All-cause Mortality In Heart Failure Research Articles

Skeletal muscle index as a prognostic marker for sudden cardiac death in heart failure patients with reduced ejection fraction

Abstract Background The association between sarcopenia and sudden cardiac death remains unclear. Therefore, this study aimed to explore the association between sarcopenia, sudden cardiac death, and all-cause mortality in heart failure (HF) patients with reduced ejection fraction based on the skeletal muscle index (SMI) predicted by a formula. Methods A total of 300 patients with heart failure who were admitted to our Hospital, National Center for Cardiovascular Diseases, between March 2013 and November 2015 and met the inclusion criteria were retrospectively analyzed. Baseline SMI was divided into two groups: lower than or equal to the median (n=151) and higher than the median (n=149). The primary endpoints were a composite outcome of sudden cardiac death (SCD) or an equivalent event (sustained ventricular events, or appropriate implantable cardioverter-defibrillator (ICD) therapy).The secondary end point was all-cause mortality. Results The average follow-up period was 24.40 ± 14.07 months. Atotal of 42 patients (42/300,14.0%) experienced sudden cardiac death or an equivalent event, and 97 patients (97/300, 32.2%) experienced all-cause mortality. Analysis of the Kaplan–Meier survival curves showed that patients with an SMI lower than or equal to the median had a higher risk of sudden cardiac death (P=0.0015) and all-cause mortality (P<0.0001). Multivariate Cox analysis showed that an SMI lower than the median was an independent risk factor for sudden cardiac death (hazard ratio (HR): 2.837, 95% confidence interval (CI): 1.009–7.980, P=0.048) and all-cause mortality (HR: 2.597, 95% CI: 1.384–4.871, P=0.003). Conclusions Decreased SMI significantly increased the risk of sudden cardiac death and all-cause mortality in patients with HF and reduced ejection fraction. In a HF population with reduced ejection fraction, attention should be paid to the effect of sarcopenia on prognosis, and active intervention is expected to improve the prognosis of patients.

Are sarcopenia and its individual components linked to all-cause mortality in heart failure? A systematic review and meta-analysis

Abstract Objective The objective of this systematic review and meta-analysis was to assess sarcopenia and its components as prognostic factors in patients with heart failure (HF). Methods From inception to December 2022, a systematic literature search was carried out utilizing PubMed, Web of Science, Scopus, and Cochrane Library databases. A meta-analysis employing a random-effects model was performed to assess the pooled effects. Results The systematic review and meta-analysis included 32 and 18 longitudinal studies, respectively. The prediction of 1- to 2-year all-cause mortality in sarcopenia was not statistically significant (hazard ratio (HR): 1.35, 95% CI 0.76–2.38, I2 = 54%, P = 0.31). The lowest combined quartile and quantile of the population were used to define low handgrip strength that showed identical results (HR: 1.24, 95% CI 0.94–1.62, I2 = 0%, P = 0.13). Low L3-L4 psoas muscle mass (HR: 2.20, 95% CI 1.26–3.83, I2 = 87%, P < 0.01) and slow gait speed (HR: 1.45, 95% CI 1.20–1.74, I2 = 0%, P < 0.01) were significant contributors to all-cause mortality risk. Additionally, a 0.1 m/s increase in gait speed demonstrated a significant reduction of all-cause mortality (HR: 0.77, 95% CI 0.66–0.90, I2 = 60%, P < 0.01). Our narrative synthesis also described appendicular lean mass (ALM) and short physical performance battery (SPPB) scores as significant prognostic factors. Conclusions Compared to patients with higher overall functional performance, those with HF and low ALM, low psoas muscle mass, low SPPB, and slow gait speed are at an increased risk of all-cause mortality. Early prevention and/or treatment of lower limb physical function deterioration may be an essential strategy to reduce the risk of premature death in HF. Graphical abstract

Abstract P3075: Neutrophil Secreted Chitinase 3-like-1 Antagonizes Macrophage Reparative Polarization And Contributes To Ventricular Dysfunction After Myocardial Infarction

