We Should Do More to Offer Evidence-Based Treatment for an Important Modifiable Risk Factor for COVID-19: Obesity

Risk of bias and certainty of evidence on the association between obesity and mortality in patients with SARS-COV-2: An umbrella review of meta-analyses

BACKGROUND & AIMS

This umbrella review of systematic reviews with meta-analysis (SR-MAs) aimed to evaluate the risk of bias and the certainty of the evidence of SR-MAs on the association between obesity and mortality in patients with SARS-CoV-2.

METHODS

We conducted a comprehensive literature search until April 22, 2022, in several databases and assessed the risk of bias of SR-MAs according to AMSTAR-2 and the certainty of evidence using the GRADE approach. The degree of overlap between meta-analyses was based on the corrected covered area (CCA) index. The results of each MA [relative risk (RR), hazard ratio (HR), or odds ratio (OR)] were extracted to evaluate the magnitude of the association between obesity and mortality.

RESULTS

A total of 24 SR-MAs were eligible, and the association between obesity and mortality was not statistically significant in eight (33.3%) of them, while the OR/HR/RR ranged from 1.14 to 3.52 in the other SR-MAs. The overlap was slight (CCA = 4.82%). The majority of SR-MAs presented critically low quality according to AMSTAR-2 (66.7%), and the certainty of the evidence for most of them (83.4%) was "very low".

CONCLUSIONS

Obesity was associated with an increased risk of death in patients with SARS-CoV-2 infection in most SR-MAs; however, a critical appraisal pointed to a high risk of bias, and the certainty of their evidence was not well graded. The dissemination of poor SR-MAs may limit the interpretation of findings, and we should always aspire to trustworthy scientific evidence.

PROSPERO

PROSPERO 2021 CRD42021253142.

A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19

OBJECTIVE

To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease 2019 (COVID-19) in the emergency department (ED).

METHODS

We developed in a 12-hospital system a model using training and validation followed by a real-time assessment. The LASSO guided feature selection included demographics, comorbidities, home medications, vital signs. We constructed a logistic regression-based ML algorithm to predict "severe" COVID-19, defined as patients requiring intensive care unit (ICU) admission, invasive mechanical ventilation, or died in or out-of-hospital. Training data included 1,469 adult patients who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within 14 days of acute care. We performed: 1) temporal validation in 414 SARS-CoV-2 positive patients, 2) validation in a PUI set of 13,271 patients with symptomatic SARS-CoV-2 test during an acute care visit, and 3) real-time validation in 2,174 ED patients with PUI test or positive SARS-CoV-2 result. Subgroup analysis was conducted across race and gender to ensure equity in performance.

RESULTS

The algorithm performed well on pre-implementation validations for predicting COVID-19 severity: 1) the temporal validation had an area under the receiver operating characteristic (AUROC) of 0.87 (95%-CI: 0.83, 0.91); 2) validation in the PUI population had an AUROC of 0.82 (95%-CI: 0.81, 0.83). The ED CDS system performed well in real-time with an AUROC of 0.85 (95%-CI, 0.83, 0.87). Zero patients in the lowest quintile developed "severe" COVID-19. Patients in the highest quintile developed "severe" COVID-19 in 33.2% of cases. The models performed without significant differences between genders and among race/ethnicities (all p-values > 0.05).

CONCLUSION

A logistic regression model-based ML-enabled CDS can be developed, validated, and implemented with high performance across multiple hospitals while being equitable and maintaining performance in real-time validation.