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Visceral Adiposity and Severe COVID-19 Disease: Application of an Artificial Intelligence Algorithm to Improve Clinical Risk Prediction

BACKGROUND: Obesity has been linked to severe clinical outcomes among people who are hospitalized with coronavirus disease 2019 (COVID-19). We tested the hypothesis that visceral adipose tissue (VAT) is associated with severe outcomes in patients hospitalized with COVID-19, independent of body mass...

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Autores principales: Goehler, Alexander, Hsu, Tzu-Ming Harry, Seiglie, Jacqueline A, Siedner, Mark J, Lo, Janet, Triant, Virginia, Hsu, John, Foulkes, Andrea, Bassett, Ingrid, Khorasani, Ramin, Wexler, Deborah J, Szolovits, Peter, Meigs, James B, Manne-Goehler, Jennifer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8244656/
https://www.ncbi.nlm.nih.gov/pubmed/34258315
http://dx.doi.org/10.1093/ofid/ofab275
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author Goehler, Alexander
Hsu, Tzu-Ming Harry
Seiglie, Jacqueline A
Siedner, Mark J
Lo, Janet
Triant, Virginia
Hsu, John
Foulkes, Andrea
Bassett, Ingrid
Khorasani, Ramin
Wexler, Deborah J
Szolovits, Peter
Meigs, James B
Manne-Goehler, Jennifer
author_facet Goehler, Alexander
Hsu, Tzu-Ming Harry
Seiglie, Jacqueline A
Siedner, Mark J
Lo, Janet
Triant, Virginia
Hsu, John
Foulkes, Andrea
Bassett, Ingrid
Khorasani, Ramin
Wexler, Deborah J
Szolovits, Peter
Meigs, James B
Manne-Goehler, Jennifer
author_sort Goehler, Alexander
collection PubMed
description BACKGROUND: Obesity has been linked to severe clinical outcomes among people who are hospitalized with coronavirus disease 2019 (COVID-19). We tested the hypothesis that visceral adipose tissue (VAT) is associated with severe outcomes in patients hospitalized with COVID-19, independent of body mass index (BMI). METHODS: We analyzed data from the Massachusetts General Hospital COVID-19 Data Registry, which included patients admitted with polymerase chain reaction–confirmed severe acute respiratory syndrome coronavirus 2 infection from March 11 to May 4, 2020. We used a validated, fully automated artificial intelligence (AI) algorithm to quantify VAT from computed tomography (CT) scans during or before the hospital admission. VAT quantification took an average of 2 ± 0.5 seconds per patient. We dichotomized VAT as high and low at a threshold of ≥100 cm(2) and used Kaplan-Meier curves and Cox proportional hazards regression to assess the relationship between VAT and death or intubation over 28 days, adjusting for age, sex, race, BMI, and diabetes status. RESULTS: A total of 378 participants had CT imaging. Kaplan-Meier curves showed that participants with high VAT had a greater risk of the outcome compared with those with low VAT (P < .005), especially in those with BMI <30 kg/m(2) (P < .005). In multivariable models, the adjusted hazard ratio (aHR) for high vs low VAT was unchanged (aHR, 1.97; 95% CI, 1.24–3.09), whereas BMI was no longer significant (aHR for obese vs normal BMI, 1.14; 95% CI, 0.71–1.82). CONCLUSIONS: High VAT is associated with a greater risk of severe disease or death in COVID-19 and can offer more precise information to risk-stratify individuals beyond BMI. AI offers a promising approach to routinely ascertain VAT and improve clinical risk prediction in COVID-19.
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spelling pubmed-82446562021-07-01 Visceral Adiposity and Severe COVID-19 Disease: Application of an Artificial Intelligence Algorithm to Improve Clinical Risk Prediction Goehler, Alexander Hsu, Tzu-Ming Harry Seiglie, Jacqueline A Siedner, Mark J Lo, Janet Triant, Virginia Hsu, John Foulkes, Andrea Bassett, Ingrid Khorasani, Ramin Wexler, Deborah J Szolovits, Peter Meigs, James B Manne-Goehler, Jennifer Open Forum Infect Dis Major Article BACKGROUND: Obesity has been linked to severe clinical outcomes among people who are hospitalized with coronavirus disease 2019 (COVID-19). We tested the hypothesis that visceral adipose tissue (VAT) is associated with severe outcomes in patients hospitalized with COVID-19, independent of body mass index (BMI). METHODS: We analyzed data from the Massachusetts General Hospital COVID-19 Data Registry, which included patients admitted with polymerase chain reaction–confirmed severe acute respiratory syndrome coronavirus 2 infection from March 11 to May 4, 2020. We used a validated, fully automated artificial intelligence (AI) algorithm to quantify VAT from computed tomography (CT) scans during or before the hospital admission. VAT quantification took an average of 2 ± 0.5 seconds per patient. We dichotomized VAT as high and low at a threshold of ≥100 cm(2) and used Kaplan-Meier curves and Cox proportional hazards regression to assess the relationship between VAT and death or intubation over 28 days, adjusting for age, sex, race, BMI, and diabetes status. RESULTS: A total of 378 participants had CT imaging. Kaplan-Meier curves showed that participants with high VAT had a greater risk of the outcome compared with those with low VAT (P < .005), especially in those with BMI <30 kg/m(2) (P < .005). In multivariable models, the adjusted hazard ratio (aHR) for high vs low VAT was unchanged (aHR, 1.97; 95% CI, 1.24–3.09), whereas BMI was no longer significant (aHR for obese vs normal BMI, 1.14; 95% CI, 0.71–1.82). CONCLUSIONS: High VAT is associated with a greater risk of severe disease or death in COVID-19 and can offer more precise information to risk-stratify individuals beyond BMI. AI offers a promising approach to routinely ascertain VAT and improve clinical risk prediction in COVID-19. Oxford University Press 2021-05-28 /pmc/articles/PMC8244656/ /pubmed/34258315 http://dx.doi.org/10.1093/ofid/ofab275 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Infectious Diseases Society of America. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) ), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Major Article
Goehler, Alexander
Hsu, Tzu-Ming Harry
Seiglie, Jacqueline A
Siedner, Mark J
Lo, Janet
Triant, Virginia
Hsu, John
Foulkes, Andrea
Bassett, Ingrid
Khorasani, Ramin
Wexler, Deborah J
Szolovits, Peter
Meigs, James B
Manne-Goehler, Jennifer
Visceral Adiposity and Severe COVID-19 Disease: Application of an Artificial Intelligence Algorithm to Improve Clinical Risk Prediction
title Visceral Adiposity and Severe COVID-19 Disease: Application of an Artificial Intelligence Algorithm to Improve Clinical Risk Prediction
title_full Visceral Adiposity and Severe COVID-19 Disease: Application of an Artificial Intelligence Algorithm to Improve Clinical Risk Prediction
title_fullStr Visceral Adiposity and Severe COVID-19 Disease: Application of an Artificial Intelligence Algorithm to Improve Clinical Risk Prediction
title_full_unstemmed Visceral Adiposity and Severe COVID-19 Disease: Application of an Artificial Intelligence Algorithm to Improve Clinical Risk Prediction
title_short Visceral Adiposity and Severe COVID-19 Disease: Application of an Artificial Intelligence Algorithm to Improve Clinical Risk Prediction
title_sort visceral adiposity and severe covid-19 disease: application of an artificial intelligence algorithm to improve clinical risk prediction
topic Major Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8244656/
https://www.ncbi.nlm.nih.gov/pubmed/34258315
http://dx.doi.org/10.1093/ofid/ofab275
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