Clinical studies show that chitinase 3-like-1 (CHI3L1) is a biomarker and predictor of all-cause mortality in heart failure. Studies from our lab demonstrated that CHI3L1 is elevated in hearts post-MI, and CHI3L1 deficiency in mice results in preserved ventricular structure and function post-MI. Furthermore, we found that CHI3L1 is secreted from activated neutrophils but has no effect on neutrophil recruitment in hearts after MI. The goal of the current study was to elucidate mechanisms whereby CHI3L1 contributes to exacerbated cardiac dysfunction after MI. Post-MI neutrophil recruitment is followed by the sequential programming of macrophages to pro-inflammatory or pro-reparative phenotypes. We sought to determine the impact of neutrophil-secreted CHI3L1 on macrophage polarization. Activated neutrophils isolated from Chil1 +/+ and Chil1 -/- mice were co-cultured with bone marrow-derived macrophages. We found that macrophages directly co-cultured with Chil1 -/- but not Chil1 +/+ neutrophils showed upregulation of the pro-reparative markers, Chil4 and Arg1 (n=5/group, p<0.05). Additionally, direct co-culture of Chil1 -/- neutrophils and macrophages showed trends towards downregulation of the pro-inflammatory polarization markers, Il1b and Tnfa relative to macrophages co-cultured with Chil +/+ neutrophils. These data suggest that neutrophil-derived CHI3L1 may antagonize signaling to polarize macrophages towards a pro-reparative phenotype that may explain delayed scar formation and exacerbated ventricular dysfunction after MI. Future efforts will elucidate the signaling role of CHI3L1 associated with neutrophil extracellular traps (NETs) on macrophage function.

Predictive value of blood urea nitrogen in heart failure: a systematic review and meta-analysis.

The mortality rate of patients with heart failure (HF) remains high, and when heart failure occurs, blood urea nitrogen (BUN) is involved in the perfusion of renal blood flow. Some studies have shown an association between heart failure prognosis and blood urea nitrogen, but the results of some other studies were inconsistent. Therefore, we conducted a comprehensive meta-analysis to investigate the value of BUN on the prognosis of patients with heart failure. A computerized systematic search of all English literature was performed in four databases, PubMed, Cochrane, Embase and Web of Science, from their inception to May 2022. The data of BUN were classified into continuous and categorical variables after passing the inclusion and exclusion criteria. The BUN data of both types were extracted separately into stata15.0 for statistical analysis. A total of 19 cohort studies involving 56,003 patients were included. When BUN was used as a categorical variable, the risk of death in heart failure was 2.29 times higher for high levels of BUN than for low levels of BUN (RR = 2.29, 95% CI:1.42-3.70, P < 0.001). The results showed statistical significance in multifactorial and univariate groups, the prospective cohort, and European and Asian groups. When BUN was used as a continuous variable, the risk of death in heart failure was 1.02 times higher for each unit increase in BUN (RR = 1.02, 95% CI:1.01-1.03, p < 0.001). Subgroup analysis showed statistical significance in retrospective cohort, American and Asian. High BUN is an independent predictor of all-cause mortality in heart failure. Lower BUN was associated with better prognosis in patients with heart failure.

Non-thyroidal illness syndrome and the prognosis of heart failure: a systematic review and meta-analysis

