Development and validation of multivariable models to predict mortality and hospitalization in patients with heart failure

International Society for Heart and Lung Transplantation Guidelines for the Evaluation and Care of Cardiac Transplant Candidates-2024

The "International Society for Heart and Lung Transplantation Guidelines for the Evaluation and Care of Cardiac Transplant Candidates-2024" updates and replaces the "Listing Criteria for Heart Transplantation: International Society for Heart and Lung Transplantation Guidelines for the Care of Cardiac Transplant Candidates-2006" and the "2016 International Society for Heart Lung Transplantation Listing Criteria for Heart Transplantation: A 10-year Update." The document aims to provide tools to help integrate the numerous variables involved in evaluating patients for transplantation, emphasizing updating the collaborative treatment while waiting for a transplant. There have been significant practice-changing developments in the care of heart transplant recipients since the publication of the International Society for Heart and Lung Transplantation (ISHLT) guidelines in 2006 and the 10-year update in 2016. The changes pertain to 3 aspects of heart transplantation: (1) patient selection criteria, (2) care of selected patient populations, and (3) durable mechanical support. To address these issues, 3 task forces were assembled. Each task force was cochaired by a pediatric heart transplant physician with the specific mandate to highlight issues unique to the pediatric heart transplant population and ensure their adequate representation. This guideline was harmonized with other ISHLT guidelines published through November 2023. The 2024 ISHLT guidelines for the evaluation and care of cardiac transplant candidates provide recommendations based on contemporary scientific evidence and patient management flow diagrams. The American College of Cardiology and American Heart Association modular knowledge chunk format has been implemented, allowing guideline information to be grouped into discrete packages (or modules) of information on a disease-specific topic or management issue. Aiming to improve the quality of care for heart transplant candidates, the recommendations present an evidence-based approach.

Impaired HDL antioxidant and anti-inflammatory functions are linked to increased mortality in acute heart failure patients

AIMS

Acute heart failure (AHF) is typified by inflammatory and oxidative stress responses, which are associated with unfavorable patient outcomes. Given the anti-inflammatory and antioxidant properties of high-density lipoprotein (HDL), this study sought to examine the relationship between impaired HDL function and mortality in AHF patients. The complex interplay between various HDL-related biomarkers and clinical outcomes remains poorly understood.

METHODS

HDL subclass distribution was quantified by nuclear magnetic resonance spectroscopy. Lecithin-cholesterol acyltransferase (LCAT) activity, cholesterol ester transfer protein (CETP) activity, and paraoxonase (PON-1) activity were assessed using fluorometric assays. HDL-cholesterol efflux capacity (CEC) was assessed in a validated assay using [3H]-cholesterol-labeled J774 macrophages.

RESULTS

Among the study participants, 74 (23.5 %) out of 315 died within three months after hospitalization due to AHF. These patients exhibited lower activities of the anti-oxidant enzymes PON1 and LCAT, impaired CEC, and lower concentration of small HDL subclasses, which remained significant after accounting for potential confounding factors. Smaller HDL particles, particularly HDL3 and HDL4, exhibited a strong association with CEC, PON1 activity, and LCAT activity.

CONCLUSIONS

In patients with AHF, impaired HDL CEC, HDL antioxidant and anti-inflammatory function, and impaired HDL metabolism are associated with increased mortality. Assessment of HDL function and subclass distribution could provide valuable clinical information and help identify patients at high risk.

Machine learning-based model for worsening heart failure risk in Chinese chronic heart failure patients

AIMS

This study aims to develop and validate an optimal model for predicting worsening heart failure (WHF). Multiple machine learning (ML) algorithms were compared, and the results were interpreted using SHapley Additive exPlanations (SHAP). A clinical risk calculation tool was subsequently developed based on these findings.

METHODS AND RESULTS

This nested case-control study included 200 patients with chronic heart failure (CHF) from the China-Japan Friendship Hospital (September 2019 to December 2022). Sixty-five variables were collected, including basic information, physical and chemical examinations, and quality of life assessments. WHF occurrence within a 3-month follow-up was the outcome event. Variables were screened using LASSO regression, univariate analysis, and comparison of key variables in multiple ML models. Eighty per cent of the data was used for training and 20% for testing. The best models were identified by integrating nine ML algorithms and interpreted using SHAP, and to develop a final risk calculation tool. Among participants, 68 (34.0%) were female, with a mean age (standard deviation, SD) of 68.57 (12.80) years. During the follow-up, 60 participants (30%) developed WHF. N-terminal pro-brain natriuretic peptide (NT-proBNP), creatinine (Cr), uric acid (UA), haemoglobin (Hb), and emotional area score on the Minnesota Heart Failure Quality of Life Questionnaire were critical predictors of WHF occurrence. The random forest (RF) model was the best model to predict WHF with an area under the curve (AUC) (95% confidence interval, CI) of 0.842 (0.675-1.000), accuracy of 0.775, sensitivity of 0.900, specificity of 0.833, negative predictive value of 0.800, and positive predictive value of 0.600 for the test set. SHAP analysis highlighted NT-proBNP, UA, and Cr as significant predictors. An online risk predictor based on the RF model was developed for personalized WHF risk assessment.

CONCLUSIONS

This study identifies NT-proBNP, Cr, UA, Hb, and emotional area scores as crucial predictors of WHF in CHF patients. Among the nine ML algorithms assessed, the RF model showed the highest predictive accuracy. SHAP analysis further emphasized NT-proBNP, UA, and Cr as the most significant predictors. An online risk prediction tool based on the RF model was subsequently developed to enhance early and personalized WHF risk assessment in clinical settings.

Incidence and prediction of hospitalization for heart failure in patients with atrial fibrillation: the REFLEJA scale

INTRODUCTION AND OBJECTIVES

Hospitalization for heart failure (HHF) is common in patients with atrial fibrillation (AF) and is associated with increased mortality. The aims of this study were to determine the incidence of HHF, identify the clinical predictors of its occurrence, and develop a new risk scale.

METHODS

The incidence of HHF was estimated using data from the prospective single-center REFLEJA registry of outpatients with AF (October 2017-October 2018). A multivariate Cox regression model was calculated to detect HHF predictors, and a nomogram was created for individual risk assessment.

RESULTS

Of the 1499 patients included (mean age 73.8±11.1 years, 48.1% women), 127 had HHF (incidence rate of 8.51 per 100 persons/y) and 319 died (rate of death from any cause of 21.1 per 100 persons/y) after a 3-year follow-up. The independent predictors of HHF were age, diabetes, chronic kidney disease, pulmonary hypertension, previous pacemaker implantation, baseline use of diuretics, and moderate-severe aortic regurgitation. The c-statistic for predicting the event was 0.762 (95%CI after boostrapping resampling, 0.753-0.791). The cumulative incidences of the main outcome for the risk scale quartiles were 1.613 (Q1), 3.815 (Q2), 8.378 (Q3), and 20.436 (Q4) cases per 100 persons/y (P <.001).

CONCLUSIONS

HHF was common in this AF cohort. The combination of certain clinical characteristics can identify patients with a very high risk of HHF.

Hallmarks of cancer in patients with heart failure: data from BIOSTAT-CHF

BACKGROUND

Within cardio-oncology, emerging epidemiologic studies have demonstrated a bi-directional relationship between heart failure (HF) and cancer. In the current study, we aimed to further explore this relationship and investigate the underlying pathophysiological pathways that connect these two disease entities.

METHODS

We conducted a post-hoc analysis in which we identified 24 Gene Ontology (GO) processes associated with the hallmarks of cancer based on 92 biomarkers in 1960 patients with HF. We performed Spearman's correlations and Cox-regression analyses to evaluate associations with HF biomarkers, severity and all-cause mortality.

RESULTS

Out of a total of 24 GO processes, 9 biological processes were significantly associated with adverse clinical outcome. Positive regulation of mononuclear cell proliferation demonstrated the highest hazard for reaching the clinical endpoint, even after adjusting for confounders: all-cause mortality HR 2.00 (95% CI 1.17-3.42), p = 0.012. In contrast, negative regulation of apoptotic process was consistently associated with a lower hazard of reaching the clinical outcome, even after adjusting for confounders: all-cause mortality HR 0.74 (95% CI 0.59-0.95), p = 0.016. All processes significantly correlated with HF biomarkers, renal function and HF severity.

CONCLUSIONS

In patients with HF, GO processes associated with hallmarks of cancer are associated with HF biomarkers, severity and all-cause mortality.

Cardiovascular disease healthcare trajectories: descriptions, similarities, mortality rates of heart failure in France

AIMS

The primary objectives of this study were to analyse the nationwide healthcare trajectories of heart failure (HF) patients in France, 2 years after their first hospitalization, and to measure sequence similarities. Secondary objectives were to identify the association between trajectories and the risk of mortality.

METHODS AND RESULTS

A retrospective, observational study was conducted using data extracted from the Echantillon Généraliste des Bénéficiaires database, covering the period from 1 January 2008 to 31 December 2018. Follow-up concluded upon death or at the end of the study. We developed a methodology specific to healthcare data by extracting frequent healthcare trajectories and measuring their similarity for use in a survival machine learning analysis. In total, 11 488 HF patients were included and followed up for an average of 2.9 ± 1.3 years. The mean age of the patients was 78.0 ± 13.2 years. The first-year mortality rate was 31.7% and increased to 78.8% at 5 years. Fifty per cent of patients experienced re-hospitalization for reasons related to cardiovascular diseases. We identified 1707 hospitalization sequences, and 21 sequences were associated with survival, while 15 sequences were linked to mortality. In all our models, age and gender emerged as the most significant predictors of mortality (permutation feature importance: 0.099 ± 0.00078 and 0.0087 ± 0.00018, respectively; weights could be interpreted in relative terms). Specifically, the age at initial hospitalization for HF was positively associated with mortality. Gender (male: 49.5%) was associated with poorer prognoses. Healthcare trajectories, including non-surgical device treatments, valve replacements, and atrial fibrillation ablation, were associated with a better prognosis (permutation feature importance: 0.0047 ± 0.00011, 0.0014 ± 0.000073, and 0.00095 ± 0.000097, respectively), except in cases where these invasive treatments preceded or followed hospitalization for cardiac decompensation. The predominant negative prognosis sequences were mostly those that included HF-related hospitalizations before or after other-related hospitalizations (permutation feature importance: 0.0007 ± 0.000091 and 0.00011 ± 0.000045, respectively).

CONCLUSIONS

We highlight the value of healthcare trajectories on frequent hospitalization sequences, mortality, and prognosis and indicate the necessary prognostic value of HF re-hospitalization. Our work may be an essential tool for better identification of at-risk patients in order to increase and improve personalized care in the future.

Role of spot urinary sodium in outpatients with heart failure

INTRODUCTION AND OBJECTIVES

Spot determination of urinary sodium (UNa+) has emerged as a useful tool for monitoring diuretic response in patients with acute heart failure (AHF). However, the evidence in outpatients is scarce. We aimed to examine the relationship between spot UNa+ levels and the risk of mortality and worsening heart failure (WHF) events in individuals with chronic HF.

