Comparison of risk scores for the prediction of stroke in African Americans: Findings from the Jackson Heart Study.
BACKGROUND
Evidence from existing cohort studies supports the prediction of incident coronary heart disease and stroke using 10-year cardiovascular disease (CVD) risk scores and the American Heart Association/American Stroke Association's cardiovascular health (CVH) metric.


METHODS
We included all Jackson Heart Study participants with complete scoring information at the baseline study visit (2000-2004) who had no history of stroke (n = 4,140). We used Kaplan-Meier methods to calculate the cumulative incidence of stroke and used Cox models to estimate hazard ratios and 95% CIs for stroke according to CVD risk and CVH score. We compared the discrimination of the 2 models according to the Harrell c index and plotted predicted vs observed stroke risk calibration plots for each of the 2 models.


RESULTS
The median age of the African American participants was 54.5 years, and 65% were female. The cumulative incidence of stroke increased across worsening categories of CVD risk and CVH. A 1-unit increase in CVD risk increased the hazard of stroke (1.07, 1.06-1.08), whereas each 1-unit increase in CVH corresponded to a decreased hazard of stroke (0.76, 0.69-0.83). As evidenced by the c statistics, the CVH model was less discriminating than the CVD risk model (0.59 [0.55-0.64] vs 0.79 [0.76-0.83]).


CONCLUSIONS
Both scores were associated with incident stroke in a dose-response fashion; however, the CVD risk model was more discriminating than the CVH model. The CVH score may still be preferable for its simplicity in application to broad patient populations and public health efforts.
Division of Epidemiology, The Ohio State University, Columbus, OH. Electronic address: rforaker@cph.osu.edu.
Randi E
RE
Foraker
Duke Clinical Research Institute, Durham, NC. Electronic address: Melissa.Greiner@duke.edu.
Melissa
M
Greiner
Department of Medicine, University of Mississippi Medical Center, Birmingham, AL. Electronic address: msims2@umc.edu.
Mario
M
Sims
Department of Clinical Laboratory and Nutritional Sciences, University of Massachusetts at Lowell, Lowell, MA. Electronic address: Katherine_Tucker@uml.edu.
Katherine L
KL
Tucker
Keck School of Medicine, University of Southern California, Los Angeles, CA. Electronic address: atowfighi@dhs.lacounty.gov.
Amytis
A
Towfighi
School of Public Health, Indiana University, Bloomington, IN. Electronic address: abidules@indiana.edu.
Aurelian
A
Bidulescu
Division of Biostatistics, The Ohio State University, Columbus, OH. Electronic address: ashoben@cph.osu.edu.
Abigail B
AB
Shoben
The Ohio State University Medical Center, Columbus, OH. Electronic address: Sakima.Smith@osumc.edu.
Sakima
S
Smith
Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD. Electronic address: stalega1@jhu.edu.
Sameera
S
Talegawkar
Center of Biostatistics and Bioinformatics, University of Mississippi Medical Center, Jackson, MS. Electronic address: cblackshear@umc.edu.
Chad
C
Blackshear
Center of Biostatistics and Bioinformatics, University of Mississippi Medical Center, Jackson, MS. Electronic address: wwang@umc.edu.
Wei
W
Wang
Duke Clinical Research Institute, Durham, NC. Electronic address: chantelle.hardy@duke.edu.
Natalie Chantelle
NC
Hardy
Duke Clinical Research Institute, Durham, NC. Electronic address: emily.obrien@duke.edu.
Emily
E
O'Brien
D000328:Adult; D001741:Black or African American / Q000706:statistics & numerical data*; D000368:Aged; D000369:Aged, 80 and over; D000572:American Heart Association; D002318:Cardiovascular Diseases / Q000453:epidemiology; D015331:Cohort Studies; D003327:Coronary Disease / Q000453:epidemiology*; D005260:Female; D006801:Humans; D015994:Incidence; D053208:Kaplan-Meier Estimate; D008297:Male; D008875:Middle Aged; D016016:Proportional Hazards Models; D018570:Risk Assessment; D012307:Risk Factors; D020521:Stroke / Q000453:epidemiology*; D014481:United States; D055815:Young Adult