Heart failure (HF) is a complex and multifactorial syndrome caused by impaired heart function. The high morbidity and mortality of HF cause a heavy burden of illness worldwide. Non-thyroidal illness syndrome (NTIS) refers to aberrant serum thyroid parameters in patients without past thyroid disease. Observational studies have indicated that NTIS is associated with a higher risk of all-cause mortality in HF. This meta-analysis aimed to investigate the association between NTIS and HF prognosis. Medline, Embase, Web of Science, and the Cochrane database were searched for any studies reporting an association between NTIS and HF prognosis from inception to July 1st 2022. A meta-analysis was then performed. The quality of studies was assessed using the Newcastle-Ottawa Scale (NOS). The heterogeneity of the results was assessed with I2 and Cochran's Q statistics. Sensitivity analysis and publication bias analysis were also conducted. A total of 626 studies were retrieved, and 18 studies were finally included in the meta-analysis. The results showed that NTIS in HF patients was significantly associated with an increased risk of all-cause mortality and major cardiovascular events (MACE), but not with in-hospital mortality. The stability of the data was validated by the sensitivity analysis. There was no indication of a publication bias in the pooled results for all-cause mortality and MACE. This meta-analysis showed that NTIS was associated with a worse outcome in HF patients. However, the association between NTIS and in-hospital mortality of HF patients requires further investigation.

Relationship between Prothrombin time and All-cause Mortality in Heart Failure.

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Effect of Sacubitril Valsartan on the Incidence of Atrial Fibrillation: A Meta-Analysis.

Sacubitril/valsartan reduces all-cause mortality in heart failure (HF) patients compared to angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs). ACEIs/ARBs have been shown to decrease the incidence of atrial fibrillation (AF). We hypothesized sacubitril-valsartan decreases the incidence of AF compared to ACEis/ARBs. Clinicaltrials.gov was searched for trials by terms sacubitril/valsartan, entresto, sacubitril, valsartan. Randomized controlled human trials of sacubitril/valsartan reporting AF were included. Data were extracted independently by two reviewers. Data was pooled using a random effect model. Publication bias was evaluated by funnel plots. A total of 11 trials including 11,458 patients on sacubitril/valsartan and 10,128 patients on ACEI/ARBs were identified. 284 AF events were reported in the sacubitril/valsartan group compared to 256 AF events in ACEIs/ARBs. Patients on sacubitril/valsartan were as likely as patients on ACEIs/ARBs to develop AF (pooled OR=1.091, 95% CI= 0.917 - 1.298, p=0.324). 6 Atrial flutter (AFl) events were reported in 6 trials; 48 out of 9165 patients in the sacubitril/valsartan group developed AFl compared to 46 out of 8759 in ACEi/ARBs group. There was no difference in AFl risk between the two groups (pooled OR=1.028, 95% CI=0.681 - 1.553, p=0.894). Finally, sacubitril/valsartan did not reduce the risk of atrial arrhythmias (AF+AFl) compared to ACEi/ARBs (pooled OR=1.081, 95% CI= 0.922 - 1.269, p=0.337). Although sacubitril/valsartan reduces mortality compared to ACEIs/ARBs in HF patients, they do not reduce AF risk compared to these drugs. This article is protected by copyright. All rights reserved.

Predictive value of remnant cholesterol level for all-cause mortality in heart failure patients.

Lower cholesterol levels are associated with increased mortality in heart failure (HF) patients. Remnant cholesterol corresponds to all cholesterol not found in high-density lipoprotein (HDL) and low-density lipoprotein (LDL). The prognostic role of remnant cholesterol in HF remains unknown. To reveal the relationship between the baseline remnant cholesterol level and all-cause mortality in HF patients. This study enrolled 2,823 patients hospitalized for HF. Kaplan-Meier analysis, Cox regression, C-statistic, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate the prognostic value of remnant cholesterol for all-cause mortality in HF. The mortality rate was lowest in the fourth quartile of remnant cholesterol, which had an adjusted hazard ratio (HR) for death of 0.56 [HR: 0.39, 95% confidence interval (CI): 0.46-0.68, p < 0.001] relative to the first quartile. After adjustment, a one-unit increase in the level of remnant cholesterol was associated with a 41% decrease in the risk of all-cause mortality (HR: 0.59, 95% CI: 0.47-0.73, p < 0.001). A refinement in risk prediction was observed after adding remnant cholesterol quartile to the original model (ΔC-statistic = 0.010, 95% CI: 0.003-0.017; NRI = 0.036, 95% CI: 0.003-0.070; IDI = 0.025, 95% CI: 0.018-0.033; all p < 0.05). Low remnant cholesterol levels are associated with increased all-cause mortality in HF patients. The addition of the remnant cholesterol quartile improved the predictive value over traditional risk factors. ClinicalTrials.gov, Unique Identifier: NCT02664818.