METHODS

This observational and ambispective study included 1145 outpatients with chronic HF followed in a single center specialized HF clinic. UNa+ assessment was carried out 1-5 days before each visit. The endpoints of the study were the association between UNa+ and risk of a) long-term death and b) AHF-hospitalization and total WHF events (including AHF-hospitalization, emergency department visits or parenteral loop-diuretic administration in HF clinic), assessed by multivariate Cox and negative binomial regressions.

RESULTS

The mean±standard deviation of age was 73±11 years, 670 (58.5%) were men, 902 (78.8%) were on stable NYHA class II, and 595 (52%) had LFEF ≥50%. The median (interquartile range) UNa+ was 72 (51-94) mmol/L. Over a median follow-up of 2.63 (1.70-3.36) years, there were 293 (25.6%) deaths and 382 WHF events (244 AHF-admissions) in 233 (20.3%) patients. After multivariate adjustment, baseline UNa+ was inverse and linearly associated with the risk of total WHF (IRR, 1.07; 95%CI, 1.02-1.12; P=.007) and AHF-admissions (IRR, 1.08; 95%CI, 1.02-1.14; P=.012) and borderline associated with all-cause mortality (HR, 1.04; 95%CI, 0.99-1.09; P=.068).

CONCLUSIONS

In outpatients with chronic HF, lower UNa+ was associated with a higher risk of recurrent WHF events.

IL-17 is associated with disease severity and targetable inflammatory processes in heart failure

AIMS

Heart failure (HF) is recognized as an inflammatory disease in which cytokines play an important role. In animal HF models, interleukin-17A (IL-17) has been linked to deterioration of cardiac function and fibrosis, whereas knock-out of IL-17 showed beneficial cardiac effects. However, there is limited evidence of IL-17 involvement in patients with HF. This study aims to investigate the clinical characteristics, outcomes, and pathophysiological processes associated with circulating IL-17 concentrations in patients with HF.

METHODS AND RESULTS

IL-17 was measured by ELISA in 2082 patients diagnosed with HF along with 363 circulating proteins using proximity extension assay technology for differential expression and pathway analysis. Data were validated in an independent cohort of 1737 patients with HF. Patients with elevated IL-17 concentrations had more severe HF, as reflected by more frequent current or previous hospitalizations for HF, higher New York Heart Association functional class (NYHA) and higher levels of N-terminal pro-B-type natriuretic peptide (NT-proBNP). High IL-17 concentrations were independently associated with an increased risk of hospitalization for HF and mortality. In both cohorts, the most strongly up-regulated proteins in patients with high IL-17 were fibroblast growth factor 21 (FGF-21), interleukin-6 (IL-6), C-X-C motif chemokine ligand 13 (CXCL13), tumour necrosis factor receptor superfamily member 6B (TNFRSF6B) and interleukin-1 receptor antagonist (IL-1RA). Pathway over-representation analysis showed increased activity of pathways related to lymphocyte-mediated immunity, leukocyte activation and regulation of the immune response.

CONCLUSIONS

In patients with HF, elevated IL-17 concentrations indicate more severe HF and increased activity of inflammatory processes known to be involved in the pathophysiology of HF. IL-17 might hold potential for identifying and targeting inflammation in HF.

Predicting the risk of 1-year mortality among patients hospitalized for acute heart failure in China

BACKGROUND

We aimed to develop and validate a model to predict 1-year mortality risk among patients hospitalized for acute heart failure (AHF), build a risk score and interpret its application in clinical decision making.

METHODS

By using data from China Patient-Centred Evaluative Assessment of Cardiac Events Prospective Heart Failure Study, which prospectively enrolled patients hospitalized for AHF in 52 hospitals across 20 provinces, we used multivariate Cox proportional hazard model to develop and validate a model to predict 1-year mortality.

RESULTS

There were 4,875 patients included in the study, 857 (17.58%) of them died within 1-year following discharge of index hospitalization. A total of 13 predictors were selected to establish the prediction model, including age, medical history of chronic obstructive pulmonary disease and hypertension, systolic blood pressure, Kansas City Cardiomyopathy Questionnaire-12 score, angiotensin converting enzyme inhibitor or angiotensin receptor blocker at discharge, discharge symptom, N-terminal pro-brain natriuretic peptide, high-sensitivity troponin T, serum creatine, albumin, blood urea nitrogen, and highly sensitive C-reactive protein. The model showed a high performance on discrimination (C-index was 0.759 [95% confidence interval: 0.739, 0.778] in development cohort and 0.761 [95% confidence interval: 0.731, 0.791] in validation cohort), accuracy, calibration, and outperformed than several existed risk scores. A point-based risk score was built to stratify low- (0-12), intermediate- (13-16), and high-risk group (≥17) among patients.

CONCLUSIONS

A prediction model using readily available predictors was developed and internal validated to predict 1-year mortality risk among patients hospitalized for AHF. It may serve as a useful tool for individual risk stratification and informing decision making to improve clinical care.

Quantifying Hemodynamic Cardiac Stress and Cardiomyocyte Injury in Normotensive and Hypertensive Acute Heart Failure

BACKGROUND

The characterization of the different pathophysiological mechanisms involved in normotensive versus hypertensive acute heart failure (AHF) might help to develop individualized treatments.

METHODS

The extent of hemodynamic cardiac stress and cardiomyocyte injury was quantified by measuring the B-type natriuretic peptide (BNP), N-terminal proBNP (NT-proBNP), and high-sensitivity cardiac troponin T (hs-cTnT) concentrations in 1152 patients presenting with centrally adjudicated AHF to the emergency department (ED) (derivation cohort). AHF was classified as normotensive with a systolic blood pressure (SBP) of 90-140 mmHg and hypertensive with SBP > 140 mmHg at presentation to the ED. Findings were externally validated in an independent AHF cohort (n = 324).

RESULTS

In the derivation cohort, with a median age of 79 years, 43% being women, 667 (58%) patients had normotensive and 485 (42%) patients hypertensive AHF. Hemodynamic cardiac stress, as quantified by the BNP and NT-proBNP, was significantly higher in normotensive as compared to hypertensive AHF [1105 (611-1956) versus 827 (448-1419) pg/mL, and 5890 (2959-12,162) versus 4068 (1986-8118) pg/mL, both p < 0.001, respectively]. Similarly, the extent of cardiomyocyte injury, as quantified by hs-cTnT, was significantly higher in normotensive AHF as compared to hypertensive AHF [41 (24-71) versus 33 (19-59) ng/L, p < 0.001]. A total of 313 (28%) patients died during 360 days of follow-up. All-cause mortality was higher in patients with normotensive AHF vs. patients with hypertensive AHF (hazard ratio 1.66, 95%CI 1.31-2.10; p < 0.001). Normotensive patients with a high BNP, NT-proBNP, or hs-cTnT had the highest mortality. The findings were confirmed in the validation cohort.

CONCLUSION

Biomarker profiling revealed a higher extent of hemodynamic stress and cardiomyocyte injury in patients with normotensive versus hypertensive AHF.

The Association of Unmet Palliative Care Needs and Physical Frailty With Clinical Outcomes: A Prospective Study of Adults With Heart Failure

BACKGROUND

People with heart failure, particularly those who are physically frail, experience complex needs that can be addressed by palliative care (PC). However, we have a limited understanding of how the intersection of unmet PC needs and physical frailty contributes to health-related quality of life (HRQOL) and risk for hospitalization or mortality.

OBJECTIVE

In this study, we sought to examine the association of unmet PC needs and physical frailty with clinical outcomes (baseline HRQOL and hospitalizations or mortality at 6 months).

METHODS

We recruited a convenience sample of community-dwelling persons with heart failure from an urban hospital system who were older than 50 years and hospitalized in the last year. We measured physical frailty using the FRAIL scale (nonfrail, 0-2; frail, 3-5), PC needs using the Integrated Palliative Outcome Scale (range, 0-58; higher scores indicating higher needs), and HRQOL using the Kansas City Cardiomyopathy Questionnaire (range, 0-100; higher scores indicate higher HRQOL). We performed multivariable linear regression to test the relationships between physical frailty, PC needs, and HRQOL, and multivariable logistic regression for associations with all-cause 6-month hospitalization or mortality. We also performed an exploratory analysis of 4 PC needs/frailty groups (high PC needs/frail, high PC needs/nonfrail, low PC needs/frail, low PC needs/nonfrail) with outcomes.

RESULTS

In our overall sample (n = 298), mean (SD) age was 68 (9.8) years, 37% were women (n = 108), 28% identified as Black/African American (n = 84), and 65% had heart failure with preserved ejection fraction (n = 194). Mean PC needs score was 19.7, and frail participants (n = 130, 44%) had a significantly higher mean PC needs score than nonfrail participants (P < .001). Those with higher PC needs (Integrated Palliative Care Outcome Scale ≥ 20) had significantly worse HRQOL (P < .001) and increased odds of hospitalization or mortality (odds ratio, 2.5; P < .01) compared with those with lower PC needs, adjusting for covariates. Physically frail participants had significantly worse HRQOL (P < .001) and higher odds of hospitalization or mortality at 6 months (odds ratio, 2.6; P < .01) than nonfrail participants, adjusting for covariates. In an exploratory analysis, physically frail participants with high PC needs had the lowest HRQOL score, with an average score of 28.6 points lower (P < .001) and 4.6 times higher odds of hospitalization or mortality (95% confidence interval, 2.03-10.43; P < .001) than low-needs/nonfrail participants.

CONCLUSION

Higher unmet PC needs and physical frailty, separately and in combination, were associated with lower HRQOL and higher odds of hospitalization or mortality. Self-reported PC needs and physical frailty assessment in clinical settings may improve identification of patients at the highest risk for poor HRQOL and hospitalization or mortality amenable to PC intervention.

Biomarker and transcriptomics profiles of serum selenium concentrations in patients with heart failure are associated with immunoregulatory processes

BACKGROUND

Low selenium concentrations are associated with worse outcomes in heart failure (HF). However, the underlying pathophysiologic mechanisms remain incompletely understood. Therefore, we aimed to contrast serum selenium concentrations to blood biomarker and transcriptomic profiles in patients with HF.

METHODS

Circulating biomarkers, whole blood transcriptomics and serum selenium measurements in a cohort of 2328 patients with HF were utilized. Penalized linear regression and gene expression analysis were used to assess biomarker and transcriptomics profiles, respectively. As a proof-of-principle, potential causal effects of selenium on excreted cytokines concentrations were investigated using human peripheral blood mononuclear cells (PBMCs).

