The role of B-type natriuretic peptide in diagnosing acute decompensated heart failure in chronic kidney disease patients

Development of a clinical risk score system for peritoneal dialysis-associated peritonitis treatment failure

OBJECTIVE

This study aimed to construct a clinical risk score system for peritoneal dialysis-associated peritonitis (PDAP) treatment failure to provide a theoretical basis for clinical workers.

METHODS

A total of 161 PDAP individuals admitted to our hospital were included, among whom 70 cases were in the treatment-improved group and 87 cases were in the treatment failure group. We compared the general condition, clinical manifestations, and laboratory examination indicators of the two groups of individuals, used multivariate logistic regression analysis to identify the factors influencing PDAP treatment failure, and developed a clinical risk score system. The diagnostic performance of the risk score system was evaluated utilizing the receiver operating characteristic (ROC) curve.

RESULTS

Significant differences (P < 0.05) were observed between the two groups in terms of contamination, peritoneal fluid culture results, blood urea nitrogen (BUN) level, C-reactive protein (CRP) level, B-type natriuretic peptide (BNP) level, average residual urine (RU) volume, and urea clearance rate (UCR). Multivariate logistic regression analysis showed that BUN level, CRP level, BNP level, average RU volume, and UCR were independent risk factors affecting PDAP patient treatment outcomes (P < 0.05). The ROC curve analysis of the risk score system for predicting treatment failure in PDAP individuals showed an area under the curve of 0.895 [95% confidence interval (0.847-0.943)]. The optimal cut-off point was 2.5 points, with corresponding sensitivity and specificity of 88.5% and 74.3%, separately.

CONCLUSION

BUN level, CRP level, BNP level, average RU volume, and UCR are independent risk factors for PDAP treatment failure. The clinical risk score system based on these five independent risk factors can accurately predict the risk of treatment failure in PDAP individuals.

Detecting cardiomyopathies in pregnancy and the postpartum period with an electrocardiogram-based deep learning model

Aims

Cardiovascular disease is a major threat to maternal health, with cardiomyopathy being among the most common acquired cardiovascular diseases during pregnancy and the postpartum period. The aim of our study was to evaluate the effectiveness of an electrocardiogram (ECG)-based deep learning model in identifying cardiomyopathy during pregnancy and the postpartum period.

Methods and results

We used an ECG-based deep learning model to detect cardiomyopathy in a cohort of women who were pregnant or in the postpartum period seen at Mayo Clinic. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. We compared the diagnostic probabilities of the deep learning model with natriuretic peptides and a multivariable model consisting of demographic and clinical parameters. The study cohort included 1807 women; 7%, 10%, and 13% had left ventricular ejection fraction (LVEF) of 35% or less, <45%, and <50%, respectively. The ECG-based deep learning model identified cardiomyopathy with AUCs of 0.92 (LVEF ≤ 35%), 0.89 (LVEF < 45%), and 0.87 (LVEF < 50%). For LVEF of 35% or less, AUC was higher in Black (0.95) and Hispanic (0.98) women compared to White (0.91). Natriuretic peptides and the multivariable model had AUCs of 0.85 to 0.86 and 0.72, respectively.

Conclusions

An ECG-based deep learning model effectively identifies cardiomyopathy during pregnancy and the postpartum period and outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting.

Introduction to Nephrocardiology

The interaction between nephrology and cardiovascular medicine is much broader than the cardiorenal syndrome. Many different aspects of cardiovascular medicine are interconnected with and substantially influenced by the conditions that fall into the realm of nephrology, and vice versa. Those aspects include pathophysiology, risk factors, epidemiology, prognosis, prevention, diagnosis, monitoring, and therapy. Discovery of the interconnected areas and development of appropriate knowledge and skill to optimally approach those circumstances can improve the quality of care and outcome of a large population of patients. Therefore, establishment of the distinct subspeciality of nephrocardiology is imperative.

Identification of key genes related to heart failure by analysis of expression profiles

Introduction

To elucidate the candidate biomarkers involved in the pathogenesis process of heart failure (HF) via analysis of differentially expressed genes (DEGs) of the dataset from the Gene Expression Omnibus (GEO).

Material and methods

The GSE76701 gene expression profiles regarding the HF and control subjects were respectively analysed. Briefly, DEGs were firstly identified and subjected to Cytoscape plug-in ClueGO + CluePedia and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A protein-protein interaction (PPI) network was then built to analyse the interaction between DEGs, followed by the construction of an interaction network by combining with hub genes with the targeted miRNA genes of DEGs to identify the key molecules of HF. In addition, potential drugs targeting key DEGs were sought using the drug-gene interaction database (DGIdb), and a drug-mRNA-miRNA interaction network was also constructed.

