Biomarkers versus traditional risk factors to predict cardiovascular events in very old adults: cross-validated prospective cohort study

Objectives To test new cardiovascular (CV) risk models in very old adults with and without a history of CV disease (CVD), based on traditional risk factors and biomarkers.Design Cross- validated prospective cohort study. The models were tested in the BELFRAIL Study and externally validated in the Leiden 85- plus Study.setting General practice, Belgium and The Netherlands.Participants The BELFRAIL cohort consisted of 266 patients aged 80 years or older without a history of CVD and 260 with a history of CVD. The Leiden 85- plus Study consisted of 264 patients aged 85 years without a history of CVD and 282 with a history of CVD.Outcome measures The model with traditional risk factors and biomarkers, as well as the model using only biomarkers, was compared with the model with only traditional risk factors to predict 3- year CV morbidity and mortality. A competing- risk analysis was performed, and the continuous net reclassification improvement (NRI), integrated discrimination improvement (IDI) and net benefit were used to compare the predictive value of the different models.results Traditional risk factors poorly predicted CV mortality and morbidity. In participants without a history of CVD, adding N- terminal pro- B- type natriuretic peptide (NT- pro- BNP) improved the prediction (NRI 0.56 (95% CI 0.16 to 0.99) and relative IDI 4.01 (95% CI 2.19 to 6.28)). In participants with a history of CVD, the NRI with the addition of NT- pro- BNP and high- sensitivity C reactive protein was 0.38 (95% CI 0.09 to 0.70), and the relative IDI was 0.53 (95% CI 0.23 to 0.90). Moreover, in participants without a history of CVD, NT- pro- BNP performed well as a stand- alone predictor (NRI 0.32 (95% CI −0.12 to 0.74) and relative IDI 3.44 (95% CI 1.56 to 6.09)).Conclusions This study tested new risk models to predict CV morbidity and mortality in very old adults. Especially, NT- pro- BNP showed a strong added predictive value. This opens perspectives for clinicians who are in need of an easily applicable strategy for CV risk prediction in very old adults