RESULTS

Mean selenium levels were 60.6 μg/L in Q1 and 122.0 μg/L in Q4. From 356 biomarkers and 20 clinical features, the penalized linear regression model yielded 44 variables with <5 % marginal false discovery rate as predictors of serum selenium. Biomarkers associated positively with selenium concentrations included: epidermal growth factor receptor (EGFR), IFN-gamma-R1, CD4, GDF15, and IL10. Biomarkers associated negatively with selenium concentrations included: PCSK9, TNFSF13, FGF21 and PAI. Additionally, 148 RNA transcripts were found differentially expressed between high and low selenium status (Padj.<0.05; log-fold-change<|0.25|). Enrichment analyses of the selected biomarkers and RNA transcripts identified similar enriched processes, including regulation processes of leukocyte differentiation and activation, as well as cytokines production. The mRNA expression of two selenoproteins (MSRB1 and GPX4) were strongly correlated with serum selenium, while GPX4, SELENOK, and SELENOS were associated with prognosis. In the in-vitro setting, PBMCs supplemented with selenium showed significantly lower abundance of several (pro-)inflammatory cytokines.

CONCLUSION

These data suggest that immunoregulation is an important mechanism through which selenium might have beneficial roles in HF. The beneficial effects of higher serum selenium concentrations are likely because of global immunomodulatory effects on the abundance of cytokines. MSRB1 and GPX4 are potential modulators of and should be pursued in future research.

Prognostic impact of MitraScore in elderly Asian patients with heart failure: sub-analysis of FRAGILE-HF

AIMS

MitraScore is a novel, simple, and manually calculatable risk score developed as a prognostic model for patients undergoing transcatheter edge-to-edge repair (TEER) for mitral regurgitation. As its components are considered prognostic in heart failure (HF), we aimed to investigate the usefulness of the MitraScore in HF patients.

METHODS AND RESULTS

We calculated MitraScore for 1100 elderly patients (>65 years old) hospitalized for HF in the prospective multicentre FRAGILE-HF study and compared its prognostic ability with other simple risk scores. The primary endpoint was all-cause deaths, and the secondary endpoints were the composite of all-cause deaths and HF rehospitalization and cardiovascular deaths. Overall, the mean age of 1100 patients was 80 ± 8 years, and 58% were men. The mean MitraScore was 3.2 ± 1.4, with a median of 3 (interquartile range: 2-4). A total of 326 (29.6%), 571 (51.9%), and 203 (18.5%) patients were classified into low-, moderate-, and high-risk groups based on the MitraScore, respectively. During a follow-up of 2 years, 226 all-cause deaths, 478 composite endpoints, and 183 cardiovascular deaths were observed. MitraScore successfully stratified patients for all endpoints in the Kaplan-Meier analysis (P < 0.001 for all). In multivariate analyses, MitraScore was significantly associated with all endpoints after covariate adjustments [adjusted hazard ratio (HR) (95% confidence interval): 1.22 (1.10-1.36), P < 0.001 for all-cause deaths; adjusted HR 1.17 (1.09-1.26), P < 0.001 for combined endpoints; and adjusted HR 1.24 (1.10-1.39), P < 0.001 for cardiovascular deaths]. The Hosmer-Lemeshow plot showed good calibration for all endpoints. The net reclassification improvement (NRI) analyses revealed that the MitraScore performed significantly better than other manually calculatable risk scores of HF: the GWTG-HF risk score, the BIOSTAT compact model, the AHEAD score, the AHEAD-U score, and the HANBAH score for all-cause and cardiovascular deaths, with respective continuous NRIs of 0.20, 0.22, 0.39, 0.39, and 0.29 for all-cause mortality (all P-values < 0.01) and 0.20, 0.22, 0.42, 0.40, and 0.29 for cardiovascular mortality (all P-values < 0.02).

CONCLUSIONS

MitraScore developed for patients undergoing TEER also showed strong discriminative power in HF patients. MitraScore was superior to other manually calculable simple risk scores and might be a good choice for risk assessment in clinical practice for patients receiving TEER and those with HF.

Urinary Marker Profiles in Heart Failure with Reduced Versus Preserved Ejection Fraction

BACKGROUND

Recent data suggest different causes of renal dysfunction between heart failure with reduced (HFrEF) versus preserved ejection fraction (HFpEF). We therefore studied a wide range of urinary markers reflecting different nephron segments in heart failure patients.

METHODS

In 2070, in chronic heart failure patients, we measured several established and upcoming urinary markers reflecting different nephron segments.

RESULTS

Mean age was 70 ± 12 years, 74% was male and 81% (n = 1677) had HFrEF. Mean estimated glomerular filtration rate (eGFR) was lower in patients with HFpEF (56 ± 23 versus 63 ± 23 ml/min/1.73 m2, P = 0.001). Patients with HFpEF had significantly higher values of NGAL (58.1 [24.0-124.8] versus 28.1 [14.6-66.9] μg/gCr, P < 0.001) and KIM-1 (2.28 [1.49-4.37] versus 1.79 [0.85-3.49] μg/gCr, P = 0.001). These differences were more pronounced in patients with an eGFR > 60 ml/min/1.73m2.

CONCLUSIONS

HFpEF patients showed more evidence of tubular damage and/or dysfunction compared with HFrEF patients, in particular when glomerular function was preserved.

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.

Comprehensive vasodilatation in women with acute heart failure: Novel insights from the GALACTIC randomized controlled trial

AIMS

Sex-specific differences in acute heart failure (AHF) are both relevant and underappreciated. Therefore, it is crucial to evaluate the risk/benefit ratio and the implementation of novel AHF therapies in women and men separately.

METHODS AND RESULTS

We performed a pre-defined sex-specific analysis in AHF patients randomized to a strategy of early intensive and sustained vasodilatation versus usual care in an international, multicentre, open-label, blinded endpoint trial. Inclusion criteria were AHF with increased plasma concentrations of natriuretic peptides, systolic blood pressure ≥100 mmHg, and plan for treatment in a general ward. Among 781 eligible patients, 288 (37%) were women. Women were older (median 83 vs. 76 years), had a lower body weight (median 64.5 vs. 77.6 kg) and lower estimated glomerular filtration rate (median 48 vs. 54 ml/min/1.73 m2 ). The primary endpoint, a composite of all-cause mortality or rehospitalization for AHF at 180 days, showed a significant interaction of treatment strategy and sex (p for interaction = 0.03; hazard ratio adjusted for female sex 1.62, 95% confidence interval 1.05-2.50; p = 0.03). The combined endpoint occurred in 53 women (38%) in the intervention group and in 35 (24%) in the usual care group. The implementation of rapid up-titration of renin-angiotensin-aldosterone system (RAAS) inhibitors was less successful in women versus men in the overall cohort and in patients with heart failure with reduced ejection fraction (median discharge % target dose in patients randomized to intervention: 50% in women vs. 75% in men).

CONCLUSION

Rapid up-titration of RAAS inhibitors was less successfully implemented in women possibly explaining their higher rate of all-cause mortality and rehospitalization for AHF.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov, unique identifier NCT00512759.

Association between Serum Free Fatty Acids and Clinical and Laboratory Parameters in Acute Heart Failure Patients

Very little is known about the association between individual serum free fatty acids (FFAs) and clinical and laboratory parameters (indicators of heart failure severity) in acute heart failure (AHF) patients. Here, the baseline serum levels of FFAs, 16:0 (palmitic acid), 16:1 (palmitoleic acid), 18:0 (stearic acid), 18:1 (oleic acid), 18:2 (linoleic acid), 18:3 (alpha-linolenic acid or gamma-linolenic acid), 20:4 (arachidonic acid), 20:5 (eicosapentaenoic acid), and 22:6 (docosahexaenoic acid), were determined in 304 AHF patients (94.7% belonged to New York Heart Association functional class IV) using gas chromatography. Spearman correlation coefficients were used to examine the associations between the individual and total (the sum of all FFAs) FFAs and clinical and laboratory parameters. After applying a Bonferroni correction to correct for multiple testing, the total FFAs, as well as the individual FFAs (except FFAs 18:0, 20:5, and 22:6), were found to be significantly positively correlated with serum albumin. Only a few additional associations were found: FFA 16:0 was significantly negatively correlated with systolic pulmonary artery pressure, FFA 18:3 was significantly negatively correlated with C-reactive protein and body mass index, and FFA 20:4 was significantly negatively correlated with blood urea nitrogen. Based on our results, we conclude that in patients with severe AHF, individual and total serum FFAs are slightly associated with established laboratory and clinical parameters, which are indicators of heart failure severity.

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.

Renin Trajectories and Outcome in Stable Heart Failure with Reduced Ejection Fraction (HFrEF) on Contemporary Therapy: A Monocentric Study from an Austrian Tertiary Hospital Outpatient Clinic

Introduction

The renin-angiotensin system (RAS) is the main target of neurohumoral therapy in heart failure with reduced ejection fraction (HFrEF) effectively reducing mortality. Reasonably, renin might serve as a biomarker for risk prediction and therapy response. Renin indeed bears some additional value to clinical risk models, albeit the effect is not pronounced. Whether assessing renin trajectories can overcome the weaknesses of single renin measurements has not been reported.

Methods

A total of 505 patients with stable HFrEF were enrolled prospectively and followed through routine clinical visits. Active plasma renin concentration was documented up to 5 years. Changes in renin were analyzed throughout the disease course, and survival was compared for different renin trajectories within the first year.

Results

Baseline renin levels were not related to all-cause mortality (crude HR for an increase of 100 μiE/ml: 1.01 (95% CI: 0.99-1.02), p = 0.414) but associated with unplanned HF hospitalizations (crude HR: 1.01 (95% CI: 1.00-1.02), p = 0.015). Renin increased during the disease course from baseline to 1-year and 2-year FUP (122.7 vs. 185.6 μIU/ml, p = 0.039, and 122.7 vs. 258.5 μIU/ml, p = 0.001). Both survival and unplanned HF hospitalization rates were comparable for different renin trajectories at 1-year FUP (p = 0.546, p = 0.357).

Conclusions

Intriguingly, renin is not a good biomarker to indicate prognosis in HF, while renin trajectories over a 1-year period do not have an additional value. Rapid physiologic plasma renin variations, but also opposing effects of angiotensinogen-derived metabolites under presence of RAS blockade, might obscure the predictive ability of renin.

Development and validation of a prediction model for in-hospital death in patients with heart failure and atrial fibrillation

BACKGROUND

To develop a prediction model for in-hospital mortality of patients with heart failure (HF) and atrial fibrillation (AF).

METHODS

This cohort study extracted the data of 10,236 patients with HF and AF upon intensive care unit (ICU) from the Medical Information Mart for Intensive Care (MIMIC). The subjects from MIMIC-IV were divided into the training set to construct the prediction model, and the testing set to verify the performance of the model. The samples from MIMIC-III database and eICU-CRD were included as the internal and external validation set to further validate the predictive value of the model, respectively. Univariate and multivariable Logistic regression analyses were used to explore predictors for in-hospital death in patients with HF and AF. The receiver operator characteristic (ROC), calibration curves and the decision curve analysis (DCA) curves were plotted to evaluate the predictive values of the model.