Associations between rheumatoid arthritis, incident heart failure, and left ventricular ejection fraction

Rheumatoid arthritis (RA) is an independent risk factor for heart failure (HF). Yet, the association between RA and left ventricular ejection fraction (LVEF) in incident HF is not well studied, nor are outcomes of HF in RA by LVEF. We identified incident HF patients between 2003 and 2018 through the Swedish Heart Failure Registry, enriched with data from national health registers. Using logistic regression, associations between a prior diagnosis of RA and LVEF among HF patients and vs age, sex, and geographical area matched general population controls without HF were assessed. Additionally, associations between HF with vs without a prior diagnosis of RA, by LVEF, and outcomes up to 5 years after HF diagnosis were investigated using Cox regression. LVEF was primarily dichotomized at 40% and secondarily categorized as <40%, 40% to 49%, and ≥50%. Covariates included demographics and cardiovascular comorbidities. Among 20,916 incident HF patients, 331 (1.6%) had RA vs 1,047/103,501 (1.0%) of HF-free controls. The odds ratio (OR) for RA was 1.4 (95% CI: 1.1-1.8) in LVEF<40% vs HF-free controls and 1.6 (95% CI: 1.3-2.0) in LVEF≥40% vs HF-free controls. Among HF patients, RA was more common in HF with LVEF ≥40% (1.9%) vs LVEF<40% (1.3%), corresponding to OR 1.4 (95% CI: 1.1-1.7). No associations between RA and cardiovascular outcomes were observed across LVEF. An association between RA and all-cause mortality was observed only for patients with LVEF<40% (hazard ratio: 1.4; 95% CI: 1.1-1.8). RA was independently associated with incident HF, particularly HF with LVEF≥40%. RA did not associate with cardiovascular outcomes following HF diagnosis but was associated with increased risk of all-cause mortality in HF with LVEF<40%.

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

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

C-Reactive Protein to Albumin Ratio Predicts In-hospital Mortality in Patients with Acute Heart Failure.

Acute heart failure remains at high risk of mortality despite recent advances in the management and treatment of heart failure. Recently, C-reactive protein to albumin ratio has been shown to predict all-cause mortality in heart failure with reduced ejection fraction. The association between C-reactive protein to albumin ratio and in-hospital mortality in patients with acute heart failure regardless of the left ventricular ejection fraction remains unknown. In this retrospective, single-center cohort study, we included 374 hospitalized patients with acute decompensated heart failure. We calculated C-reactive protein to albumin ratio and evaluated the relationship between the values and in-hospital mortality. During hospitalization duration of 10 [6-17] days, need for hemodialysis/ultrafiltration, acute ischemic hepatitis, coagulopathy, ventricular tachycardia, invasive mechanical ventilation, and shock were more prevalent in the high C-reactive protein to albumin ratio (< 0.78) group compared to low C-reactive protein to albumin ratio ( ≥ 0.78) group. Mortality was higher in the high C-reactive protein to albumin ratio group in comparison to the low C-reactive protein to albumin ratio group (36.7% vs. 12%; P < 0.001). C-reactive protein to albumin ratio was independently and significantly associated with in-hospital mortality (hazard ratio = 1.69, 95% CI: 1.02-2.82; P = 0.042) by multivariate Cox proportional hazard analysis. In receiver operating characteristic analysis, C-reactive protein to albumin ratio was able to predict in-hospital mortality (area under the curve value for in-hospital mortality was 0.72; P < 0.001). Admission C-reactive protein to albumin ratio was associated with increased all-cause mortality in hospitalized patients with acute decompensated heart failure.

