Dataset Information

Novel and conventional biomarkers for prediction of incident cardiovascular events in the community.

ABSTRACT: Prior studies have demonstrated conflicting results regarding how much information novel biomarkers add to cardiovascular risk assessment.To evaluate the utility of contemporary biomarkers for predicting cardiovascular risk when added to conventional risk factors.Cohort study of 5067 participants (mean age, 58 years; 60% women) without cardiovascular disease from Malmö, Sweden, who attended a baseline examination between 1991 and 1994. Participants underwent measurement of C-reactive protein (CRP), cystatin C, lipoprotein-associated phospholipase 2, midregional proadrenomedullin (MR-proADM), midregional proatrial natriuretic peptide, and N-terminal pro-B-type natriuretic peptide (N-BNP) and underwent follow-up until 2006 using the Swedish national hospital discharge and cause-of-death registers and the Stroke in Malmö register for first cardiovascular events (myocardial infarction, stroke, coronary death).Incident cardiovascular and coronary events.During median follow-up of 12.8 years, there were 418 cardiovascular and 230 coronary events. Models with conventional risk factors had C statistics of 0.758 (95% confidence interval [CI], 0.734 to 0.781) and 0.760 (0.730 to 0.789) for cardiovascular and coronary events, respectively. Biomarkers retained in backward-elimination models were CRP and N-BNP for cardiovascular events and MR-proADM and N-BNP for coronary events, which increased the C statistic by 0.007 (P = .04) and 0.009 (P = .08), respectively. The proportion of participants reclassified was modest (8% for cardiovascular risk, 5% for coronary risk). Net reclassification improvement was nonsignificant for cardiovascular events (0.0%; 95% CI, -4.3% to 4.3%) and coronary events (4.7%; 95% CI, -0.76% to 10.1%). Greater improvements were observed in analyses restricted to intermediate-risk individuals (cardiovascular events: 7.4%; 95% CI, 0.7% to 14.1%; P = .03; coronary events: 14.6%; 95% CI, 5.0% to 24.2%; P = .003). However, correct reclassification was almost entirely confined to down-classification of individuals without events rather than up-classification of those with events.Selected biomarkers may be used to predict future cardiovascular events, but the gains over conventional risk factors are minimal. Risk classification improved in intermediate-risk individuals, mainly through the identification of those unlikely to develop events.

SUBMITTER: Melander O

PROVIDER: S-EPMC3090639 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Novel and conventional biomarkers for prediction of incident cardiovascular events in the community.

Melander Olle O Newton-Cheh Christopher C Almgren Peter P Hedblad Bo B Berglund Göran G Engström Gunnar G Persson Margaretha M Smith J Gustav JG Magnusson Martin M Christensson Anders A Struck Joachim J Morgenthaler Nils G NG Bergmann Andreas A Pencina Michael J MJ Wang Thomas J TJ

JAMA 20090701 1

<h4>Context</h4>Prior studies have demonstrated conflicting results regarding how much information novel biomarkers add to cardiovascular risk assessment.<h4>Objective</h4>To evaluate the utility of contemporary biomarkers for predicting cardiovascular risk when added to conventional risk factors.<h4>Design, setting, and participants</h4>Cohort study of 5067 participants (mean age, 58 years; 60% women) without cardiovascular disease from Malmö, Sweden, who attended a baseline examination between ...[more]

PMID: 19567439

Similar Datasets

Project description:the purpose of this study was to assess the predictive accuracy of conventional cardiovascular risk factors for incident heart failure and atrial fibrillation, and the added benefit of multiple biomarkers reflecting diverse pathophysiological pathways.heart failure and atrial fibrillation are interrelated cardiac diseases associated with substantial morbidity and mortality and increasing incidence. Data on prediction and prevention of these diseases in healthy individuals are limited.in 5,187 individuals from the community-based MDCS (Malmö Diet and Cancer Study), we studied the performance of conventional risk factors and 6 biomarkers including midregional pro-atrial natriuretic peptide (MR-proANP), N-terminal pro-B-type natriuretic peptide (NT-proBNP), midregional pro-adrenomedullin, cystatin C, C-reactive protein (CRP), and copeptin.during a mean follow-up of 14 years, 112 individuals were diagnosed with heart failure and 284 individuals with atrial fibrillation. NT-proBNP (hazard ratio [HR]: 1.63 per SD, 95% confidence interval [CI]: 1.29 to 2.06, p < 0.001), CRP (HR: 1.57 per SD, 95% CI: 1.28 to 1.94, p < 0.001), and MR-proANP (HR: 1.26 per SD, 95% CI: 1.02 to 1.56, p = 0.03) predicted incident heart failure independently of conventional risk factors and other biomarkers. MR-proANP (HR: 1.62, 95% CI: 1.42 to 1.84, p < 0.001) and CRP (HR: 1.18, 95% CI: 1.03 to 1.34, p = 0.01) independently predicted atrial fibrillation. Addition of biomarkers to conventional risk factors improved c-statistics from 0.815 to 0.842 for heart failure and from 0.732 to 0.753 for atrial fibrillation and the integrated discrimination improvement for both diseases (p < 0.001). Net reclassification improvement (NRI) with biomarkers was observed in 22% of individuals for heart failure (NRI, p < 0.001) and in 7% for atrial fibrillation (NRI, p = 0.06), mainly due to up-classification of individuals who developed disease (heart failure: 29%, atrial fibrillation: 19%). Addition of CRP to natriuretic peptides did not improve discrimination or reclassification.conventional cardiovascular risk factors predict incident heart failure and atrial fibrillation with reasonable accuracy in middle-age individuals free from disease. Natriuretic peptides, but not other biomarkers, improve discrimination modestly for both diseases above and beyond conventional risk factors and substantially improve risk classification for heart failure.

