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This study investigated whether adding biomarkers, comorbidities, anthropometric measurements, and social factors to the SMART2 model improves the prediction of recurrent cardiovascular events in patients with established atherosclerotic cardiovascular disease (ASCVD). Using data from 179,382 patients across 11 cohorts, the study found that NT-proBNP and troponin I provided the largest improvements in C-statistic, with other factors like employment, heart failure, and atrial fibrillation also showing statistically significant but smaller improvements. The flexible add-on approach enabled more personalized risk estimation.
Adding factors like NT-proBNP, troponin I, heart failure history, and atrial fibrillation to the SMART2 model can improve the prediction of recurrent cardiovascular events in patients with established ASCVD.
BACKGROUND Guidelines recommend using the SMART2 model, estimating the risk of recurrent cardiovascular (CV) events, to support treatment decisions in patients with established atherosclerotic CV disease (ASCVD). They further outline that adding biomarkers, comorbidities, anthropometric, and social factors may improve these predictions. AIMS To investigate the added predictive value of guideline-outlined factors including biomarkers, comorbidities, anthropometric, and social factors on top of the SMART2 model using an approach enabling their use as add-on predictors. METHODS Patients aged 40-80 with ASCVD were included from 11 cohorts (n=179,382 with 25,789 recurrent CV events). Additional factors included biomarkers (troponin I, NT-proBNP, albuminuria), comorbidities (heart failure, atrial fibrillation, coronary multivessel disease), anthropometric measurements (body-mass index, waist and hip circumference), social (employment, education), and other factors (former smoking, parental CV history). Cross-cohort availability of these factors ranged from 2 cohorts for albuminuria to all cohorts for BMI. These factors were assessed as add-on predictors to the SMART2 model using Fine-Gray models with SMART2 coefficients as offset with recurrent CV events as primary outcome (non-fatal myocardial infarction or stroke, or CV death). Added predictive value was assessed through cohort cross validation by change (Δ) in C--statistic, calibration, and net benefit through decision curve analysis. RESULTS Sub distribution hazard ratios for additional factors ranged from 0.77 [95% confidence interval 0.75-0.80] for employment status to 1.69 [1.63-1.76] for heart failure history. ΔC-statistic was largest for NT-proBNP (0.0127 [0.0060-0.0193]) and troponin I (0.0100 [0.0020-0.0181]), with statistically significant but smaller ΔC-statistics for employment, heart failure, and atrial fibrillation. Calibration was adequate before and after integration of additional factors. Decision curve analysis demonstrated added net benefit beyond SMART2 for NT-proBNP, heart failure history, atrial fibrillation, albuminuria, current employment, coronary multivessel disease, and education level across clinically relevant thresholds up to 40% predicted risk. CONCLUSIONS The flexible add-on of guideline-outlined factors on top of SMART2 enables more personalised and improved estimation of recurrent CV event risk in patients with established ASCVD.
