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

被引:0
|
作者
Rochmawati, Ike Dhiah [1 ,2 ]
Deo, Salil [3 ]
Lees, Jennifer S. [4 ]
Mark, Patrick B. [4 ]
Sattar, Naveed [4 ]
Celis-Morales, Carlos [4 ,5 ,6 ]
Pell, Jill P. [1 ]
Welsh, Paul [4 ]
Ho, Frederick K. [1 ]
机构
[1] Univ Glasgow, Sch Hlth & Wellbeing, 90 Byres Rd, Glasgow G12 8TB, Scotland
[2] Univ Surabaya, Fac Pharm, Dept Clin & Community Pharm, Jalan Raya Kalirungkut, Surabaya 60293, Indonesia
[3] Louis Stokes Cleveland VA Med Ctr, 10701 East Blvd, Cleveland, OH 44106 USA
[4] Univ Glasgow, Sch Cardiovasc & Metab Hlth, 126 Univ Pl, Glasgow G12 8TA, Scotland
[5] Univ Catolica Maule, Educ Phys Act & Hlth Res Unit, Human Performance Lab, San Miguel Ave 3605, Talca 3466706, Chile
[6] Univ Arturo Prat, Ctr Invest Med Altura CEIMA, Av Arturo Prat, Iquique 2120, Chile
关键词
Cardiovascular disease; SMART2; Risk prediction model; Secondary prevention; CYSTATIN-C; ALKALINE-PHOSPHATASE; ASSOCIATION; SAMPLE;
D O I
10.1093/eurjpc/zwae352
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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 (Delta C = 0.0064, P < 0.001), Cystatin C (Delta C = 0.0037, P < 0.001), GGT (Delta C = 0.0023, P < 0.001), AST (Delta C = 0.0007, P < 0.005) or ALP (Delta C = 0.0010, P < 0.001) or replacing eGFRCr with eGFRCysC (Delta C = 0.0036, P < 0.001) or eGFRCr-CysC (Delta C = 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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页数:11
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