Ahmed Hasanin

BACKGROUND: We aimed to evaluate the ability of diaphragmatic excursion at hospital admission to predict outcomes in patients with coronavirus disease-2019 (COVID-19).

METHODS: In this prospective observational study, we included adult patients with severe COVID-19 admitted to a tertiary hospital. Ultrasound examination of the diaphragm was performed within 12 h of admission. Other collected data included peripheral oxygen saturation (SpO), respiratory rate, and computed tomography (CT) score. The outcomes included the ability of diaphragmatic excursion, respiratory rate, SpO, and CT score at admission to predict the need for ventilatory support (need for non-invasive or invasive ventilation) and patient mortality using the area under the receiver operating characteristic curve (AUC) analysis. Univariate and multivariable analyses about the need for ventilatory support and mortality were performed.

RESULTS: Diaphragmatic excursion showed an excellent ability to predict the need for ventilatory support, which was the highest among respiratory rate, SpO, and CT score; the AUCs (95% confidence interval [CI]) was 0.96 (0.85-1.00) for the right diaphragmatic excursion and 0.94 (0.82-0.99) for the left diaphragmatic excursion. The right diaphragmatic excursion also had the highest AUC for predicting mortality in relation to respiratory rate, SpO, and CT score. Multivariable analysis revealed that low diaphragmatic excursion was an independent predictor of mortality with an odds ratio (95% CI) of 0.55 (0.31-0.98).

CONCLUSION: Diaphragmatic excursion on hospital admission can accurately predict the need for ventilatory support and mortality in patients with severe COVID-19. Low diaphragmatic excursion was an independent risk factor for in-hospital mortality.

OBJECTIVES: Accurate estimation of a critically ill patient's caloric requirements is essential for a proper nutritional plan. This study aimed to evaluate the use of point-of-care ultrasound (US) to predict the resting energy expenditure (REE) in critically ill patients.

METHODS: In 69 critically ill patients, we measured the REE using indirect calorimetry (REE_IC), muscle layer thicknesses (MLTs), and cardiac output (CO). Muscle thickness was measured at the biceps and the quadriceps muscles. Patients were randomly split into a model development group (n = 46) and a cross-validation group (n = 23). In the model development group, a multiple regression analysis was applied to generate REE using US (REE_US) values. In the cross-validation group, REE was calculated by the REE_US and the resting energy expenditure using the Harris-Benedict equation (REE_HB), and both were compared to the REE_IC.

RESULTS: In the model development group, the REE_US was predicted by the following formula: predicted REE_US (kcal/d) = 206 + 173.5 × CO (L/min) + 137 × MLT (cm) - 230 × (women = 1; men = 0) (R  = 0.8; P < .0001). In the cross-validated group, the REE_IC and REE_US values were comparable (mean difference, -66 [-3.3%] kcal/d; P = .14). However, the difference between the mean REE_IC and the mean REE_HB was 455.8 (26%) kcal/d (P < .001). According to a Bland-Altman analysis, the REE_US agreed well with the REE_IC, whereas the REE_HB did not.

CONCLUSIONS: Resting energy expenditure could be estimated from US measurements of MLTs and CO. Our point-of-care US model explains 80% of the change in the REE in critically ill patients.