Stratification Of Heart Failure Patients Research Articles

The Use of Metabolomes in Risk Stratification of Heart Failure Patients: Protocol for a Scoping Review.

Heart failure (HF) is a significant health problem that is often associated with major morbidity and mortality. Metabolic abnormalities occur in HF and may be used to identify individuals at risk of developing the condition. Furthermore, these metabolic changes may play a role in the pathogenesis and progression of HF. Despite this knowledge, the utility of metabolic changes in diagnosis, management, prognosis, and therapy for patients with chronic HF has not been systematically reviewed. This scoping review aims to systematically appraise the literature on metabolic changes in patients with HF, describe the role of these changes in pathogenesis, progression, and care, and identify knowledge gaps to inform future research. This review will be conducted using a strategy based on previous reports, the JBI Manual for Evidence Synthesis, and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines. A comprehensive search of electronic databases (Medline, EBSCOhost, Scopus, and Web of Science) will be conducted using keywords related to HF, myocardial failure, metabolomes, metabonomics, and analytical chemistry techniques. The search will include original peer-reviewed research papers (clinical studies conducted on humans and systematic reviews with or without a meta-analysis) published between January 2010 and September 2023. Studies that include patients with HF younger than 18 years or those not published in English will be excluded. Two authors (UGA and MB) will screen the titles and abstracts independently and perform a full-text screen of the relevant and eligible papers. Relevant data will be extracted and synthesized, and a third author or group will be consulted to resolve discrepancies. This scoping review will span from January 2010 to September 2023, and the results will be published in a peer-reviewed, open-access journal as a scoping review in 2024. The presentation of the findings will use the PRISMA-ScR flow diagram and descriptive and narrative formats, including tables and graphical displays, to provide a comprehensive overview of the extracted data. This review aims to collect and analyze the available evidence on metabolic changes in patients with HF, aiming to enhance our current understanding of this topic. Additionally, this review will identify the most commonly used and suitable sample, analytical method, and specific metabolomes to facilitate standardization, reproducibility of results, and application in the diagnosis, treatment, and risk stratification of patients with HF. Finally, it is hoped that this review's outcomes will inspire further research into the metabolomes of patients with HF in low- and middle-income countries. Open Science Framework; https://osf.io/sp6xj. DERR1-10.2196/53905.

Prevalently carried HLA allele associates with inflammation and poor prognosis in HF patients via co-stimulatory, adhesion and MHC-II enhanceosome regulation

Abstract Background Heart failure with reduced ejection fraction (HFrEF) is associated with impaired inflammation resolution, adverse remodeling, restricted function, and poor prognosis. Furthermore, immune cells play crucial roles in pathological remodeling in cardiovascular diseases. Thus identifying subsets of patients with unique inflammatory profiles may aid in better risk stratification and precision therapies for chronic HFrEF patients with altered immune profiles. Methods and Results A pilot study using scRNA-seq of circulating immune cells (n=16) showed strong T cell activation in HFrEF patients, which was validated via flow cytometry in HFrEF patients and healthy controls (n=180 patients) with a shift into the memory/effector phenotype (p<0.0001). Thus, HFrEF patients may have unique MHC signatures promoting aberrant T cell activation, leading to chronic inflammation and pathology. We investigated antigen-presenting cells (APCs) that activate CD4+ T cells via MHC-II and costimulatory molecules. Indeed, we found MHC-II and costimulatory molecule signatures associated with HFrEF and T cell activation, such as HLA-DRB5 (DRB5) and ICAM-1, indicating both T cell activation potential and a pro-inflammatory status. To study whether HLA-DRB5 selectively promotes immune dysregulation, circulating monocytes from healthy DRB5 carriers and non-carriers were investigated using scRNA-seq showing enhanced antigen presentation, co-stimulatory and inflammatory molecule expression (e.g. HLA-DRA, ICAM-2, JAK2). ScRNA-seq confirmed HLA-DRB5 regulation of MHC-II, inflammatory and adhesion (e.g. ITGAM and ITGB1) properties by silencing HLA-DRB5 in THP1 cells. In silico analysis of monocyte epigenome data predicted unique transcription factors (e.g. ERG) controlling of HLA-DRB5 via regulatory elements suggesting a role in selective MHC-II molecule activation. To gain insights as to whether HLA carrier status might associate with prognosis, we performed HLA sequencing on age and gender matched HFrEF patients (n=93). During follow up of this cohort we identified HLA-DRB5*0101, which belongs to the DR2-supertype, as a predictor for death (p<0.05). We hypothesized whether HLA-DRB5 expression could impact T cell infiltration into the heart. Thus, we stimulated naïve T cells with pre-treated THP1-derived DCs (stress v. no stress cardiac organoid medium) and found HLA-DRB5 silencing reduced the infiltration of T cells into unstressed cardiac organoids compared to control group. Conclusions This is the first study to provide mechanistic insights for how HLA-DRB5 regulates innate and adaptive immunity in the context of heart failure, while also showing corresponding changes in T cell activation, infiltration capacity and prognosis in HFrEF patients. These data provide novel insights for risk stratification of heart failure patients and novel immunological interventions which may improve prognosis in HFrEF patients.

