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

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.

Introduction

End-stage kidney disease (ESKD) represents a significant global public health challenge, with rapidly increasing patient numbers worldwide [1]. According to the latest Chinese National Renal Data System (CNRDS) report, by 2021, 877,470 patients in China were receiving kidney replacement therapies treatment, with approximately 82.4% dependent on maintenance hemodialysis (MHD) to sustain life, posing substantial burdens to healthcare infrastructure, patient quality of life, and healthcare economics [2–5]. Conventionally, MHD is predominantly delivered through centralized dialysis centers in mainland China, requiring patients to make frequent visits (typically 3 times per week). This model significantly limits patients’ autonomy, disrupts their participation in employment or education, imposes transportation challenges, and proves especially problematic during public health emergencies, such as pandemics, natural disasters, or regional crises [6–12].

In response to these challenges, global healthcare innovation has increasingly focused on enhancing the flexibility, accessibility, and convenience of dialysis services [13]. Mobile dialysis units have emerged internationally as an effective alternative, notably exemplified by the “Big Red Kidney Bus” initiative in Australia [14]. Such platforms have demonstrated the capacity to safely provide dialysis treatments at patient-preferred locations, such as vacation sites or underserved regions, improving patient satisfaction and quality of life [14]. While mobile dialysis units have been implemented and studied in several countries [14–16], there have been no reported studies evaluating the safety and efficacy of mobile dialysis in mainland China. Moreover, existing mobile dialysis solutions primarily emphasize hardware mobility – integrating traditional dialysis equipment into vehicles – but rarely incorporate advanced healthcare technologies for real-time monitoring, patient management, or intelligent decision support systems.

With the rapid advancement of digital health technologies, the integration of intelligent systems – including artificial intelligence (AI), internet of things (IoT), and real-time data analytics – into dialysis treatment represents a substantial innovation [17–19]. Such integration could significantly enhance dialysis quality, safety, and patient experience by providing clinicians with immediate access to comprehensive patient data, enabling proactive clinical interventions, and allowing remote oversight. Importantly, intelligent mobile dialysis platforms could proactively identify and prevent treatment-related complications (e.g., hypotension), predict clinical deterioration using advanced AI algorithms, and personalize dialysis treatment parameters to individual patient needs in real-time, thus greatly surpassing conventional mobile dialysis models [20, 21].

Our team at Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, has pioneered the first home hemodialysis program in mainland China and established an innovative remote monitoring system based on Internet technology [22, 23] and is now advancing this concept further by developing the first intelligent mobile dialysis platform domestically. This novel platform uniquely integrates mobile dialysis equipment with advanced intelligent healthcare systems, including IoT-enabled real-time monitoring of dialysis parameters, remote patient monitoring through 5 G telecommunication technology, AI-driven predictive analytics for early identification of clinical risks, and automated patient scheduling and management systems. By combining these technologies, the mobile intelligent dialysis platform aims not only to bring dialysis treatment closer to patients’ daily lives but also to significantly elevate the safety, efficiency, and personalization of dialysis care.

Therefore, this exploratory clinical trial aims to evaluate the clinical safety and efficacy of our innovative mobile intelligent dialysis platform in a real-world clinical setting. If proven successful, it could represent a landmark advancement in dialysis technology, enhancing the resilience and flexibility of dialysis services, and substantially improving patient autonomy, satisfaction, and outcomes.

Methods

Study Design

This study is a prospective, single-center, exploratory clinical trial. The illustration of the proposed study design is shown in Figure 1. The detailed study plan of the study is shown in Table 1.

Fig. 1.

Study design – flowchart of the mobile intelligent hemodialysis platform trial.

Table 1.

