Predictive Value of Pro-BNP for Heart Failure Readmission after an Acute Coronary Syndrome

Heart failure biomarkers and prediction of early left ventricle remodeling after acute coronary syndromes

INTRODUCTION

Several biomarkers are characteristically elevated in patients with acute heart failure (AHF). Our hypothesis was they could predict early changes in left ventricular (LV) characteristics in acute coronary syndrome (ACS) patients. The objective of this study was two-fold: a) compare circulating concentrations of NT-pro BNP, CA-125, ST2, galectin-3 and pro-adrenomedullin among 4 groups of individuals (healthy controls; patients with ACS without AHF; patients with ACS and AHF and patients admitted for AHF); and b) evaluate whether these biomarkers predict adverse LV remodeling and ejection fraction changes in ACS.

METHODS

6 biomarkers (NT-pro BNP, CA-125, ST2, galectin-3, pro-adrenomedullin and C-reactive) were measured within the first 48 h of admission. Echocardiograms were performed during admission and at 3 months. Variables associated with LV end-diastolic volume (EDV) and ejection fraction (LVEF) change were assessed by multivariate linear regression.

RESULTS

We analyzed 51 patients with ACS, 16 with AHF and, 20 healthy controls. NT-pro BNP and ST2 concentrations were elevated at similar values in patients admitted for AHF and ACS complicated with HF but CA-125 concentrations were higher in AHF patients. NT-pro BNP concentrations were positively correlated with CA-125 (rho = 0.58; p < 0.001), ST2 (rho = 0.58; p < 0.001) and galectin-3 (rho = 0.37; p < 0.001) Median change (median days was 83 days after) in EDV and LVEF was 5 %. CA-125 concentrations were positively associated to LV EDV change (β-coefficient 1.56) and negatively with LVEF trend (β-coefficient = -0.86). No other biomarker predicted changes in EDV or LVEF.

CONCLUSIONS

CA-125 correlates with early LV remodeling and LVEF deterioration in ACS patients.

First time ACS in patients with on-target lipid levels: Inflammation at admission and re-event rate at follow-up

BACKGROUND

Dyslipidaemia, inflammation and elevated Lp(a) levels are associated with the progression of atherosclerosis. This study investigates whether patients with a first-time presentation of chest pain and on-target LDL-C levels and intermediate FRS/ESC-Score risks, display a high inflammatory burden linked to myocardial injury and whether inflammation at admission affects the re-event rate up to 6 years follow-up.

METHODS

Blind assessments of novel inflammatory markers such as Glycoprotein A and B via nuclear magnetic resonance (NMR), cytokines, hsCRP, Neutrophil-to-Lymphocyte ratio (NLR) and Lipoprotein(a) levels were examined. Out of 198 chest pain patients screened, 97 met the inclusion criteria at admission.

RESULTS

cTnI(+) patients (>61 ng/L) with elevated Lipoprotein(a), showed significantly increased levels of Glycoprotein A and B, hsCRP, IL-6, a high NLR and a reduced left ventricular ejection fraction (%) compared to cTnI(-) individuals. Those patients, with a higher inflammatory burden at hospital admission (hsCRP, IL-6, Glycoprotein A and B, and Lipoprotein(a)) had a higher re-event rate at follow-up.

CONCLUSIONS

Inflammation and Lipoprotein(a) levels were particularly prominent in patients presenting with reduced left ventricular ejection fraction. Notably, Glycoproteins A/B emerge as novel markers of inflammation in these patients. Our study highlights the significantly higher impact of inflammatory burden in patients with chest pain and high level of myocardial damage than in those with lower myocardial affectation, even when they all had lipid levels well controlled. Inflammation at the time of admission influenced the re-event rate over a follow-up period of up to 6 years.

