Skip to main content
Top
Published in: Critical Care 1/2020

Open Access 01-12-2020 | Obesity | Research

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

Authors: Alexander Decruyenaere, Johan Steen, Kirsten Colpaert, Dominique D. Benoit, Johan Decruyenaere, Stijn Vansteelandt

Published in: Critical Care | Issue 1/2020

Login to get access

Abstract

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/m2. 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.
Appendix
Available only for authorised users
Literature
1.
go back to reference World Health Organization. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000;894(i-xii):1–253. World Health Organization. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000;894(i-xii):1–253.
2.
go back to reference NCD Risk Factor Collaboration (NCD-RisC). Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19.2 million participants. Lancet. 2016;387(10026):1377–96. NCD Risk Factor Collaboration (NCD-RisC). Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19.2 million participants. Lancet. 2016;387(10026):1377–96.
3.
go back to reference The GBD 2015 Obesity Collaborators. Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med. 2017;377(1):13–27. The GBD 2015 Obesity Collaborators. Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med. 2017;377(1):13–27.
4.
go back to reference Schetz M, De Jong A, Deane AM, Druml W, Hemelaar P, Pelosi P, et al. Obesity in the critically ill: a narrative review. Intensive Care Med. 2019;45(6):757–69.PubMed Schetz M, De Jong A, Deane AM, Druml W, Hemelaar P, Pelosi P, et al. Obesity in the critically ill: a narrative review. Intensive Care Med. 2019;45(6):757–69.PubMed
5.
go back to reference Sakr Y, Alhussami I, Nanchal R, Wunderink RG, Pellis T, Wittebole X, et al. Being overweight is associated with greater survival in ICU patients: results from the intensive care over nations audit. Crit Care Med. 2015;43(12):2623–32.PubMed Sakr Y, Alhussami I, Nanchal R, Wunderink RG, Pellis T, Wittebole X, et al. Being overweight is associated with greater survival in ICU patients: results from the intensive care over nations audit. Crit Care Med. 2015;43(12):2623–32.PubMed
6.
go back to reference Hutagalung R, Marques J, Kobylka K, Zeidan M, Kabisch B, Brunkhorst F, et al. The obesity paradox in surgical intensive care unit patients. Intensive Care Med. 2011;37(11):1793–9.PubMed Hutagalung R, Marques J, Kobylka K, Zeidan M, Kabisch B, Brunkhorst F, et al. The obesity paradox in surgical intensive care unit patients. Intensive Care Med. 2011;37(11):1793–9.PubMed
7.
go back to reference Pepper DJ, Demirkale CY, Sun J, Rhee C, Fram D, Eichacker P, et al. Does obesity protect against death in sepsis? A retrospective cohort study of 55,038 adult patients. Crit Care Med. 2019;47(5):643–50.PubMedPubMedCentral Pepper DJ, Demirkale CY, Sun J, Rhee C, Fram D, Eichacker P, et al. Does obesity protect against death in sepsis? A retrospective cohort study of 55,038 adult patients. Crit Care Med. 2019;47(5):643–50.PubMedPubMedCentral
8.
go back to reference Pepper DJ, Sun J, Welsh J, Cui X, Suffredini AF, Eichacker PQ. Increased body mass index and adjusted mortality in ICU patients with sepsis or septic shock: a systematic review and meta-analysis. Crit Care. 2016;20(1):181.PubMedPubMedCentral Pepper DJ, Sun J, Welsh J, Cui X, Suffredini AF, Eichacker PQ. Increased body mass index and adjusted mortality in ICU patients with sepsis or septic shock: a systematic review and meta-analysis. Crit Care. 2016;20(1):181.PubMedPubMedCentral
9.
go back to reference Pickkers P, de Keizer N, Dusseljee J, Weerheijm D, van der Hoeven JG, Peek N. Body mass index is associated with hospital mortality in critically ill patients: an observational cohort study. Crit Care Med. 2013;41(8):1878–83.PubMed Pickkers P, de Keizer N, Dusseljee J, Weerheijm D, van der Hoeven JG, Peek N. Body mass index is associated with hospital mortality in critically ill patients: an observational cohort study. Crit Care Med. 2013;41(8):1878–83.PubMed
10.
go back to reference Zhao Y, Li Z, Yang T, Wang M, Xi X. Is body mass index associated with outcomes of mechanically ventilated adult patients in intensive critical units? A systematic review and meta-analysis. PLoS One. 2018;13(6):e0198669.PubMedPubMedCentral Zhao Y, Li Z, Yang T, Wang M, Xi X. Is body mass index associated with outcomes of mechanically ventilated adult patients in intensive critical units? A systematic review and meta-analysis. PLoS One. 2018;13(6):e0198669.PubMedPubMedCentral
11.
