Ramos-Acevedo Samuel, Rodríguez-Gómez Luis, López-Cisneros Sonia, González-Ortiz Ailema, Espinosa-Cuevas Ángeles
Department of Nephrology and Mineral Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.
Programa de Maestría y Doctorado en Ciencias Médicas y Odontológicas y de la Salud, Universidad Nacional Autónoma de México, Mexico City, Mexico.
Front Nutr. 2022 May 18;9:881719. doi: 10.3389/fnut.2022.881719. eCollection 2022.
Estimating energy requirements (ER) is crucial for nutritional attention to chronic kidney disease (CKD) patients. Current guidelines recommend measuring ER with indirect calorimetry (IC) when possible. Due to clinical settings, the use of simple formulas is preferred. Few studies have modeled equations for estimating ER for CKD. Nevertheless, variables of interest such as nutritional status and strength have not been explored in these models. This study aimed to develop and validate a model for estimating REE in patients with CKD stages 3-5, who were not receiving renal replacement therapy (RTT), using clinical variables and comparing it with indirect calorimetry as the gold standard.
In this study 80 patients with CKD participated. Indirect calorimetry (IC) was performed in all patients. The calorimeter analyzed metabolic measurements every minute for 15 min after autocalibration with barometric pressure, temperature, and humidity. Bioelectrical Impedance Analysis (BIA) was performed. Fat-free mass (FFM) was registered among other bioelectrical components. Handgrip strength (HGS) was evaluated and an average of 3 repetitions was recorded. Nutritional status was assessed with the subjective global assessment (SGA). Patients categorized as B or C were then considered as having malnutrition.
We analyzed 71 patients and 3 models were generated. Model 1a included FFM; Model 2a included weight; Model 3c included handgrip strength (HGS). All other variables were stepwise, computer-selected with a < 0.01 significance level; Malnutrition was consistently associated with ER among other clinical variables in all models ( < 0.05). The model that included BIA-FFM had = 0.46, while the model that included weight (Kg) had an adjusted = 0.44. The models had moderate concordance, LC = 0.60-0.65 with the gold standard, whereas other energy expenditure estimation equations had LC = 0.36 and 0.55 with indirect calorimetry. Using these previously validated equations as a reference, our models had concordance values ranging from 0.66 to 0.80 with them.
Models incorporating nutritional status and other clinical variables such as weight, FFM, comorbidities, gender, and age have a moderate agreement with REE. The agreement between our models and others previously validated for the CKD patient is good; however, the agreement between the latter and IC measurements is moderate. The KDOQI lowest recommendation (25 Kcals/kg body weight) considering the 22% difference with respect to the IC for total energy expenditure rather than for REE.
估算能量需求（ER）对于关注慢性肾脏病（CKD）患者的营养状况至关重要。当前指南建议尽可能采用间接测热法（IC）来测量能量需求。由于临床实际情况，更倾向于使用简单公式。很少有研究为估算CKD患者的能量需求建立模型。然而，这些模型尚未探索诸如营养状况和力量等相关变量。本研究旨在利用临床变量开发并验证一个用于估算3 - 5期未接受肾脏替代治疗（RTT）的CKD患者静息能量消耗（REE）的模型，并将其与作为金标准的间接测热法进行比较。
本研究纳入了80例CKD患者。所有患者均进行了间接测热法（IC）检测。在根据气压、温度和湿度进行自动校准后，热量计每分钟分析代谢测量值，持续15分钟。进行了生物电阻抗分析（BIA）。记录了无脂肪量（FFM）以及其他生物电成分。评估了握力（HGS），并记录3次重复测量的平均值。采用主观全面评定法（SGA）评估营养状况。被归类为B或C级的患者被视为存在营养不良。
我们分析了71例患者并生成了3个模型。模型1a纳入了FFM；模型2a纳入了体重；模型3c纳入了握力（HGS）。所有其他变量均采用逐步法、在显著性水平α < 0.01时由计算机选择；在所有模型中，营养不良在其他临床变量中始终与能量需求相关（P < 0.05）。纳入BIA - FFM的模型r² = 0.46，而纳入体重（kg）的模型调整后r² = 0.44。这些模型与金标准具有中等一致性，一致性系数（LC） = 0.60 - 0.65，而其他能量消耗估算方程与间接测热法的一致性系数（LC）分别为0.36和0.55。以这些先前验证的方程为参考，我们的模型与它们的一致性值范围为0.66至0.80。
纳入营养状况以及体重、FFM、合并症、性别和年龄等其他临床变量的模型与静息能量消耗具有中等程度的一致性。我们的模型与先前为CKD患者验证的其他模型之间一致性良好；然而，后者与IC测量值之间的一致性为中等。考虑到与IC相比总能量消耗存在22%的差异，而不是针对静息能量消耗，肾脏疾病改善全球预后（KDOQI）的最低推荐值为25千卡/千克体重。
