Improving the Prediction of Death from Cardiovascular Causes with Multiple Risk Markers

BackgroundTraditional risk factors including demographics, blood pressure, cholesterol, and diabetes status are successfully able to predict a proportion of cardiovascular disease (CVD) events. Whether including additional routinely measured factors improves CVD prediction is unclear. To determine whether a comprehensive risk factor list, including clinical blood measures, blood counts, anthropometric measures, and lifestyle factors, improves prediction of CVD deaths beyond traditional factors. MethodsThe analysis comprised of 21,982 participants aged 40 years and older (mean age=59.4 years at baseline) from the National Health and Nutrition Examination Survey (NHANES) from 2001 to 2016 survey cycles. Data were linked with the National Death Index mortality data through 2019 and split into 80:20 training and testing sets. Relative to the traditional risk factors (age, sex, race/ethnicity, smoking status, systolic blood pressure, total and high-density lipoprotein cholesterol, antihypertensive medications, and diabetes), we compared models with an additional 22 clinical blood biomarkers, 20 complete blood counts, 7 anthropometric measures, 51 dietary factors, 13 cardiovascular health-related questions, and all 113 predictors together. To build prediction models for CVD mortality, we performed Cox proportional hazards regression, elastic-net (ENET) penalized Cox regression, and random survival forest, and compared classification using C-index and net reclassification improvement. ResultsDuring follow-up (median, 9.3 years), 3,075 participants died; 30.9% (1,372/3,075) deaths were from cardiovascular causes. In Cox proportional hazards models with traditional risk factors (C-index=0.850), CVD mortality classification improved with incorporation of clinical blood biomarkers (C-index=0.867), blood counts (C-index=0.861), and all predictors (C-index=0.871). Net CVD mortality reclassification improved 13.2% by adding clinical blood biomarkers and 12.2% by adding all predictors. Results for ENET-penalized Cox regression and random survival forest were similar. No improvement was observed in separate models for anthropometric measures, dietary nutrient intake, or cardiovascular health-related questions. ConclusionsThe addition of clinical blood biomarkers and blood counts substantially improves CVD mortality prediction, beyond traditional risk factors. These biomarkers may serve as an important clinical and public health screening tool for the prevention of CVD deaths. Clinical PerspectiveO_ST_ABSWhat is new?C_ST_ABSO_LIWe tested the predictive value of a combination of 113 potential predictors, including 22 clinical blood biomarkers, 20 complete blood counts, 7 anthropometric measures, 51 dietary factors, and 13 cardiovascular health-related questions, beyond traditional risk factors, for CVD mortality in adults in the United States. C_LIO_LIThe addition of predictors, specifically blood biomarkers such as glucose, uric acid, bicarbonate, urea nitrogen, total protein, creatinine, calcium, globulin, and phosphorus, improved CVD mortality prediction. C_LI What are the clinical implications?O_LIAccurate prediction of CVD mortality is essential for identifying those at risk and targeting interventions. C_LIO_LIOur findings highlight the clinical translational utility of predictors, including the biomarkers already well established and routinely applied in clinical practice, for CVD mortality prediction. C_LI