PS-C24-2: SYSTOLIC BLOOD PRESSURE AND MORTALITY IN ELDERLY PATIENTS WITH HEART FAILURE

Background: Optimal blood pressure levels in elderly patients are controversial. Our previous results demonstrated that systolic blood pressure (SBP) lowering to less than 140 mmHg might not improve the prognosis in elderly hypertensive patients with frailty. Optimal blood pressure levels for elderly patients with heart failure (HF) have not yet been established. Objective: To examine the association between SBP level and mortality in elderly outpatients with or without HF. Design and setting and participants: A prospective cohort study of older patients in an outpatient setting in Okinawa (Nambu Cohort Study) included 630 patients. Data were collected from 2017 to 2021. Patients were stratified into four categories according to SBP levels of more or less than 140mmHg and presence or absence of HF. Results: A total of 630 patients, with a median (IQR) age of 78 (71 - 84) years and 50% of whom were men, were followed for a median duration of 42 (34 - 43) months. 56% of them had SBP less than 140 mmHg, and 21% were diagnosed as HF. All-cause mortality occurred in 39 (11%) and 31 (11%) of patients with SBP less than 140 mmHg vs. SBP more than 140 mmHg, respectively (HR 1.02, 95%CI 0.64 - 1.65). Likewise, 32 (24%) and 38 (8%) of patients with HF vs. without HF were dead, respectively (HR 3.75, 95% CI 2.33 - 6.00). Adjusted HR (95%CI) for all-cause death of each category patients was as follows; SBP less than 140 mmHg / Non-HF (Reference), SBP more than 140mmHg / Non-HF 1.41 (0.74 - 2.71), SBP less than 140 mmHg / HF 2.71 (1.40 - 5.29), SBP more than 140mmHg / HF 3.75 (1.67 - 8.09) (Figure). Conclusion: In an elderly outpatient setting, an SBP level of more than 140mmHg was likely associated with an increased risk of all-cause mortality in HF and non-HF patients but not statistically significant. On the other hand, HF was associated with increased mortality risk.

Markers of bone metabolism are associated in all-cause mortality in heart failure with reduced ejection fraction

Abstract Background In patients with heart failure an increased risk of fracture-related mortality has been reported. A growing body of evidence suggests that, heart failure (HF) itself may be related to disturbances in bone metabolism, but the exact mechanism remains unclear. In patients with HF previous studies indicated a loss of renal function and alterations in bone micro-architecture, but the clinical relevance remains unclear. Purpose We aimed to evaluate the association between markers of bone metabolism with mortality and pro-brain natriuretic peptide (proBNP) in patients with HF with reduced ejection fraction (HFrEF). Methods We analysed participants of a prospective cohort study of patients referred to coronary angiography (CA). The baseline examination was performed at a tertiary care centre in Germany. Indications for CA were based on clinical routine. For the current analysis only patients with HFrEF were included. Patients were selected based on echocardiographic left ventricular (LV)-EF≤40%, symptoms of heart failure and elevated proBNP concentrations, as recommended by current clinical guidelines. We estimated the risk associated with parathyroid hormone (PTH), osteocalcin (OC), beta-crosslaps (βCL) and alkaline phosphatase (AP) and all-cause mortality using Cox proportional hazard models. Adjustments for demographic and clinical characteristics from the index hospital presentation were used as covariates. We included all individuals who had complete data for all variables in the study in our primary analysis. Additionally, we used linear regression analysis to investigate the correlation between proBNP and its association with PTH, OC, βCL and AP. Results A total of 297 participants (63.9±9.9 years; 17.9% females) were included in the analysis. Median follow-up was 10 years. Median LV-EF was 35%, median New York Heart Association functional class was 2 (IQR=1–3), mean pulmonary capillary wedge pressure was 15.3±7.4 mmHg and median proBNP levels of 2282 (IQR 1875–3758) ng/ml. Participants had in 42.3% multi-vessel atherosclerotic CAD (with stenosis of ≥50% considered diagnostic) with 66.4% had a previous myocardial infarction. In multivariate cox proportionate hazard models OC, βCL and AP were statistically significant associated with an increased risk for all-cause mortality (hazard ratio (HR)=1.6 [95% CI 1.2–2.2], HR=1.8 [95% CI 1.1–2.9] and HR=2.1 [95% CI 1.5–2.9], respectively). In multivariate analysis proBNP was associated with PTH (β-coefficient = 0.076; P≤0.001), AP (β-coefficient = 0.125; P=0.001) and βCL (β-coefficient = 0.062; P=0.046), but not with osteocalcin (β-coefficient = −0.055; P=0.145). Conclusion In patients with HFrEF markers of bone metabolism were significantly associated with mortality and proBNP concentrations. Future studies should focus on different aspects of bone metabolism, fracture rates and fracture related mortality in patients with different stages of heart failure. Funding Acknowledgement Type of funding sources: None.