The study plan of the mobile intelligent hemodialysis platform

​	Visit 0	Visit 1	Visit 2d
	−7d ∼ −1d	0d	3±1d
ICF	Х	​	​
Screen inclusion and exclusion criteria	Х	​	​
Demographic characteristics	Х	​	​
Medical history	Х	​	​
Etiology of ESKD	Х	​	​
Vascular access	Х	​	​
Height, cm	Х	​	​
Dry weight, kg	Х	​	​
Pre-dialysis weight, kg	​	Х	​
Post-dialysis weight, kg	​	Х	​
Vital signs	​	Х	​
Physical examination	​	Х	​
Dialysis adequacya	​	Х	​
Dialysis prescription	​	Х	​
Pre-dialysis biochemistry measurementsb	​	Х	​
Post-dialysis biochemistry measurementsb	​	Х	​
Pre-dialysis blood gas analysis	​	Х	​
Post-dialysis blood gas analysis	​	Х	​
Pre-dialysis CBC	​	Х	​
Post-dialysis CBC	​	Х	​
Pre-dialysis anticoagulation monitoringc	​	Х	​
Intra-dialysis anticoagulation monitoringc	​	Х	​
Comfort Assessment Questionnaire	​	Х	Х
AE	​	Х	Х
SAE	​	Х	Х

ICF, informed consent form; ESKD, end-stage kidney disease; CBC, complete blood count; AE, adverse event; SAE, severe adverse event; HD, hemodialysis; spKt/V, single-pool Kt/V; URR, Urea Reduction Ratio; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUN, blood urea nitrogen; APTT, activated partial thromboplastin time; PT, prothrombin time; TT, thrombin time; Fg, fibrinogen.

Vital signs are continuously monitored from 30 min before initiation of HD to 30 min after HD procedure. Physical examination, pre-dialysis weight, pre-dialysis biochemistry measurements, pre-dialysis blood, pre-dialysis blood gas analysis, pre-dialysis CBC, and pre-dialysis anticoagulation monitoring should be collected within 30 min before initiation of HD. Intra-dialysis anticoagulation monitoring should be collected within 1 h ± 15 min after initiation of HD. Post-dialysis weight, post-dialysis biochemistry measurements and post-dialysis blood gas analysis should be collected within 30 min after HD procedure.

^aDialysis adequacy includes spKt/V and URR.

^bBiochemistry measurements include serum albumin, ALT, AST, serum creatinine, BUN, and uric acid.

^cAnticoagulation monitoring involves measuring anti-Xa activity in HD patients using low molecular weight heparin, and measuring APTT, PT, TT, and Fg in HD patients receiving nafamostat anticoagulation.

^dVisit 2 refers to the next regular dialysis session following the completion of visit 1.

All blood samples will be measured at local laboratory.

Participants

This study will include 20 patients with ESKD (aged 18–75 years) receiving MHD treatment 3 times per week. Written informed consent will be obtained from all the participants. Patients will be eligible to be included in the study only if all of the following criteria are applicable:

Inclusion Criteria

• 1.Aged 18–75 years
• 2.Receiving regular MHD for at least 3 months, 3 times per week
• 3.Vascular access via arteriovenous fistula or graft, with blood flow ≥200 mL/min
• 4.Capable of giving signed informed consent.

Exclusion Criteria

• 1.Participation in other clinical trials within the previous month
• 2.Pregnant or breastfeeding women
• 3.New York Heart Association (NYHA) Class IV heart failure or acute coronary syndrome within 3 months
• 4.Active bleeding within the past 2 weeks
• 5.Unstable blood pressure (pre-dialysis systolic ≥180 or ≤90 mm Hg, diastolic ≥100 or ≤60 mm Hg)
• 6.Severe anemia (hemoglobin ≤60 g/L) or high coagulation risk
• 7.Severe infection (CRP >10 times upper normal limit)
• 8.History of drug abuse or severe psychiatric disorders
• 9.Positive for human immunodeficiency virus, hepatitis B virus, hepatitis C virus, or Treponema pallidum.

Procedures for Withdrawal

Participants were withdrawn from the study based on the following prespecified criteria:

• 1.Voluntary withdrawal with retraction of informed consent by the participant
• 2.Inability to complete hemodialysis treatment on the mobile intelligent dialysis platform due to adverse events
• 3.Death or pregnancy during the study period
• 4.Investigator’s determination that study discontinuation was in the best interest of the participant.