Clustering of Heart Failure Phenotypes in Johannesburg Using Unsupervised Machine Learning

Background: The diagnosis and therapy of heart failure are guided mainly by a single imaging parameter, the left ventricular ejection fraction (LVEF). Recent studies have reported on the value of machine learning in characterising the various phenotypes of heart failure patients. Therefore, this study aims to use unsupervised machine learning algorithms to phenotype heart failure patients into different clusters using multiple clinical parameters. Methods: Seven unsupervised machine learning clustering algorithms were used to cluster heart failure patients hospitalised with acute and chronic heart failure. Results: The agglomerative clustering algorithm identified three clusters with a silhouette score of 0.72. Cluster 1 (uraemic cluster) comprised 229 (36.0%) patients with a mean age of 56.2 ± 17.2 years and a serum urea of 14.5 ± 31.3 mmol/L. Cluster 2 (hypotensive cluster) comprised 117 (18.4%) patients with a minimum systolic and diastolic blood pressure of 91 and 60 mmHg, respectively. In cluster 3 (congestive cluster), patients predominantly had symptoms of fluid overload, and 93 (64.6%) patients had ascites. Among the 636 heart failure patients studied, the median LVEF was 32% (interquartile range: 25–45), and the rate of in-hospital all-cause mortality was 14.5%. Systolic and diastolic blood pressure, age, and the LVEF had the most substantial impact on discriminating between the three clusters. Conclusions: Clinicians without access to echocardiography could potentially rely on blood pressure measurements and age to risk stratify heart failure patients. However, larger prospective studies are mandatory for the validation of these clinical parameters.

Predicting in-hospital all-cause mortality in heart failure using machine learning.

The age of onset and causes of heart failure differ between high-income and low-and-middle-income countries (LMIC). Heart failure patients in LMIC also experience a higher mortality rate. Innovative ways that can risk stratify heart failure patients in this region are needed. The aim of this study was to demonstrate the utility of machine learning in predicting all-cause mortality in heart failure patients hospitalised in a tertiary academic centre. Six supervised machine learning algorithms were trained to predict in-hospital all-cause mortality using data from 500 consecutive heart failure patients with a left ventricular ejection fraction (LVEF) less than 50%. The mean age was 55.2 ± 16.8 years. There were 271 (54.2%) males, and the mean LVEF was 29 ± 9.2%. The median duration of hospitalisation was 7 days (interquartile range: 4-11), and it did not differ between patients discharged alive and those who died. After a prediction window of 4 years (interquartile range: 2-6), 84 (16.8%) patients died before discharge from the hospital. The area under the receiver operating characteristic curve was 0.82, 0.78, 0.77, 0.76, 0.75, and 0.62 for random forest, logistic regression, support vector machines (SVM), extreme gradient boosting, multilayer perceptron (MLP), and decision trees, and the accuracy during the test phase was 88, 87, 86, 82, 78, and 76% for random forest, MLP, SVM, extreme gradient boosting, decision trees, and logistic regression. The support vector machines were the best performing algorithm, and furosemide, beta-blockers, spironolactone, early diastolic murmur, and a parasternal heave had a positive coefficient with the target feature, whereas coronary artery disease, potassium, oedema grade, ischaemic cardiomyopathy, and right bundle branch block on electrocardiogram had negative coefficients. Despite a small sample size, supervised machine learning algorithms successfully predicted all-cause mortality with modest accuracy. The SVM model will be externally validated using data from multiple cardiology centres in South Africa before developing a uniquely African risk prediction tool that can potentially transform heart failure management through precision medicine.

Invasive exercise hemodynamics predict survival in patients with clinical heart failure irrespective of left ventricular systolic function