RESULTS

The mean survival time of participants from MIMIC-III was 11.29 ± 10.05 days and the mean survival time of participants from MIMIC-IV was 10.56 ± 9.19 days. Simplified acute physiology score (SAPSII), red blood cell distribution width (RDW), beta-blocker, race, respiratory rate, urine output, coronary artery bypass grafting (CABG), Charlson comorbidity index, renal replacement therapies (RRT), antiarrhythmic, age, and anticoagulation were predictors finally included in the prediction model. The AUC of our prediction model was 0.810 (95%CI: 0.791-0.828) in the training set, 0.757 (95%CI: 0.729-0.786) in the testing set, 0.792 (95%CI: 0.774-0.810) in the internal validation set, and 0.724 (95%CI: 0.687-0.762) in the external validation set. The calibration curves of revealed that the predictive probabilities of our model for the in-hospital death in patients with HF and AF deviated slightly from the ideal model. The DCA curves revealed that the use of our prediction model increased the net benefit than use no model.

CONCLUSION

The prediction model had good discriminative ability, and might provide a tool to timely identify patients with HF complicated with AF who were at high risk of in-hospital mortality.

Relationship between comorbidity and health outcomes in patients with heart failure: a systematic review and meta-analysis

BACKGROUND

The prevalence of heart failure (HF) is expected to rise due to increased survivorship and life expectancy of patients with acute heart conditions. Patients with HF and other multiple comorbid conditions are likely to have poor health outcomes. This study aimed to assimilate the current body of knowledge and to provide the pooled effect of HF patients' comorbid conditions on health outcomes.

METHODS

A systematic search was performed using MEDLINE, EMBASE and CINAHL databases. Observational studies evaluating the relationship between comorbid conditions and the health outcomes of HF were included. The pooled effect sizes of comorbidity on the identified health outcomes were calculated using a random effects model, and the heterogeneity was evaluated using I2 statistics.

RESULTS

A total of 42 studies were included in this review, and a meta-analysis was performed using the results of 39 studies. In the pooled analysis, the presence of a comorbid condition showed a significant pooled effect size in relation to the prognostic health outcomes: all-cause mortality (HR 1.31; 95% CI 1.18, 1.45), all-cause readmission (HR 1.16; 95% CI 1.09, 1.23), HF-related readmission (HR 1.13; 95% CI 1.05, 1.23), and non-HF-related readmission (HR 1.17; 95% CI 1.07, 1.27). Also, comorbidity was significantly associated with health-related quality of life and self-care confidence. Furthermore, we identified a total of 32 comorbid conditions from included studies. From these, 16 individual conditions were included in the meta-analyses, and we identified 10 comorbid conditions to have negative effects on overall prognostic outcomes: DM (HR 1.16, 95% CI 1.11, 1.22), COPD (HR 1.31, 95% CI 1.23, 1.39), CKD (HR 1.18, 95% CI 1.14, 1.23, stroke (HR 1.25, 95% CI 1.17, 1.31), IHD (HR 1.17, 95% CI 1.11, 1.23), anemia (HR 1.42, 95% CI 1.14, 1.78), cancer (HR 1.17, 95% CI 1.04, 1.32), atrial fibrillation (HR 1.25, 95% CI 1.01, 1.54), dementia (HR 1.19, 95% CI 1.03, 1.36) and depression (HR 1.17, 95% CI 1.04, 1.31).

CONCLUSIONS

Comorbid conditions have significantly negative pooled effects on HF patient health outcomes, especially in regard to the prognostic health outcomes. Clinicians should carefully identify and manage these conditions when implementing HF interventions to improve prognostic outcomes.

Influence of serum transferrin concentration on diagnostic criteria for iron deficiency in chronic heart failure

AIMS

Transferrin saturation (TSAT), a marker of iron deficiency, reflects both serum concentrations of iron (SIC) and transferrin (STC). TSAT is susceptible to changes in each of these biomarkers. Little is known about determinants of STC and its influence on TSAT and mortality in patients with heart failure. Accordingly, we studied the relationship of STC to clinical characteristics, to markers of iron deficiency and inflammation and to mortality in chronic heart failure (CHF).

METHODS AND RESULTS

Prospective cohort of patients with CHF attending a clinic serving a large local population. A total of 4422 patients were included (median age 75 (68-82) years; 40% women; 32% with left ventricular ejection fraction ≤40%). STC ≤ 2.3 g/L (lowest quartile) was associated with older age, lower SIC and haemoglobin and higher high-sensitivity C-reactive protein, ferritin and N-terminal pro-brain natriuretic peptide compared with those with STC > 2.3 g/L. In the lowest STC quartile, 624 (52%) patients had SIC ≤13 μmol/L, of whom 38% had TSAT ≥20%. For patients in the highest STC quartile, TSAT was <20% when SIC was >13 μmol/L in 185 (17%) patients. STC correlated inversely with ferritin (r = -0.52) and high-sensitivity C-reactive protein (r = -0.17) and directly with albumin (r = 0.29); all P < 0.001. In models adjusted for age, N-terminal pro-brain natriuretic peptide and haemoglobin, both higher SIC (hazard ratio 0.87 [95% CI: 0.81-0.95]) and STC (hazard ratio 0.82 [95% CI: 0.73-0.91]) were associated with lower mortality. SIC was more strongly associated with both anaemia and mortality than either STC or TSAT.

CONCLUSIONS

Many patients with CHF and a low STC have low SIC even when TSAT is >20% and serum ferritin >100 μg/L; such patients have a high prevalence of anaemia and a poor prognosis and might have iron deficiency but are currently excluded from clinical trials of iron repletion.

Development and validation of a multivariate model for predicting heart failure hospitalization and mortality in patients receiving maintenance hemodialysis

BACKGROUND

Heart failure (HF) in patients undergoing maintenance hemodialysis (MHD) increases their hospitalization rates, mortality, and economic burden significantly. We aimed to develop and validate a predictive model utilizing contemporary deep phenotyping for individual risk assessment of all-cause mortality or HF hospitalization in patients on MHD.

MATERIALS AND METHODS

A retrospective review was conducted from January 2017 to October 2022, including 348 patients receiving MHD from four centers. The variables were adjusted by Cox regression analysis, and the clinical prediction model was constructed and verified.

RESULTS

The median follow-up durations were 14 months (interquartile range [IQR] 9-21) for the modeling set and 14 months (9-20) for the validation set. The composite outcome occurred in 72 (29.63%) of 243 patients in the modeling set and 39 (37.14%) of 105 patients in the validation set. The model predictors included age, albumin, history of cerebral hemorrhage, use of angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers/"sacubitril/valsartan", left ventricular ejection fraction, urea reduction ratio, N-terminal prohormone of brain natriuretic peptide, and right atrial size. The C-index was 0.834 (95% CI 0.784-0.883) for the modeling set and 0.853 (0.798, 0.908) for the validation set. The model exhibited excellent calibration across the complete risk profile, and the decision curve analysis (DCA) suggested its ability to maximize patient benefits.

CONCLUSION

The developed prediction model offered an accurate and personalized assessment of HF hospitalization risk and all-cause mortality in patients with MHD. It can be employed to identify high-risk patients and guide treatment and follow-up.

Usefulness of HANBAH Score in Japanese Patients With Acute Heart Failure

The HANBAH score is a novel simple risk score consisting of hemoglobin level, age, sodium (N) level, blood urea nitrogen level, atrial fibrillation, and high-density lipoprotein. We aimed to validate this score in an external population. This retrospective study included 744 patients hospitalized for acute heart failure between 2015 and 2019. Each of the following criteria was scored as 1 point: hemoglobin level (<13.0 g/L for men and <12.0 g/L for women), atrial fibrillation, age (>70 years), serum blood urea nitrogen level (>26 mg/100 ml for men and >28 mg/100 ml for women), serum high-density lipoprotein level (<25 mg/100 ml), and serum sodium level (<135 mg/100 ml). HANBAH scores were available for 736 patients (age, 75 ± 13 years; 60% male; reduced [<40%] and preserved ejection fraction [≥50%]: 35% and 49%, respectively). All-cause death during follow-up, a composite of death and heart failure rehospitalization, and in-hospital death were observed in 173, 274, and 51 patients, respectively. The HANBAH score was significantly associated with these end points after adjustment for covariates (adjusted hazard ratio 1.38 [95% confidence interval 1.16 to 1.64], p <0.001; 1.27 [1.11 to 1.45], p <0.001; and 1.66 [1.18 to 2.33], p <0.001, respectively). Receiver operating characteristic and net reclassification improvement analyses showed that the HANBAH score performed significantly better than AHEAD (atrial fibrillation, hemoglobin [anemia], elderly, abnormal renal parameters, diabetes mellitus) and AHEAD-U (AHEAD with uric acid) scores and similar to the multi-domain ACUTE HF score for all end points. In conclusion, the HANBAH score showed powerful risk stratification in this external Japanese cohort. Despite its simplicity, it performed better than other simple risk scores and similar to a multidomain risk score.

Low Valine Serum Levels Predict Increased 1-Year Mortality in Acute Heart Failure Patients

Considering the relationship between disease severity and the extent of metabolic derangement in heart failure, we hypothesized that the serum levels of metabolites may have prognostic value for 1-year mortality in acute heart failure (AHF). The AHF study was a prospective, observational study enrolling consecutive patients hospitalized due to AHF. Metabolites were measured in serum collected at admission using NMR spectroscopy. Out of 315 AHF patients, 118 (37.5%) died within 1 year after hospitalization for AHF. The serum levels of 8 out of 49 identified metabolites were significantly different between patients who were alive and those who died within 1 year after hospitalization for AHF. Of these, only valine was significantly associated with 1-year mortality (hazard ratio 0.73 per 1 standard deviation increase, 95% confidence interval: 0.59-0.90, p = 0.003) in the multivariable Cox regression analyses. Kaplan-Maier analysis showed significantly higher survival rates in AHF patients with valine levels above the median (>279.2 µmol/L) compared to those with valine levels ≤ 279.2 µmol/L. In a receiver operating characteristics curve analysis, valine was able to discriminate between the two groups with an area under the curve of 0.65 (95% CI 0.59-0.72). We conclude that valine serum levels might be of prognostic value in AHF.

Heart failure diagnosis in the general community - Who, how and when? A clinical consensus statement of the Heart Failure Association (HFA) of the European Society of Cardiology (ESC)

A significant proportion of patients experience delays in the diagnosis of heart failure due to the non-specific signs and symptoms of the syndrome. Diagnostic tools such as measurement of natriuretic peptide concentrations are fundamentally important when screening for heart failure, yet are frequently under-utilized. This clinical consensus statement provides a diagnostic framework for general practitioners and non-cardiology community-based physicians to recognize, investigate and risk-stratify patients presenting in the community with possible heart failure.

Systemic oxidative stress associates with disease severity and outcome in patients with new-onset or worsening heart failure

BACKGROUND

Oxidative stress may be a key pathophysiological mediator in the development and progression of heart failure (HF). The role of serum-free thiol concentrations, as a marker of systemic oxidative stress, in HF remains largely unknown.

OBJECTIVE

The purpose of this study was to investigate associations between serum-free thiol concentrations and disease severity and clinical outcome in patients with new-onset or worsening HF.