Intervention and Procedures

Participants will receive a single 4-h dialysis session on the mobile intelligent dialysis platform. Dialysis parameters include a blood flow rate of 250 ± 50 mL/min, dialysate flow rate of 500 mL/min, and individualized ultrafiltration. Low molecular weight heparin or nafamostat mesylate anticoagulation will be used. Continuous ECG monitoring will be applied during treatment. Blood samples for biochemical analysis will be collected before and after dialysis.

Mobile Intelligent Hemodialysis Platform Configuration

The mobile intelligent hemodialysis platform is a specialized medical vehicle integrating comprehensive dialysis capabilities through six strategically arranged functional zones as illustrated in Figure 2: dialysate water preparation zone at the rear with water purification system and activated carbon filter; adjacent dialysate disposal zone equipped with an onboard waste-liquid pre-treatment system, in which dialysis effluent is pre-treated on the platform and subsequently discharged into a compliant medical-wastewater sewer at the hospital or designated docking site, in accordance with national medical-wastewater discharge standards; centrally positioned patient treatment zone equipped with hemodialysis machines and dialysis beds; adjoining clinical work zone featuring central control touchscreen, surveillance systems, a medical oxygen supply, and a dedicated emergency response kit comprising a portable ventilator, a cardiac defibrillator, and emergency medications; medical electrical power zone housing the medical-grade power distribution box and 5 G router for stable connectivity; and the vehicle operator zone at the front with the heating, ventilation, and air conditioning system. This integrated design optimizes space utilization while meeting strict medical safety standards, ensuring proper infection control through medical-grade materials, and providing comprehensive functionality that seamlessly combines treatment capabilities, water purification, power management, and digital connectivity in a mobile healthcare solution. In addition, a dedicated emergency response plan (standard operating procedure) has been established to ensure timely management of acute medical events, such as cardiac arrest or respiratory failure that may occur during transport and treatment.

Fig. 2.

Schematic layout of the mobile intelligent hemodialysis platform with functional zones and key medical components.

Digital Management System

The mobile hemodialysis platform incorporates an intelligent clinical decision support system (Fig. 3) that integrates 3 key data domains: clinical assessment, teleconsultation, and hemodialysis device parameters. Patient care begins with electronic registration of demographic information and medical history, followed by automated upload of pre-dialysis vital signs and weight measurements. The AI clinical guidance system (AICG) analyzes these data to generate treatment prescriptions for remote physician review and approval prior to implementation by nursing staff. During hemodialysis, the AICG continuously monitors device parameters (ultrafiltration rate, transmembrane pressure, dialysate conductivity, etc.) and provides real-time personalized treatment recommendations. Machine learning algorithms detect patterns suggesting potential clinical deterioration, automatically alerting healthcare providers and enabling remote nephrologists to authorize treatment modifications via the teleconsultation platform. The system functions across multiple interfaces (workstations, mobile devices, web platforms) to deliver comprehensive capabilities: vital sign monitoring with automated alerts, prescription management, electronic health record integration, comprehensive patient journey coordination, clinical abnormality detection, evidence-based decision support, and resource management optimization. The AICG component draws on a dataset of MHD patients, comprising demographic characteristics, dialysis prescriptions, intradialytic vital signs and device parameters (e.g., ultrafiltration rate, transmembrane pressure, dialysate conductivity, and blood pressure trajectories), and pre- and post-dialysis biochemical data. For tasks such as prediction of intradialytic hypotension, model performance is assessed using standard metrics, including area under the receiver operating characteristic curve, sensitivity, and specificity. In the present study, the real-world data generated on the mobile intelligent hemodialysis platform will be used to validate and further refine model performance and generalizability in the mobile setting.

Fig. 3.

Schematic representation of the intelligent clinical decision support system for mobile hemodialysis.

Endpoints

Primary Endpoint

The primary endpoint will be the successful completion rate of hemodialysis sessions on the mobile intelligent dialysis platform, defined as the percentage of participants who completed a standard hemodialysis session without platform-related technical failures (including treatment interruptions or data transmission disruptions). The success rate was calculated as: (number of participants with successful dialysis completion/total number of study participants) × 100%.