Abstract Funding Acknowledgements Type of funding sources: None. Introduction When aiming to stratify heart failure patients for mortality risk, exercise stress testing has proven to be relevant in specific populations of heart failure patients, but is not universally applied. Analysis of left ventricular systolic function (LVEF) remains the cornerstone for stratifying heart failure patients for prognosis and clinical treatment. The aim of the present study is to investigate the prognostic capabilities of invasive exercise testing in a real-world cohort of suspected heart failure patients in whom non-cardiac causes of dyspnea were excluded. Methods We retrospectively analyzed survival of patients who underwent right heart catheterization at rest and during exercise between 2007 to 2017 for dyspnea and expected heart failure. Pulmonary capillary wedge pressure (PCWP) at rest and the PCWP response to exercise, expressed as the ratio of PCWP at peak exercise to workload normalized to body weight [PCWL (mmHg/W/kg)], were determined. Mortality data was deducted from the official German death registry. Results A total of 682 patients [aged 64.1 ± 12 years, 269 (39.2%) women] were included. Based on LVEF, 515 patients (76.4%) had sustained LVEF ≥50%, 71 patients (10.5%) had heart failure with mildly-impaired LVEF (40-49%) and 88 patients (13.1%) had heart failure with reduced LVEF (≤40%). Absolute LVEF was not associated with all-cause mortality (HR 0.995; 95% CI 0.99-1.00; p=0.231). While increases in PCWP at rest an PCWL during exercise were both associated with death, the discriminative properties of pressure measurements derived during exercise were superior in ROC-analysis (AUC 0.637 for PCWP and AUC 0.728 for PCWL, p<0.001) Exercise hemodynamics and systolic function were next used to re-classify patients in four groups based on normal vs. impaired LVEF (≥/< 50%) and response to exercise (PCWL≥/<25.5 mmHg/W/kg for normal LVEF and PCWL≥/<34.7 mmHg/W/kg for impaired LVEF). In multivariate models adjusted for pertinent baseline parameters, patients with pathological response to exercise carried a 2.0-fold (with sustained LVEF, 95% CI 1.17-3.52, p=0.011) to 3.2-fold (with impaired LVEF, 95% CI 1.83-5.91, p<0.001) increased risk for death. Patients with impaired LVEF but featuring a normal response to exercise did not carry an increased risk for death (HR 1.117, 95% CI 0.453-2.757, p=0.810). Discussion: In patients with clinical heart failure, invasive exercise testing improves the prediction of mortality. Subjects with a normal response to exercise have a relatively low mortality irrespective of systolic function.

Abstract 6950: Precision Quantification of Cardiac Biomarkers Using Reagent-Free Electrochemical Aptasensors

Introduction: Precision quantification of cardiac biomarkers in unprocessed patient samples could reveal critical information in the event of acute myocardial infarction and chronic heart failure. Consequential research efforts have been made in recent years to develop self-contained analytical devices for accurate and rapid quantification of molecular analytes in unprocessed biological fluids, however, their poor sensitivity and compromised robustness in complex biofluids greatly limit their clinical usability. We aimed to investigate the utility of a new biomolecular analysis technique for accurately quantifying BNP and NT-pro BNP directly in whole blood using only a sensor-modified electrode chip. Methods and Results: Recently, we have developed a new class of reagentless biosensors using a molecular pendulum (MP) for monitoring physiologically relevant proteins directly in unprocessed body fluids. The sensing strategy is based on the kinetics of a MP tethered to an electrode surface where the motion of the MP is modulated by the presence of target analyte. Using an antibody as the bioreceptor unit, our MP sensors demonstrated to detect 1 pg/mL of cardiac troponin I in several bio-fluids. Since aptamers hold great promise for developing next-generation of diagnostics and therapeutic tools, we employed them to develop reagent-free MP aptasensors that quantify different heart failure biomarkers including BNP and NT-pro BNP directly in whole human blood. Our MP aptasensors show excellent performance matrices, such as a wide dynamic range (10 fg/mL to 10 ng/mL of BNP), a limit of quantification of 93.5 fg/mL, excellent specificity, and long-term stability, measured directly in whole human blood. Moreover, the analytical resolving capability of the sensors can distinguish samples that differ by only 20 pg/mL of BNP, which is critical for the BNP guided therapy for heart failure. We were also able to quantify BNP and NT-pro BNP simultaneously from the same sample by multiplexing the sensors on the same electronic chip. Conclusion: The analytical strength of our reagent-free MP aptasensors warrants their accelerated development as they could play crucial roles for earlier identification and better risk stratification of heart failure patients.

Frailty phenotypes in patients with heart failure in the early post-discharge period: insights from the iCOR randomised controlled trial and a machine learning-based clustering analysis

