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

医学 内科学 疾病 队列 二级预防 前瞻性队列研究 队列研究 重症监护医学
作者
Ike Dhiah Rochmawati,Salil V. Deo,Jennifer S. Lees,Patrick B. Mark,Naveed Sattar,Carlos Celis‐Morales,Jill P. Pell,Paul Welsh,Frederick K. Ho
出处
期刊:European Journal of Preventive Cardiology [Oxford University Press]
被引量:2
标识
DOI:10.1093/eurjpc/zwae352
摘要

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.
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