Secondary Endpoints

• The secondary endpoints were assessed before and after hemodialysis session, comprising: Dialysis adequacy measurements: The proportion of patients achieving target urea reduction ratio (URR) (defined as URR ≥65%) during each dialysis session; the proportion of patients achieving target Kt/V (defined as Kt/V >1.2).

• Hemodynamic stability: The proportion of patients experiencing intradialytic hypotension during each dialysis session. Blood pressure measurements at standardized time points (pre-dialysis, intradialytic at hours 1, 2, 3, and post-dialysis).

• Biochemical parameters: The mean difference between pre- and post-dialysis serum potassium concentrations, with pre-dialysis levels measured immediately prior to initiation of dialysis and post-dialysis levels obtained within 15 min of treatment completion during each hemodialysis session; the mean difference between pre- and post-dialysis serum bicarbonate concentrations, measured concurrently with potassium sampling, to assess acid-base homeostasis throughout the dialysis treatment.

• Patient-reported outcomes: Treatment comfort and patient satisfaction, assessed after the dialysis session on the mobile intelligent platform using a structured Comfort Assessment Questionnaire.

Safety Endpoints

• Safety assessments comprised the following measures: Vital signs: Continuous monitoring of vital parameters during dialysis sessions; documentation of any significant deviations from baseline measurements.

• Adverse events: All adverse events will be graded according to the Common Terminology Criteria for Adverse Events (CTC-AE) version 4.0. particular emphasis will be placed on: cardiovascular and cerebrovascular events and platform-specific technical adverse events. The frequency and severity of adverse events were documented, with special attention to serious adverse events (grade 3 or higher per CTC-AE 4.0) and device-related adverse events associated with the mobile intelligent dialysis platform.

Data Collection and Management

Data will be collected systematically by trained researchers, monitored for completeness and accuracy, and securely stored in electronic databases compliant with data protection standards.

Statistical Analysis

Analysis will be performed using SPSS 20.0. Normally distributed continuous data will be reported as mean ± standard deviation and analyzed using paired t tests. Nonparametric data will be reported as median (interquartile range) and analyzed with Wilcoxon signed-rank tests. Paired categorical data will be analyzed using the paired Chi-square test (McNemar’s test). A p value <0.05 will indicate statistical significance.

Quality Control

Quality control of the hemodialysis procedure on the mobile intelligent platform will follow the same standards applied in our in-center dialysis unit. The onboard water purification system will be monitored regularly, with chemical contaminants, microbial counts, and endotoxin levels maintained within the limits specified by the relevant standards for hemodialysis water and dialysis fluid (ISO 23500/ISO 13959), and ultrapure dialysate will be used. Hemodialysis machines and monitoring devices will undergo routine calibration, preventive maintenance, and disinfection between sessions according to the manufacturers’ instructions, with disinfection records documented. Vascular access cannulation, anticoagulation, and the dialysis prescription will be carried out by experienced dialysis nurses and physicians in accordance with standardized operating procedures will be monitored to verify treatment quality. Throughout the procedure, single-use disposables, medical-grade materials, and standardized aseptic techniques will be used, consistent with the platform’s infection-control design. In addition, treatment and device parameters captured by the digital management system will be checked for completeness and accuracy and verified against source records, and all operators will be trained and credentialed before participating in the study.

Ethics and Dissemination

This study has been approved by the Ethics Committee of Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University (Approval No: LY2024-313-A). The study will be conducted in accordance with the International Conference on Harmonisation (ICH) Guidelines for Good Clinical Practice and the Declaration of Helsinki. Any protocol modifications, amendments to informed consent documents, severe adverse events, or decisions regarding trial continuation or early termination will be promptly reported to the IRB for review and approval. Protocol deviations will be documented and reported according to institutional requirements. The findings of this study will be disseminated through publication in peer-reviewed scientific journals and presentation at international academic conferences. All investigators who contributed substantively to the trial design, protocol development, and manuscript preparation will be included as authors in accordance with the International Committee of Medical Journal Editors (ICMJE).

Expected Results

This study expects to demonstrate high dialysis completion rates, adequate dialysis efficiency, and a safety profile similar or superior to conventional dialysis treatments, highlighting the feasibility of mobile intelligent dialysis as a routine clinical option.

