Alcorta M Duque, Alvarez P Chanca, Cabetas R Nuñez, Martín Mj Alcaide, Valero M, Candela C Gómez
Department of Laboratory Medicine. La Paz University Hospital, Madrid, Spain.
Department of Laboratory Medicine. La Paz University Hospital, Madrid, Spain.
Clin Nutr ESPEN. 2018 Jun;25:110-113. doi: 10.1016/j.clnesp.2018.03.124. Epub 2018 Apr 13.
The global health community has recognized the role of food and nutrition in health maintenance and disease prevention. Undernutrition is an important problem in clinical circles but it is still not highly considered by specialists. It is well known the consequences of undernutrition on the immunological systems. Furthermore, the main consequences are an increase of morbidity-mortality rates, postoperative complications, length of stay and number of hospital early readmissions. These are all reasons to lead to increase health-care financial costs. The total assistance quality could be improved by the arrangement of an automatic detection system of undernutrition. In our hospital, we use the screening tool "CONtrolling NUTritional status" (CONUT). To measure albumin, the laboratory could use bromocresol green (BCG) and bromocresol purple (BCP) method. The aim of this study is to evaluate the CONNUT tool to classify patients using two different albumin methods to measure.
The albumin and cholesterol performed in Advia 2400 analyzer using bromocresol green and purple methods to measure albumin. The total lymphocytes performed in Advia 2120. We calculate CONNUT index and classify the patients based on nutritional status. When we classified our patients based on nutritional status (CONNUT), 28% were misclassified, almost in moderate and severe groups. This is very important because this tool generates a multidisciplinary action to the patient. Therefore, in the Clinical Laboratory we have to know the methods we use, the validity of these methods in future tools/index and the management and outcome of the patients.
全球卫生界已经认识到食物和营养在维持健康及预防疾病方面的作用。营养不良在临床领域是一个重要问题,但仍未得到专家的高度重视。营养不良对免疫系统的影响是众所周知的。此外,其主要后果是发病率和死亡率上升、术后并发症、住院时间延长以及医院早期再入院次数增加。这些都是导致医疗保健财务成本增加的原因。通过安排营养不良自动检测系统可以提高整体医疗援助质量。在我们医院,我们使用“控制营养状况”(CONUT)筛查工具。为了检测白蛋白,实验室可以使用溴甲酚绿(BCG)和溴甲酚紫(BCP)方法。本研究的目的是评估CONNUT工具在使用两种不同白蛋白检测方法对患者进行分类时的效果。
在Advia 2400分析仪上使用溴甲酚绿和溴甲酚紫方法检测白蛋白和胆固醇。在Advia 2120上检测总淋巴细胞。我们计算CONNUT指数并根据营养状况对患者进行分类。当我们根据营养状况(CONNUT)对患者进行分类时,有28%的患者被错误分类,几乎都在中度和重度组。这非常重要,因为该工具会对患者产生多学科行动。因此,在临床实验室中,我们必须了解我们使用的方法、这些方法在未来工具/指数中的有效性以及患者的管理和预后情况。