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

Rochmawati, Ike Dhiah and Deo, Salil and Lees, Jennifer and Mark, Patrick and Sattar, Naveed and Celis-morales, Carlos and Pell, Jill and Welsh, Paul and Ho, Frederick (2024) Adding traditional and emerging biomarkers for risk assessment in secondary prevention: a prospective cohort study of 20 656 patients with cardiovascular disease. European Journal of Preventive Cardiology, 00. pp. 1-11. ISSN 2047-4873; E-ISSN 2047-4881

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Abstract

Aims This study aims to explore whether conventional and emerging biomarkers could improve risk discrimination and calibration in the secondary prevention of recurrent atherosclerotic cardiovascular disease (ASCVD), based on a model using predictors from SMART2 (Secondary Manifestations of ARTerial Disease). Methods and results 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 lipoprotein A (LP-a), apolipoprotein B, Cystatin C, Hemoglobin A1c (HbA1c), gamma-glutamyl transferase (GGT), aspartate aminotransferase (AST), alanine aminotransferase, and alkaline phosphatase (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. Compared with the baseline model (C index = 0.663), modest increments 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 modified calibration. Conclusion Adding several biomarkers, most notably Cystatin C and HbA1c, but not LP-a, in a model using SMART2 predictors modestly improved discrimination.

Item Type:	Article
Uncontrolled Keywords:	Cardiovascular disease; SMART2; Risk prediction model; Secondary prevention
Subjects:	R Medicine > R Medicine (General)
Divisions:	Faculty of Pharmacy > Department of Pharmacy
Depositing User:	IKE DHIAH ROCHMAWATI
Date Deposited:	15 Jan 2025 04:15
Last Modified:	15 Jan 2025 04:15
URI:	http://repository.ubaya.ac.id/id/eprint/47723

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