IQR 2.4-5.5) additional years of overall and HF hospitalization-free survival, respectively. CONCLUSION: The LIFE-HF model enables estimation of lifelong overall and HF hospitalization-free survival, and (lifetime) treatment benefit for individual patients with HFrEF. It could serve as a tool to improve the management of HFrEF by facilitating personalized medicine and shared decision-making.

Association Between Diabetes, Chronic Kidney Disease, and Outcomes in People With Heart Failure From Asia.

Background: Diabetes mellitus (DM), chronic kidney disease (CKD), and heart failure (HF) are pathophysiologically linked and increasing in prevalence in Asian populations, but little is known about the interplay of DM and CKD on outcomes in HF. Objectives: This study sought to investigate outcomes in patients with heart failure with preserved ejection fraction (HFpEF) vs heart failure with reduced ejection fraction (HFrEF) in relation to the presence of DM and CKD. Methods: Using the multinational ASIAN-HF registry, we investigated associations between DM only, CKD only, and DM+CKD with: 1) composite of 1-year mortality or HF hospitalization; and 2) Kansas City Cardiomyopathy Questionnaire scores, according to HF subtype. Results: In 5,239 patients with HF (74.6% HFrEF, 25.4% HFpEF; mean age 63 years; 29.1% female), 1,107 (21.1%) had DM only, 1,087 (20.7%) had CKD only, and 1,400 (26.7%) had DM+CKD. Compared with patients without DM nor CKD, DM+CKD was associated with 1-year all-cause mortality or HF hospitalization in HFrEF (adjusted HR: 2.07; 95% CI: 1.68-2.55) and HFpEF (HR: 2.37; 95% CI: 1.40-4.02). In HFrEF, DM only and CKD only were associated with 1-year all-cause mortality or HF hospitalization (both HRs: 1.43; 95% CI: 1.14-1.80), while in HFpEF, CKD only (HR: 2.54; 95% CI: 1.46-4.41) but not DM only (HR: 1.01; 95% CI: 0.52-1.95) was associated with increased risk (interaction P < 0.01). Adjusted Kansas City Cardiomyopathy Questionnaire scores were lower in patients with DM+CKD (HFrEF: mean 60.50, SEM 0.77, HFpEF: mean 70.10, SEM 1.06; P < 0.001) than with no DM or CKD (HFrEF: mean 66.00, SEM 0.65; and HFpEF: mean 75.80, SEM 0.99). Conclusions: Combined DM and CKD adversely effected outcomes independently of HF subtype, with CKD a consistent predictor of worse outcomes. Strategies to prevent and treat DM and CKD in HF are urgently required.