#UKPDS RISK ENGINE FREE#

Patients: Of the 815 FDS participants with type 2 diabetes who were initially CVD-free, 791 (97%) were eligible for assessment using the UKPDS equations, and 697 (86%) using the Framingham equation.

#UKPDS RISK ENGINE FREE#

Objective: To assess the performance of the Framingham and United Kingdom Prospective Diabetes Study (UKPDS) cardiovascular risk equations in Australian patients with type 2 diabetes who were initially free of cardiovascular disease (CVD).ĭesign and setting: The Fremantle Diabetes Study (FDS), a community-based longitudinal observational study data for the period 1993–2006 were used.

Statistics,epidemiology and research design.

Statistics, epidemiology and research design.

Accuracy and impact of risk assessment in the primary prevention of cardiovascular disease: a systematic review. Predicting cardiovascular risk: so what do we do now? Arch. Critical appraisal of CRP measurement for the prediction of coronary heart disease events: new data and systematic review of 31 prospective cohorts. An assessment of incremental coronary risk prediction using C-reactive protein and other novel risk markers: the atherosclerosis risk in communities study. Evaluation of the Framingham risk score in the European Prospective Investigation of Cancer-Norfolk cohort: does adding glycated hemoglobin improve the prediction of coronary heart disease events? Arch. Are the methods of risk prediction satisfactory? Eur. Performance of the UK Prospective Diabetes Study Risk Engine and the Framingham Risk Equations in Estimating Cardiovascular Disease in the EPIC-Norfolk Cohort. Prognostic value of the Framingham cardiovascular risk equation and the UKPDS risk engine for coronary heart disease in newly diagnosed Type 2 diabetes: results from a United Kingdom study. Cardiovascular risk assessment scores for people with diabetes: a systematic review. Diabetes: Which risk engines should be used to assess patients with diabetes? Nat.

10 Further research might address other potential uses of risk scores, for example, to better understand the interaction between clinicians and patients when CVD risk has been assessed, and the effects on physician prescribing and patient health behaviors such as diet, smoking, physical activity and medication adherence. Indeed, there is no strong evidence that a CVD risk assessment performed by a clinician improves CVD-related health outcomes. 9 More precise risk prediction by itself will not improve health outcomes. 7, 8 There may be little to gain from developing novel CVD risk scores, incorporating expensive biomarkers, in historical cohorts including individuals dissimilar to patients in whom the risk score would be used. However, very few novel biomarkers improve risk prediction models over and above the inclusion of traditional risk factors, and any improvement is likely to be marginal. Ray and Sattar conclude by suggesting that future studies on T2DM should incorporate data on emerging prognostic biomarkers to help improve CVD risk prediction and thus risk reduction. 3, 4, 5 Furthermore, adding a measure of glycemia to the FRS improves discrimination slightly in men but not in women, without significant improvement in reclassification of risk category. 2 Validation studies comparing the predictive performance of the FRS and the UKPDS risk engine report conflicting results. However, our systematic review demonstrates that scant evidence exists to suggest that diabetes-specific risk tools estimate CVD risk more accurately than those developed in the general population. The authors suggest that the UK Prospective Diabetes Study (UKPDS) risk engine has several advantages over the Framingham risk score (FRS), particularly as it incorporates a measure of glycemia and duration of diabetes mellitus, alongside other traditional risk factors. 5, 302–303 2009)) 1 raises a number of interesting points regarding the role of cardiovascular disease (CVD) risk assessment tools in the prevention of CVD among people with type 2 diabetes mellitus (T2DM). The News and Views article by Ray and Sattar (Diabetes: Which risk engines should be used to assess patients with diabetes? Nat.

Ukpds risk engine