Abstract P352: Is Brain Natriuretic Peptide or Pro- Brain Natriuretic Peptide of value for Early Cardiovascular Disease Risk Stratification in asymptomatic Subjects?

Circulation ◽  
2014 ◽  
Vol 129 (suppl_1) ◽  
Author(s):  
Mahfouz El Shahawy ◽  
Omar Y El Shahawy ◽  
Miglena O Entcheva
Keyword(s):  
Cardiovascular Disease ◽  
Risk Stratification ◽  
Brain Natriuretic Peptide ◽  
Natriuretic Peptide ◽  
Risk Score ◽  
Left Ventricular ◽  
Structural Abnormality ◽  
Close Monitoring ◽  
Cvd Risk ◽  
Asymptomatic Subjects

Introduction: There has been much discussion in recent years about the use of Brain natriuretic peptide (BNP) or Pro-brain natriuretic peptide (Pro-BNP) for cardiovascular disease (CVD) risk stratification. The purpose of this study is to assess the association between the presence of abnormal BNP/pro BNP with Left ventricular Hypertrophy (LVH) in asymptomatic subjects with or without hypertension and their value for early CVD risk stratification. Methods: We evaluated 2230 subjects aged 23-80 years, who underwent screening for CVD risk using Early CVD Risk Score (ECVDRS), also known as Rasmussen Risk Score (RRS). ECVDRS consists of 10 non-invasive tests: large (C1) and small (C2) artery stiffness, blood pressure (BP) at rest and post mild exercise (BP PME), Carotid Intima Media Thickness (CIMT), abdominal aorta ultrasound, retinal photography, microalbuminuria, electrocardiogram (ECG), left ventricular ultrasound, and BNP/Pro-BNP. Results: Among the 2230 asymptomatic participants, we analyzed 933 who were treated by at least one or more cardiovascular medication for Diabetes, Hypertension (HTN) or Hyperlipidemia. Our hypothesis was that asymptomatic subjects with LVH have a significantly higher BNP/ProBNP values than those without LVH independent of HTN management. We used 3 Chi-square tests to test the independence between the two groups (with or without LVH) regarding their BNP/ProBNP status for the whole sample and further stratified by their HTN control. The findings in three tests were highly significant; the proportion of abnormal LVH was 0.17 and 0.39 respectively with normal vs elevated BNP/ProBNP for the whole sample, χ 2 (1, N = 933) = 36.693, p < 0.001. When we stratified by HTN control, with uncontrolled HTN the proportion of abnormal LVH was 0.25 and 0.46 respectively with the normal vs elevated BNP/ProBNP, χ 2 (1, N = 271) = 11.983, p =0.0005. Among participants with controlled HTN the proportion of abnormal LVH was 0.15 and 0.33 respectively with normal vs elevated BNP/ProBNP, χ 2 (1, N = 662) = 18.257, p < 0.001. Conclusions: Abnormal BNP/pro-BNP levels are significantly associated with abnormal LVH findings in asymptomatic subjects independent of their HTN control. Thus, ProBNP/BNP could be considered an important follow up test for early detection of serious CV structural abnormality like LVH during the management of patients with HTN regardless of the degree of their HTN control. This finding may justify a more aggressive treatment approach and close monitoring with BNP/ProBNP as a possible screener for CVD manifestations like LVH in HTN patients.

scholarly journals Machine learning approaches improve risk stratification for secondary cardiovascular disease prevention in multiethnic patients

Open Heart ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. e001802
Author(s):  
Ashish Sarraju ◽  
Andrew Ward ◽  
Sukyung Chung ◽  
Jiang Li ◽  
David Scheinker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Risk Stratification ◽  
Secondary Prevention ◽  
Risk Score ◽  
Characteristic Curve ◽  
World Population ◽  
Learning Approaches ◽  
Cvd Risk ◽  
Improve Risk Stratification