Project description:ObjectivesEvaluation of the feasibility of using cardiovascular magnetic resonance (CMR) radiomics in the prediction of incident atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), and stroke using machine learning techniques.MethodsWe identified participants from the UK Biobank who experienced incident AF, HF, MI, or stroke during the continuous longitudinal follow-up. The CMR indices and the vascular risk factors (VRFs) as well as the CMR images were obtained for each participant. Three-segmented regions of interest (ROIs) were computed: right ventricle cavity, left ventricle (LV) cavity, and LV myocardium in end-systole and end-diastole phases. Radiomics features were extracted from the 3D volumes of the ROIs. Seven integrative models were built for each incident cardiovascular disease (CVD) as an outcome. Each model was built with VRF, CMR indices, and radiomics features and a combination of them. Support vector machine was used for classification. To assess the model performance, the accuracy, sensitivity, specificity, and AUC were reported.ResultsAF prediction model using the VRF+CMR+Rad model (accuracy: 0.71, AUC 0.76) obtained the best result. However, the AUC was similar to the VRF+Rad model. HF showed the most significant improvement with the inclusion of CMR metrics (VRF+CMR+Rad: 0.79, AUC 0.84). Moreover, adding only the radiomics features to the VRF reached an almost similarly good performance (VRF+Rad: accuracy 0.77, AUC 0.83). Prediction models looking into incident MI and stroke reached slightly smaller improvement.ConclusionsRadiomics features may provide incremental predictive value over VRF and CMR indices in the prediction of incident CVDs.Key points• Prediction of incident atrial fibrillation, heart failure, stroke, and myocardial infarction using machine learning techniques. • CMR radiomics, vascular risk factors, and standard CMR indices will be considered in the machine learning models. • The experiments show that radiomics features can provide incremental predictive value over VRF and CMR indices in the prediction of incident cardiovascular diseases.

Project description:Some variables including age, comorbidity of diabetes, and so on at dialysis initiation are associated with patient prognosis. Cardiovascular (CV) events are a major cause of death, and adequate models that predict prognosis in dialysis patients are warranted. Therefore, we created models using some variables at dialysis initiation. We used a database of 1,520 consecutive dialysis patients (median age, 70 years; 492 women [32.4%]) from a multicenter prospective cohort study. We established the primary endpoint as a composite of the incidence of first CV events or all-cause death. A multivariable Cox proportional hazard regression model was used to construct a model. We considered a complex and a simple model. We used area under the receiver operating characteristic curve (AUROC) to assess and compare the predictive performances of the prediction models and evaluated the improvement in discrimination using the complex model versus the simple model using net reclassification improvement (NRI). We then assessed integrated discrimination improvement (IDI) to evaluate improvements in average sensitivity and specificity. Of 392 deaths, 152 were CV-related. Totally, 506 CV events occurred during the follow-up period (median 1,285 days). Finally, 692 patients reached the primary endpoint. Baseline data were set at dialysis initiation. AUROC for the primary endpoint was 0.737 (95% confidence interval [CI], 0.712-0.761) in the simple model and 0.765 (95% CI, 0.741-0.788) in the complex model. There were significant intergroup differences in NRI (0.44; 95% CI, 0.34-0.53; p < 0.001) and IDI (0.02; 95% CI, 0.02-0.03; p < 0.001). We prepared a Shiny R application for each model to automatically calculate the predicted occurrence probability (https://statacademy.shinyapps.io/App_inaguma_20190717/). The complex model made more accurate predictions than the simple model. However, the intergroup difference was not significant. Hence, the simple model was more useful than the complex model. The tool was useful in a real-world clinical setting because it required only routinely available variables. Moreover, we emphasized that the tool could predict the incidence of CV events or all-cause mortality for individual patients. In the future, we must confirm its external validity in other prospective cohorts.

Project description:It is unclear whether high-density lipoprotein (HDL) cholesterol concentration plays a causal role in atherosclerosis. A more important factor may be HDL cholesterol efflux capacity, the ability of HDL to accept cholesterol from macrophages, which is a key step in reverse cholesterol transport. We investigated the epidemiology of cholesterol efflux capacity and its association with incident atherosclerotic cardiovascular disease outcomes in a large, multiethnic population cohort.We measured HDL cholesterol level, HDL particle concentration, and cholesterol efflux capacity at baseline in 2924 adults free from cardiovascular disease who were participants in the Dallas Heart Study, a probability-based population sample. The primary end point was atherosclerotic cardiovascular disease, defined as a first nonfatal myocardial infarction, nonfatal stroke, or coronary revascularization or death from cardiovascular causes. The median follow-up period was 9.4 years.In contrast to HDL cholesterol level, which was associated with multiple traditional risk factors and metabolic variables, cholesterol efflux capacity had minimal association with these factors. Baseline HDL cholesterol level was not associated with cardiovascular events in an adjusted analysis (hazard ratio, 1.08; 95% confidence interval [CI], 0.59 to 1.99). In a fully adjusted model that included traditional risk factors, HDL cholesterol level, and HDL particle concentration, there was a 67% reduction in cardiovascular risk in the highest quartile of cholesterol efflux capacity versus the lowest quartile (hazard ratio, 0.33; 95% CI, 0.19 to 0.55). Adding cholesterol efflux capacity to traditional risk factors was associated with improvement in discrimination and reclassification indexes.Cholesterol efflux capacity, a new biomarker that characterizes a key step in reverse cholesterol transport, was inversely associated with the incidence of cardiovascular events in a population-based cohort. (Funded by the Donald W. Reynolds Foundation and others.).

Dataset Information

Novel and conventional biomarkers for prediction of incident cardiovascular events in the community.

Publications

Novel and conventional biomarkers for prediction of incident cardiovascular events in the community.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets