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Cited by 141Atrial Fibrillation Management and OutcomesGenetic Associations and EpidemiologyCardiac electrophysiology and arrhythmias

The DOI matches its Crossref record; 30 of the 31 references it cites were found in a registry or an index.

Novel genetic markers improve measures of atrial fibrillation risk prediction

Open access
Everett, Brendan M., Cook, Nancy R., Conen, David, Chasman, Daniel I., Ridker, Paul M, Albert, Christine M.·2013·European Heart Journal
DOI 10.1093/eurheartj/eht033PMC PMC3730133
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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.

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Crossref · 10.1093/eurheartj/eht033

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Figure 1 Clinical reclassification of participants in the validation cohort for the age alone model when compared with the novel Women's Health Study atrial fibrillation risk prediction algorithm (WHS Model). In total, 1546 participants were reclassified, 1546 (100%) correctly. Reclassification χ2 calibration statistics calculated from this table were 25.3 (P = 0.0001) for the age alone model and 4.51 (P = 0.48) for the novel Women's Health Study atrial fibrillation risk prediction algorithm.
Figure 1 Clinical reclassification of participants in the validation cohort for the age alone model when compared with the novel Women's Health Study atrial fibrillation risk prediction algorithm (WHS Model). In total, 1546 participants were reclassified, 1546 (100%) correctly. Reclassification χ2 calibration statistics calculated from this table were 25.3 (P = 0.0001) for the age alone model and 4.51 (P = 0.48) for the novel Women's Health Study atrial fibrillation risk prediction algorithm.CC BY-NC · PubMed Central
Figure 2 Adjusted relative risk of incident atrial fibrillation for increasing quintiles of the weighted and unweighted genetic risk scores in the entire Women's Health Study cohort (n = 20 437). Estimates of relative risk and 95% confidence intervals are adjusted for the covariates included in the Women's Health Study atrial fibrillation risk prediction algorithm [ln(age), weight, height, systolic blood pressure, alcohol use (≥2 drinks per day) and ever smoking status].
Figure 2 Adjusted relative risk of incident atrial fibrillation for increasing quintiles of the weighted and unweighted genetic risk scores in the entire Women's Health Study cohort (n = 20 437). Estimates of relative risk and 95% confidence intervals are adjusted for the covariates included in the Women's Health Study atrial fibrillation risk prediction algorithm [ln(age), weight, height, systolic blood pressure, alcohol use (≥2 drinks per day) and ever smoking status].CC BY-NC · PubMed Central
Figure 3 Clinical reclassification of participants in the validation cohort for the Women's Health Study model plus the atrial fibrillation weighted genetic risk score, when compared with the Women's Health Study model without genetic information. In total, 1011 participants were reclassified, 591 (58.5%) correctly. Reclassification χ2 calibration statistics calculated from this table were 6.12 (P = 0.30) for the Women's Health Study model and 3.96 (P = 0.56) for the Women's Health Study model plus the atrial fibrillation genetic risk score.
Figure 3 Clinical reclassification of participants in the validation cohort for the Women's Health Study model plus the atrial fibrillation weighted genetic risk score, when compared with the Women's Health Study model without genetic information. In total, 1011 participants were reclassified, 591 (58.5%) correctly. Reclassification χ2 calibration statistics calculated from this table were 6.12 (P = 0.30) for the Women's Health Study model and 3.96 (P = 0.56) for the Women's Health Study model plus the atrial fibrillation genetic risk score.CC BY-NC · PubMed Central

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Metadata

Authors
Everett, B. M., Cook, N. R., Conen, D., Chasman, D. I., Ridker, P. M., & Albert, C. M.
Affiliations
  • Brigham and Women's Hospital (US)
  • University Hospital of Basel (CH)
Corresponding author
Everett, Brendan M.
ORCID
Venue
European Heart Journal
Publisher
Oxford University Press (OUP)
Published
2013-02-26
Volume
34
Issue
29
Pages
2243-2251
ISSN
  • 0195-668X (ISSN-L)
  • 1522-9645
DOI
10.1093/eurheartj/eht033
PMC
PMC3730133
OpenAlex
W2115751016
Cited by
141
References
31
Citations by year
2026: 3 · 2025: 7 · 2024: 5 · 2023: 3 · 2022: 8 · 2021: 16 · 2020: 11 · 2019: 5 · 2018: 13 · 2017: 15 · 2016: 26 · 2015: 12 · 2014: 12 · 2013: 4
FWCI
12.23
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • European Heart Journal (publishedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • reroDoc Digital Library (submittedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
  • HighWire Press Open Archive (submittedVersion)
Funders
  • Donald W. Reynolds Foundation (CA-047988)
  • American Heart Association (0835304N)
  • Amgen (HL-043851, HL-080467, CA-047988)
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (133681)
  • National Institutes of Health (5r01hl043851-10)
  • National Heart, Lung, and Blood Institute (R01HL080467, HL043851, HL-093613, HL099355, R01HL116690, R21HL093613, RC1HL099355, HL080467)
  • National Cancer Institute (HL-080467, HL-099355, HL-043851, CA 047988, R01CA047988)
Topics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Atrial Fibrillation Management and Outcomes
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
Keywords
atrial fibrillation, polygenic risk score, risk prediction model, genetic risk markers, Women's Health Study, risk reclassification, integrated discrimination improvement
MeSH terms
Age Factors, Alcohol Drinking, Algorithms, Atrial Fibrillation, Body Height, Body Weight, Female, Genetic Markers, Humans, Middle Aged, Prospective Studies, Risk Factors, Smoking, Risk Assessment, Polymorphism, Single Nucleotide

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