title: Validity of a long-term cardiovascular disease risk prediction equation for low-incidence populations: the CAMUNI-MATISS Cohorts Collaboration study.
abstract: BACKGROUND
  Before introducing long-term cardiovascular disease (CVD) risk models in clinical practice, their external validity should be investigated. We assessed the validity of the CArdiovascular Monitoring Unit in Northern Italy (CAMUNI) 20-year risk score, developed in Northern Italy, and published previously, when applied to a population with different risk factors distribution and event incidence.
  
  
  METHODS
  The validation sample consisted of 5307 35-69 year-old subjects (2418 men) enrolled in Central Italy during the 1980s (Malattia ATerosclerotica Istituto Superiore di Sanità (MATISS) study). Baseline risk factor assessment and follow-up procedures, including MONICA definition of acute events, followed a shared protocol with the derivation cohorts. We estimated model calibration and discrimination (area under the ROC curve, AUC) in the validation set; as well as the net benefit of using the CAMUNI risk score as second-level screening in subjects at different levels of short-term risk.
  
  
  RESULTS
  The 20-year risk of event was 14% in men and 7% in women. Model calibration was satisfactory, and the strength of the association between predictors and the endpoint was the same as in the derivation population. The AUC was 0.734 (men) and 0.802 (women). The net benefit of the CAMUNI score was 3.9 (95% confidence interval: 2.1-5.7) and 2.9 (1.7-4.3) in men and women at low 10-year risk, respectively. Among subjects at high short-term risk, a significant net benefit of 9.8 was observed in men only. A pooled CAMUNI-MATISS risk score is provided.
  
  
  CONCLUSIONS
  In this low-incidence European population, long-term CVD prediction through the CAMUNI risk score is accurate and it has the potential to improve current primary prevention strategies based on short-term risk scores alone.
authors.affiliation: Department of Clinical and Experimental Medicine. University of Insubria, Italy giovanni.veronesi@uninsubria.it.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Veronesi
authors.affiliation: Department of Clinical and Experimental Medicine. University of Insubria, Italy.
authors.forename: Francesco
authors.initials: F
authors.lastname: Gianfagna
authors.affiliation: Istituto Superiore di Sanità, Roma, Italy.
authors.forename: Simona
authors.initials: S
authors.lastname: Giampaoli
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, USA.
authors.forename: Lloyd E
authors.initials: LE
authors.lastname: Chambless
authors.affiliation: Clinica Medica, Università Milano-Bicocca, Italy Istituto di Ricerca a Carattere Scientifico IRCCS Multimedica, Sesto San Giovanni (Milano), Italy.
authors.forename: Guido
authors.initials: G
authors.lastname: Grassi
authors.affiliation: Research Center for Public Health, University of Milano-Bicocca, Italy.
authors.forename: Giancarlo
authors.initials: G
authors.lastname: Cesana
authors.affiliation: Department of Clinical and Experimental Medicine. University of Insubria, Italy.
authors.forename: Marco M
authors.initials: MM
authors.lastname: Ferrario
mesh_terms: D000328:Adult; D000368:Aged; D019540:Area Under Curve; D002318:Cardiovascular Diseases / Q000175:diagnosis / Q000453:epidemiology* / Q000517:prevention & control; D003661:Decision Support Techniques; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D015994:Incidence; D007558:Italy / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D015995:Prevalence; D011322:Primary Prevention; D012372:ROC Curve; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors
keywords: Cardiovascular disease; Italy; calibration; clinical utility; discrimination; prediction model; primary prevention; validity