Abstract Background/Introduction Several variables such as clinical, socioeconomic, functional or cognitive, among others can have an impact on the prognosis of heart failure (HF) patients despite the optimisation of follow-up strategies (e.g. telemedicine [TM] solutions). The clustering of HF patients may to identify different patient frailty phenotypes. Purpose The aim of this study was to perform a machine learning-based clustering analysis to identify different patient frailty phenotypes in a cohort of HF patients recruited in a randomized clinical trial (The Insuficiència Cardíaca Optimitzaciό Remota [iCOR] study). Methods We performed the clustering analysis on the basis of 8 frailty-related dimensions. To define the number of clusters, dissimilarity matrix was calculated with Gower's distance. Then, hierarchical divisive clustering was performed. Using then Elbow and Silhouette to analyse how the within sum of squares changes for the different number of clusters, the final number of clusters were chosen. The incidence proportion of the each of the study endpoints (non-fatal HF events as primary endpoint and all-cause hospitalization, all-cause death and the composite endpoint combining of all-cause death or non-fatal HF events as secondary endpoints) was calculated for cluster. Results 5 different frailty phenotypes were identified. Cluster 1 (29 patients, 16%) comprised patients with the best reported self-perceived health status (QoL), fair emotional-affective status, but low levels of self-care. Cluster 2 (41 patients, 23%) included the youngest patients with the highest level of education and a better level of cognition. Cluster 3 (68, 38%) encompassed the patients who had the best level of self-care behaviour (18.9±9.8), greater physical and instrumental functioning for activities of daily living (ADL) and a lower rate of comorbidities. Patients in the Cluster 4 (30 patients, 17%) tended to be elderly females with poor health-related QoL, and a higher level of functional dependence. Finally, Cluster 5 was the smallest group (10 patients, 6%), encompassing the oldest patients with low level of education, a worse affective-emotional state, a significant cognitive decline and a higher proportion of comorbidities compared to the other clusters. Cluster 4 had the highest incidence rate of the primary endpoint (57 per 100 patient-years at risk, 95% CI [37.4–74–5]) and a higher incidence of all-cause hospitalization and of the combined variable of all-cause of death or non-fatal HF events. Conclusion(s) Using the cluster analysis, we were able to stratify HF patients according to the stage of their impairment and vulnerability in each of the different frailty domains. This will allow clinicians to incorporate holistic multi-domain assessments in HF programmes to identify patients' needs and provide each patient with personalised and structured follow-up programme according to patient's needs (personalised and precision medicine). Funding Acknowledgement Type of funding sources: None. Radar chart to compare frailty clustersClinical endpoints according clusters

Could a Multi-Marker and Machine Learning Approach Help Stratify Patients with Heart Failure?

Half of the patients with heart failure (HF) have preserved ejection fraction (HFpEF). To date, there are no specific markers to distinguish this subgroup. The main objective of this work was to stratify HF patients using current biochemical markers coupled with clinical data. The cohort study included HFpEF (n = 24) and heart failure with reduced ejection fraction (HFrEF) (n = 34) patients as usually considered in clinical practice based on cardiac imaging (EF ≥ 50% for HFpEF; EF < 50% for HFrEF). Routine blood tests consisted of measuring biomarkers of renal and heart functions, inflammation, and iron metabolism. A multi-test approach and analysis of peripheral blood samples aimed to establish a computerized Machine Learning strategy to provide a blood signature to distinguish HFpEF and HFrEF. Based on logistic regression, demographic characteristics and clinical biomarkers showed no statistical significance to differentiate the HFpEF and HFrEF patient subgroups. Hence a multivariate factorial discriminant analysis, performed blindly using the data set, allowed us to stratify the two HF groups. Consequently, a Machine Learning (ML) strategy was developed using the same variables in a genetic algorithm approach. ML provided very encouraging explorative results when considering the small size of the samples applied. The accuracy and the sensitivity were high for both validation and test groups (69% and 100%, 64% and 75%, respectively). Sensitivity was 100% for the validation and 75% for the test group, whereas specificity was 44% and 55% for the validation and test groups because of the small number of samples. Lastly, the precision was acceptable, with 58% in the validation and 60% in the test group. Combining biochemical and clinical markers is an excellent entry to develop a computer classification tool to diagnose HFpEF. This translational approach is a springboard for improving new personalized treatment methods and identifying “high-yield” populations for clinical trials.

Differential β‐arrestin2 binding of the human β 1 ‐adrenoceptor Arg389Gly polymorphic variants in cardiac myocytes

Rationale The β1-adrenergic receptor (AR) is the primary receptor in cardiomyocytes through which catecholamines increase cardiac function, i.e. contractility and heart rate (positive inotropy and chronotropy). The human β1AR gene (Adrb1), which encodes a G protein-coupled receptor (GPCR), displays high nucleotide sequence variability. One of its several non-synonymous polymorphisms identified to date is the Gly/Arg variation at aminoacid position 389 (C1165G in the open reading frame's nucleotide sequence), with the Arg389 variant being much less frequent in African-Americans than in Caucasians (58% vs. 73%, respectively). Since amino acid 389 is located in the intracellular C-terminus of the encoded receptor, this polymorphism is expected, and has been shown to affect both signaling/function and desensitization of the β1AR. Indeed, Arg389 β1AR has been found to be hyperfunctional, compared to its Gly389 counterpart, in transfected cell lines and in cardiomyocytes in vitro. It is also known to affect the response of individuals to the cardiac stimulant (β1AR-agonist) dobutamine, as well as the responses of heart failure patients to several β-blockers (β1AR-antagonists), including carvedilol, to which the Arg allele confers greater responses. Consistent with this, homozygous Arg389 heart failure patients seem to fare much better than their homozygous Gly389 or heterozygous Arg389Gly counterparts when treated with the same β-blocker drug, a finding that has led to the idea of utilizing genotyping for the β1AR Arg389Gly polymorphism for the purposes of stratification of heart failure patients and of tailoring their b-blocker therapy. Objective To examine the interaction of the two β1AR variants with the signal transducers β-arrestins in neonatal rat ventricular myocytes (NRVMs), in order to identify molecular mechanisms underlying their functional differences. Methods We tested the interaction of the polymorphic β1ARs with β-arrestins via β1AR immunoprecipitation followed by β-arrestin immunoblotting in NRVM protein extracts. Results The β-arrestin1 isoform binds both variants equally well in NRVMs. Conversely, the potentially beneficial in the heart β-arrestin2 isoform interacts only with the Arg389 receptor (not with the Gly389 allele) in NRVMs. Conclusion Arg389 confers unique β-arrestin2-interacting properties to the β1AR in cardiac myocytes, potentially underlying this variant's gain-of-function phenotype.

