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超越营养不良筛查工具:评估握力和胃肠道症状以预测未接受肾脏替代治疗的慢性肾脏病门诊患者的营养不良情况。

Beyond the Malnutrition Screening Tool: Assessing Hand Grip Strength and Gastrointestinal Symptoms for Malnutrition Prediction in Outpatients with Chronic Kidney Disease Not on Kidney Replacement Therapy.

作者信息

Young Maya, Dawson Jessica, Katz Ivor J, Turner Kylie, Chan Maria

机构信息

Department of Nutrition and Dietetics, St George Hospital, Kogarah, NSW 2217, Australia.

NHMRC Clinical Trials Centre, University of Sydney, Sydney, NSW 2006, Australia.

出版信息

Nutrients. 2025 Jul 29;17(15):2471. doi: 10.3390/nu17152471.

DOI:10.3390/nu17152471
PMID:40806056
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12348737/
Abstract

: The Malnutrition Screening Tool (MST) is commonly used to identify malnutrition risk; however it has demonstrated poor sensitivity to detect malnutrition in inpatients with chronic kidney disease (CKD) and kidney replacement therapy (KRT) populations. Gastrointestinal symptoms, such as poor appetite, may better detect malnutrition. The accuracy of MST or other nutrition-related parameters to detect malnutrition in ambulatory patients with CKD stages 4-5 without KRT has not been evaluated. : A single site retrospective audit of outpatient records from May 2020 to March 2025 was conducted. Patients with eGFR < 25 mL/min/1.73 m without KRT who had both MST and a 7-point Subjective Global Assessment (SGA) within 7 days were included. Sensitivity, specificity, and ROC-AUC analyses compared nutritional parameters against SGA-defined malnutrition. Nutritional parameters tested included MST, hand grip strength, upper gastrointestinal symptom burden, poor appetite and a combination of some of these parameters. : Among 231 patients (68.8% male, median age 69 years, median eGFR 15), 29.9% were at risk of malnutrition (MST ≥ 2) and 33.8% malnourished (SGA ≤ 5). All potential screening tools had AUC ranging from 0.604 to 0.710, implying a poor-to-moderate discriminator ability to detect malnutrition. Combining HGS ≤ 29.5 kg or MST ≥2 demonstrated high sensitivity (95.5%) and negative predictive value (93.3%), but low specificity (33.3%) for detecting malnutrition, indicating this approach is effective for ruling out malnutrition but may over-identify at-risk individuals. : MST and other tested tools showed limited overall accuracy to identify malnutrition. Using combined nutritional markers of HGS or MST score was the most sensitive tool for detecting malnutrition in this advanced CKD without KRT population.

摘要

营养不良筛查工具(MST)通常用于识别营养不良风险;然而,它在检测慢性肾脏病(CKD)住院患者和肾脏替代治疗(KRT)人群中的营养不良方面表现出较差的敏感性。胃肠道症状,如食欲不振,可能更能检测出营养不良。MST或其他营养相关参数在未接受KRT的CKD 4-5期门诊患者中检测营养不良的准确性尚未得到评估。

对2020年5月至2025年3月的门诊记录进行了单中心回顾性审计。纳入估算肾小球滤过率(eGFR)<25 mL/min/1.73 m²且未接受KRT、在7天内同时进行了MST和7分主观全面评定法(SGA)的患者。通过敏感性、特异性和ROC-AUC分析,将营养参数与SGA定义的营养不良进行比较。测试的营养参数包括MST、握力、上消化道症状负担、食欲不振以及其中一些参数的组合。

在231名患者中(68.8%为男性,中位年龄69岁,中位eGFR为15),29.9%有营养不良风险(MST≥2),33.8%营养不良(SGA≤5)。所有潜在筛查工具的AUC范围为0.604至0.710,这意味着其检测营养不良的鉴别能力较差至中等。结合握力≤29.5 kg或MST≥2在检测营养不良方面显示出高敏感性(95.5%)和阴性预测值(93.3%),但特异性低(33.3%),表明该方法在排除营养不良方面有效,但可能会过度识别有风险个体。

MST和其他测试工具在识别营养不良方面总体准确性有限。在这个未接受KRT的晚期CKD人群中,使用握力或MST评分的联合营养标志物是检测营养不良最敏感的工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0644/12348737/2213537d23f9/nutrients-17-02471-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0644/12348737/2213537d23f9/nutrients-17-02471-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0644/12348737/2213537d23f9/nutrients-17-02471-g001.jpg

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本文引用的文献

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Br J Nutr. 2025 Jan 28;133(2):231-238. doi: 10.1017/S0007114524003192. Epub 2024 Dec 13.
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ESPEN practical guideline on clinical nutrition in hospitalized patients with acute or chronic kidney disease.ESPEN 临床实践指南:急/慢性肾脏病住院患者的临床营养治疗。
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Association of Handgrip Strength and Nutritional Status in Non-Dialysis-Dependent Chronic Kidney Disease Patients: Results from the KNOW-CKD Study.
非透析依赖型慢性肾脏病患者握力与营养状况的相关性:来自 KNOW-CKD 研究的结果。
Nutrients. 2024 Jul 26;16(15):2442. doi: 10.3390/nu16152442.
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Malnutrition as a potential predictor of mortality in chronic kidney disease patients on dialysis: A systematic review and meta-analysis.营养不良作为透析慢性肾脏病患者死亡的潜在预测因素:系统评价和荟萃分析。
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