Evidence map›Paper›PMID 33009533›Full record

ArticleThe journal of nutrition, health & aging2020

A Cross-Sectional Study of GLIM-Defined Malnutrition Based on New Validated Calf Circumference Cut-off Values and Different Screening Tools in Hospitalised Patients over 70 Years Old.

J-Y Xu, M-W Zhu, H Zhang, L Li, P-X Tang, W Chen, J-M Wei

Open access · greenAbstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in The journal of nutrition, health & aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 4 pooled it
3.5field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 4 syntheses or guidelines pooled it, 40 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

J-Y XuJun-Min Wei, Department of General Surgery, Beijing Hospital, No 1, Dahua Road, Beijing 100730, China, E-mail address: weijunminsci@163.com.
M-W Zhu
H Zhang
L Li
P-X Tang
W Chen
J-M Wei
Chinese Academy of Medical Sciences & Peking Union Medical College · CNPeking Union Medical College Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimsThe Global Leadership Initiative on Malnutrition (GLIM) is new criteria for diagnosing malnutrition that need validation adjusted to race. Our aim is to determine the optimal reference values of calf circumference (CC), investigate the prevalence of GLIM-defined malnutrition based on different screening tools in inpatients over 70 years old in China and assess its relationship with clinical outcomes.

methodsWe designed two continuity studies by analyzing a prospective multicenter database. First, we estimated and validated the CC cut-off values by receiver operating characteristic analyses against in-hospital mortality. Then the patients who were at risk by NRS 2002, MNA-SF and MUST were assessed by the GLIM criteria using the new CC values. Some clinical parameters and outcome data were evaluated.

resultsThe optimal cut-off values of CC were 29.6 cm for male patients and 27.5 cm for female patients. The prevalence of GLIM-defined malnutrition was 27.5% by using NRS2002, 32.6% by using MNA-SF and 25.4% by using MUST. Patients with GLIM-defined malnutrition showed significantly worse values in BMI, total protein, albumin, neutrophil/lymphocyte ratio, CC, rate of complication, in-hospital mortality, length of stay, and total hospital cost than normal patients. Multivariate logistic regression showed the odds ratio of in-hospital mortality was significantly associated with GLIM defined malnutrition by using MNA-SF [OR = 1.231, 95%CI (1.022, 1.484), P = 0.029].

conclusionsThe Chinese reference values of CC for inpatients over 70 years old were validated by in-hospital mortality, which could be implemented in GLIM criteria. And this population possessed a high prevalence of nutrition risk and malnutrition. GLIM criteria with MNA-SF seems to be the first choice to diagnose malnutrition.

Indexed as

Nutrition AssessmentAgedAged, 80 and overAnthropometryCross-Sectional StudiesFemaleGeriatric AssessmentHumansLegMaleMalnutritionMass ScreeningNutritional StatusProspective Studiescalf circumferenceelderlyGLIMmalnutritionnutritional risk screening

Identifiers

PMID33009533
PMCPMC12876683
OpenAlexW3024088484

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.