Predictive Accuracy of a Polygenic Risk Score-Enhanced Prediction Model vs a Clinical Risk Score for Coronary Artery Disease

The incremental value of polygenic risk scores in addition to well-established risk prediction models for coronary artery disease (CAD) is uncertain. To examine whether a polygenic risk score for CAD improves risk prediction beyond pooled cohort equations. Observational study of UK Biobank participa...

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Vydané v:JAMA : the journal of the American Medical Association Ročník 323; číslo 7; s. 636
Hlavní autori: Elliott, Joshua, Bodinier, Barbara, Bond, Tom A, Chadeau-Hyam, Marc, Evangelou, Evangelos, Moons, Karel G M, Dehghan, Abbas, Muller, David C, Elliott, Paul, Tzoulaki, Ioanna
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 18.02.2020
Predmet:
Adult
Aged
Case-Control Studies
Coronary Artery Disease - genetics
Female
Genetic Predisposition to Disease
Genome-Wide Association Study
Genotype
Humans
Male
Middle Aged
Multifactorial Inheritance
Predictive Value of Tests
Risk
Risk Assessment - methods
ROC Curve
ISSN:1538-3598, 1538-3598
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Shrnutí:The incremental value of polygenic risk scores in addition to well-established risk prediction models for coronary artery disease (CAD) is uncertain. To examine whether a polygenic risk score for CAD improves risk prediction beyond pooled cohort equations. Observational study of UK Biobank participants enrolled from 2006 to 2010. A case-control sample of 15 947 prevalent CAD cases and equal number of age and sex frequency-matched controls was used to optimize the predictive performance of a polygenic risk score for CAD based on summary statistics from published genome-wide association studies. A separate cohort of 352 660 individuals (with follow-up to 2017) was used to evaluate the predictive accuracy of the polygenic risk score, pooled cohort equations, and both combined for incident CAD. Polygenic risk score for CAD, pooled cohort equations, and both combined. CAD (myocardial infarction and its related sequelae). Discrimination, calibration, and reclassification using a risk threshold of 7.5% were assessed. In the cohort of 352 660 participants (mean age, 55.9 years; 205 297 women [58.2%]) used to evaluate the predictive accuracy of the examined models, there were 6272 incident CAD events over a median of 8 years of follow-up. CAD discrimination for polygenic risk score, pooled cohort equations, and both combined resulted in C statistics of 0.61 (95% CI, 0.60 to 0.62), 0.76 (95% CI, 0.75 to 0.77), and 0.78 (95% CI, 0.77 to 0.79), respectively. The change in C statistic between the latter 2 models was 0.02 (95% CI, 0.01 to 0.03). Calibration of the models showed overestimation of risk by pooled cohort equations, which was corrected after recalibration. Using a risk threshold of 7.5%, addition of the polygenic risk score to pooled cohort equations resulted in a net reclassification improvement of 4.4% (95% CI, 3.5% to 5.3%) for cases and -0.4% (95% CI, -0.5% to -0.4%) for noncases (overall net reclassification improvement, 4.0% [95% CI, 3.1% to 4.9%]). The addition of a polygenic risk score for CAD to pooled cohort equations was associated with a statistically significant, yet modest, improvement in the predictive accuracy for incident CAD and improved risk stratification for only a small proportion of individuals. The use of genetic information over the pooled cohort equations model warrants further investigation before clinical implementation.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:1538-3598
1538-3598
DOI:10.1001/jama.2019.22241