Fast Strain-Encoded Cardiac Magnetic Resonance for Diagnostic Classification and Risk Stratification of Heart Failure Patients

ObjectivesThe purpose of this study was to compare the ability of fast-strain encoded magnetic resonance (fast-SENC) cardiac magnetic resonance (CMR) to classify and risk stratify all-comer patients with different stages of chronic heart failure (Stages of heart failure A to D) based on American College of Cardiology/American Heart Association guidelines with standard clinical and CMR imaging data. BackgroundHeart failure is a major cause of morbidity and mortality, resulting in millions of deaths and hospitalizations annually. MethodsThe study population consisted of 1,169 consecutive patients between September 2017 and February 2019 who underwent CMR for clinical reasons, and 61 healthy volunteers. In addition, clinical follow-up was performed in Stages A and B patients after 1.9 ± 0.4 years. Wall motion score and late gadolinium enhancement score indexes, left ventricular (LV) ejection fraction, and global circumferential and longitudinal strain based on fast-SENC acquisitions, were calculated in all subjects. The percentage of myocardial segments with strain ≤−17% (% normal myocardium) was determined in all subjects. ResultsLV ejection fraction, global circumferential and longitudinal strain, and % normal myocardium significantly decreased with increasing heart failure stages (p < 0.001 for all by analysis of variance). By multivariable analysis, % normal myocardium remained an independent predictor of heart failure stages, exhibiting closer association than LV ejection fraction (rpartial = 0.76 vs. rpartial = 0.30; p < 0.001). Importantly, 149 of 399 (37%) with Stage A were reclassified to Stage B, that is, as having subclinical LV dysfunction based on % normal myocardium <80%. Such patients exhibited significantly higher rates of all-cause mortality and hospital stay due to heart failure during follow-up, compared with patients with % normal myocardium ≥80% (chi-square = 6.9; p = 0.03). ConclusionsThe % normal myocardium, determined by fast-SENC, enables improved identification of asymptomatic patients with subclinical LV dysfunction compared with LV ejection fraction and risk stratification of patients with so far asymptomatic heart failure. The identification of such presumably healthy patients at high risk for heart failure-related outcomes may bear important medical implications.

Comparison of deep learning with traditional models to predict preventable acute care use and spending among heart failure patients

Recent health reforms have created incentives for cardiologists and accountable care organizations to participate in value-based care models for heart failure (HF). Accurate risk stratification of HF patients is critical to efficiently deploy interventions aimed at reducing preventable utilization. The goal of this paper was to compare deep learning approaches with traditional logistic regression (LR) to predict preventable utilization among HF patients. We conducted a prognostic study using data on 93,260 HF patients continuously enrolled for 2-years in a large U.S. commercial insurer to develop and validate prediction models for three outcomes of interest: preventable hospitalizations, preventable emergency department (ED) visits, and preventable costs. Patients were split into training, validation, and testing samples. Outcomes were modeled using traditional and enhanced LR and compared to gradient boosting model and deep learning models using sequential and non-sequential inputs. Evaluation metrics included precision (positive predictive value) at k, cost capture, and Area Under the Receiver operating characteristic (AUROC). Deep learning models consistently outperformed LR for all three outcomes with respect to the chosen evaluation metrics. Precision at 1% for preventable hospitalizations was 43% for deep learning compared to 30% for enhanced LR. Precision at 1% for preventable ED visits was 39% for deep learning compared to 33% for enhanced LR. For preventable cost, cost capture at 1% was 30% for sequential deep learning, compared to 18% for enhanced LR. The highest AUROCs for deep learning were 0.778, 0.681 and 0.727, respectively. These results offer a promising approach to identify patients for targeted interventions.