Discussion

This study represents an innovative step forward in the integration of intelligent mobile healthcare technologies into chronic dialysis treatment. The proposed mobile intelligent dialysis platform addresses critical gaps in current dialysis care models by combining mobility with cutting-edge IoT, AI monitoring, and real-time clinical data management. The clinical workflow of our mobile intelligent dialysis platform is illustrated in Figure 4. We anticipate significant benefits including improved patient autonomy, greater convenience, reduced travel-associated burdens, and increased flexibility for dialysis scheduling, which together could enhance the overall quality of life and patient satisfaction.

Fig. 4.

Schematic representation of the sequential clinical workflow for patients receiving treatment in the intelligent mobile hemodialysis platform.

The rising prevalence of ESKD necessitates more flexible and accessible dialysis modalities. Although home hemodialysis (HHD) represents a theoretical solution [24–27], with our team pioneering mainland China’s first HHD program in 2021 [22, 23], its widespread implementation faces significant challenges, including supply chain complexities, intensive training requirements, limited emergency response capabilities, and patient concerns about self-cannulation. The mobile intelligent dialysis platform emerges as a promising intermediate solution between in-center and HHD, potentially offering greater acceptability and feasibility in mainland China by combining professional oversight with treatment location flexibility.

Previous studies, such as Australia’s “Big Red Kidney Bus,” have demonstrated that mobile dialysis units are capable of providing safe, effective, and patient-centered care, positively impacting patient wellbeing [14–16, 28, 29]. However, existing mobile dialysis models predominantly focus on hardware integration without adequately leveraging advanced digital health technologies. Our platform, by contrast, integrates sophisticated digital health management systems, including continuous physiological monitoring, remote clinical oversight, and predictive analytics for patient safety management, potentially setting a new standard for mobile dialysis service delivery.

The potential clinical implications of this study extend beyond routine patient convenience. In situations such as public health emergencies, pandemics (e.g., COVID-19), or natural disasters, conventional dialysis centers face operational disruptions, risking patient lives due to interrupted treatment schedules. A mobile intelligent platform capable of rapidly deploying dialysis care in alternative locations could serve as a critical infrastructure solution, maintaining uninterrupted dialysis services, and potentially reducing emergency healthcare burdens.

Additionally, from a socioeconomic perspective, a mobile dialysis solution could reduce indirect costs associated with dialysis care, such as transportation, caregiver time, and patient absenteeism from work or education. By providing treatment closer to or even within patients’ communities, the mobile platform may also reduce healthcare access disparities, particularly benefiting elderly patients, patients with mobility restrictions, and patients living in rural or underserved areas.

Nevertheless, we recognize that this study has inherent limitations. The small sample size (n = 20) and single-session design limit the generalizability of the results, particularly regarding long-term safety and effectiveness. In addition, although the eligible age range (18–75 years) is relatively broad for a cohort of only 20 participants, the present study is an exploratory, single-center safety study and is therefore neither designed nor powered for age-stratified efficacy inference; the planned future multicenter study with an expanded sample size will allow stratified analyses across age-groups. Furthermore, the single-center setting could restrict applicability across different regional healthcare contexts. These limitations underline the necessity of subsequent multicenter randomized controlled trials with larger populations and extended observation periods to robustly confirm the platform’s clinical efficacy, safety, and scalability.

Building on the present exploratory study, our next step will be a multicenter, randomized controlled confirmatory trial. This subsequent trial is planned to enroll a substantially larger sample size across multiple dialysis centers and to randomize patients between the mobile intelligent hemodialysis platform and conventional in-center hemodialysis, with an extended observation period covering multiple consecutive dialysis sessions. The primary endpoint will be dialysis adequacy together with the incidence of treatment-related adverse events, and secondary endpoints will include hemodynamic stability, patient-reported comfort and satisfaction, and health-economic outcomes, so as to robustly confirm the efficacy, safety, and scalability of the platform across diverse healthcare settings. Future research should investigate long-term clinical outcomes, patient-reported outcomes, cost-effectiveness analyses, and comparisons between mobile intelligent dialysis platforms and traditional dialysis centers or home dialysis options. Exploring the integration of additional remote healthcare services such as telemedicine consultations and chronic disease management programs could further enhance the value and scope of mobile dialysis solutions.