Results

A total of 489 DEGs were verified between HF and control, which mainly enriched in type I interferon and leukocyte migration according to molecular function. Significantly increased levels of GAPDH, GALM1, MMP9, CCL5, and GNAL2 were found in the HF setting and were identified as the hub genes based on the PPI network. Furthermore, according to the drug-mRNA-miRNA network, FCGR2B, CCND1, and NF-κb, as well as corresponding miRNA-605-5p, miRNA-147a, and miRNA-671-5p were identified as the drug targets of HF.

Conclusions

The hub genes GAPDH, GALM1, MMP9, CCL5, and GNAL2 were significantly increased in HF. miRNA-605-5p, miRNA-147a, and miRNA-671-5p were predicted as the drug target-interacted gene-miRNA of HF.

Are there any further modalities for prediction of subclinical volume overload in advanced stages of chronic kidney disease?

BACKGROUND

Subclinical volume overload in chronic kidney disease (CKD) patient represents a debatable issue. Although many tools were used to detect volume overload in such patients, many non-specific results were due to presence of comorbidities. Bioimpedance spectroscopy is an objective fluid status assessment method, which is shown superior to classical methods in many studies. Combining some of these tools may improve their accuracy and specificity. Inferior vena cava collapsibility index (IVCCI) with brain natriuretic peptide (BNP) can be combined for more specific volume assessment. This study was performed to assess the usage of combined IVCCI and BNP levels in CKD patients to predict subclinical volume overload.

METHODS

One hundred and ten patients with CKD (stages 4 and 5) not on dialysis and having normal left ventricular systolic function were included in this study. Exclusion criteria were: (1) patients with other causes of raised BNP than volume overload and (2) patients on diuretics. A complete medical history was obtained, and thorough examination and laboratory tests were performed for all included patients. IVCCI and BNP serum levels were evaluated. The patients who exhibited an overhydration (OH)/extracellular water (ECW) ratio of >15% were considered to have volume overload.

RESULTS

Twenty-six patients (23.6%) had subclinical hypervolemia as diagnosed by OH/ECW ratio of >15%. IVCCI ≤ 38% had higher diagnostic performance than BNP ≥ 24 pg/mL. Combining both IVCCI ≤ 38% and BNP ≥ 24 pg/mL increased the specificity and positive predictive value for detection of subclinical hypervolemia.

CONCLUSION

Combined elevated BNP level and decreased IVCCI are more precise tools for subclinical volume overload detection in CKD patients.

Length of stay and readmission in older adults hospitalized for heart failure

INTRODUCTION

Hospital length of stay (LoS) and hospital readmissions are metrics of healthcare performance. We examined the association between these two metrics in older patients hospitalized with decompensated heart failure (HF).

MATERIAL AND METHODS

Eight thousand and forty-nine patients hospitalized for HF in 106 U.S. hospitals had a median LoS of 5 days; among them, 3777 had a LoS > 5 days. Using propensity scores for LoS > 5 days, we assembled 2723 pairs of patients with LoS 1-5 vs. > 5 days. The matched cohort of 5446 patients was balanced on 40 baseline characteristics. We repeated the above process in 7045 patients after excluding those with LoS > 10 days, thus assembling a second matched cohort of 2399 pairs of patients with LoS 1-5 vs. 6-10 days. Hazard ratios (HR) and 95% confidence intervals (CI) for outcomes associated with longer LoS were estimated in matched cohorts.

RESULTS

In the primary matched cohort (n = 5446), LoS > 5 days was associated with a higher risk of all-cause readmission at 30 days (HR = 1.16; 95% CI: 1.04-1.31; p = 0.010), but not during longer follow-up. A longer LoS was also associated with a higher risk of mortality during 8.8 years of follow-up (HR = 1.13; 95% CI: 1.06-1.21; p < 0.001). LoS had no association with HF readmission. Similar associations were observed among the matched sensitivity cohort (n = 4798) that excluded patients with LoS > 10 days.

CONCLUSIONS

In propensity score-matched balanced cohorts of patients with HF, a longer LoS was independently associated with poor outcomes, which persisted when LoS > 10 days were excluded.

Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea

BACKGROUND

Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this study was to assess the accuracy of an artificial intelligence-enabled ECG to identify patients presenting with dyspnea who have left ventricular systolic dysfunction (LVSD).

