The equations to predict cardiovascular risk developed in Framingham [1] have been very useful decision-making tools in clinical practice to prevent and control cardiovascular risk factors. Nonetheless, these equations were constructed within the framework of a population with high cardiovascular risk and their validity in the Medterranean population, which has a much lower incidence of cardiovascular disease, was questionable. For this reason, following a published methodology [2] for adapting the risk equation to populations that with differed from Framingham in the incidence and prevalence of risk factors, the original equation was adapted to the Spanish population. The approach included replacing Framingham equation’s incidence rate for that observed in the population of Girona (province), recorded in the REGICOR population register of cardiac disease, and replacing the prevalences with those observed over time in individuals who were free of cardiovascular disease in the 1995 cohort [3]. Based on the adapted risk function, screening charts were developed for ease of use in clinical practice, showing the 10-year risk of heart attack according to a patient’s profile of risk factors (cholesterol, blood pressure, etc).

Shortly thereafter, the VERIFICA study was designed to validate the REGICOR adaptation of the Framingham equation for use in the Spanish population [4]. Information was collected from participants in studies throughout Spain with varying risk profiles. The results were satisfactory and the risk predicted by the Framingham-REGICOR adapted function was very similar to the observed risk, while the original Framingham calculation, as had been suspected, clearly over-estimated the risk.

The next step was to incorporate new variables in addition to those used in the Framingham equation. One of those proposed was treatment for hypertension and for hypercholesterolemia, both of which were infrequent at the time the original equation was developed. One of the objectives of the FRESCO study [5] is to construct new functions that incorporate these treatments as a variable. On the other hand, with the growing awareness and collection of genetic data in recent years, the degree to which predictive functions can be improved by incorporating genetic variants is also being evaluated [6, 7]. Along these lines, various other biomarkers related to metabolism are being considered, including inflammation, hemodynamics, hemostasis and myocardial damage.

In order to adequately verify the risk equations and assess the degree of improvement achieved by incorporating new predictive variables, the proper statistical techniques have been incorporated, including the modification of the Hosmer-Lemeshow test for cohort follow-up (response with censored information) [8]. Among the statistical methods used to measure improvement in predictive capacity achieved by incorporating new variables into the risk equation are the Net Reclassification Improvement Index (NRI), which measures the proportion of individuals with an event who were re-assigned to a higher risk group, and Integrated Discriminant Improvement (IDI) [9]. Both methods have also been adapted for cohort study.

References:

  1. Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998 May 12;97(18):1837-47. PubMed PMID: 9603539.
  2. D'Agostino RB Sr, Grundy S, Sullivan LM, Wilson P; CHD Risk Prediction Group. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA. 2001 Jul 11;286(2):180-7. PubMed PMID: 11448281.
  3. Marrugat J, Solanas P, D'Agostino R, Sullivan L, Ordovas J, Cordón F, Ramos R, Sala J, Masiá R, Rohlfs I, Elosua R, Kannel WB[Coronary risk estimation in Spain using a calibrated Framingham function]. Rev Esp Cardiol 2003; 56: 253-61
  4. Marrugat J, Subirana I, Comín E, Cabezas C, Vila J, Elosua R, Nam BH, Ramos R, Sala J, Solanas P, Cordón F, Gené-Badía J, D'Agostino RB Validity of an adaptation of the Framingham cardiovascular risk function: the VERIFICA Study. J Epidemiol Community Health 2007; 61: 40-7
  5. Marrugat J. et al. Derivation and validation of a set of 10-year cardiovascular risk predictive functions in 11 population Spanish cohorts: the FRESCO Study. Not yet submitted.
  6. Lluis-Ganella C, Subirana I, Lucas G, Tomás M, Muñoz D, Sentí M, Salas E, Sala J, Ramos R, Ordovas JM, Marrugat J, Elosua R. Assessment of the value of a genetic risk score in improving the estimation of coronary risk. Atherosclerosis. 2012 Jun;222(2):456-63. doi: 10.1016/j.atherosclerosis.2012.03.024. Epub 2012 Mar 30. PubMed PMID: 22521901.
  7. Lluís-Ganella C, Lucas G, Subirana I, Sentí M, Jimenez-Conde J, Marrugat J, Tomás M, Elosua R. Additive effect of multiple genetic variants on the risk of coronary artery disease. Rev Esp Cardiol. 2010 Aug;63(8):925-33. English, Spanish. PubMed PMID: 20738937.
  8. D'Agostino R B, Sr, Nam B H. Evaluation of the performance of survival analysis models: discrimination and calibration measures. In: Balakrishnan N, Rao C, eds. Advances in survival analysis: handbook of statistics. Vol 23. Amsterdam: Elsevier, 2004. 1–25.25.
  9. Pencina MJ, D'Agostino RB Sr, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011 Jan 15;30(1):11-21. doi: 10.1002/sim.4085. Epub 2010 Nov 5. PubMed PMID: 21204120; PubMed Central PMCID: PMC3341973.

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