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Adding traditional and emerging biomarkers for risk assessment in secondary prevention: a prospective cohort study of 20 656 patients with cardiovascular disease
| dc.contributor.author | Rochmawati, Ike Dhiah | |
| dc.contributor.author | Deo, Salil | |
| dc.contributor.author | Lees, Jennifer | |
| dc.contributor.author | Mark, Patrick B. | |
| dc.contributor.author | Sattar, Naveed | |
| dc.contributor.author | Celis-Morales, Carlos | |
| dc.contributor.author | Pell, Jill P. | |
| dc.contributor.author | Welsh, Paul | |
| dc.contributor.author | Ho, Frederick K. | |
| dc.date.accessioned | 2025-07-07T18:20:46Z | |
| dc.date.available | 2025-07-07T18:20:46Z | |
| dc.date.issued | 2025 | |
| dc.identifier.uri | http://repositorio.ucm.cl/handle/ucm/6200 | |
| dc.description.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. | es_CL |
| dc.language.iso | en | es_CL |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | * |
| dc.source | European Journal of Preventive Cardiology, 32(7), 585-595 | es_CL |
| dc.subject | Cardiovascular disease | es_CL |
| dc.subject | SMART2 | es_CL |
| dc.subject | Risk prediction model | es_CL |
| dc.subject | Secondary prevention | es_CL |
| dc.title | Adding traditional and emerging biomarkers for risk assessment in secondary prevention: a prospective cohort study of 20 656 patients with cardiovascular disease | es_CL |
| dc.type | Article | es_CL |
| dc.ucm.indexacion | Scopus | es_CL |
| dc.ucm.indexacion | Isi | es_CL |
| dc.ucm.uri | oxfordjournals.ucm.elogim.com/eurjpc/article/32/7/585/7849694 | es_CL |
| dc.ucm.doi | doi.org/10.1093/eurjpc/zwae352 | es_CL |
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