Visceral fat area measured by electrical bioimpedance as an aggravating factor of COVID-19: a study on body composition

INTRODUCTION

Severe forms of COVID-19 are more common in patients with abnormal fat distribution, particularly high visceral adiposity. The patient's muscle strength may be reduced during the acute phase of the infection. Electrical bioimpedance (BIA) is a non-invasive method for measuring body compartments and estimating visceral fat area (VFA) that can be used at the bedside.

OBJECTIVE

To assess the association between several body composition parameters, primarily high adipose tissue and high VFA, in patients with and without a diagnosis of COVID-19 infection, and whether it worsened the severity parameters.

METHODS

This retrospective cohort study was conducted in a private hospital in the city of São Paulo from March 2020 to August 2021. The demographic and clinical data was collected from medical reports. Body composition is assessed using the InBODY® model S10 bioelectrical impedance device and a Jamar® digital hydraulic manual dynamometer with a scale from 0 to 90 kg is used to measure handgrip strength (HGS).

RESULTS

A total of 96 patients with a mean age of 69.1 years (SD 15) were divided into two groups of 48 individuals, with and without COVID-19 infection. Body mass index (odds ratio [OR]: 4.47, 95% confidence interval [CI]: 1.69, 11.83), fat mass (OR: 2.03, 95% CI: 0.48, 8.55), and VFA (OR: 1.08, 95% CI: 0.33, 3.53) were all higher in the infection group. When COVID-19 patients were evaluated, those with higher VFA had longer hospital stays (OR: 0.99, 95% CI: 0.97, 1.01) and used more vasoactive drugs (p = 0.043). Patients with COVID-19 with poor handgrip strength were 3.29 times more likely to require a prolonged intensive care unit (ICU) stay.

CONCLUSION

The study concluded that excess weight and body fat are significantly associated with COVID-19 involvement, but the severity is primarily related to a greater area of visceral fat. The use of bioimpedance for visceral fat measurement was effective, as it is a simple method performed in the hospital setting that does not require the use of radiation.

Automated healthcare-associated infection surveillance using an artificial intelligence algorithm

Healthcare-associated infections (HAIs) are among the most common adverse events in hospitals. We used artificial intelligence (AI) algorithms for infection surveillance in a cohort study. The model correctly detected 67 out of 73 patients with HAIs. The final model used a multilayer perceptron neural network achieving an area under receiver operating curve (AUROC) of 90.27%; specificity of 78.86%; sensitivity of 88.57%. Respiratory infections had the best results (AUROC ≥93.47%). The AI algorithm could identify most HAIs. AI is a feasible method for HAI surveillance, has the potential to save time, promote accurate hospital-wide surveillance, and improve infection prevention performance.