Evidence map›Paper›PMID 42609889›Full record

ArticleKidney diseases (Basel, Switzerland)

Safety and Efficacy of the First Mobile Intelligent Hemodialysis Platform in Mainland China: Protocol for a Single-Center Prospective Exploratory Study in Maintenance Hemodialysis Patients.

Renhua Lu, Yan Fang, Qisheng Lin, Yifei Lu, Yijun Zhou, Xiaojun Zeng, Tingting Liu, Wangshu Wu, Kewei Xie, Haifen Zhang and 4 more

Abstract read
In one paragraph

Article in Kidney diseases (Basel, Switzerland). 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

14 authors.

Renhua LuDepartment of Nephrology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yan FangDepartment of Nephrology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Qisheng LinDepartment of Nephrology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yifei LuDepartment of Nephrology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yijun ZhouDepartment of Nephrology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiaojun ZengDepartment of Nephrology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Tingting LiuDepartment of Nephrology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Wangshu WuDepartment of Nephrology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Kewei XieDepartment of Nephrology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Haifen ZhangDepartment of Nephrology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shan MouDepartment of Nephrology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Haijiao JinDepartment of Nephrology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Zhaohui NiDepartment of Nephrology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Leyi GuDepartment of Nephrology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: End-stage kidney disease (ESKD) is a growing global public health burden, with millions of patients dependent on maintenance hemodialysis (MHD). Conventional center-based MHD imposes significant constraints on patient flexibility, accessibility, and quality of life, while home hemodialysis faces substantial implementation barriers in China. Advances in digital health technologies - including artificial intelligence, internet of things (IoT), and real-time data analytics - offer opportunities to fundamentally transform dialysis care delivery. This study presents the protocol for the first clinical trial evaluating a mobile intelligent hemodialysis platform in mainland China. Methods: This is a prospective, single-center, exploratory clinical trial to be conducted at Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine. Twenty adult ESKD patients (aged 18-75 years) receiving regular MHD 3 times weekly for at least 3 months will be enrolled. Participants will undergo a single 4-h hemodialysis session on the mobile intelligent dialysis platform, a specialized medical vehicle integrating water purification, dialysis equipment, and an intelligent clinical decision support system across six functional zones. The primary endpoint is the successful session completion rate, defined as completion without platform-related technical failure or premature termination. Secondary endpoints include dialysis adequacy (urea reduction ratio ≥65%; Kt/V >1.2), hemodynamic stability, and pre- to post-dialysis changes in biochemical parameters. Safety assessments include continuous vital sign monitoring and adverse event grading according to Common Terminology Criteria for Adverse Events (CTC-AE) version 4.0. The study has been approved by the Ethics Committee of Ren Ji Hospital (approval number: LY2024-313-A) and registered in the Chinese Clinical Trial Registry (ChiCTR2500100356). Conclusion: This trial is expected to demonstrate that the mobile intelligent hemodialysis platform can achieve high session completion rates, adequate dialysis efficiency, and a safety profile comparable to conventional center-based dialysis. If validated, this platform has the potential to improve service accessibility, reduce indirect costs, enhance patient autonomy, and provide a resilient dialysis option in public health emergencies and disaster settings, representing a meaningful paradigm shift in dialysis care delivery.

Indexed as

Clinical trial protocolDigital healthEnd-stage kidney diseaseHemodialysisMobile dialysis

Identifiers

PMID42609889
PMCPMC13480982

What OpenQuestion holds

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Registered trials

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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.