METHODS

Serum-free thiol concentrations were determined by colorimetric detection in 3802 patients from the BIOlogy Study to TAilored Treatment in Chronic Heart Failure (BIOSTAT-CHF). Associations between free thiol concentrations and clinical characteristics and outcomes, including all-cause mortality, cardiovascular mortality, and a composite of HF hospitalization and all-cause mortality during a 2-years follow-up, were reported.

RESULTS

Lower serum-free thiol concentrations were associated with more advanced HF, as indicated by worse NYHA class, higher plasma NT-proBNP (P < 0.001 for both) and with higher rates of all-cause mortality (hazard ratio (HR) per standard deviation (SD) decrease in free thiols: 1.253, 95% confidence interval (CI): 1.171-1.341, P < 0.001), cardiovascular mortality (HR per SD: 1.182, 95% CI: 1.086-1.288, P < 0.001), and the composite outcome (HR per SD: 1.058, 95% CI: 1.001-1.118, P = 0.046).

CONCLUSIONS

In patients with new-onset or worsening HF, a lower serum-free thiol concentration, indicative of higher oxidative stress, is associated with increased HF severity and poorer prognosis. Our results do not prove causality, but our findings may be used as rationale for future (mechanistic) studies on serum-free thiol modulation in heart failure. Associations of serum-free thiol concentrations with heart failure severity and outcomes.

Clinical and prognostic associations of autoantibodies recognizing adrenergic/muscarinic receptors in patients with heart failure

AIMS

The importance of autoantibodies (AABs) against adrenergic/muscarinic receptors in heart failure (HF) is not well-understood. We investigated the prevalence and clinical/prognostic associations of four AABs recognizing the M2-muscarinic receptor or the β1-, β2-, or β3-adrenergic receptor in a large and well-characterized cohort of patients with HF.

METHODS AND RESULTS

Serum samples from 2256 patients with HF from the BIOSTAT-CHF cohort and 299 healthy controls were analysed using newly established chemiluminescence immunoassays. The primary outcome was a composite of all-cause mortality and HF rehospitalization at 2-year follow-up, and each outcome was also separately investigated. Collectively, 382 (16.9%) patients and 37 (12.4%) controls were seropositive for ≥1 AAB (P = 0.045). Seropositivity occurred more frequently only for anti-M2 AABs (P = 0.025). Amongst patients with HF, seropositivity was associated with the presence of comorbidities (renal disease, chronic obstructive pulmonary disease, stroke, and atrial fibrillation) and with medication use. Only anti-β1 AAB seropositivity was associated with the primary outcome [hazard ratio (95% confidence interval): 1.37 (1.04-1.81), P = 0.024] and HF rehospitalization [1.57 (1.13-2.19), P = 0.010] in univariable analyses but remained associated only with HF rehospitalization after multivariable adjustment for the BIOSTAT-CHF risk model [1.47 (1.05-2.07), P = 0.030]. Principal component analyses showed considerable overlap in B-lymphocyte activity between seropositive and seronegative patients, based on 31 circulating biomarkers related to B-lymphocyte function.

CONCLUSIONS

AAB seropositivity was not strongly associated with adverse outcomes in HF and was mostly related to the presence of comorbidities and medication use. Only anti-β1 AABs were independently associated with HF rehospitalization. The exact clinical value of AABs remains to be elucidated.

Machine Learning for Mortality Prediction in Patients With Heart Failure With Mildly Reduced Ejection Fraction

Background Machine-learning-based prediction models (MLBPMs) have shown satisfactory performance in predicting clinical outcomes in patients with heart failure with reduced and preserved ejection fraction. However, their usefulness has yet to be fully elucidated in patients with heart failure with mildly reduced ejection fraction. This pilot study aims to evaluate the prediction performance of MLBPMs in a heart failure with mildly reduced ejection fraction cohort with long-term follow-up data. Methods and Results A total of 424 patients with heart failure with mildly reduced ejection fraction were enrolled in our study. The primary outcome was all-cause mortality. Two feature selection strategies were introduced for MLBPM development. The "All-in" (67 features) strategy was based on feature correlation, multicollinearity, and clinical significance. The other strategy was the CoxBoost algorithm with 10-fold cross-validation (17 features), which was based on the selection result of the "All-in" strategy. Six MLBPMs with 5-fold cross-validation based on the "All-in" and the CoxBoost algorithm with 10-fold cross-validation strategy were developed by the eXtreme Gradient Boosting, random forest, and support vector machine algorithms. The logistic regression model with 14 benchmark predictors was used as a reference model. During a median follow-up of 1008 (750, 1937) days, 121 patients met the primary outcome. Overall, MLBPMs outperformed the logistic model. The "All-in" eXtreme Gradient Boosting model had the best performance, with an accuracy of 85.4% and a precision of 70.3%. The area under the receiver-operating characteristic curve was 0.916 (95% CI, 0.887-0.945). The Brier score was 0.12. Conclusions The MLBPMs could significantly improve outcome prediction in patients with heart failure with mildly reduced ejection fraction, which would further optimize the management of these patients.

Palliative Care Needs of Physically Frail Community-Dwelling Older Adults With Heart Failure

CONTEXT

Physical frailty is emerging as a potential "trigger" for palliative care (PC) consultation, but the PC needs of physically frail persons with heart failure (HF) in the outpatient setting have not been well described.

OBJECTIVES

This study describes the PC needs of community dwelling, physically frail persons with HF.

METHODS

We included persons with HF ≥50 years old who experienced ≥1 hospitalization in the prior year and excluded those with moderate/severe cognitive impairment, hospice patients, or non-English speaking persons. Measures included the FRAIL scale (0-5: 0 = robust, 1-2 = prefrail, 3-5 = frail) and the Integrated Palliative Outcome Scale (IPOS) (17 items, score 0-68; higher score = higher PC needs). Multiple linear regression tested the association between frailty group and palliative care needs.

RESULTS

Participants (N = 286) had a mean age of 68 (range 50-92) were majority male (63%) and White (68%) and averaged two hospitalizations annually. Most were physically frail (44%) or prefrail (41%). Mean PC needs (IPOS) score was 19.7 (range 0-58). On average, participants reported 5.86 (SD 4.28) PC needs affecting them moderately, severely, or overwhelmingly in the last week. Patient-perceived family/friend anxiety (58%) weakness/lack of energy (58%), and shortness of breath (47%) were the most prevalent needs. Frail participants had higher mean PC needs score (26) than prefrail (16, P < 0.001) or robust participants (11, P < 0.001). Frail participants experienced an average of 8.32 (SD 3.72) moderate/severe/overwhelming needs compared to prefrail (4.56, SD 3.77) and robust (2.39, SD 2.91) participants (P < 0.001). Frail participants reported higher prevalence of weakness/lack of energy (83%), shortness of breath (66%), and family/friend anxiety (69%) than prefrail (48%, 39%, 54%) or robust (13%, 14%, 35%) participants (P < 0.001).

CONCLUSION

Physically frail people with HF have higher unmet PC needs than those who are nonfrail. Implementing PC needs and frailty assessments may help identify vulnerable patients with unmet needs requiring further assessment and follow-up.

What drives performance in machine learning models for predicting heart failure outcome?

Aims

The development of acute heart failure (AHF) is a critical decision point in the natural history of the disease and carries a dismal prognosis. The lack of appropriate risk-stratification tools at hospital discharge of AHF patients significantly limits clinical ability to precisely tailor patient-specific therapeutic regimen at this pivotal juncture. Machine learning-based strategies may improve risk stratification by incorporating analysis of high-dimensional patient data with multiple covariates and novel prediction methodologies. In the current study, we aimed at evaluating the drivers for success in prediction models and establishing an institute-tailored artificial Intelligence-based prediction model for real-time decision support.

Methods and results

We used a cohort of all 10 868 patients AHF patients admitted to a tertiary hospital during a 12 years period. A total of 372 covariates were collected from admission to the end of the hospitalization. We assessed model performance across two axes: (i) type of prediction method and (ii) type and number of covariates. The primary outcome was 1-year survival from hospital discharge. For the model-type axis, we experimented with seven different methods: logistic regression (LR) with either L1 or L2 regularization, random forest (RF), Cox proportional hazards model (Cox), extreme gradient boosting (XGBoost), a deep neural-net (NeuralNet) and an ensemble classifier of all the above methods. We were able to achieve an area under receiver operator curve (AUROC) prediction accuracy of more than 80% with most prediction models including L1/L2-LR (80.4%/80.3%), Cox (80.2%), XGBoost (80.5%), NeuralNet (80.4%). RF was inferior to other methods (78.8%), and the ensemble model was slightly superior (81.2%). The number of covariates was a significant modifier (P < 0.001) of prediction success, the use of multiplex-covariates preformed significantly better (AUROC 80.4% for L1-LR) compared with a set of known clinical covariates (AUROC 77.8%). Demographics followed by lab-tests and administrative data resulted in the largest gain in model performance.

Conclusions

The choice of the predictive modelling method is secondary to the multiplicity and type of covariates for predicting AHF prognosis. The application of a structured data pre-processing combined with the use of multiple-covariates results in an accurate, institute-tailored risk prediction in AHF.

Prognostic Value of Self-Reported Subjective Exercise Capacity in Patients With Acute Dyspnea

Background

Self-reported exercise capacity is a well-established prognostic measure in stable ambulatory patients with cardiac and pulmonary disease.

Objectives

The authors aimed to directly compare the prognostic accuracy of quantified self-reported exercise capacity using the Duke Activity Status Index (DASI) with the established objective disease-severity marker B-type natriuretic peptide (BNP) in patients presenting with acute dyspnea to the emergency department.

Methods

The DASI was obtained in a prospective multicenter diagnostic study recruiting unselected patients presenting with acute dyspnea to the emergency department. The prognostic accuracy of DASI and BNP for 90-day and 720-day all-cause mortality was evaluated using C-index.

Results

Among 1,019 patients eligible for this analysis, 75 (7%) and 297 (29%) patients died within 90 and 720 days after presentation, respectively. Unadjusted hazard ratios (HRs) and multivariable adjusted hazard ratios (aHRs) for 90- and 720-day mortality increased continuously from the fourth (best self-reported exercise capacity) to the first DASI quartile (worst self-reported exercise capacity). For 720-day mortality the HR of the first quartile vs the fourth was 9.1 (95% CI, 5.5-14.9) vs (aHR: 6.1, 95% CI: 3.7-10.1), of the second quartile 6.4 (95% CI: 3.9-10.6) vs (aHR: 4.4, 95% CI: 2.6-7.3), while of the third quartile the HR was 3.2 (95% CI: 1.9-5.5) vs (aHR: 2.4, 95% CI: 1.4-4.0). The prognostic accuracy of the DASI score was high, and higher than that of BNP concentrations (720-day mortality C-index: 0.67 vs 0.62; P = 0.024).

Conclusions

Quantification of self-reported subjective exercise capacity using the DASI provides high prognostic accuracy and may aid physicians in risk stratification. (Basics in Acute Shortness of Breath EvaLuation [BASEL V] Study [BASEL V]; NCT01831115).