Combined-task deep network based on LassoNet feature selection for predicting the comorbidities of acute coronary syndrome

Acute coronary syndrome (ACS) is a multifaceted cardiovascular condition frequently accompanied by multiple comorbidities, which can have significant implications for patient outcomes and treatment approaches. Precisely predicting these comorbidities is crucial for providing personalized care and making well-informed clinical decisions. However, there is a shortage of research investigating the identification of risk factors associated with ACS comorbidities and accurately predicting their likelihood of occurrence beyond heart failure. In this study, an approach called Combined-task Deep Network based on LassoNet feature selection (CDNL) is presented for predicting ACS comorbidities, including hypertension, diabetes, hyperlipidemia, and heart failure. In order to identify crucial biomarkers associated with ACS comorbidities, the proposed framework first incorporates LassoNet, which extends Lasso regression to the deep network by adding a skip (residual) layer. Additionally, a correlation score calculation method across tasks is introduced based on measuring the overlap of identified biomarkers and their assigned importance. This method enables the development of an optimal combined-task prediction model for each ACS comorbidity, addressing the challenge of limited representations in traditional multi-task learning. Our evaluation, conducted through a meticulous cross-sectional study at a tertiary hospital in China, involved a dataset of 2941 samples with 42 clinical features. The results demonstrate that CDNL facilitates the identification of significant biomarkers and achieves an average improvement in AUC of 4.93% and 8.58% compared to deep learning multi-layer neural network (DNN) and SVM, respectively. Additionally, it shows an average improvement of 2.64% and 1.92% compared to two state-of-the-art multi-task models.

Association among β2-adrenergic receptor autoantibodies and proximal left anterior descending artery lesions in patients with initial ST-segment elevation myocardial infarction

BACKGROUND

β2 -adrenergic receptor autoantibody (β2 -AA) are widely present in patients with many different types of cardiovascular diseases. Proximal left anterior descending (LAD) artery lesions are associated with adverse prognostic events in patients with ST-segment elevation myocardial infarction (STEMI).

HYPOTHESIS

β2 -AA is associated with the presence of proximal LAD lesions in patients with STEMI.

METHODS

A cohort of 153 patients with STEMI who underwent primary percutaneous coronary intervention (PPCI) was enrolled in the study. Baseline characteristics were compared between the proximal LAD group (n = 62) and the nonproximal LAD group (n = 91). Admission serum of patients was collected to detect the level of β2 -AA. Data for echocardiogram within 24 hours after PPCI and at the 6-month follow-up were recorded.

RESULTS

The optical density values and positive rates of β2 -AA in the proximal LAD group were higher than those in the nonproximal LAD group (p < 0.05). β2 -AA positively correlated with high sensitivity C-reactive protein and peak N-terminal pro-B type natriuretic peptide levels in the proximal LAD group, but those were not relevant in the nonproximal LAD group. Multivariate logistic regression analysis revealed that high β2 -AA levels was independently associated with the presence of proximal LAD lesions in patients with STEMI. Furthermore, a receiver operating characteristic curve was used to show the efficiency of β2 -AA levels to detect proximal LAD lesions, and the AUC of the β2-AA OD value was 0.658 (95% confidence interval 0.568-0.749; p = 0.001).

CONCLUSIONS

The STEMI patients with high β2 -AA levels had a greater possibility having proximal LAD lesions.

HDL Function and Size in Patients with On-Target LDL Plasma Levels and a First-Onset ACS

Patients admitted for acute coronary syndrome (ACS) usually have high cardiovascular risk scores with low levels of high-density lipoprotein cholesterol (HDL-C) and high low-density lipoprotein cholesterol (LDL-C) levels. Here, we investigated the role of lipoprotein functionality as well as particle number and size in patients with a first-onset ACS with on-target LDL-C levels. Ninety-seven patients with chest pain and first-onset ACS with LDL-C levels of 100 ± 4 mg/dL and non-HDL-C levels of 128 ± 4.0 mg/dL were included in the study. Patients were categorized as ACS and non-ACS after all diagnostic tests were performed (electrocardiogram, echocardiogram, troponin levels and angiography) on admission. HDL-C and LDL-C functionality and particle number/size by nuclear magnetic resonance (NMR) were blindly investigated. A group of matched healthy volunteers (n = 31) was included as a reference for these novel laboratory variables. LDL susceptibility to oxidation was higher and HDL-antioxidant capacity lower in the ACS patients than in the non-ACS individuals. ACS patients had lower HDL-C and Apolipoprotein A-I levels than non-ACS patients despite the same prevalence of classical cardiovascular risk factors. Cholesterol efflux potential was impaired only in the ACS patients. ACS-STEMI (Acute Coronary Syndrome—ST-segment-elevation myocardial infarction) patients, had a larger HDL particle diameter than non-ACS individuals (8.4 ± 0.02 vs. 8.3 ± 0.02 and, ANOVA test, p = 0.004). In conclusion, patients admitted for chest pain with a first-onset ACS and on-target lipid levels had impaired lipoprotein functionality and NMR measured larger HDL particles. This study shows the relevance of HDL functionality rather than HDL-C concentration in ACS patients.