In conclusion, if successfully validated, this mobile intelligent dialysis platform has the potential to transform dialysis care delivery significantly, benefiting both patients and healthcare systems by improving service accessibility, reducing economic burdens, enhancing patient autonomy, and ensuring dialysis continuity in challenging circumstances. This research thus aligns closely with global trends toward personalized, flexible, and intelligent chronic disease management.

Acknowledgments

The authors thank Guoxin Wang’an (Shanghai) Big Data Technology Co., Ltd. for their technical assistance with this research.

Statement of Ethics

This study has been approved by the Ethics Committee of Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University (Approval No: LY2024-313-A). The findings will be disseminated through peer-reviewed publications and international academic conferences. The study will be conducted in accordance with International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) Guidelines for Good Clinical Practice and the Declaration of Helsinki. Written informed consent was obtained from all participants.

Conflict of Interest Statement

The authors have no conflicts of interest to declare.

Funding Sources

This study was funded by the National Natural Science Foundation of China (Grant No. 82400790); Noncommunicable Chronic Diseases-National Science and Technology Major Project (2025ZD0547600); Shanghai Medical Youth Talent Training and Support Program ([2025]71); Institute of Molecular Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai Key Laboratory of Nucleic Acid Chemistry and Nanomedicine, “Clinical+” Excellence Project (2024ZY004); Clinical Application Evaluation of a Mobile Intelligent Hemodialysis Platform (IIT-2024-0351); Out-standing Youth Project from Shanghai Key Laboratory of Nucleic Acid Chemistry and Nanomedicine (2025ZYC013); Physician-Scientist Development Award from Shanghai Immune Therapy Institute. The funding sources had no role in the study design, execution, and analysis, or in the manuscript conception, planning, writing, and decision to publish.

Author Contributions

Renhua Lu, Yan Fang, Qisheng Lin, and Yifei Lu contributed equally to this work and share first authorship. They were responsible for the conceptualization, study design, data acquisition, and drafting of the manuscript. Yijun Zhou and Xiaojun Zeng assisted with patient recruitment, clinical procedures, and data verification. Tingting Liu, Wangshu Wu, and Kewei Xie were involved in the implementation of the mobile hemodialysis sessions, safety monitoring, and clinical data analysis. Haifen Zhang and Shan Mou supported statistical analyses, visualization, and critical revision of the manuscript. Yan Fang was responsible for addressing the reviewers’ comments and revising the manuscript, and undertook the comprehensive organization and refinement of the study protocol and figures presented in the manuscript. Zhaohui Ni supervised the study and provided methodological input. Haijiao Jin, Zhaohui Ni and Leyi Gu served as the corresponding authors, overseeing project administration, resource coordination, and final manuscript approval. All authors had full access to the data, reviewed the manuscript, and approved the final version for submission.

Funding Statement

This study was funded by the National Natural Science Foundation of China (Grant No. 82400790); Noncommunicable Chronic Diseases-National Science and Technology Major Project (2025ZD0547600); Shanghai Medical Youth Talent Training and Support Program ([2025]71); Institute of Molecular Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai Key Laboratory of Nucleic Acid Chemistry and Nanomedicine, “Clinical+” Excellence Project (2024ZY004); Clinical Application Evaluation of a Mobile Intelligent Hemodialysis Platform (IIT-2024-0351); Out-standing Youth Project from Shanghai Key Laboratory of Nucleic Acid Chemistry and Nanomedicine (2025ZYC013); Physician-Scientist Development Award from Shanghai Immune Therapy Institute. The funding sources had no role in the study design, execution, and analysis, or in the manuscript conception, planning, writing, and decision to publish.

Data Availability Statement

The data that support the findings of this study are not publicly available due to privacy and ethical restrictions but are available from the corresponding author upon reasonable request, subject to approval by the Institutional Review Board.