Comparison of linear and non-linear machine learning models for time-dependent readmission or mortality prediction among hospitalized heart failure patients

Although many models are available to predict prognosis of heart failure patients, most tools combining survival analysis are based on proportional hazard model. Non-linear machine learning algorithms would overcome the limitation of the time-independent hazard ratio assumption and provide more information in readmission or mortality prediction among heart failure patients. The present study collected the clinical information of 1796 hospitalized heart failure patients surviving during hospitalization in a Chinese clinical center from December 2016 to June 2019. A traditional multivariate Cox regression model and three machine learning survival models were developed in derivation cohort. Uno's concordance index and integrated Brier score in validation cohort were calculated to evaluate the discrimination and calibration of different models. Time-dependent AUC and Brier score curves were plotted to assess the performance of models at different time phases.

Circulating immune checkpoints predict heart failure outcomes

AIMS

There are limited data examining the role of immune checkpoint (IC) ligands in the pathophysiology of heart failure (HF). Therefore, we explore this in three HF animal models and in three different human cohorts (healthy, stable, and worsening HF).

METHODS AND RESULTS

Transcriptomic analyses of cardiac tissue of three different HF mouse models revealed differentially expressed IC receptors and their ligands compared with control mice. Based on this observation, serum levels of three well-known IC ligands (i.e. sPD-L1, sPD-L2 and galectin-9) were measured in stable HF patients from the Vitamin D Chronic Heart Failure (VitD-CHF) study (n = 101), as well as healthy individuals from the Prevention of Renal and Vascular End-stage Disease (PREVEND) study (n = 58). sPD-L1, sPD-L2, and galectin-9 were all associated with New York Heart Association classification. In multivariate linear regression analyses, all three IC ligands were associated with galectin-3 (β = 0.230, β = 0.283, and β = 0.304, respectively). sPD-L1 and galectin-9 were also associated with hs-troponin-T (β = 0.386 and β = 0.314). Regarding prognosis, higher serum levels of sPD-L1 and galectin-9 were significantly associated with increased risk for HF hospitalization and all-cause mortality [hazard ratio 1.69 (1.09-2.59) and hazard ratio 1.50 (1.06-2.12)]. Furthermore, the importance of IC ligands was tested in another stage of HF, namely worsening HF patients. In the worsening HF cohort (The BIOlogy Study to Tailored Treatment in Chronic Heart Failure) (n = 2032), sPD-L2 and galectin-9 were associated with New York Heart Association classification and significantly predicted outcome with an increased relative risk of 15% and 20%, after multivariable adjustment, respectively.

CONCLUSIONS

IC ligands are expressed in cardiac disease models, and serum levels of IC ligands are elevated in HF patients, are associated with disease severity, and significantly predict prognosis. These data indicate a potential role for IC ligands in HF pathogenesis.

One-year rehospitalisation and mortality after acute heart failure hospitalisation: a competing risk analysis

OBJECTIVE

To identify factors that independently predict the risk of rehospitalisation and death after acute heart failure (AHF) hospital discharge in a real-world setting, considering death without rehospitalisation as a competing event.

METHODS

Single-centre, retrospective, observational study enrolling 394 patients discharged from an index AHF hospitalisation. Overall survival was evaluated using Kaplan-Meier and Cox regression models. For the risk of rehospitalisation, survival analysis considering competing risks was performed: rehospitalisation was the event of interest, and death without rehospitalisation was the competing event.

RESULTS

During the first year after discharge, 131 (33.3%) patients were rehospitalised for AHF and 67 (17.0%) died without being readmitted; the remaining 196 patients (49.7%) lived without further hospitalisations. The 1-year overall survival estimate was 0.71 (SE=0.02). After adjusting for gender, age and left ventricle ejection fraction, the results showed that the risk of death was higher in patients with dementia, higher levels of plasma creatinine (PCr), lower levels of platelet distribution width (PDW) and at Q4 of red cell distribution width (RDW). Multivariable models showed that the risk of rehospitalisation was increased in patients with atrial fibrillation, higher PCr or taking beta-blockers at discharge. Furthermore, the risk of death without AHF rehospitalisation was higher in males, those aged ≥80 years, patients with dementia or RDW at Q4 on admission (compared with Q1). Taking beta-blockers at discharge and having a higher PDW on admission reduced the risk of death without rehospitalisation.

CONCLUSION

When assessing rehospitalisation as a study endpoint, death without rehospitalisation should be considered a competing event in the analyses. Data from this study reveal that patients with atrial fibrillation, renal dysfunction or taking beta-blockers are more likely to be rehospitalised for AHF, while older men with dementia or high RDW are more prone to die without hospital readmission.

Association between serum albumin and outcomes in heart failure and secondary mitral regurgitation: the COAPT trial

AIMS

Low serum albumin levels are associated with poor prognosis in numerous chronic disease states but the relationship between albumin and outcomes in patients with heart failure (HF) and secondary mitral regurgitation (SMR) has not been described.

METHODS AND RESULTS

The randomized COAPT trial evaluated the safety and effectiveness of transcatheter edge-to-edge repair (TEER) with the MitraClip^TM plus guideline-directed medical therapy (GDMT) versus GDMT alone in patients with symptomatic HF and moderate-to-severe or severe SMR. Baseline serum albumin levels were measured at enrolment. Among 614 patients enrolled in COAPT, 559 (91.0%) had available baseline serum albumin levels (median 4.0 g/dl, interquartile range 3.7-4.2 g/dl). Patients with albumin <4.0 g/dl compared with ≥4.0 g/dl were older and more likely to have ischaemic cardiomyopathy and a hospitalization within the year prior to enrolment. After multivariable adjustment, patients with albumin <4.0 g/dl had higher 4-year rates of all-cause death (63.7% vs. 47.6%; adjusted hazard ratio 1.34, 95% confidence interval 1.02-1.74; p = 0.032), but there were no significant differences in HF hospitalizations (HFH) or all-cause hospitalizations according to baseline serum albumin level. The relative effectiveness of TEER plus GDMT versus GDMT alone was consistent in patients with low and high albumin levels (p_interaction  = 0.19 and 0.35 for death and HFH, respectively).

CONCLUSION

Low baseline serum albumin levels were independently associated with reduced 4-year survival in patients with HF and severe SMR enrolled in the COAPT trial, but not with HFH. Patients treated with TEER derived similarly robust reductions in both death and HFH regardless of baseline albumin level.

Inflammation, dysregulated iron metabolism, and cardiovascular disease

Iron is an essential trace element associated with both pathologic deficiency and toxic overload. Thus, systemic and cell iron metabolism are highly controlled processes regulated by protein expression and localization, as well as turnover, through the action of cytokines and iron status. Iron metabolism in the heart is challenging because both iron overload and deficiency are associated with cardiac disease. Also associated with cardiovascular disease is inflammation, as many cardiac diseases are caused by or include an inflammatory component. In addition, iron metabolism and inflammation are closely linked. Hepcidin, the master regulator of systemic iron metabolism, is induced by the cytokine IL-6 and as such is among the acute phase proteins secreted by the liver as part of the inflammatory response. In an inflammatory state, systemic iron homeostasis is dysregulated, commonly resulting in hypoferremia, or low serum iron. Less well characterized is cardiac iron metabolism in general, and even less is known about how inflammation impacts heart iron handling. This review highlights what is known with respect to iron metabolism in the heart. Expression of iron metabolism-related proteins and processes of iron uptake and efflux in these cell types are outlined. Evidence for the strong co-morbid relationship between inflammation and cardiac disease is also reviewed. Known connections between inflammatory processes and iron metabolism in the heart are discussed with the goal of linking inflammation and iron metabolism in this tissue, a connection that has been relatively under-appreciated as a component of heart function in an inflammatory state. Therapeutic options connecting inflammation and iron balance are emphasized, with the main goal of this review being to bring attention to alterations in iron balance as a component of inflammatory diseases of the cardiovascular system.

Quantifying inflammation using interleukin-6 for improved phenotyping and risk stratification in acute heart failure

AIMS

Systemic inflammation may be central in the pathophysiology of acute heart failure (AHF). We aimed to assess the possible role of systemic inflammation in the pathophysiology, phenotyping, and risk stratification of patients with AHF.

METHODS AND RESULTS

Using a novel Interleukin-6 immunoassay with unprecedented sensitivity (limit of detection 0.01 ng/L), we quantified systemic inflammation in unselected patients presenting with acute dyspnoea to the emergency department in a multicentre study. One-year mortality was the primary prognostic endpoint. Among 2042 patients, 1026 (50.2%) had an adjudicated diagnosis of AHF, 83.7% of whom had elevated interleukin-6 concentrations (>4.45 ng/L). Interleukin-6 was significantly higher in AHF patients compared to patients with other causes of dyspnoea (11.2 [6.1-26.5] ng/L vs. 9.0 [3.2-32.3] ng/L, p < 0.0005). Elevated interleukin-6 concentrations were independently predicted by increasing N-terminal pro-B-type natriuretic peptide and high-sensitivity cardiac troponin T, as well as the clinical diagnosis of infection. Among the different AHF phenotypes, interleukin-6 concentrations were highest in patients with cardiogenic shock (25.7 [14.0-164.2] ng/L) and lowest in patients with hypertensive AHF (9.3 [4.8-21.6] ng/L, p = 0.001). Inflammation as quantified by interleukin-6 was a strong and independent predictor of 1-year mortality both in all AHF patients, as well as those without clinically overt infection at presentation (adjusted hazard ratio [95% confidence interval] 1.45 [1.15-1.83] vs. 1.48 [1.09-2.00]). The addition of interleukin-6 significantly improved the discrimination of the BIOSTAT-CHF risk score.

CONCLUSION

An unexpectedly high percentage of patients with AHF have subclinical systemic inflammation as quantified by interleukin-6, which seems to contribute to AHF phenotype and to the risk of death.

Multi-omics analyses identify molecular signatures with prognostic values in different heart failure aetiologies

BACKGROUND

Heart failure (HF) is the leading cause of morbidity and mortality worldwide, and there is an urgent need for more global studies and data mining approaches to uncover its underlying mechanisms. Multiple omics techniques provide a more holistic molecular perspective to study pathophysiological events involved in the development of HF.

METHODS

In this study, we used a label-free whole myocardium multi-omics characterization from three commonly used mouse HF models: transverse aortic constriction (TAC), myocardial infarction (MI), and homozygous Phospholamban-R14del (PLN-R14^Δ/Δ). Genes, proteins, and metabolites were analysed for differential expression between each group and a corresponding control group. The core transcriptome and proteome datasets were used for enrichment analysis. For genes that were upregulated at both the RNA and protein levels in all models, clinical validation was performed by means of plasma level determination in patients with HF from the BIOSTAT-CHF cohort.

RESULTS

Cell death and tissue repair-related pathways were upregulated in all preclinical models. Fatty acid oxidation, ATP metabolism, and Energy derivation processes were downregulated in all investigated HF aetiologies. Putrescine, a metabolite known for its role in cell survival and apoptosis, demonstrated a 4.9-fold (p < 0.02) increase in PLN-R14^Δ/Δ, 2.7-fold (p < 0.005) increase in TAC mice, and 2.2-fold (p < 0.02) increase in MI mice. Four Biomarkers were associated with all-cause mortality (PRELP: Hazard ratio (95% confidence interval) 1.79(1.35, 2.39), p < 0.001; CKAP4: 1.38(1.21, 1.57), p < 0.001; S100A11: 1.37(1.13, 1.65), p = 0.001; Annexin A1 (ANXA1): 1.16(1.04, 1.29) p = 0.01), and three biomarkers were associated with HF-Related Rehospitalization, (PRELP: 1.88(1.4, 2.53), p < 0.001; CSTB: 1.15(1.05, 1.27), p = 0.003; CKAP4: 1.18(1.02, 1.35), P = 0.023).

CONCLUSIONS

Cell death and tissue repair pathways were significantly upregulated, and ATP and energy derivation processes were significantly downregulated in all models. Common pathways and biomarkers with potential clinical and prognostic associations merit further investigation to develop optimal management and therapeutic strategies for all HF aetiologies.

Albuminuria as a marker of systemic congestion in patients with heart failure

AIMS

Albuminuria is common in patients with heart failure and associated with worse outcomes. The underlying pathophysiological mechanism of albuminuria in heart failure is still incompletely understood. The association of clinical characteristics and biomarker profile with albuminuria in patients with heart failure with both reduced and preserved ejection fractions were evaluated.

METHODS AND RESULTS

Two thousand three hundred and fifteen patients included in the index cohort of BIOSTAT-CHF were evaluated and findings were validated in the independent BIOSTAT-CHF validation cohort (1431 patients). Micro-albuminuria and macro-albuminuria were defined as urinary albumincreatinine ratio (UACR) 30 mg/gCr and 300 mg/gCr in spot urines, respectively. The prevalence of micro- and macro-albuminuria was 35.4 and 10.0, respectively. Patients with albuminuria had more severe heart failure, as indicated by inclusion during admission, higher New York Heart Association functional class, more clinical signs and symptoms of congestion, and higher concentrations of biomarkers related to congestion, such as biologically active adrenomedullin, cancer antigen 125, and N-terminal pro-B-type natriuretic peptide (NT-proBNP) (all P 0.001). The presence of albuminuria was associated with increased risk of mortality and heart failure (re)hospitalization in both cohorts. The strongest independent association with log UACR was found for log NT-proBNP (standardized regression coefficient 0.438, 95 confidence interval 0.350.53, P 0.001). Hierarchical clustering analysis demonstrated that UACR clusters with markers of congestion and less with indices of renal function. The validation cohort yielded similar findings.

CONCLUSION

In patients with new-onset or worsening heart failure, albuminuria is consistently associated with clinical, echocardiographic, and circulating biomarkers of congestion.

Biomarker signature and pathophysiological pathways in patients with chronic heart failure and metabolic syndrome

AIM

The comorbidities that collectively define metabolic syndrome are common in patients with heart failure. However, the role of metabolic syndrome in the pathophysiology of heart failure is not well understood. We therefore investigated the clinical and biomarker correlates of metabolic syndrome in patients with heart failure.

METHODS AND RESULTS

In 1103 patients with heart failure, we compared the biomarker expression using a panel of 363 biomarkers among patients with (n = 468 [42%]) and without (n = 635 [58%]) metabolic syndrome. Subsequently, a pathway overrepresentation analysis was performed to identify key biological pathways. Findings were validated in an independent cohort of 1433 patients with heart failure of whom 615 (43%) had metabolic syndrome. Metabolic syndrome was defined as the presence of three or more of five criteria, including central obesity, elevated serum triglycerides, reduced high-density lipoprotein cholesterol, insulin resistance and hypertension. The most significantly elevated biomarkers in patients with metabolic syndrome were leptin (log2 fold change 0.92, p = 5.85 × 10^-21 ), fatty acid-binding protein 4 (log2 fold change 0.61, p = 1.21 × 10^-11 ), interleukin-1 receptor antagonist (log2 fold change 0.47, p = 1.95 × 10^-13 ), tumour necrosis factor receptor superfamily member 11a (log2 fold change 0.35, p = 4.16 × 10^-9 ), and proto-oncogene tyrosine-protein kinase receptor Ret (log2 fold change 0.31, p = 4.87 × 10^-9 ). Network analysis identified 10 pathways in the index cohort and 6 in the validation cohort, all related to inflammation. The primary overlapping pathway in both the index and validation cohorts was up-regulation of the natural killer cell-mediated cytotoxicity pathway.

CONCLUSION

Metabolic syndrome is highly prevalent in heart failure and is associated with biomarkers and pathways relating to obesity, lipid metabolism and immune responses underlying chronic inflammation.

Performance of the heart failure risk scores in predicting 1 year mortality and short-term readmission of patients

AIMS

The aim of this study was to assess the performance of these main scores in predicting prognosis in patients with heart failure (HF).

METHODS AND RESULTS

A total of 2008 patients who were admitted to the Fourth People's Hospital of Zigong, Sichuan, from December 2016 to June 2019 and diagnosed with HF were included in the study. We compared the prognostic predictive performance of Seattle Heart Failure Model (SHFM), Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC-HF) risk score, Get With the Guidelines-Heart Failure programme (GWTG-HF), Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure (ASCEND) risk scores, the Acute Decompensated Heart Failure National Registry (ADHERE) model, Barcelona Bio-Heart Failure (BCN-Bio-HF) risk calculator, and Gruppo Italiano per lo Studio della Streptochinasi nell'Infarto Miocardico-Heart Failure (GISSI-HF) for the endpoints. The primary endpoint was 1 year all-cause mortality and the secondary endpoint was the incidence of 28 day readmission post-discharge. At 1 year follow-up, 44 (2.21%) patients with HF died. Discrimination analyses showed that all risk scores performed reasonably well in predicting 1 year mortality, with areas under the receiver operating characteristic curve (AUCs) fluctuating between 0.757 and 0.822. GISSI-HF showed the best discrimination with the AUC of 0.822 (0.768-0.876), followed by MAGGIC-HF, BCN-Bio-HF, ASCEND, SHFM, GWTG-HF, and ADHERE with AUCs of 0.819 (0.756-0.883), 0.812 (0.758-0.865), 0.802 (0.742-0.862), 0.787 (0.725-0.849), 0.762 (0.684-0.840), and 0.757 (0.681-0.833), respectively. All risk scores were similarly predictive of 28 day emergency readmissions, with AUCs fluctuating between 0.609 and 0.680. Overestimation of mortality occurred in all scores except the ASCEND. The risk scores remained with good prognostic discrimination in patients with biventricular HF and in the subgroup of patients taking angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker.

CONCLUSIONS

Currently assessed risk scores have limited clinical utility, with fair accuracy and calibration in assessing patients' 1 year risk of death and poor accuracy in assessing patients' risk of readmission. There is a need to incorporate more patient-level information, use more advanced technologies, and develop models for different subgroups of patients to achieve more practical, innovative, and accurate risk assessment tools.

External validation of the ACUTE HF score for risk stratification in acute heart failure

BACKGROUND

The ACUTE HF score is a simple risk score that predicts the prognosis of patients with acute heart failure (HF) using clinical and echocardiographic parameters. As this score was developed for a small European population, we aimed to validate this score in an external population.

METHODS AND RESULTS

This retrospective observational cohort analysis included patients hospitalized with acute HF during 2015-2019. Of 744 patients, 703 patients with available ACUTE HF scores were analyzed (75 ± 13 years; 61% male; left ventricular ejection fraction [LVEF] 49 ± 17%). Approximately one-third (34.4%) of the patients had reduced LVEF (<40%), and 51.4% exhibited preserved LVEF (≥50%). During a median follow-up of 452 days, primary and secondary outcomes were observed in 110 and 204 patients, respectively. The ACUTE HF score successfully stratified patients for primary (all-cause mortality) and secondary endpoints (a composite of all-cause mortality and heart failure rehospitalization) in Kaplan-Meier analyses (log-rank test, P < 0.001). Multivariable Cox proportional hazards models showed that the score was significantly independently associated with both primary and secondary endpoints after adjusted by covariates (P < 0.001).

CONCLUSION

We validated the risk prediction ability of ACUTE HF score in an Asian population. This score may be applicable in clinical practice.

A Novel Nomogram Integrated with Systemic Inflammation Markers and Traditional Prognostic Factors for Adverse Events' Prediction in Patients with Chronic Heart Failure in the Southwest of China

Objective

Inflammation contributes to the pathogenesis and progression of heart failure (HF). This study aimed to construct a nomogram based on systemic inflammatory markers and traditional prognostic factors to assess the risk of adverse outcomes (cardiovascular readmission and all-cause death) in patients with chronic heart failure (CHF).

Methods

Data were retrospectively collected from patients with HF admitted to the Department of Cardiovascular Medicine at the First Affiliated Hospital of Chongqing Medical University from January 2018 to April 2020, and each patient had complete follow-up information. The follow-up duration was from June 2018 to May 31, 2022. 550 patients were included and randomly assigned to the derivation and validation cohorts with a ratio of 7:3, and prognostic risk factors of CHF were identified by Cox regression analysis. The nomogram chart scoring model was constructed.

Results

The Cox multivariate regression analysis showed that traditional prognostic factors such as age (P=0.011), BMI (P=0.048), NYHA classification (P<0.001), creatinine (P<0.001), and systemic inflammatory markers including LMR (P=0.001), and PLR (P=0.015) were independent prognostic factors for CHF patients. Integrated with traditional and inflammatory prognostic factors, a nomogram was established, which yielded a C-index value of 0.739 (95% CI: 0.714-0.764) in the derivation cohort and 0.713 (95% CI: 0.668-0.758) in the validation cohort, respectively. The calibration curves exhibited good performance of the nomogram in predicting the adverse outcomes for patients with CHF. In subgroups (HFrEF, HFmrEF, and HFpEF groups), the systematic inflammatory markers-based nomograms proved to be effective prediction tools for patients' adverse overcomes, as well.

Conclusion

The nomogram combining systemic inflammatory markers and traditional risk factors has satisfactory predictive performance for adverse outcomes (mortality and readmission) in patients with CHF.

