Abstract
Introduction Neck adiposity has been related to cardiovascular risk in healthy and nonhealthy individuals. Our objective was to evaluate the utility of anatomic neck measurements extracted from computed tomography (CT) examinations as a predictor of cardiovascular disease and its risk factors. Methods We investigated patients who had a CT neck examination with intravenous contrast performed at two hospitals between 2013 and 2020. Patients with a neck malignancy, prior neck surgery, age <18 years, incomplete demographic information, and inadequate image quality were excluded. We performed 18 separate measurements of neck anatomy which were correlated with cardiovascular risk factors and disease, as well as relevant lab values and medications. All multivariable linear regressions were controlled for gender and BMI. Associations with p<0.05 were considered statistically significant. The measurements were then used to predict hypertension using random forest, a non-linear prediction algorithm. Results Approximately 20,000 neck CT examinations with contrast were performed between 2013-2020. After applying the inclusion criteria, 458 patients remained in the study population. Eight measurements (all of which include a component of neck adiposity) showed a statistically significant association between anatomic measurements and cardiovascular risk factors. The risk factor most often associated with increases in CT measurements was type 2 diabetes. Accordingly, patients on insulin treatment had a significantly higher average for all eight measurements. Significant measurement increases were also found in those previously diagnosed with hyperlipidemia and in those being treated with hypertension medications. The area under the receiver operating characteristic (AUROC) value of the random forest prediction algorithm was 0.68, meaning our measurements were a good predictor of hypertensive disease status. Conclusion Adipose tissue measurements extracted from CT examinations of the neck are associated with cardiovascular risk factors including hypertension, diabetes, and hyperlipidemia. Machine learning models of anatomic neck measurements can potentially identify patients at risk for cardiovascular disease.