go back to reference Zhi G, Xin W, Ying W, Guohong X, Shuying L. "Obesity paradox" in acute respiratory distress syndrome: asystematic review and meta-analysis. PLoS One. 2016;11(9):e0163677.PubMedPubMedCentral Zhi G, Xin W, Ying W, Guohong X, Shuying L. "Obesity paradox" in acute respiratory distress syndrome: asystematic review and meta-analysis. PLoS One. 2016;11(9):e0163677.PubMedPubMedCentral
12.
go back to reference Ball L, Serpa Neto A, Pelosi P. Obesity and survival in critically ill patients with acute respiratory distress syndrome: a paradox within the paradox. Crit Care. 2017;21(1):114.PubMedPubMedCentral Ball L, Serpa Neto A, Pelosi P. Obesity and survival in critically ill patients with acute respiratory distress syndrome: a paradox within the paradox. Crit Care. 2017;21(1):114.PubMedPubMedCentral
13.
go back to reference Patel JJ, Rosenthal MD, Miller KR, Codner P, Kiraly L, Martindale RG. The critical care obesity paradox and implications for nutrition support. Curr Gastroenterol Rep. 2016;18(9):45.PubMed Patel JJ, Rosenthal MD, Miller KR, Codner P, Kiraly L, Martindale RG. The critical care obesity paradox and implications for nutrition support. Curr Gastroenterol Rep. 2016;18(9):45.PubMed
14.
go back to reference Aune D, Sen A, Prasad M, Norat T, Janszky I, Tonstad S, et al. BMI and all cause mortality: systematic review and non-linear dose-response meta-analysis of 230 cohort studies with 3.74 million deaths among 30.3 million participants. BMJ. 2016;353:i2156.PubMedPubMedCentral Aune D, Sen A, Prasad M, Norat T, Janszky I, Tonstad S, et al. BMI and all cause mortality: systematic review and non-linear dose-response meta-analysis of 230 cohort studies with 3.74 million deaths among 30.3 million participants. BMJ. 2016;353:i2156.PubMedPubMedCentral
15.
go back to reference Hernan MA, Robins JM. Causal inference: what if. Boca Raton: Chapman & Hall/CRC; 2020. Hernan MA, Robins JM. Causal inference: what if. Boca Raton: Chapman & Hall/CRC; 2020.
16.
go back to reference Banack HR, Kaufman JS. Estimating the time-varying joint effects of obesity and smoking on all-cause mortality using marginal structural models. Am J Epidemiol. 2016;183(2):122–9.PubMed Banack HR, Kaufman JS. Estimating the time-varying joint effects of obesity and smoking on all-cause mortality using marginal structural models. Am J Epidemiol. 2016;183(2):122–9.PubMed
17.
go back to reference Banack HR, Stokes A. The 'obesity paradox' may not be a paradox at all. Int J Obes. 2017;41(8):1162–3. Banack HR, Stokes A. The 'obesity paradox' may not be a paradox at all. Int J Obes. 2017;41(8):1162–3.
18.
go back to reference Emerson P, Brooks D, Quasim T, Puxty A, Kinsella J, Lowe DJ. Factors influencing intensive care admission: a mixed methods study of EM and ICU. Eur J Emerg Med. 2017;24(1):29–35.PubMed Emerson P, Brooks D, Quasim T, Puxty A, Kinsella J, Lowe DJ. Factors influencing intensive care admission: a mixed methods study of EM and ICU. Eur J Emerg Med. 2017;24(1):29–35.PubMed
19.
go back to reference Smith G, Nielsen M. ABC of intensive care. Criteria for admission. BMJ (Clinical research ed). 1999;318(7197):1544–7. Smith G, Nielsen M. ABC of intensive care. Criteria for admission. BMJ (Clinical research ed). 1999;318(7197):1544–7.
20.
go back to reference Banack HR, Kaufman JS. Does selection bias explain the obesity paradox among individuals with cardiovascular disease? Ann Epidemiol. 2015;25(5):342–9.PubMed Banack HR, Kaufman JS. Does selection bias explain the obesity paradox among individuals with cardiovascular disease? Ann Epidemiol. 2015;25(5):342–9.PubMed
21.
go back to reference Toft-Petersen AP, Wulff J, Harrison DA, Ostermann M, Margarson M, Rowan KM, et al. Exploring the impact of using measured or estimated values for height and weight on the relationship between BMI and acute hospital mortality. J Crit Care. 2018;44:196–202.PubMed Toft-Petersen AP, Wulff J, Harrison DA, Ostermann M, Margarson M, Rowan KM, et al. Exploring the impact of using measured or estimated values for height and weight on the relationship between BMI and acute hospital mortality. J Crit Care. 2018;44:196–202.PubMed
22.
go back to reference Wacharasint P, Boyd JH, Russell JA, Walley KR. One size does not fit all in severe infection: obesity alters outcome, susceptibility, treatment, and inflammatory response. Crit Care. 2013;17(3):R122.PubMedPubMedCentral Wacharasint P, Boyd JH, Russell JA, Walley KR. One size does not fit all in severe infection: obesity alters outcome, susceptibility, treatment, and inflammatory response. Crit Care. 2013;17(3):R122.PubMedPubMedCentral
23.
