Global cardiovascular risk evaluation: pattern of algorithm use and risk modification in 'real life'

J Cardiovasc Med (Hagerstown). 2016 Aug;17(8):581-6. doi: 10.2459/JCM.0000000000000124.


Aims: Although calculation of the global cardiovascular risk is strongly recommended, limited data are available regarding the use and the utility of this tool in clinical practice. We aimed at answering the following questions in the setting of Italian general practice: how many patients are evaluated via the cardiovascular risk algorithm; what are their characteristics; and what happens after their evaluation.

Methods: We used the Health Search/CSD Longitudinal Patient Database. The software used by about 800 participating GPs allows the calculation of the global cardiovascular risk in automatic. The following data were yearly extracted from the database within 2004-2008: age, sex, and recorded diagnosis of the main cardiovascular and other information encompassing smoking habits, blood pressure, total cholesterol, high density lipoprotein cholesterol (i.e., variables used to calculate cardiovascular risk), BMI, physical activity, triglycerides, glucose and creatinine; wherever available, current cardiovascular therapy and the automatically computed global cardiovascular risk were also extracted.

Results: In 2008, the observed population, aged 35-69 years, numbered 438 922 individuals; 78 617 (17.9%) had at least one calculation of cardiovascular risk; 20 181 patients were re-evaluated at least once: 61.1% among high-risk patients, 43.8% among moderate-risk patients, and 27.2% among low-risk patients. The level of cardiovascular risk measured at baseline increased in 6863 (34%), decreased in 11 791 (58.4%), and did not change in 1527 (7.6%) individuals. Overall, mean cardiovascular risk decreased over 4 years in 2.25% (SD 6.41%; P < 0.01) of patients.

Conclusion: The calculation of global cardiovascular risk is underused by GPs, who generally assign a higher priority to high-risk individuals. In addition, the use of this algorithm seems to favor a reduction of risk in moderate-risk and high-risk patients.

MeSH terms

  • Adult
  • Aged
  • Algorithms*
  • Blood Pressure
  • Cardiovascular Diseases / blood
  • Cardiovascular Diseases / epidemiology*
  • Cholesterol, HDL / blood
  • Databases, Factual
  • Exercise
  • Female
  • Humans
  • Italy
  • Male
  • Middle Aged
  • Risk Assessment / methods*
  • Risk Factors
  • Triglycerides / blood


  • Cholesterol, HDL
  • Triglycerides