ObjectivesIdentifying high-risk patients is crucial for effective cardiovascular disease (CVD) prevention. It is not known whether electronic health record (EHR)-based machine-learning (ML) models can improve CVD risk stratification compared with a secondary prevention risk score developed from randomised clinical trials (Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention, TRS 2°P).MethodsWe identified patients with CVD in a large health system, including atherosclerotic CVD (ASCVD), split into 80% training and 20% test sets. A rich set of EHR patient features was extracted. ML models were trained to estimate 5-year CVD event risk (random forests (RF), gradient-boosted machines (GBM), extreme gradient-boosted models (XGBoost), logistic regression with an L2 penalty and L1 penalty (Lasso)). ML models and TRS 2°P were evaluated by the area under the receiver operating characteristic curve (AUC).ResultsThe cohort included 32 192 patients (median age 74 years, with 46% female, 63% non-Hispanic white and 12% Asian patients and 23 475 patients with ASCVD). There were 4010 events over 5 years of follow-up. ML models demonstrated good overall performance; XGBoost demonstrated AUC 0.70 (95% CI 0.68 to 0.71) in the full CVD cohort and AUC 0.71 (95% CI 0.69 to 0.73) in patients with ASCVD, with comparable performance by GBM, RF and Lasso. TRS 2°P performed poorly in all CVD (AUC 0.51, 95% CI 0.50 to 0.53) and ASCVD (AUC 0.50, 95% CI 0.48 to 0.52) patients. ML identified nontraditional predictive variables including education level and primary care visits.ConclusionsIn a multiethnic real-world population, EHR-based ML approaches significantly improved CVD risk stratification for secondary prevention.

scholarly journals N-Terminal Pro-Brain Natriuretic Peptide and Right Ventricular Diameter Are Related to Aspirin Resistance in Coronary Artery Disease Patients

Medicina ◽  
2021 ◽  
Vol 57 (7) ◽  
pp. 706
Author(s):  
Kamila Marika Cygulska ◽  
Łukasz Figiel ◽  
Dariusz Sławek ◽  
Małgorzata Wraga ◽  
Marek Dąbrowa ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Brain Natriuretic Peptide ◽  
Right Ventricular ◽  
Left Ventricular Mass ◽  
Natriuretic Peptide ◽  
Hemoglobin Concentration ◽  
Left Ventricular ◽  
Ventricular Mass ◽  
Artery Disease

Background and Objectives: Resistance to ASA (ASAres) is a multifactorial phenomenon defined as insufficient reduction of platelet reactivity through incomplete inhibition of thromboxane A2 synthesis. The aim is to reassess the prevalence and predictors of ASAres in a contemporary cohort of coronary artery disease (CAD) patients (pts) on stable therapy with ASA, 75 mg o.d. Materials and Methods: We studied 205 patients with stable CAD treated with daily dose of 75 mg ASA for a minimum of one month. ASAres was defined as ARU (aspirin reaction units) ≥550 using the point-of-care VerifyNow Aspirin test. Results: ASAres was detected in 11.7% of patients. Modest but significant correlations were detected between ARU and concentration of N-terminal pro-brain natriuretic peptide (NT-proBNP) (r = 0.144; p = 0.04), body weight, body mass index, red blood cell distribution width, left ventricular mass, and septal end-systolic thickness, with trends for left ventricular mass index and prothrombin time. In multivariate regression analysis, log(NT-proBNP) was identified as the only independent predictor of ARU—partial r = 0.15, p = 0.03. Median concentrations of NT-proBNP were significantly higher in ASAres patients (median value 311.4 vs. 646.3 pg/mL; p = 0.046) and right ventricular diameter was larger, whereas mean corpuscular hemoglobin concentration was lower as compared to patients with adequate response to ASA. Conclusions: ASAres has significant prevalence in this contemporary CAD cohort and NT-proBNP has been identified as the independent correlate of on-treatment ARU, representing a predictor for ASAres, along with right ventricular enlargement and lower hemoglobin concentration in erythrocytes.

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