title: Repeat Cardiovascular Risk Assessment after Four Years: Is There Improvement in Risk Prediction?
abstract: BACKGROUND
  Framingham risk equations are widely used to predict cardiovascular disease based on health information from a single time point. Little is known regarding use of information from repeat risk assessments and temporal change in estimated cardiovascular risk for prediction of future cardiovascular events. This study was aimed to compare the discrimination and risk reclassification of approaches using estimated cardiovascular risk at single and repeat risk assessments.
  
  
  METHODS
  Using data on 12,197 individuals enrolled in EPIC-Norfolk cohort, with 12 years of follow-up, we examined rates of cardiovascular events by levels of estimated absolute risk (Framingham risk score) at the first and second health examination four years later. We calculated the area under the receiver operating characteristic curve (aROC) and risk reclassification, comparing approaches using information from single and repeat risk assessments (i.e., estimated risk at different time points).
  
  
  RESULTS
  The mean Framingham risk score increased from 15.5% to 17.5% over a mean of 3.7 years from the first to second health examination. Individuals with high estimated risk (≥20%) at both health examinations had considerably higher rates of cardiovascular events than those who remained in the lowest risk category (<10%) in both health examinations (34.0 [95%CI 31.7-36.6] and 2.7 [2.2-3.3] per 1,000 person-years respectively). Using information from the most up-to-date risk assessment resulted in a small non-significant change in risk classification over the previous risk assessment (net reclassification improvement of -4.8%, p>0.05). Using information from both risk assessments slightly improved discrimination compared to information from a single risk assessment (aROC 0.76 and 0.75 respectively, p<0.001).
  
  
  CONCLUSIONS
  Using information from repeat risk assessments over a period of four years modestly improved prediction, compared to using data from a single risk assessment. However, this approach did not improve risk classification.
authors.affiliation: MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom.|Cardio-Metabolic Research Group, Department of Social Medicine, Sanpasitthiprasong Hospital, Ubon Ratchathani, Thailand.
authors.forename: Parinya
authors.initials: P
authors.lastname: Chamnan
authors.affiliation: MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Rebecca K
authors.initials: RK
authors.lastname: Simmons
authors.affiliation: MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Sharp
authors.affiliation: Clinical Gerontology Unit, University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital, Cambridge, United Kingdom.
authors.forename: Kay-Tee
authors.initials: KT
authors.lastname: Khaw
authors.affiliation: MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Nicholas J
authors.initials: NJ
authors.lastname: Wareham
authors.affiliation: MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Simon J
authors.initials: SJ
authors.lastname: Griffin
mesh_terms: D000328:Adult; D000368:Aged; D002318:Cardiovascular Diseases / Q000453:epidemiology*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D062486:Public Health Surveillance; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors