Evidence map›Paper›PMID 39420101›Full record

ArticleScientific reports2024

Malnutrition is associated with severe outcome in elderly patients hospitalised with COVID-19.

Xiaoman Wang, Jingyao Ke, Rui Cheng, Hongfei Xian, Jingyi Li, Yongsen Chen, Bin Wu, Mengqi Han, Yifan Wu, Weijie Jia and 4 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Xiaoman WangDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China, 66 Xuefu Avenue, 330006.
Jingyao KeDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China, 66 Xuefu Avenue, 330006.
Rui ChengDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China, 66 Xuefu Avenue, 330006.
Hongfei XianDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China, 66 Xuefu Avenue, 330006.
Jingyi LiDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China, 66 Xuefu Avenue, 330006.
Yongsen ChenDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China, 66 Xuefu Avenue, 330006.
Bin WuDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China, 66 Xuefu Avenue, 330006.
Mengqi HanDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China, 66 Xuefu Avenue, 330006.
Yifan WuDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China, 66 Xuefu Avenue, 330006.
Weijie JiaDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China, 66 Xuefu Avenue, 330006.
Pengfei YuDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China, 66 Xuefu Avenue, 330006.
Jianmo LiuDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China, 66 Xuefu Avenue, 330006.
Haowen LuoDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China, 66 Xuefu Avenue, 330006.
Yingping YiDepartment of Medical Big Data Research Centre, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China, 66 Xuefu Avenue, 330006. yyp66@126.com.ORCID 0000-0002-6603-7442

Funding

the National Key R&D Program of China 2020YFC2002901 and 2018YFC1312902the National Natural Science Foundation of China 81960609The Second Affiliated Hospital of Nanchang University Funding Program 2021efyB03
6 · The paper itself

Abstract

Some studies have identified influencing factors of COVID-19 illness in elderly, such as underlying diseases, but research on the effect of nutritional status is still lacking. This study retrospectively examined the influence of nutritional status on the outcome of elderly COVID-19 inpatients. A retrospective analysis of the clinical data of 4241 COVID-19 patients who were admitted to a third-class hospital of Nanchang between November 1, 2022 and January 31, 2023 was conducted. Nutritional status was assessed using the prognostic nutritional index (PNI) and controlling nutritional status score (CONUT). The influence of nutritional status on the outcome of COVID-19 patients was determined through multivariate adjustment analysis, restrictive cubic spline, and receiver operating characteristic curve (ROC). Compared with mild/no malnutrition, severe malnutrition substantially increased the critical outcome of COVID-19. A linear relationship was observed between the odds ratio (OR) and PNI and CONUT (P > 0.05). The area under the ROC curve indicated that PNI was the better predictor. The optimal cutoff value of PNI was 38.04 (95%CI: 0.797 ~ 0.836, AUC = 0.817), with a sensitivity of 70.7% and a specificity of 79.6%. The critical illness of elderly COVID-19 patients shows a linear relationship with malnutrition at admission. The use of PNI to assess the prognosis of COVID-19 eldeely patients is reliable, highlighting the importance for doctors to closely pay attention to the nutritional status of COVID-19 patients. Focusing on nutritional status in clinical practice can effectively reduce the critical illness of elderly COVID-19 patients.

Indexed as

COVID-19HospitalizationMalnutritionNutritional StatusNutrition AssessmentAgedAged, 80 and overFemaleHumansMalePrognosisRetrospective StudiesROC CurveSARS-CoV-2Severity of Illness IndexControlling nutritional status scoreCOVID-19Elderly peopleNutritional statusPrognostic nutritional indexRetrospective cohort study

Identifiers

PMID39420101
PMCPMC11486994

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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 Socratic graph.