Novel genetic markers improve measures of atrial fibrillation risk prediction

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Novel genetic markers improve measures of atrial fibrillation risk prediction

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Title: Novel genetic markers improve measures of atrial fibrillation risk prediction
Author: Everett, Brendan M.; Cook, Nancy R.; Conen, David; Chasman, Daniel I.; Ridker, Paul M; Albert, Christine M.

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Citation: Everett, Brendan M., Nancy R. Cook, David Conen, Daniel I. Chasman, Paul M Ridker, and Christine M. Albert. 2013. “Novel genetic markers improve measures of atrial fibrillation risk prediction.” European Heart Journal 34 (29): 2243-2251. doi:10.1093/eurheartj/eht033. http://dx.doi.org/10.1093/eurheartj/eht033.
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Abstract: Aims Atrial fibrillation (AF) is associated with adverse outcome. Whether recently discovered genetic risk markers improve AF risk prediction is unknown. Methods and results We derived and validated a novel AF risk prediction model from 32 possible predictors in the Women's Health Study (WHS), a cohort of 20 822 women without cardiovascular disease (CVD) at baseline followed prospectively for incident AF (median: 14.5 years). We then created a genetic risk score (GRS) comprised of 12 risk alleles in nine loci and assessed model performance in the validation cohort with and without the GRS. The newly derived WHS AF risk algorithm included terms for age, weight, height, systolic blood pressure, alcohol use, and smoking (current and past). In the validation cohort, this model was well calibrated with good discrimination [C-index (95% CI) = 0.718 (0.684–0.753)] and improved all reclassification indices when compared with age alone. The addition of the genetic score to the WHS AF risk algorithm model improved the C-index [0.741 (0.709–0.774); P = 0.001], the category-less net reclassification [0.490 (0.301–0.670); P < 0.0001], and the integrated discrimination improvement [0.00526 (0.0033–0.0076); P < 0.0001]. However, there was no improvement in net reclassification into 10-year risk categories of <1, 1–5, and 5+% [0.041 (−0.044–0.12); P = 0.33]. Conclusion: Among women without CVD, a simple risk prediction model utilizing readily available risk markers identified women at higher risk for AF. The addition of genetic information resulted in modest improvements in predictive accuracy that did not translate into improved reclassification into discrete AF risk categories.
Published Version: doi:10.1093/eurheartj/eht033
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3730133/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:11855752
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