Obesity is a risk factor for developing critical condition in COVID‐19 patients: A systematic review and meta‐analysis

Summary The disease course of COVID‐19 varies from asymptomatic infection to critical condition leading to mortality. Identification of prognostic factors is important for prevention and early treatment. We aimed to examine whether obesity is a risk factor for the critical condition in COVID‐19 patients by performing a meta‐analysis. The review protocol was registered onto PROSPERO (CRD42020185980). A systematic search was performed in five scientific databases between 1 January and 11 May 2020. After selection, 24 retrospective cohort studies were included in the qualitative and quantitative analyses. We calculated pooled odds ratios (OR) with 95% confidence intervals (CIs) in meta‐analysis. Obesity was a significant risk factor for intensive care unit (ICU) admission in a homogenous dataset (OR = 1.21, CI: 1.002‐1.46; I2 = 0.0%) as well as for invasive mechanical ventilation (IMV) (OR = 2.05, CI: 1.16‐3.64; I2 = 34.86%) in COVID‐19. Comparing body mass index (BMI) classes with each other, we found that a higher BMI always carries a higher risk. Obesity may serve as a clinical predictor for adverse outcomes; therefore, the inclusion of BMI in prognostic scores and improvement of guidelines for the intensive care of patients with elevated BMI are highly recommended.


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Eligibility criteria 6 Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale.

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Information sources 7 Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched.

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Search 8 Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated.

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Study selection 9 State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis).

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Data collection process 10 Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators.

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Data items 11 List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made.

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Risk of bias in individual studies 12 Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis.

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Synthesis of results 14 Describe the methods of handling data and combining results of studies, if done, including measures of consistency (e.g., I 2 ) for each meta-analysis.

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Additional analyses 16 Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified.

Study selection
17 Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram.

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Study characteristics 18 For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations.

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Risk of bias within studies 19 Present data on risk of bias of each study and, if available, any outcome level assessment (see item 12).
(8/14) + Table S5 Results of individual studies 20 For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot.
6-8/14 + Figure  S1, Table  S3-4 Synthesis of results 21 Present results of each meta-analysis done, including confidence intervals and measures of consistency.

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Risk of bias across studies 22 Present results of any assessment of risk of bias across studies (see Item 15).

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Additional analysis 23 Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta-regression [see Item 16]).

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DISCUSSION Summary of evidence 24 Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policy makers).

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Limitations 25 Discuss limitations at study and outcome level (e.g., risk of bias), and at review-level (e.g., incomplete retrieval of identified research, reporting bias  The primary outcome of this study was the prevalence of patients receiving invasive mechanical ventilation (IMV) following admission to intensive care. The use of IMV was determined when oxygen therapy (≥ 10 L/min) with target spO2 (90-94%) was ineffective, and when respiratory rate was above 25/min, with signs of acute respiratory failure, despite maximal oxygen therapy.

06/04/2020
All patients were diagnosed with COVID-19 pneumonia according to World Health Organization interim guidance (11) with SARS symptoms characterized by dyspnea, increased respiratory frequency, decreased blood oxygen saturation, and need for oxygen support therapy for at least 6 L/min. Throat swab samples were obtained from all patients at admission and tested using real-time reverse transcriptasepolymerase chain reaction   Results of the modified GUIPS tool. By study participation the article was considered as carrying low risk of bias, if the diagnosis of COVID-19 was clearly stated, unclear risk of bias was given in the case of lacking description and high risk of bias was assessed if suspected or unclear cases were also involved. Study attrition was only assessed in the cases of prospective studies, where green indicates the clear description of follow-up, while yellow means a lacking description. Obesity as the only investigated prognostic factor was considered carrying low risk of bias on individual study level if BMI was assessed. A clear description of the outcomes was needed to achieve low risk of bias. In the case of confounding factors, green indicates a multivariate analysis, yellow means a lacking description and red is associated with the presence of a major confounding factor (e.g. age, gender, treatment etc.). As none of the studies has previously published protocol with statistical plan, we waived the need for the assessment of the statistical analysis reporting.