Evaluation of the added value of Brain Natriuretic Peptide to a validated mortality risk-prediction model in older people using a standardised international clinical assessment tool

The ability to accurately predict the one-year survival of older adults is challenging for clinicians as they endeavour to provide the most appropriate care. Standardised clinical needs assessments are routine in many countries and some enable application of mortality prediction models. The added value of blood biomarkers to these models is largely unknown. We undertook a proof of concept study to assess if adding biomarkers to needs assessments is of value. Assessment of the incremental value of a blood biomarker, Brain Naturetic Peptide (BNP), to a one year mortality risk prediction model, RiskOP, previously developed from data from the international interRAI-HomeCare (interRAI-HC) needs assessment. Participants were aged ≥65 years and had completed an interRAI-HC assessment between 1 January 2013 and 21 August 2021 in Canterbury, New Zealand. Inclusion criteria was a BNP test within 90 days of the date of interRAI-HC assessment. The primary outcome was one-year mortality. Incremental value was assessed by change in Area Under the Receiver Operating Characteristic Curve (AUC) and Brier Skill, and the calibration of the final model. Of 14,713 individuals with an interRAI-HC assessment 1,537 had a BNP within 90 days preceding the assessment and all data necessary for RiskOP. 553 (36.0%) died within 1-year. The mean age was 82.6 years. Adding BNP improved the overall AUC by 0.015 (95% CI:0.004 to 0.028) and improved predictability by 1.9% (0.26% to 3.4%). In those with no Congestive Heart Failure the improvements were 0.029 (0.004 to 0.057) and 4.0% (0.68% to 7.6%). Adding a biomarker to a risk model based on standardised needs assessment of older people improved prediction of 1-year mortality. BNP added value to a risk prediction model based on the interRAI-HC assessment in those patients without a diagnosis of congestive heart failure.

Clinical implications of left atrial changes after optimization of medical therapy in patients with heart failure

AIMS

Limited data exist regarding the prognostic relevance of changes in left atrial (LA) dimensions in patients with heart failure (HF). We assessed changes in LA dimension and their relation with outcomes after optimization of guideline-directed medical therapy (GDMT) in patients with new-onset or worsening HF.

METHODS AND RESULTS

Left atrial diameter was assessed at baseline and 9 months after GDMT optimization in 632 patients (mean age 65.8 ± 12.1 years, 22.3% female) enrolled in BIOSTAT-CHF. LA adverse remodelling (LAAR) was defined as an increase in LA diameter on transthoracic echocardiography between baseline and 9 months. After the 9-month visit, patients were followed for a median of 13 further months. LAAR was observed in 247 patients (39%). Larger baseline LA diameter (odds ratio [OR] 0.90; 95% confidence interval [CI] 0.87-0.93; p < 0.001) and up-titration to higher doses of angiotensin-converting enzyme inhibitors or angiotensin receptor blockers (ACEi/ARBs) (OR 0.56; 95% CI 0.34-0.92; p = 0.022) were independently associated with lower likelihood of LAAR. LAAR was associated with an increased risk of the composite of all-cause mortality or HF hospitalization (log-rank p = 0.007 and adjusted hazard ratio 1.73, 95% CI 1.22-2.45, p = 0.002). The association was more pronounced in patients without a history of atrial fibrillation (p for interaction = 0.009).

CONCLUSION

Among patients enrolled in BIOSTAT-CHF, LAAR was associated with an unfavourable outcome and was prevented by ACEi/ARB up-titration. Changes in LA dimension may be a useful marker of response to treatment and improve risk stratification in patients with HF.

A prediction model for major adverse cardiovascular events in patients with heart failure based on high-throughput echocardiographic data

Background

Heart failure (HF) is a serious end-stage condition of various heart diseases with increasing frequency. Few studies have combined clinical features with high-throughput echocardiographic data to assess the risk of major cardiovascular events (MACE) in patients with heart failure. In this study, we assessed the relationship between these factors and heart failure to develop a practical and accurate prognostic dynamic nomogram model to identify high-risk groups of heart failure and ultimately provide tailored treatment options.

Materials and methods

We conducted a prospective study of 468 patients with heart failure and established a clinical predictive model. Modeling to predict risk of MACE in heart failure patients within 6 months after discharge obtained 320 features including general clinical data, laboratory examination, 2-dimensional and Doppler measurements, left ventricular (LV) and left atrial (LA) speckle tracking echocardiography (STE), and left ventricular vector flow mapping (VFM) data, were obtained by building a model to predict the risk of MACE within 6 months of discharge for patients with heart failure. In addition, the addition of machine learning models also confirmed the necessity of increasing the STE and VFM parameters.

Results

Through regular follow-up 6 months after discharge, MACE occurred in 156 patients (33.3%). The prediction model showed good discrimination C-statistic value, 0.876 (p < 0.05), which indicated good identical calibration and clinical efficacy. In multiple datasets, through machine learning multi-model comparison, we found that the area under curve (AUC) of the model with VFM and STE parameters was higher, which was more significant with the XGboost model.

Conclusion

In this study, we developed a prediction model and nomogram to estimate the risk of MACE within 6 months of discharge among patients with heart failure. The results of this study can provide a reference for clinical physicians for detection of the risk of MACE in terms of clinical characteristics, cardiac structure and function, hemodynamics, and enable its prompt management, which is a convenient, practical and effective clinical decision-making tool for providing accurate prognosis.

Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: A retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases

Background

Heart failure (HF) combined with hypertension is an extremely important cause of in-hospital mortality, especially for the intensive care unit (ICU) patients. However, under intense working pressure, the medical staff are easily overwhelmed by the large number of clinical signals generated in the ICU, which may lead to treatment delay, sub-optimal care, or even wrong clinical decisions. Individual risk stratification is an essential strategy for managing ICU patients with HF combined with hypertension. Artificial intelligence, especially machine learning (ML), can develop superior models to predict the prognosis of these patients. This study aimed to develop a machine learning method to predict the 28-day mortality for ICU patients with HF combined with hypertension.

Methods

We enrolled all critically ill patients with HF combined with hypertension in the Medical Information Mart for IntensiveCare Database-IV (MIMIC-IV, v.1.4) and the eICU Collaborative Research Database (eICU-CRD) from 2008 to 2019. Subsequently, MIMIC-IV was divided into training cohort and testing cohort in an 8:2 ratio, and eICU-CRD was designated as the external validation cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression with internal tenfold cross-validation was used for data dimension reduction and identifying the most valuable predictive features for 28-day mortality. Based on its accuracy and area under the curve (AUC), the best model in the validation cohort was selected. In addition, we utilized the Shapley Additive Explanations (SHAP) method to highlight the importance of model features, analyze the impact of individual features on model output, and visualize an individual’s Shapley values.

Results

A total of 3,458 and 6582 patients with HF combined with hypertension in MIMIC-IV and eICU-CRD were included. The patients, including 1,756 males, had a median (Q1, Q3) age of 75 (65, 84) years. After selection, 22 out of a total of 58 clinical parameters were extracted to develop the machine-learning models. Among four constructed models, the Neural Networks (NN) model performed the best predictive performance with an AUC of 0.764 and 0.674 in the test cohort and external validation cohort, respectively. In addition, a simplified model including seven variables was built based on NN, which also had good predictive performance (AUC: 0.741). Feature importance analysis showed that age, mechanical ventilation (MECHVENT), chloride, bun, anion gap, paraplegia, rdw (RDW), hyperlipidemia, peripheral capillary oxygen saturation (SpO₂), respiratory rate, cerebrovascular disease, heart rate, white blood cell (WBC), international normalized ratio (INR), mean corpuscular hemoglobin concentration (MCHC), glucose, AIDS, mean corpuscular volume (MCV), N-terminal pro-brain natriuretic peptide (Npro. BNP), calcium, renal replacement therapy (RRT), and partial thromboplastin time (PTT) were the top 22 features of the NN model with the greatest impact. Finally, after hyperparameter optimization, SHAP plots were employed to make the NN-based model interpretable with an analytical description of how the constructed model visualizes the prediction of death.

Conclusion

We developed a predictive model to predict the 28-day mortality for ICU patients with HF combined with hypertension, which proved superior to the traditional logistic regression analysis. The SHAP method enables machine learning models to be more interpretable, thereby helping clinicians to better understand the reasoning behind the outcome and assess in-hospital outcomes for critically ill patients.

Heart failure with normal LVEF in BIOSTAT-CHF

AIMS

Several studies have shown that heart failure (HF) drug treatment seems to benefit patients with preserved ejection fraction (HFpEF) and a left ventricular ejection fraction (LVEF) up to 55-60% but not with higher LVEF. Certain HF drugs are now indicated in patients with HFpEF and a LVEF below normal. However, not much is known about patients with a normal LVEF. Therefore, we investigated the prevalence, clinical characteristics and outcome of patients with HF and a normal LVEF.

METHODS AND RESULTS

Normal LVEF was defined according to the Recommendations for Cardiac Chamber Quantification from the American Society of Echocardiography as a LVEF ≥62% for men and ≥ 64% for women. Preserved ejection fraction was defined as a LVEF ≥50% and reduced ejection fraction as a LVEF <50%. In the total cohort of 1568 studied patients with heart failure (mean age 73 years; 33.6% female) 57 patients (3.6%) had a normal LVEF. These patients least likely had a previous myocardial infarction (p < 0.001) or diabetes (p = 0.045), had the lowest Left Ventricular End Diastolic Diameter (p < 0.001), the highest rate of previous HF hospitalization in the last year (p = 0.015), the highest cardiac output (p < 0.001) and were most frequently women (p < 0.001). Patients with a normal LVEF had the lowest risk for the primary combined outcome of all-cause mortality and HF hospitalization.

CONCLUSION

Only 3.6% of patients with HF had a sex-adjusted normal LVEF. Despite the sex-adjusted cut-offs they were more frequently female with less ischemic heart disease, higher cardiac output and better clinical outcomes.

Insulin-like growth factor binding protein 7 (IGFBP7), a link between heart failure and senescence

AIMS

Insulin like growth factor binding protein 7 (IGFBP7) is a marker of senescence secretome and a novel biomarker in patients with heart failure (HF). We evaluated the prognostic value of IGFBP7 in patients with heart failure and examined associations to uncover potential new pathophysiological pathways related to increased plasma IGFBP7 concentrations.

METHODS AND RESULTS

We have measured plasma IGFBP7 concentrations in 2250 subjects with new-onset or worsening heart failure (BIOSTAT-CHF cohort). Higher IGFBP7 plasma concentrations were found in older subjects, those with worse kidney function, history of atrial fibrillation, and diabetes mellitus type 2, and in subjects with higher number of HF hospitalizations. Higher IGFBP7 levels also correlate with the levels of several circulating biomarkers, including higher NT-proBNP, hsTnT, and urea levels. Cox regression analyses showed that higher plasma IGFBP7 concentrations were strongly associated with increased risk of all three main endpoints (hospitalization, all-cause mortality, and combined hospitalization and mortality) (HR 1.75, 95% CI 1.25-2.46; HR 1.71, 95% CI 1.39-2.11; and HR 1.44, 95% CI 1.23-1.70, respectively). IGFBP7 remained a significant predictor of these endpoints in patients with both reduced and preserved ejection fraction. Likelihood ratio test showed significant improvement of all three risk prediction models, after adding IGFBP7 (P < 0.001). A biomarker network analysis showed that IGFBP7 levels activate different pathways involved in the regulation of the immune system. Results were externally validated in BIOSTAT-CHF validation cohort.

CONCLUSIONS

IGFPB7 presents as an independent and robust prognostic biomarker in patients with HF, with both reduced and preserved ejection fraction. We validate the previously published data showing IGFBP7 has correlations with a number of echocardiographic markers. Lastly, IGFBP7 pathways are involved in different stages of immune system regulation, linking heart failure to senescence pathways.