METHODS

We retrospectively applied a validated artificial intelligence-enabled ECG algorithm for the identification of LVSD (defined as LV ejection fraction ≤35%) to a cohort of patients aged ≥18 years who were evaluated in the ED at a Mayo Clinic site with dyspnea. Patients were included if they had at least one standard 12-lead ECG acquired on the date of the ED visit and an echocardiogram performed within 30 days of presentation. Patients with prior LVSD were excluded. We assessed the model performance using area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity.

RESULTS

A total of 1606 patients were included. Median time from ECG to echocardiogram was 1 day (Q1: 1, Q3: 2). The artificial intelligence-enabled ECG algorithm identified LVSD with an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86-0.91) and accuracy of 85.9%. Sensitivity, specificity, negative predictive value, and positive predictive value were 74%, 87%, 97%, and 40%, respectively. To identify an ejection fraction <50%, the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were 0.85 (95% CI, 0.83-0.88), 86%, 63%, and 91%, respectively. NT-proBNP (N-terminal pro-B-type natriuretic peptide) alone at a cutoff of >800 identified LVSD with an area under the receiver operating characteristic curve of 0.80 (95% CI, 0.76-0.84).

CONCLUSIONS

The ECG is an inexpensive, ubiquitous, painless test which can be quickly obtained in the ED. It effectively identifies LVSD in selected patients presenting to the ED with dyspnea when analyzed with artificial intelligence and outperforms NT-proBNP. Graphic Abstract: A graphic abstract is available for this article.

Pathophysiology and significance of natriuretic peptides in patients with end-stage kidney disease

Natriuretic peptides (NP), especially B type (BNP) and its N-terminal pro-B type natriuretic peptide (NT-proBNP), have long been regarded as biomarkers of volume overload and tools to exclude heart failure in the general population. However, their role in end-stage kidney disease (ESKD) is less certain given that BNP and NT-proBNP are excreted by the kidney and so serum concentrations of NPs are nearly universally elevated compared to controls. Nevertheless, the accumulated evidence suggests thatserum concentrations of NPs in patients with ESKD show moderate or strong positive relationships with underlying heart disease, abnormal cardiac structure or function and mortality. Limited evidence also supports the role of BNP including NT-proBNP, ANP in some studies, rather than CNP or DNP in risk stratification among ESKD patients as well as the utility of BNP samplings pre- and post- hemodialysis. However, studies of the cut-off values of NPs have yielded inconsistent results, such that further large-scale studies are needed to clarify these issues. This review summarizes the pathophysiology and significance of NPs in ESKD patients, especially their potential role as risk stratification biomarkers in clinical management.

Point-of-care ultrasound modalities in terms of diagnosing acute decompensated heart failure in emergency department; a diagnostic accuracy study

This study aimed to compare the diagnostic accuracy of heart, lung and inferior vena cava (IVC) ultrasonography modalities, alone and combined, for possible added accuracy in diagnosing acute decompensated heart failure (ADHF), in a group of patients with the final diagnosis of ADHF based on plasma level of B-type natriuretic peptide (BNP) as the standard. The present study is a diagnostic accuracy study, which was carried out in the emergency department of Imam Khomeini hospital, affiliated to Tehran University of Medical Sciences, in 2014-2015. All patients over 18 years old, who were referred to emergency department with complaint of acute dyspnea were regarded as eligible and no exclusion criteria were considered. All ultrasounds were performed by a trained emergency medicine resident and then saved and classified for each patient, separately, and reviewed by the attending emergency medicine physician. In this study, patients with BNP levels higher than 500 pg/ml were considered positive for dyspnea caused by heart failure. A total of 120 patients with an average age of 60.83 ± 16.528 years were studied, 64 (53%) of which were male. In total, 47.5% of patients had a BNP level over 500 pg/ml. Among patients with positive ultrasound, 94.7% were true positive and among those with a negative ultrasound, 61.4% were true negative. Based on the findings, B-line ≥ 10 has the highest specificity and left ventricular ejection fraction (LVEF) < 45% has the highest sensitivity. The combination of LVEF and IVC collapsibility index (IVC-CI), LVEF and BLC, IVC-CI and BLC, and IVC-CI and BBPC had a higher specificity rate and combination of LVEF and BBPC and BLC and BBPC had the highest sensitivity. Sensitivity, specificity, positive predictive value and negative predictive value of all three ultrasounds combined were 31.6%, 98.4%, 94.7% and 61.4%, respectively. In this study, the diagnostic accuracy of double and triple ultrasonography of heart, lung and IVC in the diagnosis of ADHF was very high, among which triple ultrasonography was more preferable.