Targeted proteomics improves cardiovascular risk prediction in secondary prevention.

ABSTRACT:

Aims

Current risk scores do not accurately identify patients at highest risk of recurrent atherosclerotic cardiovascular disease (ASCVD) in need of more intensive therapeutic interventions. Advances in high-throughput plasma proteomics, analysed with machine learning techniques, may offer new opportunities to further improve risk stratification in these patients.

Methods and results

Targeted plasma proteomics was performed in two secondary prevention cohorts: the Second Manifestations of ARTerial disease (SMART) cohort (n = 870) and the Athero-Express cohort (n = 700). The primary outcome was recurrent ASCVD (acute myocardial infarction, ischaemic stroke, and cardiovascular death). Machine learning techniques with extreme gradient boosting were used to construct a protein mo

SUBMITTER: Nurmohamed NS 

PROVIDER: S-EPMC9020984 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature