Prevention & lipidsCohort study
Adding traditional and emerging biomarkers for risk assessment in secondary prevention: a prospective cohort study of 20 656 patients with cardiovascular disease
- Prospective cohort study
Authors' conclusion
Adding several biomarkers, most notably Cystatin C and HbA1c, but not LP-a, in a model using SMART2 predictors modestly improved discrimination.
Study context
- Population
- UK Biobank participants with a history of atherosclerotic cardiovascular disease (ASCVD) (secondary prevention)UK Biobank
- Participants
- 20,658 participants
- Exposure
- SMART2-predictor model with vs without added biomarkers
- Outcome
- Improvement in C index and NRI for recurrent major CV events after adding biomarkers to a SMART2-predictor model
Main finding
As reported in the abstract
Discrimination (ΔC vs baseline model, C index = 0.663)
- Baseline model C index
- 0.663
- Adding Hemoglobin A1c (HbA1c): ΔC
- 0.0064, P < 0.001
- Adding Cystatin C: ΔC
- 0.0037, P < 0.001
- Adding gamma-glutamyl transferase (GGT): ΔC
- 0.0023, P < 0.001
- Adding AST: ΔC
- 0.0007, P < 0.005
- Adding ALP: ΔC
- 0.0010, P < 0.001
- Replacing eGFRCr with eGFRCysC: ΔC
- 0.0036, P < 0.001
- Replacing eGFRCr with eGFRCr-CysC: ΔC
- 0.00336, P < 0.001
Observational data: associations, not effects.
Further findings
Net reclassification index (NRI)
- Adding HbA1c
- 0.014
- Adding Cystatin C
- 0.006
- Replacing eGFRCr with eGFRCr-CysC
- 0.001
- Replacing eGFRCr with eGFRCysC
- 0.002
Calibration
- No evidence that adding biomarkers modified calibration
Original publication
Adding traditional and emerging biomarkers for risk assessment in secondary prevention: a prospective cohort study of 20 656 patients with cardiovascular disease. Eur J Prev Cardiol. Published 30 October 2024. doi:10.1093/eurjpc/zwae352
Summarised from the published abstract; every number is checked against it