Non-linear association of cystatin C and all-cause mortality of heart failure: A secondary analysis based on a published database.

BackgroundPrior reports have revealed that basal Cystatin-C (CysC) is positively associated with all-cause death in patients with heart failure (HF). Yet, this positive association is not necessarily generalizable to Chinese HF patients due to methodological limitations and lack of data from Chinese patients.Materials and methodsWe performed secondary data mining based on a retrospective cohort dataset published on the internet. This dataset contains 2008 patients with HF who were admitted to a tertiary hospital in Sichuan Province, China from 2016 to 2019. The exposure variable was baseline CysC and the outcome variable was all-cause death on day 28, day 90, and month 6. Covariates were baseline measurements, including demographic data, drug use, comorbidity score, organ function status (heart, kidney), and severity of heart failure.ResultsAmong 1966 selected participants, the mortality rates at 28 days, 90 days and 6 months were 1.83% (36/1966), 2.09% (41/1966) and 2.85% (56/1966) respectively. After adjustment for confounders, the non-linear associations between CysC and all-cause deaths were observed. We calculated the inflection points were about 2.5 mg/L of CysC. On the right of inflection point, each increase of 1 mg/L in CysC was associated with an increase in the risk of 28-day mortality (Relative risk [RR], 2.07; 95% confidence interval [CI], 1.09 to 3.93; P = 0.0266), 90-day mortality (RR, 2.51; 95% CI, 1.38 to 4.57; P = 0.003), and 6-month mortality (RR,2.25; 95% CI, 1.37 to 3.70; P < 0.001).ConclusionOur findings suggest that values about 2.5 mg/l of cystatin could be a danger threshold for the short-term risk of death in heart failure. Exceeding this threshold, for every 1 mg/L increase in CysC, the risk of all-cause mortality increased by more than one time.

The Value of SII in Predicting the Mortality of Patients with Heart Failure.

Background The main purpose of this study was to explore the predictive value of the systemic immune inflammation index (SII), a novel clinical marker, in heart failure (HF) patients. Methods Critically ill patients with HF were identified from the Medical Information Mart for Intensive Care III (MIMIC III) database. Patients were divided into three groups according to tertiles of SII (group 1, group 2, group 3). We used Kaplan-Meier curves and Cox proportional hazards regression models to evaluate the association between the SII and all-cause mortality in HF. Subgroup analysis was used to verify the predictive effect of the SII on mortality. Results This study included 9107 patients with a diagnosis of HF from the MIMIC III database. After 30, 60, 180, and 365 days of follow-up, 25.60%, 32.10%, 41.30%, and 47.50% of the patients in group 3 had died. Using the Kaplan-Meier curve, we observed that patients with higher SII values had a shorter survival time (log rank p < 0.001). The Cox proportional hazards regression model adjusted for all possible confounders and indicated that the higher SII group had a higher mortality (30-day: HR = 1.304, 95%CI = 1.161 − 1.465, 60-day: HR = 1.266, 95% CI = 1.120 − 1.418, 180-day: HR = 1.274, 95%CI = 1.163 − 1.395, and 365-day: HR = 1.255, 95%CI = 1.155 − 1.364). Conclusions SII values could be used as a predictor of prognosis in critically ill patients with HF.

