For more information see the published Development and Use of Prediction Models for Classification of Cardiovascular Risk of Remote Indigenous Australians (full-text article).
Authors: An Tran-Duy1*, Robyn McDermott2,3, Josh Knight1, Xinyang Hua1, Elizabeth LM Barr4,5, Kerry Arabena6, Andrew Palmer1,7, Philip M. Clarke1,8
Affiliations:
1 Centre for Health Policy, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.
2 Centre for Chronic Disease Prevention, James Cook University, Cairns, Australia
3 School of Health Sciences, University of South Australia, Adelaide, Australia
4 Menzies School of Health Research, Darwin, Australia
5 Baker Heart and Diabetes Institute, Melbourne, Australia
6 Centre for Health Equity, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
7 Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
8 Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
Indigenous Australians have a high rate of mortality from cardiovascular disease (CVD), but many who are at a high risk of CVD do not receive evidence-based treatments that have been shown to effectively reduce the risk. In primary prevention of CVD, absolute CVD risk assessment is an important component that helps to ensure that higher-risk individuals are targeted for initiation of appropriate therapies. However, there is widespread belief that current tools have deficiencies in assessing CVD risk of Indigenous Australians. Several studies have shown that the equations derived from the Framingham study underestimate the CVD risk in this high-risk population. While the current Australian guidelines for prevention of CVD provide an algorithm for classifying CVD risk of Indigenous people, the developers of these guidelines acknowledge that there is little empirical evidence supporting this classification system (a combination of level D weak evidence plus a consensus-based recommendation).
In a recent study, we developed two prediction models for 5-year risk of CVD using data from an Indigenous Australian cohort. In the primary model (i.e. one that includes any relevant factors), the risk score consists of sex, age, systolic blood pressure, diabetes mellitus, waist circumference, triglycerides, and albumin creatinine ratio. In the reduced information model (i.e. one that does not contain laboratory variables), the risk score consists of sex, age, systolic blood pressure, diabetes mellitus and waist circumference. The internal validation showed that these models performed far better compared to the Australian guidelines and the recalibrated 2008 Framingham equation in terms of agreement between predicted and observed CVD risks. Using these models as a standard tool for identification of a person with a high 5-year CVD risk, we found potential misclassification of the high risk and moderate-low risk people by the Australian guidelines.
As our models are able to better classify the CVD risk of Indigenous people compared to the algorithm recommended by the Australian guidelines, they should be considered as a contemporarily evidence-based tool for CVD risk assessment in remote Indigenous Australians. The reduced information model provides a convenient and reasonable tool for a quick assessment of 5-year CVD risk where no laboratory variables are required.
A risk calculator, designed and developed by An Tran-Duy in the form of a mobile app, is available in
Google Play Store. This risk calculator app can be freely downloaded and installed on Android smart phones and tablets, and used to quickly
calculate the absolute CVD risk of remote Indigenous Australians based on either the primary or reduced-information model.
