The objective of this study was to develop a new predictive equation of resting energy expenditure (REE) for acute kidney injury patients (AKI) on dialysis. A cross-sectional descriptive study was carried out of 114 AKI patients, consecutively selected, on dialysis and mechanical ventilation, aged between 19 and 95 years. For construction of the predictive model, 80% of cases were randomly separated to training and 20% of unused cases to validation. Several machine learning models were tested in the training data: linear regression with stepwise, rpart, support vector machine with radial kernel, generalised boosting machine and random forest. The models were selected by ten-fold cross-validation and the performances evaluated by the root mean square error. There were 364 indirect calorimetry measurements in 114 patients, mean age of 60.65 ± 16.9 years and 68.4% were males. The average REE was 2081 ± 645 kcal. REE was positively correlated with C-reactive protein, minute volume (MV), expiratory positive airway pressure, serum urea, body mass index and inversely with age. The principal variables included in the selected model were age, body mass index, use of vasopressors, expiratory positive airway pressure, MV, C-reactive protein, temperature and serum urea. The final r-value in the validation set was 0.69. We propose a new predictive equation for estimating the REE of AKI patients on dialysis that uses a non-linear approach with better performance than actual models.

中文翻译：

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关于估计急性肾损伤患者透析时静息能量消耗的新方程式的建议：一种机器学习方法

这项研究的目的是为透析中的急性肾损伤患者（AKI）建立一个新的静息能量消耗（REE）预测方程。对连续选择的114例AKI患者进行了横断面描述性研究，年龄在19至95岁之间，涉及透析和机械通气。为了构建预测模型，将80％的病例随机分开进行训练，将20％的未使用病例进行验证。在训练数据中测试了几种机器学习模型：逐步回归线性回归，rpart，具有径向核的支持向量机，广义提升机和随机森林。通过十倍交叉验证选择模型，并通过均方根误差评估性能。在114位患者中进行了364次间接量热法测量，平均年龄为60.65±16。男性为9岁，占68.4％。REE平均为2081±645 kcal。REE与C反应蛋白，分钟体积（MV），呼气气道正压，血清尿素，体重指数呈正相关，与年龄呈负相关。所选模型中包括的主要变量是年龄，体重指数，使用血管加压药，呼气气道正压，MV，C反应蛋白，温度和血清尿素。验证集中的最终r值为0.69。我们提出了一种新的预测方程式，用于估计AKI患者透析的REE，该方程式使用的非线性方法比实际模型具有更好的性能。体重指数与年龄成反比。所选模型中包括的主要变量是年龄，体重指数，使用血管加压药，呼气气道正压，MV，C反应蛋白，温度和血清尿素。验证集中的最终r值为0.69。我们提出了一种新的预测方程式，用于估计AKI患者透析的REE，该方程式使用的非线性方法比实际模型具有更好的性能。体重指数与年龄成反比。所选模型中包括的主要变量是年龄，体重指数，使用血管加压药，呼气气道正压，MV，C反应蛋白，温度和血清尿素。验证集中的最终r值为0.69。我们提出了一种新的预测方程式，用于估计AKI患者透析的REE，该方程式使用的非线性方法比实际模型具有更好的性能。