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Serum syndecan-1 concentration in hospitalized patients with heart failure may predict readmission-free survival.

ABSTRACT: Syndecan-1 is found in the endothelial glycocalyx and is released into the bloodstream during stressed conditions, including severe diseases such as acute kidney injury, chronic kidney disease, and cardiovascular disease. This study investigated the prognostic value of serum syndecan-1 concentration in patients with heart failure upon admission. Serum syndecan-1 concentration was analyzed in 152 patients who were hospitalized for worsening heart failure from September 2017 to June 2018. The primary outcome of the study was readmission-free survival, defined as the time from the first admission to readmission for worsened heart failure or death from any cause, which was assessed at 30 months after discharge from the hospital. The secondary outcome of the study was survival time. Blood samples and echocardiogram data were analyzed. Univariate and multivariable time-dependent Cox regression analyses adjusted for age, creatinine levels, and use of antibiotics were conducted. The serum syndecan-1 concentration was significantly associated with readmission-free survival. Subsequently, the syndecan-1 concentration may have gradually decreased with treatment. The administration of human atrial natriuretic peptide and antibiotics may have modified the relationship between readmission-free survival and serum syndecan-1 concentration (p = 0.01 and 0.008, respectively). Serum syndecan-1 concentrations, which may indicate injury to the endothelial glycocalyx, predict readmission-free survival in patients with heart failure.

SUBMITTER: Kitagawa Y

PROVIDER: S-EPMC8654157 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Serum syndecan-1 concentration in hospitalized patients with heart failure may predict readmission-free survival.

Kitagawa Yuichiro Y Kawamura Itta I Suzuki Keiko K Okada Hideshi H Ishihara Takuma T Tomita Hiroyuki H Suzuki Kodai K Takada Chihiro C Sampei So S Kano Soichiro S Kondo Kohei K Asano Hirotaka H Wakayama Yugo Y Kamidani Ryo R Kawasaki Yuki Y Fukuda Hirotsugu H Nishio Ayane A Miyake Takahito T Fukuta Tetsuya T Yasuda Ryu R Oiwa Hideaki H Kakino Yoshinori Y Miyazaki Nagisa N Watanabe Takatomo T Yoshida Takahiro T Doi Tomoaki T Suzuki Akio A Yoshida Shozo S Matsuo Hitoshi H Ogura Shinji S

PloS one 20211208 12

Syndecan-1 is found in the endothelial glycocalyx and is released into the bloodstream during stressed conditions, including severe diseases such as acute kidney injury, chronic kidney disease, and cardiovascular disease. This study investigated the prognostic value of serum syndecan-1 concentration in patients with heart failure upon admission. Serum syndecan-1 concentration was analyzed in 152 patients who were hospitalized for worsening heart failure from September 2017 to June 2018. The prim ...[more]

PMID: 34879094

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Project description:Background: Perturbation of energy metabolism exacerbates cardiac dysfunction, serving as a potential therapeutic target in congestive heart failure. Although circulating free fatty acids (FFAs) are linked to insulin resistance and risk of coronary heart disease, it still remains unclear whether circulating FFAs are associated with the prognosis of patients with acute heart failure (AHF). Methods: This single-center, observational cohort study enrolled 183 AHF patients (de novo heart failure or decompensated chronic heart failure) in the Second Affiliated Hospital, Zhejiang University School of Medicine. All-cause mortality and heart failure (HF) rehospitalization within 1 year after discharge were investigated. Serum FFAs were modeled as quartiles as well as a continuous variable (per SD of FFAs). The restricted cubic splines and cox proportional hazards models were applied to evaluate the association between the serum FFAs level and all-cause mortality or HF rehospitalization. Results: During a 1-year follow-up, a total of 71 (38.8%) patients had all-cause mortality or HF rehospitalization. The levels of serum FFAs positively contributed to the risk of death or HF rehospitalization, which was not associated with the status of insulin resistance. When modeled with restricted cubic splines, the serum FFAs increased linearly for the incidence of death or HF rehospitalization. In a multivariable analysis adjusting for sex, age, body-mass index, coronary artery disease, diabetes mellitus, hypertension, left ventricular ejection fraction and N-terminal pro-brain natriuretic peptid, each SD (303.07 μmol/L) higher FFAs were associated with 26% higher risk of death or HF rehospitalization (95% confidence interval, 2-55%). Each increasing quartile of FFAs was associated with differentially elevated hazard ratios for death or HF rehospitalization of 1 (reference), 1.71 (95% confidence interval, [0.81, 3.62]), 1.41 (95% confidence interval, [0.64, 3.09]), and 3.18 (95% confidence interval, [1.53, 6.63]), respectively. Conclusion: Serum FFA levels at admission among patients with AHF were associated with an increased risk of adverse outcomes. Additional studies are needed to determine the causal-effect relationship between FFAs and acute cardiac dysfunction and whether FFAs could be a potential target for AHF management.

Project description:BACKGROUND:Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body.Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at highlighting patterns and correlations otherwise undetectable by medical doctors. Machine learning, in particular, can predict patients' survival from their data and can individuate the most important features among those included in their medical records. METHODS:In this paper, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning classifiers to both predict the patients survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests, and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone. RESULTS:Our results of these two-feature models show not only that serum creatinine and ejection fraction are sufficient to predict survival of heart failure patients from medical records, but also that using these two features alone can lead to more accurate predictions than using the original dataset features in its entirety. We also carry out an analysis including the follow-up month of each patient: even in this case, serum creatinine and ejection fraction are the most predictive clinical features of the dataset, and are sufficient to predict patients' survival. CONCLUSIONS:This discovery has the potential to impact on clinical practice, becoming a new supporting tool for physicians when predicting if a heart failure patient will survive or not. Indeed, medical doctors aiming at understanding if a patient will survive after heart failure may focus mainly on serum creatinine and ejection fraction.

Dataset Information

Serum syndecan-1 concentration in hospitalized patients with heart failure may predict readmission-free survival.

Publications

Serum syndecan-1 concentration in hospitalized patients with heart failure may predict readmission-free survival.

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