Effects of educational intervention on mortality and patient-reported outcomes in individuals with heart failure: A randomized controlled trial

ObjectiveTo examine the effects of an educational intervention on patient-reported outcomes and all-cause mortality in heart failure (HF) patients MethodsIn this randomized controlled trial, we enrolled 122 hospitalized patients with HF. The intervention group (n = 60) received an individual nurse-led education session on HF self-management during hospitalization and three telephone calls after discharge. The control group (n = 62) received care as usual. Patient-reported outcomes were measured at baseline and at 3 and 6 months. Mortality status was determined using the National Death Records. Intervention effects were evaluated using the Cox proportional hazards regression model and linear mixed models. ResultsDuring the follow-up (median: 568 days), 7 deaths (12%) in the intervention group and 15 deaths (24%) in the control group occurred (adjusted hazard ratio, 0.40; 95% confidence interval, 0.16–0.98; P = .046). From baseline to 3 and 6 months, the intervention group showed greater improvements in HF knowledge (difference=6.14, P = .03; difference=5.76, P = .02, respectively), self-care (difference=−6.08, P < .001; difference=−6.16, P < .001, respectively), and health-related quality of life (difference=−11.90, P = .01; difference=−14.57, P = .004, respectively) than the control group. ConclusionEducational intervention with telephone follow-up reduced all-cause mortality and improved patient-reported outcomes. Practice implicationEducational intervention should be considered as part of routine care for HF patients.

Abstract 13836: Unbiased Deep Learning Approach Utilizing Longitudinal Data in Assessing All-Cause Mortality in Patients With a De Novo or Worsened Heart Failure

Introduction: Heart failure (HF) is a heterogenous syndrome with complex pathophysiology. Biomarkers and clinical risk scores often fail to capture modifications in the treatment continuity and provide suboptimal patient-level precision in the prognostic stratification. Electronic patient records provide necessary granularity yielding opportunities to develop new artificial intelligence (AI) based strategies for comprehensive prognostic re-stratification. Hypothesis: We assessed the hypothesis that, utilizing longitudinal patient data in an AI approach, yields superior performance predicting all-cause mortality in a cohort of patients hospitalized with a de novo or worsened HF, compared to single observational time point predictions. Methods: In a cohort of 2449 HF patients hospitalized between 2011-2017, we utilized 151 451 patient exams from 422 parameters. Features included clinical phenotyping, medication, ECG, laboratory, echocardiography, catheterization data or percutaneous and surgical interventions gathered on a routine clinical basis reflecting standard of care as captured in individual electronic records. AI models were developed, and their performance on the validation set was compared to industry standard clinical scores. Results: AI models yielded performance ranging from 0.83 to 0.89 AUC on the outcome-balanced validation set in predicting all-cause mortality at 30-, 90-, 180-, 360- and 720-day time-limits. The primary endpoint, 1-year mortality prediction model, recorded 0.85 AUC on the validation set compared to 0.7 AUC (Seattle HF model) and 0.73 AUC (MAGGIC HF Score) respectively. Conclusions: Our findings present a novel, patient-level, AI-based risk prediction approach of all-cause mortality in heart failure utilizing all historical data available in electronic health records. This suggests the potential of AI based predictive models in a point-of-care approach to guide clinical risk stratification.

Deep learning for mortality prediction in patients with a de-novo or worsened heart failure

