Evidence map›Paper›PMID 39991651›Full record

ArticleClinical kidney journal2025

Equations for estimating resting energy expenditure in patients on peritoneal dialysis.

Xiao Xu, Nanzha Abi, Zhikai Yang, Tiantian Ma, Nan Zhang, Yingdong Zheng, Jie Dong

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical kidney journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Xiao XuRenal Division, Department of Medicine, Peking University First Hospital; Institute of Nephrology, Peking University; Key Laboratory of Renal Disease, Ministry of Health; Key Laboratory of Renal Disease, Ministry of Education; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences; Beijing, China.
Nanzha AbiRenal Division, Department of Medicine, Peking University First Hospital; Institute of Nephrology, Peking University; Key Laboratory of Renal Disease, Ministry of Health; Key Laboratory of Renal Disease, Ministry of Education; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences; Beijing, China.
Zhikai YangRenal Division, Department of Medicine, Peking University First Hospital; Institute of Nephrology, Peking University; Key Laboratory of Renal Disease, Ministry of Health; Key Laboratory of Renal Disease, Ministry of Education; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences; Beijing, China.
Tiantian MaRenal Division, Department of Medicine, Peking University First Hospital; Institute of Nephrology, Peking University; Key Laboratory of Renal Disease, Ministry of Health; Key Laboratory of Renal Disease, Ministry of Education; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences; Beijing, China.
Nan ZhangRenal Division, Department of Medicine, Peking University First Hospital; Institute of Nephrology, Peking University; Key Laboratory of Renal Disease, Ministry of Health; Key Laboratory of Renal Disease, Ministry of Education; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences; Beijing, China.
Yingdong ZhengDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China.
Jie DongRenal Division, Department of Medicine, Peking University First Hospital; Institute of Nephrology, Peking University; Key Laboratory of Renal Disease, Ministry of Health; Key Laboratory of Renal Disease, Ministry of Education; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences; Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In peritoneal dialysis (PD) patients, determining energy expenditure is essential for recommending energy intake in nutrition management. Objective: We aimed to develop and validate a resting energy expenditure (REE) equation for patients with PD and compare it to previously available REE equations in dialysis patients. Design: This cross-sectional study enrolled 200 patients with PD from two hospitals in Beijing, China. Stepwise linear regression analysis was used to derive a new REE equation (eREE-PD) based on actual REE (aREE) measured using indirect calorimetry (IC) in the development dataset. The eREE-PD value was then validated with aREE in the validation dataset and compared with values from existing equations obtained in general populations and those developed for chronic kidney disease and dialysis patients, in terms of bias, precision, and accuracy. Results: The bias, precision, and accuracy of the eREE-PD equation were significantly better than those of the Harris-Benedict, WHO, and Schofield equations ( Conclusion: The eREE-PD equation, based on age, sex, and weight data, may serve as a reliable and practical tool for estimating REE in patients with PD, aiding in individualized nutritional management. However, external validation in other populations is required to confirm its generalizability beyond the studied cohort.

Indexed as

chronic kidney diseaseindirect calorimetryperitoneal dialysisresting energy expenditure

Identifiers

PMID39991651
PMCPMC11842989

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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.