Adiponectin is valuable in the diagnosis of acute heart failure with renal insufficiency

Development and validation of a dynamic nomogram for short-term survival in acute heart failure patients with acute kidney injury upon ICU admission

Objective

The objective of this study is to develop and validate an effective prognostic nomogram for predicting the short-term survival rate of patients with acute heart failure (AHF) complicated by acute kidney injury (AKI) who are admitted to the intensive care unit (ICU).

Patients and methods

We conducted an analysis of data from patients of AHF with AKI spanning the period from 2008 to 2019, utilizing the MIMIC-IV database. Patients were randomly divided into training and validation sets. The training set employed the least absolute shrinkage and selection operator regression model to identify predictors of AKI. Subsequently, a dynamic nomogram was constructed using multivariate Cox regression analysis within the training set and was subsequently validated using the validation set. The nomogram's predictive accuracy, calibration, and clinical utility were evaluated through the concordance index (C-index), calibration plots, and decision curve analysis (DCA).

Results

A total of 978 AHF patients with AKI were analyzed. Multivariate analysis identified serum creatinine, race, age, use of human albumin, use of vasoactive drug, and hemoglobin as independent predictors significantly influencing the short-term prognosis of AHF patients with AKI upon ICU admission. The C-index for the training and validation sets were 0.81 (95%CI: 0.74-0.87) and 0.80 (95 % CI: 0.67-0.92), respectively. The calibration plot of the nomogram demonstrated a close alignment between predicted and observed probabilities. Furthermore, the DCA confirmed the clinical utility of the nomogram.

Conclusions

This study presents a dynamic nomogram that incorporates clinical risk factors and can be conveniently utilized to predict short-term prognosis for AHF patients with AKI upon ICU admission.

Identification of crucial genes related to heart failure based on GEO database

BACKGROUND

The molecular biological mechanisms underlying heart failure (HF) remain poorly understood. Therefore, it is imperative to use innovative approaches, such as high-throughput sequencing and artificial intelligence, to investigate the pathogenesis, diagnosis, and potential treatment of HF.

METHODS

First, we initially screened Two data sets (GSE3586 and GSE5406) from the GEO database containing HF and control samples from the GEO database to establish the Train group, and selected another dataset (GSE57345) to construct the Test group for verification. Next, we identified the genes with significantly different expression levels in patients with or without HF and performed functional and pathway enrichment analyses. HF-specific genes were identified, and an artificial neural network was constructed by Random Forest. The ROC curve was used to evaluate the accuracy and reliability of the constructed model in the Train and Test groups. Finally, immune cell infiltration was analyzed to determine the role of the inflammatory response and the immunological microenvironment in the pathogenesis of HF.

RESULTS

In the Train group, 153 significant differentially expressed genes (DEGs) associated with HF were found to be abnormal, including 81 down-regulated genes and 72 up-regulated genes. GO and KEGG enrichment analyses revealed that the down-regulated genes were primarily enriched in organic anion transport, neutrophil activation, and the PI3K-Akt signaling pathway. The upregulated genes were mainly enriched in neutrophil activation and the calcium signaling. DEGs were identified using Random Forest, and finally, 16 HF-specific genes were obtained. In the ROC validation and evaluation, the area under the curve (AUC) of the Train and Test groups were 0.996 and 0.863, respectively.

CONCLUSIONS

Our research revealed the potential functions and pathways implicated in the progression of HF, and designed an RNA diagnostic model for HF tissues using machine learning and artificial neural networks. Sensitivity, specificity, and stability were confirmed by ROC curves in the two different cohorts.

Urine albumin-to-creatinine ratio on admission predicts early rehospitalization in patients with acute decompensated heart failure

Detecting high-risk patients for early rehospitalization is crucial in heart failure patient care. An association of albuminuria with cardiovascular events is well known. However, its predictive impact on rehospitalization for acute decompensated heart failure (ADHF) remains unknown. In this study, 190 consecutive patients admitted due to ADHF between 2017 and April 2019 who underwent urinalysis were enrolled. Among them, 140 patients from whom urine albumin-to-creatinine ratio (UACR) was measured with spot urine samples on admission were further analyzed. The association between UACR and rehospitalization due to HF during 1 year after discharge was evaluated. The mean age of 140 participants was 77.6 years and 55% were men. Only 18% (n = 25) of patients presented with normoalbuminuria (UACR < 30 mg/g∙creatinine), whereas 59% (n = 83) and 23% (n = 32) showed microalbuminuria (UACR 30-300 mg/g·creatinine) and macroalbuminuria (UACR > 300 mg/g·creatinine), respectively. The level of UACR on admission was correlated with the risk of subsequent rehospitalization due to HF (p = 0.017). The receiver operating characteristic analysis indicated that the best cut-off values for the UACR and B-type natriuretic peptide (BNP) levels to predict ADHF rehospitalization were 50 mg/g·creatinine and 824 pg/ml, respectively. When the patients were divided into four groups using both cut-off values, the individual predictive impacts of UACR and BNP on rehospitalization were comparable. Patients with both elevated UACR and BNP levels had a higher rate of HF rehospitalization than those with elevated BNP levels alone (p < 0.05). The combination of both values enabled more accurate prediction of HF rehospitalization than BNP levels alone. In conclusion, UACR could be a new useful biomarker to predict HF rehospitalization in patients with ADHF, especially in combination with the levels of BNP, and should be further evaluated in a prospective study.

High plasma adiponectin is associated with increased pulmonary blood flow and reduced right ventricular function in patients with pulmonary hypertension

BACKGROUND

Adiponectin is a biomarker closely related to heart failure. However, its role in pulmonary hypertension remains unclear. In this study, we investigated the association between adiponectin and hemodynamic abnormalities, right ventricular function in patients with congenital heart disease associated pulmonary hypertension (CHD-PH).

METHODS

Patients with CHD-PH were enrolled in this cross-sectional study. Linear regression analysis was performed to assess the association between adiponectin, N-terminal pro-Brain Natriuretic Peptide (NT-proBNP) and different clinical parameters. Results were depicted as beta-estimates(ß) with 95%-confidence intervals (95% CI). In addition, mediation and receiver operating characteristic curve analyses were used to analyze the relationships among adiponectin, NT-proBNP and right ventricular function.

RESULTS

A total of 86 CHD-PH patients were included. The overall mean adiponectin concentration was 7.9 ± 5.8 μg/ml. Log adiponectin was positively correlated with pulmonary circulation index (ß = 2.2, 95% CI 0.5, 4.0), log NT-proBNP (ß = 0.22, 95% CI 0.04, 0.41) and inversely with the tricuspid annular plane systolic excursion (TAPSE, ß = -4.7, 95% CI -8.6, - 0.8). The mediation analysis revealed the association between NT-proBNP and TAPSE was fully mediated by adiponectin (total effect c = - 5.4, 95% CI -9.4, - 1.5, p = 0.013; direct effect c' = - 3.7, 95% CI -7.5, 0.1, p = 0.067). Additionally, the efficiency of adiponectin for detecting right ventricular dysfunction was not inferior to NT-proBNP (AUC = 0.84, 95% CI 0.67-1.00 vs AUC = 0.74, 95% CI 0.51-0.97, p = 0.23).

CONCLUSIONS

Adiponectin is closely correlated with pulmonary blood flow and right ventricular function and may be a valuable biomarker for disease assessment in patients with pulmonary hypertension.