Hemodiafiltration affects NT-proBNP but not ST2 serum concentration in end-stage renal disease patients

Soluble suppression of tumorigenicity 2 is a potential predictor of post-liver transplant renal outcomes

Acute kidney injury is considered an independent prognostic factor for mortality in patients with liver cirrhosis. Non-treated acute kidney injury can progress to hepatorenal syndrome with a poor prognosis. As suppression of tumorigenicity 2 (ST2) is a member of the interleukin-1 receptor family that aggravates inflammation and fibrotic changes in multiple organs, we measured soluble ST2 (sST2) level in the serum and urine of liver-transplant recipients at the time of transplantation. The serum sST2 level significantly increased in liver-transplant recipients with suppressed kidney function compared with that in recipients with normal function. In recipients with severely decreased liver function (model for end-stage liver disease score ≥ 30), the serum sST2 level was higher than that in recipients with preserved liver function (model for end-stage liver disease score ≤ 20, P = 0.028). The serum sST2 level in recipients with hepatorenal syndrome was higher than that in liver-transplant recipients without hepatorenal syndrome (P = 0.003). The serum sST2 level in patients with hepatorenal syndrome was higher than that in recipients without a history of acute kidney injury (P = 0.004). Recipients with hepatorenal syndrome and recovered kidney function showed higher sST2 levels than those who did not recover (P = 0.034). Collectively, an increase in the serum sST2 level reflects a decrease in both kidney and liver functions. Thus, measuring sST2 level at the time of liver transplantation can help predict renal outcomes.

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.

Serum Cardiac Biomarkers in Asymptomatic Hemodialysis Patients: Role of Soluble Suppression of Tumorigenicity-2

INTRODUCTION

Cardiovascular events (CVE) remain the leading cause of mortality in hemodialysis (HD) patients. The ability to assess the risk of short-term CVE is of great importance. Soluble suppression of tumorogenicity-2 (sST2) is a novel biomarker that better stratifies risk of CVE than troponins in patients with heart failure. Few studies have investigated the role of sST2 in the HD population. The aim of this single-center study was to assess the predictive ability of sST2 on CVE in comparison to high-sensitive cardiac troponin I (hs-cTnI) and B-type natriuretic peptide (BNP) in HD patients.

METHODS

This study used a prospective, observational cohort design. We enrolled 40 chronic HD patients asymptomatic for chest pain and without recent history of acute coronary syndrome. We tested sST2 pre-/post-HD, hs-cTnI, and BNP. Demographic/dialytic/echocardiographic data were evaluated. We recorded the number of CVE for 12 months. The patients were classified into 2 groups: those who developed CVE and those who did not.

RESULTS

Ten of the 40 patients (25%) developed CVE during a 12-month follow-up. Increased sST2 levels (p < 0.0001) as well as hs-cTnI and BNP are predictive of CVE. When analyzing biomarkers as binary variables for values above or below the normal range, the correlation remained significant only for sST2 (p = 0.001). A small variation in sST2 levels before and after HD sessions was found (-2.1 ng/mL). sST2 was correlated with left ventricular (LV) echocardiographic data: LV mass index (p = 0.0001), LV ejection fraction (p = 0.01), and diastolic bulging of septum (p = 0.015). BNP and sST2 combination increased the prediction of CVE in a statistical model.

CONCLUSION

Our study confirms that sST2 is useful for stratifying CV risk in the HD population. sST2 can be evaluated simply as a dichotomous value higher or lower than the normal range, making it easily interpretable. Dialysis and residual diuresis did not affect significantly sST2. A multimarker approach that incorporates sST2 and BNP may improve the prediction of CVE.

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.

Prognostic value of soluble ST2 and soluble LR11 on mortality and cardiovascular events in peritoneal dialysis patients

Background

Although the soluble form of suppression of tumorigenicity 2 (sST2) and soluble low-density lipoprotein receptor relative with 11 ligand-binding repeats (sLR11) have emerged as novel cardiovascular biomarkers in patients with cardiovascular disease, their prognostic value has not been fully investigated in peritoneal dialysis (PD) patients.

