Validation of continuous clinical indices of cardiometabolic risk in a cohort of Australian adults

BMC Cardiovascular Disorders

Mark Daniel⁴  Anne W Taylor¹  Robert J Adams¹  Natasha J Howard³  Catherine Paquet²  Suzanne J Carroll³ 

[1] Discipline of Medicine, The University of Adelaide, Adelaide, South Australia, Australia;Research Centre of the Douglas Mental Health University Institute, Verdun, Québec, Canada;Spatial Epidemiology and Evaluation Research Group, School of Population Health and Sansom Institute for Health Research, University of South Australia, Adelaide, South Australia, Australia;Department of Medicine, The University of Melbourne, St. Vincent’s Hospital, Melbourne, VIC, Australia

关键词: Validation;    AUC;    ROC;    Risk scores;    Type 2 diabetes;    Cardiovascular disease;    Cardiometabolic;   

Others  :  855347
DOI  :  10.1186/1471-2261-14-27

 received in 2013-11-25, accepted in 2014-02-19,  发布年份 2014

【 摘 要 】

Background

Indicators of cardiometabolic risk typically include non-clinical factors (e.g., smoking). While the incorporation of non-clinical factors can improve absolute risk prediction, it is impossible to study the contribution of non-clinical factors when they are both predictors and part of the outcome measure. Metabolic syndrome, incorporating only clinical measures, seems a solution yet provides no information on risk severity. The aims of this study were: 1) to construct two continuous clinical indices of cardiometabolic risk (cCICRs), and assess their accuracy in predicting 10-year incident cardiovascular disease and/or type 2 diabetes; and 2) to compare the predictive accuracies of these cCICRs with existing risk indicators that incorporate non-clinical factors (Framingham Risk Scores).

Methods

Data from a population-based biomedical cohort (n = 4056) were used to construct two cCICRs from waist circumference, mean arteriole pressure, fasting glucose, triglycerides and high density lipoprotein: 1) the mean of standardised risk factors (cCICR-Z); and 2) the weighted mean of the two first principal components from principal component analysis (cCICR-PCA). The predictive accuracies of the two cCICRs and the Framingham Risk Scores were assessed and compared using ROC curves.

Results

Both cCICRs demonstrated moderate accuracy (AUCs 0.72 – 0.76) in predicting incident cardiovascular disease and/or type 2 diabetes, among men and women. There were no significant differences between the predictive accuracies of the cCICRs and the Framingham Risk Scores.

Conclusions

cCICRs may be useful in research investigating associations between non-clinical factors and health by providing suitable alternatives to current risk indicators which include non-clinical factors.

【 授权许可】

   
2014 Carroll et al.; licensee BioMed Central Ltd.

【 图 表 】

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