go back to reference Selim BJ, Ramar K, Surani S. Obesity in the intensive care unit: risks and complications. Hosp Pract (1995). 2016;44(3):146–56. Selim BJ, Ramar K, Surani S. Obesity in the intensive care unit: risks and complications. Hosp Pract (1995). 2016;44(3):146–56.
24.
go back to reference Lederer DJ, Bell SC, Branson RD, Chalmers JD, Marshall R, Maslove DM, et al. Control of confounding and reporting of results in causal inference studies. Guidance for authors from editors of respiratory, sleep, and critical care journals. Ann Am Thorac Soc. 2019;16(1):22–8.PubMed Lederer DJ, Bell SC, Branson RD, Chalmers JD, Marshall R, Maslove DM, et al. Control of confounding and reporting of results in causal inference studies. Guidance for authors from editors of respiratory, sleep, and critical care journals. Ann Am Thorac Soc. 2019;16(1):22–8.PubMed
25.
go back to reference National Institutes of Health. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults - the evidence report. Obes Res. 1998;6(Suppl 2):51S–209S. National Institutes of Health. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults - the evidence report. Obes Res. 1998;6(Suppl 2):51S–209S.
27.
go back to reference Schuler MS, Rose S. Targeted maximum likelihood estimation for causal inference in observational studies. Am J Epidemiol. 2017;185(1):65–73.PubMed Schuler MS, Rose S. Targeted maximum likelihood estimation for causal inference in observational studies. Am J Epidemiol. 2017;185(1):65–73.PubMed
28.
go back to reference van der Laan MJ, Rubin D. Targeted maximum likelihood learning. Int J Biostat. 2006;2(1):Art 11. van der Laan MJ, Rubin D. Targeted maximum likelihood learning. Int J Biostat. 2006;2(1):Art 11.
29.
go back to reference van der Laan M, Rose S. Targeted learning in data science: causal inference for complex longitudinal studies. Cham: Springer International Publishing; 2018. van der Laan M, Rose S. Targeted learning in data science: causal inference for complex longitudinal studies. Cham: Springer International Publishing; 2018.
30.
go back to reference van der Laan M, Gruber S. One-step targeted minimum loss-based estimation based on universal least favorable one-dimensional submodels. Int J Biostat. 2016;12(1):351–78.PubMedPubMedCentral van der Laan M, Gruber S. One-step targeted minimum loss-based estimation based on universal least favorable one-dimensional submodels. Int J Biostat. 2016;12(1):351–78.PubMedPubMedCentral
31.
go back to reference Lendle SD, Subbaraman MS, van der Laan MJ. Identification and efficient estimation of the natural direct effect among the untreated. Biometrics. 2013;69(2):310–7.PubMedPubMedCentral Lendle SD, Subbaraman MS, van der Laan MJ. Identification and efficient estimation of the natural direct effect among the untreated. Biometrics. 2013;69(2):310–7.PubMedPubMedCentral
32.
go back to reference LeDell E, van der Laan MJ, Petersen M. AUC-maximizing ensembles through metalearning. Int J Biostat. 2016;12(1):203–18.PubMedPubMedCentral LeDell E, van der Laan MJ, Petersen M. AUC-maximizing ensembles through metalearning. Int J Biostat. 2016;12(1):203–18.PubMedPubMedCentral
33.
go back to reference van der Laan MJ, Polley EC, Hubbard AE, et al. Stat Appl Genet Mol Biol. 2007;6:Article25.PubMed van der Laan MJ, Polley EC, Hubbard AE, et al. Stat Appl Genet Mol Biol. 2007;6:Article25.PubMed
34.
go back to reference van Buuren S, Groothuis-Oudshoorn K. mice: multivariate imputation by chained equations in R. J Statistical Software. 2011;45(3):67. van Buuren S, Groothuis-Oudshoorn K. mice: multivariate imputation by chained equations in R. J Statistical Software. 2011;45(3):67.
35.
go back to reference Rubin D. Multiple imputation for nonresponse in surveys. Hoboken: Wiley; 1987. Rubin D. Multiple imputation for nonresponse in surveys. Hoboken: Wiley; 1987.
37.
go back to reference Vansteelandt S. Asking too much of epidemiologic studies: the problem of collider bias and the obesity paradox. Epidemiology (Cambridge, Mass). 2017;28(5):e47–e9. Vansteelandt S. Asking too much of epidemiologic studies: the problem of collider bias and the obesity paradox. Epidemiology (Cambridge, Mass). 2017;28(5):e47–e9.
Metadata
Title
The obesity paradox in critically ill patients: a causal learning approach to a casual finding
Authors
Alexander Decruyenaere
Johan Steen
Kirsten Colpaert
Dominique D. Benoit
Johan Decruyenaere
Stijn Vansteelandt
Publication date
01-12-2020
Publisher
BioMed Central
Keywords
Obesity
Obesity
Published in
Critical Care / Issue 1/2020
Electronic ISSN: 1364-8535
DOI
https://doi.org/10.1186/s13054-020-03199-5

Other articles of this Issue 1/2020

Critical Care 1/2020 Go to the issue