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

Publication ,  Journal Article
Magudia, K; Bridge, CP; Bay, CP; Farah, S; Babic, A; Fintelmann, FJ; Brais, LK; Andriole, KP; Wolpin, BM; Rosenthal, MH
Published in: AJR Am J Roentgenol
February 2023

BACKGROUND. CT-based body composition (BC) measurements have historically been too resource intensive to analyze for widespread use and have lacked robust comparison with traditional weight metrics for predicting cardiovascular risk. OBJECTIVE. The aim of this study was to determine whether BC measurements obtained from routine CT scans by use of 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 (5519 women and 4233 men; mean age, 53.2 years; 890 patients self-reported their race as Black and 8862 self-reported their race as White) who underwent routine abdominal CT at a single health system from January 2012 through December 2012 and who were given no major cardiovascular or oncologic diagnosis within 3 months of undergoing CT. Using publicly available code, fully automated deep learning BC analysis was performed at the L3 vertebral body level to determine 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 (HRs) for MI or stroke within 5 years after CT for the three BC area z scores, with adjustment 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 quartile compared with lowest quartile, 1.31 [95% CI, 1.03-1.67], p = .04 for overall effect) and stroke risk (HR of highest compared with lowest quartile, 1.46 [95% CI, 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 derived 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 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.

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Published In

AJR Am J Roentgenol

DOI

EISSN

1546-3141

Publication Date

February 2023

Volume

220

Issue

2

Start / End Page

236 / 244

Location

United States

Related Subject Headings

  • Tomography, X-Ray Computed
  • Stroke
  • Risk Factors
  • Retrospective Studies
  • Outpatients
  • Nuclear Medicine & Medical Imaging
  • Middle Aged
  • Male
  • Humans
  • Female
 

Citation

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Magudia, K., Bridge, C. P., Bay, C. P., Farah, S., Babic, A., Fintelmann, F. J., … Rosenthal, M. H. (2023). Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular Events. AJR Am J Roentgenol, 220(2), 236–244. https://doi.org/10.2214/AJR.22.27977
Magudia, Kirti, Christopher P. Bridge, Camden P. Bay, Subrina Farah, Ana Babic, Florian J. Fintelmann, Lauren K. Brais, Katherine P. Andriole, Brian M. Wolpin, and Michael H. Rosenthal. “Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular Events.AJR Am J Roentgenol 220, no. 2 (February 2023): 236–44. https://doi.org/10.2214/AJR.22.27977.
Magudia, Kirti, et al. “Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular Events.AJR Am J Roentgenol, vol. 220, no. 2, Feb. 2023, pp. 236–44. Pubmed, doi:10.2214/AJR.22.27977.
Magudia K, Bridge CP, Bay CP, Farah S, Babic A, Fintelmann FJ, Brais LK, Andriole KP, Wolpin BM, Rosenthal MH. Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular Events. AJR Am J Roentgenol. 2023 Feb;220(2):236–244.

Published In

AJR Am J Roentgenol

DOI

EISSN

1546-3141

Publication Date

February 2023

Volume

220

Issue

2

Start / End Page

236 / 244

Location

United States

Related Subject Headings

  • Tomography, X-Ray Computed
  • Stroke
  • Risk Factors
  • Retrospective Studies
  • Outpatients
  • Nuclear Medicine & Medical Imaging
  • Middle Aged
  • Male
  • Humans
  • Female