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The obesity paradox in critically ill patients: a causal learning approach to a casual finding

BACKGROUND: While obesity confers an increased risk of death in the general population, numerous studies have reported an association between obesity and improved survival among critically ill patients. This contrary finding has been referred to as the obesity paradox. In this retrospective study, t...

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Autores principales: Decruyenaere, Alexander, Steen, Johan, Colpaert, Kirsten, Benoit, Dominique D., Decruyenaere, Johan, Vansteelandt, Stijn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7405433/
https://www.ncbi.nlm.nih.gov/pubmed/32758295
http://dx.doi.org/10.1186/s13054-020-03199-5
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author Decruyenaere, Alexander
Steen, Johan
Colpaert, Kirsten
Benoit, Dominique D.
Decruyenaere, Johan
Vansteelandt, Stijn
author_facet Decruyenaere, Alexander
Steen, Johan
Colpaert, Kirsten
Benoit, Dominique D.
Decruyenaere, Johan
Vansteelandt, Stijn
author_sort Decruyenaere, Alexander
collection PubMed
description BACKGROUND: While obesity confers an increased risk of death in the general population, numerous studies have reported an association between obesity and improved survival among critically ill patients. This contrary finding has been referred to as the obesity paradox. In this retrospective study, two causal inference approaches were used to address whether the survival of non-obese critically ill patients would have been improved if they had been obese. METHODS: The study cohort comprised 6557 adult critically ill patients hospitalized at the Intensive Care Unit of the Ghent University Hospital between 2015 and 2017. Obesity was defined as a body mass index of ≥ 30 kg/m(2). Two causal inference approaches were used to estimate the average effect of obesity in the non-obese (AON): a traditional approach that used regression adjustment for confounding and that assumed missingness completely at random and a robust approach that used machine learning within the targeted maximum likelihood estimation framework along with multiple imputation of missing values under the assumption of missingness at random. 1754 (26.8%) patients were discarded in the traditional approach because of at least one missing value for obesity status or confounders. RESULTS: Obesity was present in 18.9% of patients. The in-hospital mortality was 14.6% in non-obese patients and 13.5% in obese patients. The raw marginal risk difference for in-hospital mortality between obese and non-obese patients was − 1.06% (95% confidence interval (CI) − 3.23 to 1.11%, P = 0.337). The traditional approach resulted in an AON of − 2.48% (95% CI − 4.80 to − 0.15%, P = 0.037), whereas the robust approach yielded an AON of − 0.59% (95% CI − 2.77 to 1.60%, P = 0.599). CONCLUSIONS: A causal inference approach that is robust to residual confounding bias due to model misspecification and selection bias due to missing (at random) data mitigates the obesity paradox observed in critically ill patients, whereas a traditional approach results in even more paradoxical findings. The robust approach does not provide evidence that the survival of non-obese critically ill patients would have been improved if they had been obese.
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spelling pubmed-74054332020-08-07 The obesity paradox in critically ill patients: a causal learning approach to a casual finding Decruyenaere, Alexander Steen, Johan Colpaert, Kirsten Benoit, Dominique D. Decruyenaere, Johan Vansteelandt, Stijn Crit Care Research BACKGROUND: While obesity confers an increased risk of death in the general population, numerous studies have reported an association between obesity and improved survival among critically ill patients. This contrary finding has been referred to as the obesity paradox. In this retrospective study, two causal inference approaches were used to address whether the survival of non-obese critically ill patients would have been improved if they had been obese. METHODS: The study cohort comprised 6557 adult critically ill patients hospitalized at the Intensive Care Unit of the Ghent University Hospital between 2015 and 2017. Obesity was defined as a body mass index of ≥ 30 kg/m(2). Two causal inference approaches were used to estimate the average effect of obesity in the non-obese (AON): a traditional approach that used regression adjustment for confounding and that assumed missingness completely at random and a robust approach that used machine learning within the targeted maximum likelihood estimation framework along with multiple imputation of missing values under the assumption of missingness at random. 1754 (26.8%) patients were discarded in the traditional approach because of at least one missing value for obesity status or confounders. RESULTS: Obesity was present in 18.9% of patients. The in-hospital mortality was 14.6% in non-obese patients and 13.5% in obese patients. The raw marginal risk difference for in-hospital mortality between obese and non-obese patients was − 1.06% (95% confidence interval (CI) − 3.23 to 1.11%, P = 0.337). The traditional approach resulted in an AON of − 2.48% (95% CI − 4.80 to − 0.15%, P = 0.037), whereas the robust approach yielded an AON of − 0.59% (95% CI − 2.77 to 1.60%, P = 0.599). CONCLUSIONS: A causal inference approach that is robust to residual confounding bias due to model misspecification and selection bias due to missing (at random) data mitigates the obesity paradox observed in critically ill patients, whereas a traditional approach results in even more paradoxical findings. The robust approach does not provide evidence that the survival of non-obese critically ill patients would have been improved if they had been obese. BioMed Central 2020-08-05 /pmc/articles/PMC7405433/ /pubmed/32758295 http://dx.doi.org/10.1186/s13054-020-03199-5 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Decruyenaere, Alexander
Steen, Johan
Colpaert, Kirsten
Benoit, Dominique D.
Decruyenaere, Johan
Vansteelandt, Stijn
The obesity paradox in critically ill patients: a causal learning approach to a casual finding
title The obesity paradox in critically ill patients: a causal learning approach to a casual finding
title_full The obesity paradox in critically ill patients: a causal learning approach to a casual finding
title_fullStr The obesity paradox in critically ill patients: a causal learning approach to a casual finding
title_full_unstemmed The obesity paradox in critically ill patients: a causal learning approach to a casual finding
title_short The obesity paradox in critically ill patients: a causal learning approach to a casual finding
title_sort obesity paradox in critically ill patients: a causal learning approach to a casual finding
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7405433/
https://www.ncbi.nlm.nih.gov/pubmed/32758295
http://dx.doi.org/10.1186/s13054-020-03199-5
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