Abstract Background Heart failure (HF) is a heterogenous syndrome with complex pathophysiology. Biomarkers and clinical risk scores often fail to provide optimal patient-level precision in the prognostic stratification. As utilizing single observational timepoint, they do not capture the entire care pathway with variations in individual patient management. Electronic patient records provide an opportunity to develop new artificial intelligence (AI) strategies for comprehensive prognostic re-stratification reflecting diagnostic and therapeutic management. Purpose We sought to use deep artificial intelligence (AI) and develop an unbiased predictive algorithm for all-cause mortality in a cohort of patients hospitalized with a de novo or worsened HF. Methods In a cohort of 2449 HF patients hospitalized between 2011–2017, we utilized 151 451 patient exams from 422 parameters. They included clinical phenotyping, medication, ECG, laboratory, echocardiography, catheterization data or percutaneous and surgical interventions gathered on a routine clinical basis reflecting standard of care as captured in individual electronic records. The AI model development consisted of 101 iterations of repeated random subsampling splits into balanced training and validation sets. Results AI models yielded performance ranging from 0.83 to 0.89 AUC on the outcome-balanced validation set in predicting all-cause mortality at 30-, 90-, 180-, 360- and 720-day time-limits (Figure 1). The primary endpoint, 1-year mortality prediction model, recorded an 0.85 AUC accuracy. We observed stable model performance across all HF phenotypes: HFpEF 0.83 AUC, HFmrEF 0.85 AUC and HFrEF 0.86 AUC, respectively). Conclusion Our findings present a novel, patient-level, AI-based risk prediction of all-cause mortality in heart failure with a robust accuracy across its phenotypes. This suggests the potential of AI based predictive models in a point-of-care approach to guide clinical risk stratification. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): VZW Cardiovascular Research Center Aalst

Lower levels of triiodothyronine are associated with poor hemodynamic profile and all-cause mortality in heart failure.

Background: We aimed to assess the association of triiodothyronine (T3) hormone with invasive hemodynamic parameters and all-cause mortality in heart failure with reduced ejection fraction (HFrEF). Results:About483 HFrEF patients were enrolled. Patients with the lowest T3 tertile had advanced New York Heart Association (NYHA) classes, had higher uric acid, brain natriuretic peptide. T3 level had a positive correlation with cardiac index (CI) and a negative correlation with pulmonary vascular resistanceand pulmonary capillary wedge pressure. Adjusted with NYHA III-IV classes, uric acid, aspartate aminotransferaseand CI, T3 level was found to be an independent predictor of all-cause mortality. In Kaplan-Meier analysis, the lowest T3 tertile had the lowest survival function. Conclusion: Free T3 is positively correlated with CI and negatively correlated with pulmonary vascular resistance and pulmonary capillary wedge pressure in patients with HFrEF. Lower levels of T3 seems to be a poor prognostic factor in this particular patient population.

Depression and one-year survival of patients with heart failure in China: analysis from the China-PEACE Prospective Heart Failure study

Abstract Background Depression is prevalent among patients with heart failure (HF), but data exploring association of depression with risk of death in patients with HF is scarce in China. We investigated the relationship between depression and all-cause mortality of heart failure in China. Methods In China PEACE 5p-HF Study, we prospectively enrolled patients primarily hospitalized with HF from 52 diverse hospitals throughout China during 2016–2018. All the patients were followed up for 1 year. About 10% patients in the cohort from 41 hospitals was included for the measurement of depression state at convenience. Depression was measured by the Patient Health Questionnaire-8 depression scale (PHQ-8) at baseline. Depression state was categorized into major depressive disorder (10–24 points), minor depression (5–10 points) and no depression (0–5 points). Cox proportional hazards regression analyses, controlling for established risk factors as age, gender, LVEF, NYHA, medication use and medical history, were used to evaluate how depression were related to end point of death from any cause. Results Total 584 patients were included in our analysis, with median age 69 (IQR 60–77) years, and 40.8% female. Among these patients, 36.0% had major depressive disorder (n=210), 33.9% had minor depression (n=198). There were 70 (12%) patients died within 1 year after discharge. Major depressive disorder was associated with higher all-cause mortality compared with no depression (hazard ratio=2.18, 95% confidence interval 1.36–3.50, p=0.001). While minor depression was not significantly associated with all-cause mortality. Conclusions Major depression is an independent risk factor for all-cause mortality in hospitalized patients with HF in China. It is necessary to screen for psychological health in hospitalized patients to targeting intervention. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): National Key Research and Development Program from the Ministry of Science and Technology of China