Evidence map›Paper›PMID 38916788›Full record

ArticleInternational urology and nephrology2024

Analysis of factors influencing the trajectory of fatigue in maintenance haemodialysis patients: a longitudinal study.

Ruobing Zhao, Wei Zhang, Mengdi Sun, Chao Yang, Xiyang Liu, Chen Chen

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Article in International urology and nephrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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6citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Ruobing ZhaoSchool of Medicine, Jiangsu University, Zhenjiang, 212000, Jiangsu, China.
Wei ZhangSchool of Life Science and Technology, Jingjiang College, Jiangsu University, Zhenjiang, 212000, Jiangsu, China. zhangwei5808088@126.com.
Mengdi SunSchool of Medicine, Jiangsu University, Zhenjiang, 212000, Jiangsu, China.
Chao YangSchool of Medicine, Jiangsu University, Zhenjiang, 212000, Jiangsu, China.
Xiyang LiuSchool of Medicine, Jiangsu University, Zhenjiang, 212000, Jiangsu, China.
Chen ChenSchool of Medicine, Jiangsu University, Zhenjiang, 212000, Jiangsu, China.

Funding

The Jiangsu Provincial Institute of Higher Education YB012
6 · The paper itself

Abstract

objectiveTo explore the potential categories and influencing factors of fatigue trajectory in maintenance haemodialysis patients.

methodsBetween June 2023 and December 2023, a convenience sample of 306 maintenance haemodialysis patients in a tertiary hospital haemodialysis centre in Zhenjiang City was selected as the study population, and patient information was collected monthly after the baseline survey using the General Information Questionnaire, Pittsburgh Sleep Quality Scale, Piper Fatigue Revision Scale, Collaborative Social Support Scale, Patient Health Questionnaire Depression Scale, Comprehensive Economic Toxicity Rating Scale, and Fear of Disease Progression Simplified Scale, for a total of six follow-up visits. In addition, the potential category growth model was used to identify the developmental trajectory of fatigue, and univariate analysis and binary logistic regression were used to analyse its determinants.

resultsThe 6 month fatigue trajectory of maintenance haemodialysis patients could be divided into two categories: persistent low-fatigue group (59.8%) and fluctuating high-fatigue group (40.2%). Age, surgical history, level of social support, sleep, economic toxicity, and changes in ultrafiltration volume during dialysis were the influencing factors for repeated fatigue in maintenance haemodialysis patients (p < 0.05).

conclusionThe fatigue trajectory of maintenance haemodialysis patients is heterogeneous, suggesting that clinical workers should focus on the haemodialysis patients with repeated fatigue and make targeted interventions to improve their fatigue status and reduce the occurrence of adverse events in patients.

Indexed as

FatigueRenal DialysisAdultAgedFemaleHumansKidney Failure, ChronicLongitudinal StudiesMaleMiddle AgedFatigueInfluencing factorsMaintenance haemodialysis patientsPotential category growth model

Identifiers

PMID38916788

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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 Socratic graph.