Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular Events.

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Abstract

Background: CT-based body composition (BC) measurements have historically been too resource intensive to analyze for widespread use and lacked robust comparison to traditional weight metrics for predicting cardiovascular risk. Objective: This study’s aim was to determine if BC measurements obtained from routine CT scans using a fully automated deep learning algorithm could predict subsequent cardiovascular events independently from weight, BMI, and additional cardiovascular risk factors. Methods: This retrospective study included 9752 outpatients (mean age, 53.2 years; 5519 women, 4233 men; self-reported race of Black in 890 patients and White in 8862 patients) who underwent routine abdominal CT at a single health system from January through December 2012, without a major cardiovascular or oncologic diagnosis within 3 months of CT. Fully automated deep learning body composition analysis was performed at the L3 vertebral body level to determinate three BC areas [skeletal muscle area (SMA), visceral fat area (VFA), and subcutaneous fat area (SFA)]. Age-, sex-, and race-normalized reference curves were used to generate z scores for the three BC areas. Subsequent myocardial infarction (MI) or stroke was determined from the electronic medical record. Multivariable-adjusted Cox proportional hazards models were used to determine hazard ratios (HR) for MI or stroke within 5 years after CT for the three BC area z scores, adjusting for normalized weight, normalized BMI, and additional cardiovascular risk factors (smoking status, diabetes diagnosis, and systolic blood pressure). Results: In multivariable models, age, race and sex-normalized VFA was associated with subsequent MI risk (HR of highest compared to lowest quartile, 1.31 [1.03-1.67], p=.04 for overall effect) and stroke risk (HR of highest compared to lowest quartile, 1.46 [1.07-2.00], p=.04 for overall effect). In multivariable models, normalized SMA, SFA, weight, and BMI, were not associated with subsequent MI or stroke risk. Conclusion: VFA from fully automated and normalized analysis of abdominal CT examinations predicts subsequent MI or stroke in Black and White patients, independent of traditional weight metrics, and should be considered as an adjunct to BMI in risk models. Clinical Impact: Fully automated and normalized BC analysis of abdominal CT has promise to augment traditional cardiovascular risk prediction models.