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Protein-Based Risk Score for CV Outcomes in Stable CHD
Study Questions:
What is the validity of a score to predict risk of cardiovascular (CV) outcomes among patients with coronary heart disease (CHD), using large-scale analysis of circulating proteins?
Methods:
This was a prospective cohort study of participants with stable CHD. For the derivation cohort (Heart and Soul study), outpatients from San Francisco were enrolled from 2000-2002, and followed up through November 2011 (≤11.1 years). For the validation cohort (HUNT3, a Norwegian population-based study), participants were enrolled from 2006-2008, and followed up through April 2012 (5.6 years). Using modified aptamers, 1,130 proteins were measured in plasma samples. A 9-protein risk score was derived and validated for 4-year probability of myocardial infarction, stroke, heart failure, and all-cause death. Tests, including the C statistic, were used to assess performance of the 9-protein risk score, which was compared with the Framingham secondary event model, refit to the cohorts in this study. Within-person change in the 9-protein risk score was evaluated in the Heart and Soul study from paired samples collected 4.8 years apart.
Results:
From the derivation cohort, 938 samples were analyzed, participants’ median age at enrollment was 67.0 years, and 82% were men. From the validation cohort, 971 samples were analyzed, participants’ median age at enrollment was 70.2 years, and 72% were men. In the derivation cohort, C statistics were 0.66 for refit Framingham, 0.74 for 9-protein, and 0.75 for refit Framingham plus 9-protein models. In the validation cohort, C statistics were 0.64 for refit Framingham, 0.70 for 9-protein, and 0.71 for refit Framingham plus 9-protein models. Adding the 9-protein risk score to the refit Framingham model increased the C statistic by 0.09 (95% confidence interval [CI], 0.06-0.12) in the derivation cohort, and in the validation cohort, the C statistic was increased by 0.05 (95% CI, 0.02-0.09). Compared with the refit Framingham model, the integrated discrimination index for the 9-protein model was 0.12 (95% CI, 0.08-0.16) in the derivation cohort and 0.08 (95% CI, 0.05-0.10) in the validation cohort. In analysis of paired samples among 139 participants with CV events after the second sample, absolute within-person annualized risk increased more for the 9-protein model (median, 1.86% [95% CI, 1.15%-2.54%]) than for the refit Framingham model (median, 1.00% [95% CI, 0.87%-1.19%]) (p = 0.002), while among 375 participants without CV events, both scores changed less and similarly (p = 0.30).
Conclusions:
The authors concluded that among patients with stable CHD, a risk score based on 9 proteins performed better than the Framingham risk score in predicting CV events.
Perspective:
This study reports that a risk score based on 9 proteins performed better than the refit Framingham secondary event risk score in predicting CV events among patients with stable CHD, but still only provided modest discriminative accuracy. Additional research is indicated to assess whether the score is more accurate in a lower-risk population. It should be noted that this study investigated only the sensitivity to increasing risk as represented by an approaching event, hence it will be important to evaluate individual medical therapies that alter risk to understand how well these proteins can discern changes in risk.
Keywords: Cardiovascular Diseases, Coronary Artery Disease, Heart Failure, Myocardial Infarction, Myocardial Ischemia, Risk Assessment, Primary Prevention, Proteomics, Stroke
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