Predictability of Inpatient Mortality of Different Comorbidities in Both Types of Acute Decompensated Heart Failure: Analysis of National Inpatient Sample

BackgroundSeveral prediction models have been proposed to assess the short outcomes and in-hospital mortality among patients with heart failure (HF). Several variables were used in common among those models. We sought to focus on other, yet important risk factors that can predict outcomes. We also sought to stratify patients based on ejection fraction, matching both groups with different risk factors.MethodsWe conducted a retrospective cohort study utilizing the Healthcare Cost and Utilization Project National Inpatient Sample (HCUP-NIS) 2016 database.ResultsThere were totally 116,189 admissions for acute decompensated heart failure (ADHF). Of these, 50.9% were for heart failure with reduced ejection fraction (HFrEF) group (n = 59,195), and 49.1% were for heart failure with preserved ejection faction (HFpEF) group (n = 56,994). Overall, in-hospital mortality was 2.5% of admissions for ADHF (n = 2,869). When stratified by HF types, admissions for HFrEF had higher mortality rate (2.7%, n = 1,594) in comparison to admissions for HFpEF (2.2%, n = 1,275) (P < 0.001). Significantly associated variables in univariate analyses were age, race, hypertension, diabetes mellitus, chronic kidney disease (CKD), atrial fibrillation/flutter, obesity, and chronic ischemic heart disease (IHD), while gender and chronic obstructive pulmonary disease (COPD) did not achieve statistical significance (P > 0.1).ConclusionsTo our knowledge, this is the first study to stratify HF patients based on ejection fraction and utilizing different predictors and in-hospital mortality. These and other data support the need for future research to utilize these predictors to create more accurate models in the future.

Incremental value of Late Gadolinium Enhancement by Cardiac MRI in risk stratification of heart failure patients with moderate and severe LV dysfunction

ObjectiveThis is a prospective study of patients with LVEF ≤40%, with the objective of correlating CV events to LGE detected and quantified by CMRI. MethodsHeart Failure (HF) patients with LVEF <40% who underwent CMRI were included. LGE volume of ≥6% of the myocardial volume was considered significant. Data of appropriate ICD shocks, CV hospitalizations and mortality were recorded. ResultsThere were 133 HF (72 ICM & 62 NIDCM) patients with a mean age of 54 ± 12 years, mean LVEF of 34 ± 6% and a follow up of 24 ± 3 months. Totally 46 CV events were recorded in 30 patients, 44 in LGE +ve & 2 in LGE -ve groups (HR 17.8, 95% CI-8.03-39.3, P = 0.000095). All the 7 deaths were in LGE +ve group. CV events were 22 (30.5%) in ICM group and 8 (13.1%) in NIDCM group (p = 0.03). All the 22 ICM patients and 6 of the 8 NIDCM with CV events were LGE +ve. The distribution of CV events amongst LGE +ve and LGE -ve were 35 vs 0 (ICM) and 9 vs 2 (NIDCM); p < 0.005.CV events in LVEF ≤ 30% group, were seen in 19 (47.5%) vs 1 (5.8%) in LGE +ve vs LGE -ve and no of events were 29 vs 1 (p = 0.003). In those with LVEF >30% the corresponding figures were 9 (22.5%) vs 1 (2.8%) and 15 vs 1 respectively (p = 0.02). ConclusionDemonstration of significant LGE by CMRI indicates high risk occurrence of CV events (CV hospitalization, appropriate shocks and total mortality) in NIDCM & ICM patients with LVEF < 40%.

Low ALT Levels Associated with Poor Outcomes in 8700 Hospitalized Heart Failure Patients.

Sarcopenia and frailty are causes for morbidity and mortality amongst heart failure (HF) patients. Low alanine transaminase (ALT) is a marker for these syndromes and, therefore, could serve as a biomarker for the prognostication of HF patients. We performed a retrospective analysis of all consecutive hospitalized HF patients in our institute in order to find out whether low ALT values would be a biomarker for poor outcomes. Our cohort included 11,102 patients, 35.6% categorized as heart failure with reduced ejection fraction. We excluded patients with ALT > 40 IU/L and cirrhosis. 8700 patients were followed for a median duration of 22 months and included in a univariate analysis. Patients with ALT < 10 IU/L were older (mean age 78.6 vs. 81.8, p < 0.001), had past stroke (24.6% vs. 19.6%, p < 0.001), dementia (7.7% vs. 4.6%, p < 0.001), and malignancy (13.4% vs. 10.2%, p = 0.003). Hospitalization length was longer in the low-ALT group (4 vs. 3 days, p < 0.001), and the rate of acute kidney injury during hospitalization was higher (19.1% vs. 15.6%; p = 0.006). The in-hospital mortality rate was higher in the low-ALT group (6.5% vs. 3.9%; p < 0.001). Long-term mortality was also higher (73.3% vs. 61.5%; p < 0.001). In a multivariate regression analysis, ALT < 10 IU/L had a 1.22 hazard ratio for mortality throughout the follow-up period (CI = 1.09–1.36; p < 0.001). Low ALT plasma level, a biomarker for sarcopenia and frailty, can assist clinicians in prognostic stratification of heart failure patients.

