Department of Anesthesia and Critical Care Medicine, Cairo University, Cairo, Egypt.
Department of Radiology, Ain Shams University, Cairo, Egypt.
J Ultrasound Med. 2021 Aug;40(8):1581-1589. doi: 10.1002/jum.15538. Epub 2020 Oct 21.
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.
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.
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.
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.
准确估计危重症患者的热量需求对于制定适当的营养计划至关重要。本研究旨在评估使用即时床旁超声（US）预测危重症患者静息能量消耗（REE）的价值。
我们使用间接热量测定法（REE_IC）、肌肉层厚度（MLTs）和心输出量（CO）测量了 69 名危重症患者的 REE。在肱二头肌和股四头肌测量肌肉厚度。患者被随机分为模型建立组（n=46）和交叉验证组（n=23）。在模型建立组中，我们使用多元回归分析生成基于 US 的 REE 值（REE_US）。在交叉验证组中，通过 REE_US 和基于 Harris-Benedict 方程的 REE（REE_HB）计算 REE，并将两者与 REE_IC 进行比较。
在模型建立组中，REE_US 由以下公式预测：预测的 REE_US（千卡/天）=206+173.5×CO（升/分钟）+137×MLT（厘米）-230×（女性=1；男性=0）（R ²=0.8；P<0.0001）。在交叉验证组中，REE_IC 和 REE_US 值相当（平均差值，-66[-3.3%]千卡/天；P=0.14）。然而，REE_IC 的平均值与 REE_HB 的平均值相差 455.8（26%）千卡/天（P<0.001）。根据 Bland-Altman 分析，REE_US 与 REE_IC 一致性良好，而 REE_HB 则不然。
可以通过 US 测量的 MLTs 和 CO 来估计 REE。我们的即时床旁 US 模型可以解释危重症患者 REE 变化的 80%。
