Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study

作者：Perry J. Pickhardt, Peter M. Graffy, Ryan D. Zea, Scott Jeffery Lee, Jiamin Liu, Veit Sandfort, Ronald M. Summers · 发表于：The Lancet Digital Health · 年份：2020 · DOI：10.1016/s2589-7500(20)30025-x · 被引用次数：207 · 研究领域：Cardiac Imaging and Diagnostics、Nutrition and Health in Aging、Radiomics and Machine Learning in Medical Imaging

Background Body CT scans are frequently done for a wide range of clinical indications, but potentially valuable biometric information typically goes unused. We aimed to compare the prognostic ability of automated CT-based body composition biomarkers derived from previously developed deep-learning and feature-based algorithms with that of clinical parameters (Framingham risk score [FRS] and body-mass index [BMI]) for predicting major cardiovascular events and overall survival in an adult screening cohort. Methods In this retrospective cohort study, mature and fully automated CT-based algorithms with predefined metrics for quantifying aortic calcification, muscle density, ratio of visceral to subcutaneous fat, liver fat, and bone mineral density were applied to a generally healthy asymptomatic outpatient cohort of adults aged 18 years or older undergoing abdominal CT for routine colorectal cancer screening. To assess the association between the predictive measures (CT-based vs FRS and BMI) and downstream adverse events (death or myocardial infarction, cerebrovascular accident, or congestive heart failure subsequent to CT scanning), we used both an event-free survival analysis and logistic regression to compute receiver operating characteristic curves (ROCs) . Findings 9223 people (mean age 57·1 years [SD 7·8]; 5152 [56%] women and 4071 [44%] men) who underwent CT scans between April, 2004, and December, 2016, were included in this analysis. In the longitudinal clinical follow-u...