识别住院病人的营养不良风险:根据 GLIM 标准对五种工具进行分析。

Postgraduate medicine Pub Date : 2024-06-01 Epub Date: 2024-06-04 DOI:10.1080/00325481.2024.2363169
Sedat Arslan, Nursel Dal, Kevser Tari Selcuk, Kezban Sahin, Ramazan Mert Atan
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引用次数: 0

摘要

背景:使用适当的筛查工具及时发现住院患者的营养不良至关重要。我们研究的目的是比较与新的 GLIM 住院病人营养不良标准相关的最值得推荐的筛查工具:在这项横断面研究中,我们分析了 2022 年 8 月至 2023 年 5 月期间在 Bandırma 培训与研究医院接受住院治疗的 1397 名患者的数据,以评估和比较他们的营养不良情况。在班德尔玛培训与研究医院内科和外科诊所接受住院治疗的患者。除 GLIM 标准外,我们还使用了 NRS-2002、MST、GMS、MUST 和 SNAQ 等营养筛查和评估工具。GLIM 标准被认为是评估灵敏度和特异性的黄金标准。五种筛查工具的接收操作特征曲线(ROC)也用于评估准确区分营养不良高危患者的能力:不同筛查工具在检测营养不良方面的表现比较显示,GMS 的灵敏度最高(87.40%),而 NRS-2002 的特异性最高(91.70%)。曲线下面积(AUC)值表明,NRS-2002、MST、GMS 和 SNAQ 的预测值极佳,而 MUST 的预测值良好(p 结论:MST 与 NRS-2002 的预测值一致:我们强调了 MST 与 GLIM 标准的一致性,强调了多学科方法对早期营养不良诊断的重要性。使用适当的筛查工具可以更快、更准确地诊断出有营养不良风险的患者,并提高治疗效果。
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Identifying malnutrition risk in hospitalized patients: an analysis of five tools in the light of GLIM criteria.

Background: The prompt identification of malnutrition among hospitalized patients using the appropriate screening tool is paramount. The objective of our study is to compare the most recommended screening tools concerning the new GLIM criteria for malnutrition in hospitalized patients.

Methods: In this cross-sectional study, we analyzed the data on 1,397 patients receiving inpatient treatment at Bandırma Training and Research Hospital between August 2022 and May 2023 to assess and compare malnutrition in them. Patients who received inpatient treatment in the internal and surgical clinics of Bandırma Training and Research Hospital. In addition to the GLIM criteria, we used nutritional screening and assessment tools such as NRS-2002, MST, GMS, MUST, and SNAQ. The GLIM criteria were considered the gold standard for the evaluation of sensitivity and specificity. Receiver operating characteristic (ROC) curves for the five screening tools were also used to assess the ability to distinguish malnutrition-risk patients accurately.

Results: The comparison of the performances of different screening tools in detecting malnutrition demonstrated that while the GMS had the highest sensitivity (87.40%), the NRS-2002 had the highest specificity (91.70%). The area under the Curve (AUC) value indicated that the predictive values of the NRS-2002, MST, GMS, and SNAQ were excellent, and the predictive value of the MUST was good (p < 0.001). While the GLIM criteria in particular appear to be an effective tool for detecting malnutrition in hospitalized individuals, other screening tools are also useful in assessing their malnutrition risk.

Conclusions: We emphasized MST's alignment with GLIM criteria, underscoring the importance of a multidisciplinary approach for early malnutrition diagnosis. Patients at risk of malnutrition can be diagnosed more quickly and accurately with appropriate screening tools and the effectiveness of treatments can be increased.

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