title: A comparison of the PROCAM and Framingham point-scoring systems for estimation of individual risk of coronary heart disease in the Second Northwick Park Heart Study.
abstract: We have compared the predictive value of the PROCAM and Framingham risk algorithms in healthy UK men from the Second Northwick Park Heart Study (NPHS-II) (50-64 years at entry), followed for a median of 10.8 years for coronary heart disease (CHD) events. For PROCAM, the area under the receiver operating characteristic (ROC) curve was 0.63 (95% CI, 0.59-0.67), and not significantly different (p = 0.46) from the Framingham score, 0.62 (0.58-0.66). Sensitivities for a 5% false-positive rate (DR(5)) were 13.8 and 12.4%, respectively. Calibration analysis for PROCAM gave a ratio of observed to expected events of 0.46 (Hosmer-Lemeshow test, p < 0.0001) and 0.47 for Framingham (p < 0.0001). Using measures taken at 5 years of high-density lipoprotein cholesterol and (estimated) low-density lipoprotein cholesterol levels increased the ROC by only 1%. An NPHS-II risk algorithm, developed using a 50% random subset, and including age, triglyceride, total cholesterol, smoking status, and systolic blood pressure at recruitment, gave an ROC of 0.64 (0.58-0.70) with a DR(5) of 10.7% when applied to the second half of the data. Adding family history and diabetes increased the DR(5) to 18.4% (p = 0.28). Adding lipoprotein(a) >26.3 mg/dL (relative risk 1.6, 1.1-2.4) gave a DR(5) of 15.5% (p = 0.55), while adding fibrinogen levels (relative risk for 1S.D. increase = 1.5, 1.1-2.0) had essentially no additional impact (DR(5) = 16.9%, p = 0.95). Thus, the PROCAM algorithm is marginally better as a risk predictor in UK men than the Framingham score, but both significantly overestimate risk in UK men. The algorithm based on NPHS-II data performs similarly to those for PROCAM and Framingham with respect to discrimination, but gave an improved ratio of observed to expected events of 0.80 (p = 0.01), although no score had a high sensitivity. Any novel factors added to these algorithms will need to have a major impact on risk to increase sensitivity above that given by classical risk factors.
authors.affiliation: Division of Cardiovascular Genetics, Department of Medicine, British Heart Foundation Laboratories, Rayne Building, Royal Free and University College Medical School, London, UK.
authors.forename: Jackie A
authors.initials: JA
authors.lastname: Cooper
authors.forename: George J
authors.initials: GJ
authors.lastname: Miller
authors.forename: Steve E
authors.initials: SE
authors.lastname: Humphries
mesh_terms: D000465:Algorithms; D002138:Calibration; D008076:Cholesterol, HDL / Q000097:blood; D008078:Cholesterol, LDL / Q000097:blood; D003327:Coronary Disease / Q000209:etiology*; D048909:Diabetes Complications; D005340:Fibrinogen / Q000378:metabolism; D006801:Humans; D008297:Male; D008499:Medical Records; D008875:Middle Aged; D011237:Predictive Value of Tests; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods
chemical_list: D008076:Cholesterol, HDL; D008078:Cholesterol, LDL; D005340:Fibrinogen