Methods

We included 74 prevalent PD patients from a prospective cohort and measured serum sST2 and sLR11 concentrations by an enzyme-linked immunosorbent assay. The association of these biomarkers and all-cause mortality and major adverse cardiac and cerebrovascular events (MACCEs) was evaluated.

Results

During a follow-up of 38.5 months, all-cause deaths and MACCEs were observed in 13 (17.6%) patients and 23 (31.3%) patients. Multivariable Cox analyses demonstrated that greater sST2 was independently associated with higher risk of all-cause mortality (≥75.8 ng/mL; hazard ratio [HR] = 5.551; 95% confidence interval [CI] = 1.360–22.660) and MACCEs (≥72.5 ng/mL; HR = 4.609; 95% CI = 1.608–13.208). Furthermore, sST2 showed additive predictive value for mortality to the base model including traditional risk factors (net reclassification index = 0.598, P = 0.04). sLR11 was not significantly associated with all-cause mortality or MACCE.

Conclusions

sST2, but not sLR11, indicated a significant prognostic value for all-cause mortality and cardiovascular events in PD patients. Further research is needed to validate emerging biomarkers in these populations.

Soluble ST2: A complex and diverse role in several diseases

The Suppression of Tumorigenicity 2 protein (ST2) is a member of the interleukin (IL) 1 receptor family with transmembrane (ST2L) and soluble (sST2) isoforms that are (over)expressed in several cells in different conditions and following various triggers (e.g. inflammation, stress). The ligand of ST2 is IL-33, which on binding to ST2L results in nuclear signalling and immunomodulatory action in various cells (tumour, immune, heart). sST2, that is released in the circulation, functions as a »decoy« receptor of IL-33 and inhibits IL-33/ST2L signalling and beneficial effects. The importance and role of the ST2/IL-33 axis and sST2 have been evaluated and confirmed in several inflammatory, cancer and cardiac diseases. sST2 is involved in homeostasis/pathogenesis of these diseases, as the counterbalance/response on IL-33/ST2L axis activation, which is triggered and expressed during developing fibrosis, tissue damage/inflammation and remodelling. In clinical studies, sST2 has been recognised as an important prognostic marker in patients with cardiac disease, including patients with chronic kidney disease where specific characteristics of sST2 enable better assessment of the risk of End-Stage Renal Disease patients on dialysis. sST2 is also recognised as an important marker for monitoring treatment in heart failure patients. However, accurate measurement and interpretation of ST2 concentration in serum/plasma samples for routine and research applications require the use of appropriate methods and recognition of essential characteristics of both the methods and the analyte that may influence the result. sST2, as one of the most promising disease biomarkers, is deserving of further study and wider application in clinical practice.

IL33/ST2 Axis in Diabetic Kidney Disease: A Literature Review

Interleukin-33 (IL-33) is a cytokine belonging to the IL-1 family, playing a role in inflammatory, infectious and autoimmune diseases and expressed in the cellular nucleus in several tissues. High levels of IL-33 are expressed in epithelial barrier tissues and endothelial barriers. ST2 is a receptor for IL-33, expressed selectively on a subset of Th2 cells, mediating some of their functions. The IL-33/ST2 axis plays an important role in several acute and chronic inflammatory diseases, including asthma and rheumatoid arthritis. Different disorders are related to the activity of IL-33, ST2, or their axis, including cardiovascular disease or renal disturbances. Therefore, in the present work, a literature review was conducted, covering the period from 1 January 2000 to 30 November 2018, in PubMed, ScienceDirect, and Google Scholar database, to assess the involvement of the IL-33/ST2 axis in diabetic kidney disease. 6 articles directly dealing with the argument were identified, highlighting a clear link between IL-33/ST2 axis and diabetic kidney disease or related nephropathy. Overall, the involvement of ST2 seems to be more predictive than IL-33, especially in investigating the deterioration of kidney function; however, both compounds are pivotal in the field of renal diseases. Future studies are required to confirm the scientific evidences on larger and more heterogeneous cohorts.

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

Acute heart failure (AHF) is a major public health issue due to its high incidence and poor prognosis; thus, efficient and timely diagnosis is critical for improving the prognosis and lowering the mortality rate. Amino-terminal pro-brain natriuretic peptide (NT-proBNP) is widely used in the diagnosis of AHF; however, its efficacy is controversial in diagnosing AHF with renal insufficiency. There were numerous studies reporting the association of adiponectin (ADPN) and heart diseases. Therefore, the present study aimed to investigate whether ADPN is helpful in identifying AHF with renal insufficiency. A total of 407 participants (218 AHF patients and 189 controls) were enrolled into the current study. The plasma levels of ADPN and NT-proBNP were measured using a sandwich enzyme-linked immunosorbent assay and an electrochemiluminescence immunoassay, respectively. In addition, these levels were compared among the various New York Health Association classes, as well as the ischemic and non-ischemic AHF cases. The correlation between the two biomarkers and the renal function was analyzed by Spearman's correlation, while the diagnostic efficiency of ADPN and NT-proBNP was evaluated in AHF patients with and without renal insufficiency. The results revealed that NT-proBNP exhibited a higher diagnostic efficiency as compared with ADPN in patients without renal insufficiency [area under the receiver operating characteristic curve (AUC), 0.905 vs. 0.775]. By contrast, the ADPN presented a better diagnostic value in comparison with NT-proBNP in AHF with renal insufficiency (AUC, 0.872 vs. 0.828). Therefore, a combination of these two biomarkers may provide an excellent efficacy in the diagnosis of AHF with renal insufficiency (AUC, 0.904; sensitivity, 71.2%; specificity, 98.3%). In conclusion, ADPN is a valuable biomarker for diagnosing AHF, particularly in patients with impaired renal function.

ST2 as a novel prognostic marker in end-stage renal disease patients on hemodiafiltration

BACKGROUND

Patients with end-stage renal disease (ESRD) are prone to severe heart failure (HF) and to several life-threatening events. Therefore, the ability to assess disease prognosis and the risk of short-term events or death is of great importance. The role of soluble ST2 (sST2) as a potential new prognostic marker in ESRD patients is not yet known. The aim of our study was to assess the prognostic value of sST2 in ESRD patients on hemodiafiltration (HDF) and compare it with NT-proBNP, an established prognostic marker for HF and renal disease.

METHODS

123 ESRD patients on HDF were prospectively followed up from the date of the sST2/NT-proBNP measurement until their death or maximally up to 829days. Patients were divided into a low sST2 group (<35ng/mL) or a high sST2 group (≥35ng/mL) according to their measured sST2 concentration at the start of the study. Kaplan-Meier survival curves, Cox regression model and ROC analyses were used in statistical analysis.

RESULTS

During follow-up 32 (26.0%) patients died (all cause mortality). Median (IQR) sST2 serum concentrations of survivors and deceased were 26 (24-29) and 36 (28-59) ng/mL, respectively. The Kaplan-Meier survival analysis showed that survival rate of the high sST2 group was statistically significant lower than of the low sST2 group (P<0.01). Cox regression model for sST2, using a dichotomized (cut-off=35ng/mL, hazard ratio (HR) (95%CI)=2.72 (1.50-4.90), P=0.001) and continuous approach with log-transformed values in univariate (HR (95%CI)=17.35 (4.84-62.22), P<0.001) and multivariate analysis (HR (95%CI)=7.19 (1.89-27.38), P=0.004) showed that sST2 alone and in combination with NT-proBNP can predict all cause mortality.

CONCLUSIONS

sST2 has confirmed prognostic value and is independent of renal function and of HDF treatment. It could be useful independent prognostic marker for stratifying ESRD patients on HDF at high risk for life-threatening events, hospitalisation and death, especially in combination with NT-proBNP.