Evidence map›Paper›PMID 33127873›Full record

ArticleMedical science monitor : international medical journal of experimental and clinical research2020

Monitoring the Changes of Upper Limb Water in Breast Cancer Patients by Segmental Multi-Frequency Bioelectrical Impedance Analysis.

Lijuan Zhang, Huizhen Zhang, Xiaohang Liu, Qiaoling Zhong, Qinghua Luo, Yiheng Zhang, Ni Gong, Huiying Qin, Anli Yang

Open access · greenAbstract read
In one paragraph

Article in Medical science monitor : international medical journal of experimental and clinical research, 2020. 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
0.1field-weighted citation impact, top 50% of its field
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, 3 citations in OpenAlex.

  1. Trial
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

9 authors at 4 institutions in 1 country.

Lijuan ZhangDepartment of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China (mainland).
Huizhen ZhangDepartment of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China (mainland).
Xiaohang LiuDepartment of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China (mainland).
Qiaoling ZhongDepartment of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China (mainland).
Qinghua LuoDepartment of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China (mainland).
Yiheng ZhangSchool of Nursing, Sun Yat-sen University, Guangzhou, Guangdong, China (mainland).
Ni GongSchool of Nursing, Jinan University, Guangzhou, Guangdong, China (mainland).
Huiying QinDepartment of Nursing, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China (mainland).
Anli YangDepartment of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China (mainland).
Sun Yat-sen University · CNState Key Laboratory of Oncology in South ChinaSun Yat-sen University Cancer Center · CNJinan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND Our study aims to investigate the role of segmental multi-frequency bioelectrical impedance analysis (SMF-BIA) in the monitoring of upper limb water changes of patients with breast cancer before and after surgery to aid in establishing a new approach to preventing lymphedema. MATERIAL AND METHODS This study included 442 female patients with breast cancer. We used SMF-BIA to monitor changes in body composition. Data were collected 1 day before surgery and 7 days and 3 months after surgery. RESULTS The average body mass index (BMI) of patients was normal but, in 22.8% of patients, the percentage of body fat exceeded the average, which is known as invisible obesity. Moreover, the weight, BMI, basal metabolic rate, inorganic salt content, muscle content, total body water, and extracellular water of patients increased 7 days after surgery (P<0.05), but recovered to preoperative levels within 3 months. In addition, protein content, skeletal muscle content, and intracellular water increased 7 days after surgery, but decreased within 3 months to even lower levels than before surgery (P<0.05). The extracellular water and total body water ratios increased continuously within the 3 months after surgery. Finally, the segmental water ratio of the healthy and affected upper limbs increased, while the bioelectrical impedance value decreased; however, they were still within the normal range. CONCLUSIONS SMF-BIA monitoring may provide more detailed information for making individual nursing care plans in patients with breast cancer. Further studies with long-term follow-up are urgently needed to establishment a lymphedema risk predictive model.

Indexed as

AdiposityBody WaterElectric ImpedanceAdultAgedBody Mass IndexBreast Cancer LymphedemaBreast NeoplasmsFemaleHumansMiddle AgedUpper Extremity

Identifiers

PMID33127873
PMCPMC7640375
OpenAlexW3095170170

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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 OpenQuestion graph.