Why the study?
Studies have paradoxically linked obesity to improved survival in critically ill patients, prompting investigation into whether survival of non-obese critically ill patients would improve if they were obese.
Does obesity improve in-hospital mortality in critically ill patients?
Does obesity improve in-hospital mortality in critically ill patients?
Using robust causal inference methods that account for missing data and model misspecification mitigates the obesity paradox in critically ill patients, suggesting no true survival benefit of obesity.
May reflect confounding rather than causality; leaves open whether obesity confers survival benefit in critical illness.
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: . 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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Decruyenaere et al. (2020) studied this question.
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