scholarly journals High-sensitivity C-reactive protein as a risk assessment tool for cardiovascular disease

2005 ◽  
Vol 28 (9) ◽  
pp. 408-412 ◽  
Author(s):  
Edward T. H. Yeh
Keyword(s):  
Cardiovascular Disease ◽  
Risk Assessment ◽  
Assessment Tool ◽  
High Sensitivity ◽  
Risk Assessment Tool ◽  
C Reactive Protein ◽  
Reactive Protein

scholarly journals External validation of the European risk assessment tool for cardio metabolic disease in a Middle Eastern population

2020 ◽  
Author(s):  
Samaneh Asgari ◽  
Fatemeh Moosaie ◽  
Davood Khalili ◽  
Fereidoun Azizi ◽  
Farzad Hadaegh
Keyword(s):  
Metabolic Disease ◽  
Cardiovascular Disease ◽  
Risk Assessment ◽  
Goodness Of Fit ◽  
Assessment Tool ◽  
External Validation ◽  
Iranian Population ◽  
Risk Assessment Tool ◽  
Goodness Of Fit Test

Abstract Background: High burden of chronic cardio-metabolic disease (CCD) including type 2 diabetes mellitus (T2DM), chronic kidney disease (CKD), and cardiovascular disease (CVD) have been reported in the Middle East and North Africa region. We aimed to externally validate a Europoid risk assessment tool designed by Alssema et al, including non-laboratory measures, for the prediction of the CCD in the Iranian population. Methods: The predictors included age, body mass index, waist circumference, use of antihypertensive, current smoking, and family history of cardiovascular disease and or diabetes. For external validation of the model in the Tehran lipids and glucose study (TLGS), the Area under the curve (AUC) and the Hosmer-Lemeshow (HL) goodness of fit test were performed for discrimination and calibration, respectively. Results: Among 1310 men and 1960 women aged 28-85 years, 29.5% and 47.4% experienced CCD during the 6 and 9-year follow-up, respectively. The model showed acceptable discrimination, with an AUC of 0.72(95% CI: 0.69-0.75) for men and 0.73(95% CI: 0.71-0.76) for women. The calibration of the model was good for both genders (min HL P=0.5). Considering separate outcomes, AUC was highest for CKD (0.76(95% CI: 0.72-0.79)) and lowest for T2DM (0.65(95% CI: 0.61-0.69)), in men. As for women, AUC was highest for CVD (0.82(95% CI: 0.78-0.86)) and lowest for T2DM (0.69(95% CI: 0.66-0.73)). The 9-year follow-up demonstrated almost similar performances compared to the 6-year follow-up. Conclusion: This model showed acceptable discrimination and good calibration for risk prediction of CCD in short and long-term follow-up in the Iranian population.

scholarly journals Markers of Myocardial Stress, Myocardial Injury, and Subclinical Inflammation and the Risk of Sudden Death

Circulation ◽  
2020 ◽  
Vol 142 (12) ◽  
pp. 1148-1158
Author(s):  
Brendan M. Everett ◽  
M.V. Moorthy ◽  
Jani T. Tikkanen ◽  
Nancy R. Cook ◽  
Christine M. Albert
Keyword(s):  
Cardiovascular Disease ◽  
High Density Lipoprotein ◽  
Cardiac Troponin ◽  
Myocardial Injury ◽  
Troponin I ◽  
Density Lipoprotein ◽  
High Sensitivity ◽  
C Reactive Protein ◽  
Cholesterol Ratio ◽  
Reactive Protein

Background: The majority of sudden cardiac deaths (SCDs) occur in low-risk populations often as the first manifestation of cardiovascular disease (CVD). Biomarkers are screening tools that may identify subclinical cardiovascular disease and those at elevated risk for SCD. We aimed to determine whether the total to high-density lipoprotein cholesterol ratio, high-sensitivity cardiac troponin I, NT-proBNP (N-terminal pro-B-type natriuretic peptide), or high-sensitivity C-reactive protein individually or in combination could identify individuals at higher SCD risk in large, free-living populations with and without cardiovascular disease. Methods: We performed a nested case-control study within 6 prospective cohort studies using 565 SCD cases matched to 1090 controls (1:2) by age, sex, ethnicity, smoking status, and presence of cardiovascular disease. Results: The median study follow-up time until SCD was 11.3 years. When examined as quartiles or continuous variables in conditional logistic regression models, each of the biomarkers was significantly and independently associated with SCD risk after mutually controlling for cardiac risk factors and other biomarkers. The mutually adjusted odds ratios for the top compared with the bottom quartile were 1.90 (95% CI, 1.30–2.76) for total to high-density lipoprotein cholesterol ratio, 2.59 (95% CI, 1.76–3.83) for high-sensitivity cardiac troponin I, 1.65 (95% CI, 1.12–2.44) for NT-proBNP, and 1.65 (95% CI, 1.13–2.41) for high-sensitivity C-reactive protein. A biomarker score that awarded 1 point when the concentration of any of those 4 biomarkers was in the top quartile (score range, 0–4) was strongly associated with SCD, with an adjusted odds ratio of 1.56 (95% CI, 1.37–1.77) per 1-unit increase in the score. Conclusions: Widely available measures of lipids, subclinical myocardial injury, myocardial strain, and vascular inflammation show significant independent associations with SCD risk in apparently low-risk populations. In combination, these measures may have utility to identify individuals at risk for SCD.

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