OBJECTIVES: To evaluate the performance of the 2013 Pooled Cohort Risk Equation (PCE-ASCVD) for predicting cardiovascular disease (CVD) in an Australian population; to compare this performance with that of three frequently used Framingham-based CVD risk prediction models. DESIGN: Prospective national population-based cohort study. SETTING: 42 randomly selected urban and non-urban areas in six Australian states and the Northern Territory. PARTICIPANTS: 5453 adults aged 40-74 years enrolled in the Australian Diabetes, Obesity and Lifestyle study and followed until November 2011. We excluded participants who had CVD at baseline or for whom data required for risk model calculations were missing. MAIN OUTCOME MEASURES: Predicted and observed 10-year CVD risks (adjusted for treatment drop-in); performance (calibration and discrimination) of four CVD risk prediction models: 1991 Framingham, 2008 Framingham, 2008 office-based Framingham, 2013 PCE-ASCVD. RESULTS: The performance of the 2013 PCE-ASCVD model was slightly better than 1991 Framingham, and each was better the two 2008 Framingham risk models, both in men and women. However, all four models overestimated 10-year CVD risk, particularly for patients in higher deciles of predicted risk. The 2013 PCE-ASCVD (7.5% high risk threshold) identified 46% of men and 18% of women as being at high risk; the 1991 Framingham model (20% threshold) identified 17% of men and 2% of women as being at high risk. Only 16% of men and 11% of women identified as being at high risk by the 2013 PCE-ASCVD experienced a CV event within 10 years. CONCLUSIONS: The 2013 PCE-ASCVD or 1991 Framingham should be used as CVD risk models in Australian. However, the CVD high risk threshold for initiating CVD primary preventive therapy requires reconsideration.
OBJECTIVES: To evaluate the performance of the 2013 Pooled Cohort Risk Equation (PCE-ASCVD) for predicting cardiovascular disease (CVD) in an Australian population; to compare this performance with that of three frequently used Framingham-based CVD risk prediction models. DESIGN: Prospective national population-based cohort study. SETTING: 42 randomly selected urban and non-urban areas in six Australian states and the Northern Territory. PARTICIPANTS: 5453 adults aged 40-74 years enrolled in the Australian Diabetes, Obesity and Lifestyle study and followed until November 2011. We excluded participants who had CVD at baseline or for whom data required for risk model calculations were missing. MAIN OUTCOME MEASURES: Predicted and observed 10-year CVD risks (adjusted for treatment drop-in); performance (calibration and discrimination) of four CVD risk prediction models: 1991 Framingham, 2008 Framingham, 2008 office-based Framingham, 2013 PCE-ASCVD. RESULTS: The performance of the 2013 PCE-ASCVD model was slightly better than 1991 Framingham, and each was better the two 2008 Framingham risk models, both in men and women. However, all four models overestimated 10-year CVD risk, particularly for patients in higher deciles of predicted risk. The 2013 PCE-ASCVD (7.5% high risk threshold) identified 46% of men and 18% of women as being at high risk; the 1991 Framingham model (20% threshold) identified 17% of men and 2% of women as being at high risk. Only 16% of men and 11% of women identified as being at high risk by the 2013 PCE-ASCVD experienced a CV event within 10 years. CONCLUSIONS: The 2013 PCE-ASCVD or 1991 Framingham should be used as CVD risk models in Australian. However, the CVD high risk threshold for initiating CVD primary preventive therapy requires reconsideration.
Authors: Evan Atlantis; James Rufus John; S L Hocking; Kath Peters; Kathryn Williams; Paul Dugdale; P Fahey Journal: BMJ Open Date: 2022-06-22 Impact factor: 3.006
Authors: Mulugeta Molla Birhanu; Roger G Evans; Ayse Zengin; Michaela Riddell; Kartik Kalyanram; Kamakshi Kartik; Oduru Suresh; Nihal Jacob Thomas; Velandai K Srikanth; Amanda G Thrift Journal: BMJ Open Date: 2022-04-22 Impact factor: 3.006
Authors: Shelda Sajeev; Stephanie Champion; Alline Beleigoli; Derek Chew; Richard L Reed; Dianna J Magliano; Jonathan E Shaw; Roger L Milne; Sarah Appleton; Tiffany K Gill; Anthony Maeder Journal: Int J Environ Res Public Health Date: 2021-03-19 Impact factor: 3.390