González-Madroño A, Mancha A, Rodríguez F J, Culebras J, de Ulibarri J I
Sección de Nutrición Clínica y Dietética, Hospital Universitario de La Princesa, Madrid, España.
Nutr Hosp. 2012 Mar-Apr;27(2):564-71. doi: 10.1590/S0212-16112012000200033.
To ratify previous validations of the CONUT nutritional screening tool by the development of two probabilistic models using the parameters included in the CONUT, to see if the CONUT´s effectiveness could be improved.
It is a two step prospective study. In Step 1, 101 patients were randomly selected, and SGA and CONUT was made. With data obtained an unconditional logistic regression model was developed, and two variants of CONUT were constructed: Model 1 was made by a method of logistic regression. Model 2 was made by dividing the probabilities of undernutrition obtained in model 1 in seven regular intervals. In step 2, 60 patients were selected and underwent the SGA, the original CONUT and the new models developed. The diagnostic efficacy of the original CONUT and the new models was tested by means of ROC curves. Both samples 1 and 2 were put together to measure the agreement degree between the original CONUT and SGA, and diagnostic efficacy parameters were calculated.
No statistically significant differences were found between sample 1 and 2, regarding age, sex and medical/surgical distribution and undernutrition rates were similar (over 40%). The AUC for the ROC curves were 0.862 for the original CONUT, and 0.839 and 0.874, for model 1 and 2 respectively. The kappa index for the CONUT and SGA was 0.680.
The CONUT, with the original scores assigned by the authors is equally good than mathematical models and thus is a valuable tool, highly useful and efficient for the purpose of Clinical Undernutrition screening.
通过利用营养风险与营养不足筛查工具（CONUT）中包含的参数开发两个概率模型，来验证该工具先前的有效性，以探讨是否可以提高CONUT的有效性。
这是一项分两步进行的前瞻性研究。在第一步中，随机选择101例患者，进行主观全面评定法（SGA）和CONUT评估。利用获得的数据建立无条件逻辑回归模型，并构建CONUT的两个变体：模型1采用逻辑回归方法构建。模型2通过将模型1中获得的营养不良概率划分为七个等距区间构建。在第二步中，选择60例患者进行SGA、原始CONUT及新开发模型的评估。通过ROC曲线检验原始CONUT和新模型的诊断效能。将样本1和样本2合并，以测量原始CONUT与SGA之间的一致性程度，并计算诊断效能参数。
在样本1和样本2之间，关于年龄、性别、内科/外科分布未发现统计学上的显著差异，且营养不良率相似（超过40%）。原始CONUT的ROC曲线下面积（AUC）为0.862，模型1和模型2的AUC分别为0.839和0.874。CONUT与SGA的kappa指数为0.680。
作者最初设定评分的CONUT与数学模型同样出色，因此是一种有价值的工具，在临床营养不良筛查方面非常有用且高效。
