The heterogeneity of the studies limited the analysis , but together they showed that provision of risk information to patients improved the accuracy of risk perception without decreasing quality of life or increasing anxiety , but had little effect on lifestyle .
There seems evidence that providing CVD risk model estimates to professionals and patients improves perceived CVD risk and medical prescribing , with little evidence of harm on psychological well-being
OBJECTIVE To systematic ally review whether the provision of information on cardiovascular disease ( CVD ) risk to healthcare professionals and patients impacts their decision-making , behaviour and ultimately patient health .
Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score.
CONTEXT Despite improved underst and ing of atherothrombosis , cardiovascular prediction algorithms for women have largely relied on traditional risk factors . OBJECTIVE To develop and vali date cardiovascular risk algorithms for women based on a large panel of traditional and novel risk factors . DESIGN , SETTING , AND PARTICIPANTS Thirty-five factors were assessed among 24 558 initially healthy US women 45 years or older who were followed up for a median of 10.2 years ( through March 2004 ) for incident cardiovascular events ( an adjudicated composite of myocardial infa rct ion , ischemic stroke , coronary revascularization , and cardiovascular death ) . We used data among a r and om two thirds ( derivation cohort , n = 16 400 ) to develop new risk algorithms that were then tested to compare observed and predicted outcomes in the remaining one third of women ( validation cohort , n = 8158 ) . MAIN OUTCOME MEASURE Minimization of the Bayes Information Criterion was used in the derivation cohort to develop the best-fitting parsimonious prediction models . In the validation cohort , we compared predicted vs actual 10-year cardiovascular event rates when the new algorithms were compared with models based on covariates included in the Adult Treatment Panel III risk score . RESULTS In the derivation cohort , a best-fitting model ( model A ) and a clinical ly simplified model ( model B , the Reynolds Risk Score ) had lower Bayes Information Criterion scores than models based on covariates used in Adult Treatment Panel III . In the validation cohort , all measures of fit , discrimination , and calibration were improved when either model A or B was used . For example , among participants without diabetes with estimated 10-year risks according to the Adult Treatment Panel III of 5 % to less than 10 % ( n = 603 ) or 10 % to less than 20 % ( n = 156 ) , model A reclassified 379 ( 50 % ) into higher- or lower-risk categories that in each instance more accurately matched actual event rates . Similar effects were achieved for clinical ly simplified model B limited to age , systolic blood pressure , hemoglobin A(1c ) if diabetic , smoking , total and high-density lipoprotein cholesterol , high-sensitivity C-reactive protein , and parental history of myocardial infa rct ion before age 60 years . Neither new algorithm provided substantive information about women at very low risk based on the published Adult Treatment Panel III score . CONCLUSION We developed , vali date d , and demonstrated highly improved accuracy of 2 clinical algorithms for global cardiovascular risk prediction that reclassified 40 % to 50 % of women at intermediate risk into higher- or lower-risk categories
The heterogeneity of the studies limited the analysis , but together they showed that provision of risk information to patients improved the accuracy of risk perception without decreasing quality of life or increasing anxiety , but had little effect on lifestyle .
There seems evidence that providing CVD risk model estimates to professionals and patients improves perceived CVD risk and medical prescribing , with little evidence of harm on psychological well-being
OBJECTIVE To systematic ally review whether the provision of information on cardiovascular disease ( CVD ) risk to healthcare professionals and patients impacts their decision-making , behaviour and ultimately patient health .