Predicting Acute Onset of Heart Failure Complicating Acute Coronary Syndrome: An Explainable Machine Learning Approach

Patients with acute coronary syndrome (ACS) are at high risk of heart failure (HF). Early prediction and management of HF among ACS patients are essential to provide timely and cost-effective care. The aim of this study is to train and evaluate a machine learning model to predict the acute onset of HF subsequent to ACS. A total of 1,028 patients with ACS admitted to Guangdong Second Provincial General Hospital between October 2019 and May 2022 were included in this study. 128 clinical features were ranked using Shapley additive exPlanations (SHAP) values and the top 20% of features were selected for building a balanced random forest (BRF) model. We compared the discriminatory capability of BRF with linear logistic regression (LLR). In the hold-out test set, the BRF model predicted subsequent HF with areas under the curve (AUC) of 0.76 (95% CI: 0.75-0.77), sensitivity of 0.97 (95% CI: 0.96-0.97), positive predictive value (PPV) of 0.73 (95% CI: 0.72-0.74), negative predictive value (NPV) of 0.63 (95% CI: 0.60-0.66), and accuracy of 0.73 (95% CI: 0.72-0.73), respectively. BRF outperforms linear logistic regression by 15.6% in AUC, 3.0% in sensitivity, and 60.8% in NPV. End-to-end machine learning approaches can predict the acute onset of HF following ACS with high prediction accuracy. This proof-of-concept study has the potential to substantially advance the management of ACS patients by utilizing the machine learning model as a triage tool to automatically identify clinically significant patients allowing for prioritization of interventions.

Prognostic Value of Systemic Immune-Inflammation Index and NT-proBNP in Patients with Acute ST-Elevation Myocardial Infarction

Objective

Our aim was to assess systemic immune-inflammation index (SII) and NT-proBNP value either in singly or in combination to predict acute ST-elevation myocardial infarction (STEMI) patient prognosis.

Methods

Analyzed retrospectively the clinical features and laboratory data of STEMI confirmed patients in our hospital from January to December 2020. The levels of SII and NT-proBNP were detected. The Kaplan-Meier approach and Spearman's rank correlation coefficient were used to construct the overall major adverse cardiac event (MACE) curve. Multivariate Cox regression analysis was applied to detect MACE predictors. In addition, the Delong test and receiver operating characteristic (ROC) curve analyzed each factor performance on its own and composite multivariate index to predict MACEs.

Results

The MACE group showed statistically significant differences in SII, NT- proBNP in comparison to the non-MACE group (P=0.003, P <0.001). Based on Kaplan-Meier analysis, SII and NT-proBNP showed positive correlation with MACE (log-rank P < 0.001). SII and NT-proBNP were independent predicting factors for long-term MACEs in multivariate Cox regression analysis (P <0.001, HR: 2.952, 95% CI 1.565-5.566; P <0.001, HR: 2.112, 95% CI 1.662-2.683). SII and NT-proBNP exhibited a positive correlation (R = 0.187, P < 0.001) in correlation analysis. According to the ROC statistical analysis, the combination exhibited 78.0% sensitivity and 88.0% specificity in the prediction of MACE. According to the results of the AUC and Delong test, the combined SII and NT-proBNP performed better as a prognostic index than each of the individual factor indexes separately (Z = 2.622, P = 0.009; Z = 3.173, P < 0.001).

Conclusion

SII and NT-proBNP were independent indicators of clinical prognosis in acute STEMI patients, and they correlated positively. These factors could be combined to improve clinical prognosis.