The Novel Use of Point-of-Care Ultrasound to Predict Resting Energy Expenditure in Critically Ill Patients.

Citation:
Mukhtar, A., M. AbdelGhany, A. Hasanin, W. Hamimy, A. Abougabal, H. Nasser, A. Elsayed, and E. Ayman, "The Novel Use of Point-of-Care Ultrasound to Predict Resting Energy Expenditure in Critically Ill Patients.", Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine, vol. 40, issue 8, pp. 1581-1589, 2021.

Abstract:

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.

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