Evaluation of an Integrated Device Diagnostics Algorithm to Risk Stratify Heart Failure Patients - Results From the SCAN-HF Study.

Integrated device diagnostics, Triage-HF, is useful in risk stratifying patients with heart failure (HF), but its performance for Japanese patients remains unknown. This is a prospective study of Japanese patients treated with a cardiac resynchronization therapy defibrillator (CRT-D), with a Medtronic OptiVol 2.0 feature.Methods and Results:A total of 320 CRT-D patients were enrolled from 2013 to 2017. All received HF treatment in the prior 12 months. Following enrollment, they were followed every 6 months for 48 months (mean, 22 months). Triage-HF-stratified patients at low, medium and high risk statuses at every 30-day period, and HF-related hospitalization occurring for the subsequent 30 days, were evaluated and repeated. The primary endpoint was to assess Triage-HF performance in predicting HF-related hospitalization risk. All device data were available for 279 of 320 patients (NYHA class II or III in 93%; mean left ventricular ejection fraction, 31%). During a total of 5,977 patient-month follow-ups, 89 HF-related hospitalization occurred in 72 patients. The unadjusted event numbers for Low, Medium and High statuses were 19 (0.7%), 42 (1.6%) and 28 (4.1%), respectively. Relative risk of Medium to Low status was 2.18 (95% CI 1.23-3.85) and 5.78 (95% CI 3.34-10.01) for High to Low status. Common contributing factors among the diagnostics included low activity, OptiVol threshold crossing, and elevated night heart rate. Triage-HF effectively stratified Japanese patients at risk of HF-related hospitalization.

P6326Which results of the cardiopulmonary exercise test deserve greatest attention to establish the prognosis in heart failure?

Abstract Background and aim Limitation of exercise tolerance is one of the cardinal manifestations of heart failure (HF). Cardiopulmonary exercise testing (CPET) provides a thorough assessment of exercise integrative physiology involving the pulmonary, muscular, and oxidative cellular systems. We aimed to identify which data collected during a CPET shows the best prognostic performance with respect to predicting mortality or the need for heart transplantation (HT). Methods Single-centre retrospective cohort study of consecutive HF patients performing a CPET for functional and prognostic HF evaluation from October 1996 till May 2018. Left ventricular ejection fraction was not an exclusion criterion. A Cox model was fit with time to death or heart transplantation (whichever recorded first within 5 years) as the dependent variable and CPET parameters as the independent variables. Both unadjusted and adjusted covariate Cox regressions were performed. ROC curve analysis was used to determine whether the significant variables, as a model, could reliably predict the study endpoint. Results The study population consisted of 513 patients, median age 58 (IQ 16) years, and 74.9% male. The majority had reduced ejection fraction (75.4%), and the most common HF aetiology was ischemic heart disease (55.8%). During the 5-years follow up, 126 patients died and 60 underwent heart transplantation. In unadjusted Cox regression, nearly all CPET variables were significantly associated with the study endpoint. After covariate adjustment, with prior exclusion of redundant variables, three measures remained associated with the study endpoint: peak VO2 consumption (hazard ratio [HR] 0.85; 95% confidence interval [CI], 0.81–0.90); VE/VCO2 slope (HR 1.02; 95% CI, 1.00–1.02); presence of oscillatory ventilatory pattern (HR 3.73; 95% CI, 2.43–5.72). As a model, these 3 variables showed a strong discriminatory ability (c-statistic 0.87; 95% CI, 0.83–0.90) (see figure) for the study endpoint. Figure 1 Conclusion When using the CPET for prognostic stratification of HF patients, the presence of an oscillatory ventilatory pattern, the peak VO2 and the VE/VCO2 slope are the most important tools on which clinicians should focus.

Prospective Real-World Performance Evaluation of a Machine Learning Algorithm to Predict 30-Day Readmissions in Patients with Heart Failure Using Electronic Medical Record Data

Background: Heart failure (HF) patients have a high readmission rate with approximately 20% of patients being readmitted within 30-days after discharge. Hospital interventions to reduce HF readmissions are resource- and effort-intensive. Widespread availability of electronic medical record data has spurred interest in using machine learning-based techniques for risk stratification of heart failure patients. The predictive performance of machine learning-based predictive models is often evaluated solely using the Area Under the Receiver Operating Characteristic (AUROC) curve. However, the AUROC is independent of prevalence therefore predictive models with the same AUROC can have differential clinical utility. Furthermore, the AUROC does not provide any insight about the presence of overfitting or decay in predictive performance of a model over time, both of which can affect its real-world performance.

Can we Further Stratify Heart Failure Patients Deemed to be at Intermediate Risk of Mortality⋯Looking Beyond Percent Predicted Peak VO2 and Ventilatory Efficiency?

