Circulation: Formula gives primary-care physicians easy CVD risk
Generally, researchers use a separate multivariable algorithm to assess the risk of specific atherosclerotic cardiovascular disease events, such as coronary heart disease, cerebrovascular disease, peripheral vascular disease, or heart failure.
Investigators at Boston University , however, developed a single multivariable risk function that predicts risk of developing all CVD . It can be determined using measurements that are available in an office or clinic.
Use of validated CVD risk prediction algorithms has lagged in primary care, in part because of the large number of algorithms, each targeted at predicting a patient's risk for an individual CVD event, the authors wrote in the Feb. 12 issue of Circulation.
“Individuals with a high overall cardiovascular disease risk require more aggressive risk factor modification.The goal of therapy for cholesterol disorders, diabetes, and hypertension should be linked to the global cardiovascular disease risk,” according to lead author Ralph B. D’Agostino Sr., PhD, of the department of mathematics and statistics.
D’Agostino and colleagues used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8,491 Framingham study participants (mean age 49 years; 4,522 women). The participants were followed for up to 12 years.
During follow-up, the researchers found that 1,174 participants developed a first CVD event, which translated into an occurrence rate of 10.08% for women and 18.09% for men. For predicting global cardiovascular disease risk, the algorithm demonstrated good accuracy for men and women, as reflected by c-statistics of 0.762 and 0.793, respectively. In contrast, an earlier Framingham risk algorithm yielded lower c-statistics of 0.756 for men and 0.778 for women, the authors wrote.
A comparison of the two algorithms resulted in a "net reclassification improvement from using the new model that was statistically significant for men and women,” according to the study.
To determine a patient's risk for individual CVD events, the researchers calculated the global risk score and then multiplied by the proportion of total CVD events represented by the specific event.
"Therefore, use of a general CVD risk score is an attractive option in office-based primary care practices," the authors wrote. "Serial assessment of global CVD risk could be used to monitor progress of patients on treatment and improvement in their multivariable risk scores."
The researchers concluded that the estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.
Investigators at Boston University , however, developed a single multivariable risk function that predicts risk of developing all CVD . It can be determined using measurements that are available in an office or clinic.
Use of validated CVD risk prediction algorithms has lagged in primary care, in part because of the large number of algorithms, each targeted at predicting a patient's risk for an individual CVD event, the authors wrote in the Feb. 12 issue of Circulation.
“Individuals with a high overall cardiovascular disease risk require more aggressive risk factor modification.The goal of therapy for cholesterol disorders, diabetes, and hypertension should be linked to the global cardiovascular disease risk,” according to lead author Ralph B. D’Agostino Sr., PhD, of the department of mathematics and statistics.
D’Agostino and colleagues used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8,491 Framingham study participants (mean age 49 years; 4,522 women). The participants were followed for up to 12 years.
During follow-up, the researchers found that 1,174 participants developed a first CVD event, which translated into an occurrence rate of 10.08% for women and 18.09% for men. For predicting global cardiovascular disease risk, the algorithm demonstrated good accuracy for men and women, as reflected by c-statistics of 0.762 and 0.793, respectively. In contrast, an earlier Framingham risk algorithm yielded lower c-statistics of 0.756 for men and 0.778 for women, the authors wrote.
A comparison of the two algorithms resulted in a "net reclassification improvement from using the new model that was statistically significant for men and women,” according to the study.
To determine a patient's risk for individual CVD events, the researchers calculated the global risk score and then multiplied by the proportion of total CVD events represented by the specific event.
"Therefore, use of a general CVD risk score is an attractive option in office-based primary care practices," the authors wrote. "Serial assessment of global CVD risk could be used to monitor progress of patients on treatment and improvement in their multivariable risk scores."
The researchers concluded that the estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.