A simple risk score using routine data for predicting cardiovascular disease in primary care.
BACKGROUND
Population-based screening for cardiovascular disease (CVD) risk, incorporating blood tests, is proposed in several countries.


OBJECTIVE
The aim of this study was to evaluate whether a simple approach to identifying individuals at high risk of CVD using routine data might be effective.


METHODS
Prospective cohort study (EPIC-Norfolk).


METHODS
Norfolk area, UK.


METHODS
A total of 21 867 men and women aged 40-74 years, who were free from CVD and diabetes at baseline, participated in the study. The discrimination (the area under the receiver operating characteristic curve [aROC]), calibration, sensitivity/specificity, and positive/negative predictive value were evaluated for different risk thresholds of the Framingham risk equations and the Cambridge diabetes risk score (as an example of a simple risk score using routine data from electronic general practice records).


RESULTS
During 203 664 person-years of follow-up, 2213 participants developed a first CVD event (10.9 per 1000 person-years). The Cambridge diabetes risk score predicted CVD events reasonably well (aROC 0.72; 95% confidence interval [CI] = 0.71 to 0.73), while the Framingham risk score had the best predictive ability (aROC 0.77; 95% CI = 0.76 to 0.78). The Framingham risk score overestimated risk of developing CVD in this representative British population by 60%.


CONCLUSIONS
A risk score incorporating routinely available data from GP records performed reasonably well at predicting CVD events. This suggests that it might be more efficient to use routine data as the first stage in a stepwise population screening programme to identify people at high risk of developing CVD before more time- and resource-consuming tests are used.
Chamnan
Parinya
P
MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge.
Simmons
Rebecca K
RK
Hori
Hiroyuki
H
Sharp
Stephen
S
Khaw
Kay-Tee
KT
Wareham
Nicholas J
NJ
Griffin
Simon J
SJ
D000328:Adult; D000368:Aged; D002318:Cardiovascular Diseases / Q000209:etiology*; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008875:Middle Aged; D011320:Primary Health Care; D011446:Prospective Studies; D018570:Risk Assessment; D012307:Risk Factors
Diabetes. 1995 Apr;44(4):369-74
7698502
Br J Cancer. 1999 Jul;80 Suppl 1:95-103
10466767
Eur J Cardiovasc Prev Rehabil. 2005 Oct;12(5):451-8
16210931
Heart. 2008 Jan;94(1):1-3
18083943
Diabetologia. 2009 Oct;52(10):2001-14
19629430
Biometrics. 1988 Sep;44(3):837-45
3203132
Am J Med. 2004 Sep 15;117(6):390-7
15380495
BMJ. 2009 Jul 07;339:b2584
19584409
Circulation. 1998 May 12;97(18):1837-47
9603539
J Med Screen. 2002;9(4):187-90
12518011
BMJ. 2008 Aug 28;337:a1395
18755771
Fam Pract. 2008 Jun;25(3):191-6
18515811
Lancet. 1997 May 3;349(9061):1269-76
9142060
Circulation. 2008 Feb 12;117(6):743-53
18212285
Lancet. 2005 Jan 29-Feb 4;365(9457):434-41
15680460
BMJ. 2002 Jul 27;325(7357):197
12142309
BMJ. 2003 Nov 29;327(7426):1267
14644971
Soc Sci Med. 1999 Dec;49(12):1571-98
10574231
Heart. 2005 Dec;91 Suppl 5:v1-52
16365341
Diabetes Metab Res Rev. 2000 May-Jun;16(3):164-71
10867715
Diabetes Care. 2004 Jan;27(1):116-22
14693976
BMJ. 2008 Jun 28;336(7659):1475-82
18573856