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.