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Original Investigation
February 18, 2020

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

Author Affiliations
  • 1Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
  • 2Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
  • 3Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
  • 4MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
  • 5National Institute for Health Research Imperial Biomedical Research Centre, Imperial College London, London, United Kingdom
JAMA. 2020;323(7):636-645. doi:10.1001/jama.2019.22241
Key Points

Question  Do polygenic risk scores have incremental value over and above prediction models that are currently used in clinical practice for cardiovascular risk stratification in general populations?

Findings  In this observational study of 352 660 individuals with no history of cardiovascular disease at baseline, the addition of a polygenic risk score to pooled cohort equations clinical risk score was associated with a modest but statistically significant improvement in discriminative accuracy for incident coronary artery disease (CAD) compared with pooled cohort equations alone (incremental C statistic, 0.02).

Meaning  The use of genetic information over the pooled cohort equations model warrants further investigation before clinical implementation.

Abstract

Importance  The incremental value of polygenic risk scores in addition to well-established risk prediction models for coronary artery disease (CAD) is uncertain.

Objective  To examine whether a polygenic risk score for CAD improves risk prediction beyond pooled cohort equations.

Design, Setting, and Participants  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.

Exposures  Polygenic risk score for CAD, pooled cohort equations, and both combined.

Main Outcomes and Measures  CAD (myocardial infarction and its related sequelae). Discrimination, calibration, and reclassification using a risk threshold of 7.5% were assessed.

Results  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%]).

Conclusions and Relevance  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.

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