Adding traditional and emerging biomarkers for risk assessment in secondary prevention: a prospective cohort study of 20 656 patients with cardiovascular disease

Abstract

Background This study aims to explore whether conventional and emerging biomarkers could improve risk discrimination and calibration in secondary prevention of recurrent atherosclerotic cardiovascular disease (ASCVD), based on a model using predictors from SMART2. Methods In a cohort of 20,658 UK Biobank participants with medical history of ASCVD, we analysed any improvement in C indices and net reclassification index (NRI) for future ASCVD events, following addition of LP-a, ApoB, cystatin C, HbA1c, GGT, AST, ALT, and ALP, to a model with predictors used in SMART2 for the outcome of recurrent major cardiovascular event. We also examined any improvement in C indices and NRIs replacing creatinine based estimated glomerular filtration rate (eGFR) with cystatin C based estimates. Calibration plots between different models were also compared. Results Compared with the baseline model (C index=0.663), modest increment in C indices were observed when adding HbA1c (ΔC=0.0064, p<0.001), cystatin C (ΔC=0.0037, p<0.001), GGT (ΔC=0.0023, p<0.001), AST (ΔC= 0.0007, p<0.005) or ALP (ΔC=0.0010, p<0.001) or replacing eGFRCr with eGFRCysC (ΔC=0.0036, p<0.001) or eGFRCr-CysC (ΔC=0.00336, p<0.001). Similarly, the strongest improvements in NRI were observed with the addition of HbA1c (NRI=0.014), or cystatin C (NRI= 0.006) or replacing eGFRCr with eGFRCr-CysC (NRI=0.001) or eGFRCysC (NRI=0.002). There was no evidence that adding biomarkers modify calibration. Conclusions Adding several biomarkers, most notably cystatin C and HbA1c, but not LP-a, in a model using SMART2 predictors modestly improved discrimination.