Physicians' Academy for Cardiovascular Education

Proteomic risk model superior to clinical risk model in predicting recurrent ASCVD events

Targeted proteomics improves cardiovascular risk prediction in secondary prevention

Literature - Nurmohamed NS, Pereira JPB, Hoogeveen RM et al. - Eur Heart J. 2022 Feb 9;ehac055. doi: 10.1093/eurheartj/ehac055.

Introduction and methods


In the last years, more and more novel therapeutic agents have become available that offer an opportunity to further reduce residual risk of ASCVD [1-9]. Traditional risk scores perform poorly in terms of discrimination [10]. More accurate methods are needed for the identification of patients who are at highest risk of recurrent events to guide risk-based therapeutic decisions.

Aim of the study

This study investigated the predictive value of a targeted plasma proteomics approach, analyzed with machine learning techniques in a secondary prevention setting.


The derivation cohort consisted of 870 individuals from the SMART cohort and the validation cohort consisted of 700 individuals from the Athero-Express cohort [11,12]. A total of 276 proteins were measured in blood samples from panels that were selected based on known associations with ASCVD.

Three models were constructed using machine learning techniques in the derivation cohort:

All three models were recalibrated for use in the validation cohort and tested for discrimination, calibration and reclassification.

In an exploratory analysis, the predictive value of the proteomic model was assessed in patients with low and high CRP levels. For this analysis, individuals in the derivation cohort were categorized according to CRP levels: high CRP (>2 mg/L, n=463) and low CRP (≤2 mg/L, n=373). Those with CRP >20 mg/L were excluded.


The primary outcome was first recurrent ASCVD event, defined as acute MI, ischemic stroke or CV death.

Main results


Calibration and reclassification

High vs low CRP levels


This study shows that a proteomic risk model comprising a panel of 50 proteins performed significantly better in terms of discrimination than a clinical risk model in predicting recurrent ASCVD events. The proteomics model was well calibrated and provided significant net reclassification improvement.


Show references

Find this article online at Eur Heart J.

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