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Novel risk stratification with time course assessment of in-hospital mortality in patients with acute heart failure.

ABSTRACT: Patients with acute heart failure (AHF) show various clinical courses during hospitalization. We aimed to identify time course predictors of in-hospital mortality and to establish a sequentially assessable risk model.We enrolled 1,035 consecutive AHF patients into derivation (n = 597) and validation (n = 438) cohorts. For risk assessments at admission, we utilized Get With the Guidelines-Heart Failure (GWTG-HF) risk scores. We examined significant predictors of in-hospital mortality from 11 variables obtained during hospitalization and developed a risk stratification model using multiple logistic regression analysis. Across both cohorts, 86 patients (8.3%) died during hospitalization. Using backward stepwise selection, we identified five time-course predictors: catecholamine administration, minimum platelet concentration, maximum blood urea nitrogen, total bilirubin, and C-reactive protein levels; and established a time course risk score that could sequentially assess a patient's risk status. The addition of a time course risk score improved the discriminative ability of the GWTG-HF risk score (c-statistics in derivation and validation cohorts: 0.776 to 0.888 [p = 0.002] and 0.806 to 0.902 [p<0.001], respectively). A calibration plot revealed a good relationship between observed and predicted in-hospital mortalities in both cohorts (Hosmer-Lemeshow chi-square statistics: 6.049 [p = 0.642] and 5.993 [p = 0.648], respectively). In each group of initial low-intermediate risk (GWTG-HF risk score <47) and initial high risk (GWTG-HF risk score ?47), in-hospital mortality was about 6- to 9-fold higher in the high time course risk score group than in the low-intermediate time course risk score group (initial low-intermediate risk group: 20.3% versus 2.2% [p<0.001], initial high risk group: 57.6% versus 8.5% [p<0.001]).A time course assessment related to in-hospital mortality during the hospitalization of AHF patients can clearly categorize a patient's on-going status, and may assist patients and clinicians in deciding treatment options.

SUBMITTER: Yagyu T

PROVIDER: S-EPMC5667756 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Novel risk stratification with time course assessment of in-hospital mortality in patients with acute heart failure.

Yagyu Takeshi T Kumada Masahiro M Nakagawa Tsutomu T

PloS one 20171102 11

<h4>Background</h4>Patients with acute heart failure (AHF) show various clinical courses during hospitalization. We aimed to identify time course predictors of in-hospital mortality and to establish a sequentially assessable risk model.<h4>Methods and results</h4>We enrolled 1,035 consecutive AHF patients into derivation (n = 597) and validation (n = 438) cohorts. For risk assessments at admission, we utilized Get With the Guidelines-Heart Failure (GWTG-HF) risk scores. We examined significant p ...[more]

PMID: 29095900

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Project description:AimsAcute heart failure (AHF) leads to a drastic increase in mortality and rehospitalization. The aim of the study was to identify prognostic variables in a real-life population of AHF patients admitted to the emergency department with acute shortness of breath.Methods and resultsWe evaluated potential predictors of mortality in 728 consecutive patients admitted to the emergency department with AHF. Possible predictors of all-cause and cardiovascular (CV) mortality were investigated by Cox and Fine and Gray models at multivariable analysis. Among the 728 patients, 256 died during the entire follow-up, 142 of these due to CV cause. The 1 year mortality rate was 20%, with the highest risk of death during the index hospitalization (with 8% estimate in-hospital mortality at 30 days). A higher risk of events during the index hospitalization was more evident for the CV deaths, for which we found a cumulative 1 year incidence of 12% with a cumulative incidence in the first 30 days of hospitalization of about 5%. At multivariable analysis, age (P < 0.001), New York Heart Association (NYHA) class IV vs. I-II-III (P = 0.001), systolic blood pressure (P < 0.001), non-cardiac co-morbidities (≥3 vs. 0, P = 0.05), oxygen saturation (P = 0.03), serum creatinine (P < 0.001), and left ventricular ejection fraction (LVEF) (40-49% vs. <40%, P = 0.004; ≥50% vs. <40%, P = 0.003) were independent predictors of all-cause mortality during the entire follow-up. Age (P = 0.03), systolic blood pressure (P = 0.01), oxygen saturation (P = 0.03), serum creatinine (P = 0.02), and LVEF (40-49% vs. <40%, P = 0.03; ≥50% vs. <40%, P = 0.004) were independent predictors of CV mortality during the entire follow-up. NYHA class IV vs. I-II-III (P < 0.001), serum creatinine (P = 0.01), and LVEF (40-49% vs. <40%, P = 0.02; ≥50% vs. <40%, P < 0.001) remained independent predictors for in-hospital death, while only serum creatinine (P = 0.04), LVEF (40-49% vs. <40%: 0.32, P = 0.04; ≥50% vs. <40%, P < 0.001), and NYHA class vs. I-II-III (P = 0.02) remained predictors for in-hospital CV mortality.ConclusionsIn this real-life cohort of patients with AHF, the results showed a similar mortality rate comparing with other analysis and with the most important registries. Age, NYHA class IV, systolic blood pressure, creatinine levels, sodium levels, and ejection fraction were independent predictors of 1 year mortality, while LVEF <40% was the only predictor of both all-cause mortality and CV mortality.

Project description:BackgroundHeart failure (HF) is a complex clinical syndrome with persistently high mortality. High-throughput proteomic technologies offer new opportunities to improve HF risk stratification, but their contribution remains to be clearly defined. We aimed to systematically review prognostic studies using high-throughput proteomics to identify protein signatures associated with HF mortality.MethodsWe searched four databases and two clinical trial registries for articles published from 2012 to 2023. HF proteomics studies measuring high numbers of proteins using aptamer or antibody-based affinity platforms on human plasma or serum with outcomes of all-cause or cardiovascular death were included. Two reviewers independently screened articles, extracted data, and assessed the risk of bias. A third reviewer resolved conflicts. We assessed the risk of bias using the Risk Of Bias In Non-randomized Studies-of Exposure tool.ResultsOut of 5131 unique articles identified, nine articles were included in the review. The nine studies were observational; three used the aptamer platform, and six used the antibody platform. We found considerable heterogeneity across studies in measurement panels, HF definitions, ejection fraction categorization, follow-up duration, and outcome definitions, and a lack of risk estimates for most protein associations. Hence, we proceeded with a systematic review rather than a meta-analysis. In two comparable aptamer studies in patients with HF with reduced ejection fraction, 21 proteins were identified in common for the association with all-cause death. Among these, one protein, WAP four-disulfide core domain protein 2 was also reported in an antibody study on HFrEF and for the association with CV death. We proposed standardized reporting criteria to facilitate the interpretation of future studies.ConclusionsIn this systematic review of nine studies evaluating the association of proteomics with mortality in HF, we identified a limited number of proteins common across several studies. Heterogeneity across studies compromised drawing broad inferences, underscoring the importance of standardized approaches to reporting.

Dataset Information

Novel risk stratification with time course assessment of in-hospital mortality in patients with acute heart failure.

Publications

Novel risk stratification with time course assessment of in-hospital mortality in patients with acute heart failure.

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