Background Percent predicted peak oxygen uptake (%ppVO2) is a useful tool to help stratify risk in patients with heart failure with reduced ejection fraction (HFrEF). However, insufficient knowledge on how to best optimize timing for advanced therapies in patients with a peak VO2 between 50% and 75% of predicted exists. Purpose Determine if risk for mortality can be further parsed in patients with a %ppVO2 between 50% and 75%, using the slope relationship between minute ventilation and carbon dioxide (VE/VCO2 slope) and clinical variables. Methods Retrospective analysis involving patients with HFrEF (EF Results 645 patients were included in this study: 66% male, 53% african american, mean (+sd) age 55+13, 63% non-ischemic etiology, and EF 23+10. There were 300 deaths over 6.4 median years of follow-up. For both 1 and 3 year mortality rates, we observed a significant difference across all three subgroups of VE/VCO2 slope ( Table 1 ) Table 2 lists those covariates that were independently associated with mortality for each subgroup of VE/VCO2 slope. No models could be identified to predict mortality with a sufficiently high degree of sensitivity and specificity. Conclusions Among patients with HFrEF at intermediate risk for mortality based on %ppVO2, an increase in VE/VCO2 is associated with increased risk for mortality and may identify patients in need of closer surveillance. However, no additional covariates or combinations of covariates could be identified to sufficiently further parse risk for any of the three VE/VCO2 subgroups.

Evidence for a hyper-reductive redox in a sub-set of heart failure patients

BackgroundOxidative stress has been linked to heart failure (HF) in humans. Antioxidant-based treatments are often ineffective. Therefore, we hypothesize that some of the HF patients might have a reductive stress (RS) condition. Investigating RS-related mechanisms will aid in personalized optimization of redox homeostasis for better outcomes among HF patients.MethodsBlood samples were collected from HF patients (n = 54) and healthy controls (n = 42) and serum was immediately preserved in − 80 °C for redox analysis. Malondialdehyde (MDA; lipid peroxidation) levels by HPLC, reduced glutathione (GSH) and its redox ratio (GSH/GSSG) using enzymatic-recycling assay in the serum of HF patients were measured. Further, the activities of key antioxidant enzymes were analyzed by UV–Vis spectrophotometry. Non-invasive echocardiography was used to relate circulating redox status with cardiac function and remodeling.ResultsThe circulatory redox state (GSH/MDA ratio) was used to stratify the HF patients into normal redox (NR), hyper-oxidative (HO), and hyper-reductive (HR) groups. While the majority of the HF patients exhibited the HO (42%), 41% of them had a normal redox (NR) state. Surprisingly, a subset of HF patients (17%) belonged to the hyper-reductive group, suggesting a strong implication for RS in the progression of HF. In all the groups of HF patients, SOD, GPx and catalase were significantly increased while GR activity was significantly reduced relative to healthy controls. Furthermore, echocardiography analyses revealed that 55% of HO patients had higher systolic dysfunction while 62.5% of the hyper-reductive patients had higher diastolic dysfunction.ConclusionThese results suggest that RS may be associated with HF pathogenesis for a subset of cardiac patients. Thus, stratification of HF patients based on their circulating redox status may serve as a useful prognostic tool to guide clinicians designing personalized antioxidant therapies.

α-1 Protein evaluation to stratify heart failure patients

Heart failure is a pathological condition characterized by cardiac dysfunction and neuroendocrine system activation. The aim of this study was to evaluate serum α-1 proteins in the characterization of heart failure patients. The study included 69 patients with documented heart failure disease and 44 healthy individuals. We included 12 out of 69 patients with preserved (>50%) left ventricular ejection fraction. α-1 protein levels were evaluated using routine capillary electrophoresis. Markers of inflammation, such as interleukin-6 (IL-6) and tumor necrosis factor-α, were measured with UltraSensitive ELISA Kits. C-reactive protein and brain natriuretic peptide were determined by automated assays. No difference in α-1 protein levels between patients with reduced versus preserved left ventricular ejection fraction was observed. IL-6, tumor necrosis factor-α, and C-reactive protein concentrations were significantly increased in patients with respect to the control group (P <0.001, P <0.01, and P <0.05, respectively). A progressive increase in α-1 protein levels across NYHA classes (P = 0.0077) was observed. Brain natriuretic peptide median value of the patient group was 287 ng/l (92–602 ng/l) and was significantly associated with α-1 proteins and IL-6 levels (P <0.05 and P <0.01, respectively). Considering recent findings and our preliminary data, we hypothesized that the overexpression of α-1 antitrypsin (AAT) protein (and probably elevated AAT levels) is a compensatory mechanism as a consequence of the loss of the antiprotease activity, induced by the increase of oxidative stress in heart failure patients. In conclusion, we assume that α-1 proteins and AAT could contribute to the prognostic stratification of heart failure patients.