ESRD (End Stage Renal Disease)NCT07466329
Research and Validation of a Big Data-Driven Intelligent Decision-Making System for Hemodialysis
An observational study of ESRD (End Stage Renal Disease), run by Huashan Hospital at 1 site in China.
Trial summary
Objectives and Scope:This observational study aims to leverage real-world data from Huashan Hospital to develop an AI-driven intelligent decision-making system for assessing dialysis adequacy in maintenance hemodialysis (MHD) patients, and to analyze early warning factors contributing to inadequate dialysis. Read more about this study
- Who is it for?
- Ages 18 to 90 years
- Conditions the study is for
- ESRD (End Stage Renal Disease)
- How many people
- 778 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2011to 30 Sep 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 90 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study population consists of patients receiving maintenance hemodialysis at the Hemodialysis Center of Huashan Hospital between January 2011 and September 2025. Eligibility criteria require patients to be aged 18-90 years with a dialysis vintage of at least 3 months. Additionally, participants must have comprehensive clinical and follow-up data maintained within the center's electronic medical record system.
Who may be able to join
- Patients undergoing long-term maintenance hemodialysis with a dialysis vintage of at least 3 months.
- Aged between 18 and 90 years.
- Possess relatively comprehensive hemodialysis records maintained within this center.
Who cannot join
- Patients with significantly incomplete dialysis-related data.
- Patients with poor compliance during dialysis or those receiving palliative dialysis.
- Other conditions deemed unsuitable by the investigator.
Trial locations and contacts
1 site in 1 country.
China1 site
Huashan Hospital, Fudan University
Shanghai, Shanghai Municipality, 200040
About this study
Objectives and Scope:This observational study aims to leverage real-world data from Huashan Hospital to develop an AI-driven intelligent decision-making system for assessing dialysis adequacy in maintenance hemodialysis (MHD) patients, and to analyze early warning factors contributing to inadequate dialysis.
Core Research Question:Can an AI-based early warning and diagnostic model, built on multidimensional big data, identify the risk of inadequate hemodialysis at an ultra-early stage and accurately diagnose composite complications such as cardiovascular and cerebrovascular diseases? Methodology:The study will conduct a retrospective analysis of adult MHD patients treated at Huashan Hospital between January 2011 and September 2025. The dataset encompasses multidimensional variables, including sociodemographics, treatment parameters, laboratory indicators, metabolomics, and physical functions. Utilizing Dynamic Network Biomarkers (DNB) technology to screen for early warning markers, combined with artificial intelligence algorithms such as Neural Networks and Support Vector Machines (SVM), the study will construct two primary models: "Ultra-early Warning" and "Disease State Diagnosis." These models are designed to provide clinical decision support for precise interventions.
Read the detailed description
Research Background：End-stage renal disease (ESRD) represents the terminal stage of chronic kidney disease (CKD) progression. By 2020, the global ESRD population exceeded 12 million, with China accounting for nearly 30%, the highest in the world. Renal replacement therapy (RRT), including hemodialysis (HD), peritoneal dialysis (PD), and kidney transplantation, is the primary treatment for ESRD. Over 3.5 million patients worldwide receive maintenance dialysis, 90% of whom undergo HD. According to the Chinese National Renal Data System (CNRDS), the total number of dialysis patients in China approached 1 million in 2022, with maintenance hemodialysis (MHD) patients reaching 840,000-a 3.5-fold increase from 2012. Addressing the rapid growth in dialysis demand by improving medical quality and promoting social reintegration has become a global healthcare priority.Dialysis adequacy is a critical survival indicator for MHD patients. Currently, clinical practice relies on the Urea Reduction Ratio (URR) and Kt/V to assess adequacy. However, the 2002 HEMO study demonstrated that high-flux dialysis based on Kt/V did not improve survival rates. Consequently, existing metrics are criticized for failing to reflect the clearance of middle-molecule toxins and lacking a direct correlation with clinical outcomes, quality of life, and long-term prognosis. Identifying which indicators and computational models best assess dialysis adequacy remains an unresolved challenge in nephrology.Recently, advancements in Artificial Intelligence (AI) have offered new research avenues for assessing dialysis adequacy. Since 2005, studies using Artificial Neural Networks (ANN) and various machine learning (ML) models-including Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGBoost)-have demonstrated superior predictive performance (AUROC up to 0.874) compared to traditional linear regression and formulas (e.g., Smye, Daugirdas). Despite this progress, existing studies often lack comprehensive variables such as dietary nutrition, neuropsychiatric status, and physical function. Furthermore, most current models are "diagnostic" in nature-identifying differences between stable "normal" and "diseased" states-making them suitable for diagnosis but insufficient for early intervention.Therefore, this retrospective study leverages real-world data (RWD) from Huashan Hospital to identify early warning factors for inadequate dialysis. The investigators aim to construct an "Ultra-early AI Warning Model" and an "AI Diagnostic Model" to form an Intelligent Decision-Making System for Hemodialysis, providing precise clinical intervention recommendations.
- Conditions
- ESRD (End Stage Renal Disease)
- Keywords
- End-Stage Renal Disease, Maintenance Hemodialysis
- Medical subject headings
- Renal Insufficiency, Chronic, Kidney Failure, Chronic
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Patients undergoing hemodialysis at this center from January 2011 to September 2025. | Information will be retrospectively collected from patients who underwent hemodialysis at this center between January 2011 and September 2025. Data are primarily sourced from electronic information systems, including the Hemodialysis Electronic Management System, the hospital Health Information System (HIS), and the Inpatient Medical Record System. The dataset encompasses personal information, laboratory results, diagnostic data, medical orders, and nutritional status. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Cardiovascular and Cerebrovascular Diseases (CCVD)
Clinicians diagnose these conditions based on the American Heart Association (AHA) professional guidelines and diagnostic criteria.
Measured: 15 years (From January 2011 to September 2025)
Secondary outcomes (1)
Composite Complications
Composite outcome including Protein-Energy Wasting (PEW), Mineral and Bone Disorder (MBD), anemia, infection, and tumors. PEW is diagnosed based on the International Society of Renal Nutrition and Metabolism (ISRNM) criteria. MBD and anemia are diagnosed following the clinical guidelines from Kidney Disease: Improving Global Outcomes (KDIGO), the Japanese Society for Dialysis Therapy (JSDT), or the Japanese Society of Nephrology (JSN). Severe infection is diagnosed according to the Systemic Inflammatory Response Syndrome (SIRS) criteria. Tumors are identified based on clinical diagnoses by physicians.
Measured: 15 years (From January 2011 to September 2025)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Mar 2026
- Study start1 Jan 2011
- Primary completion30 Sep 2025
- Study completion30 Sep 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for ESRD (End Stage Renal Disease)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 778 participants (actual)
Sponsor and investigators
- Lead sponsor
- Huashan Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Chen Jing, Huashan Hospital
- Investigators
- Jing Chen, Principal investigator, Huashan Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07466329
- Sponsor's protocol ID
- KY2025-1424
- Status last verified
- Jan 2026
- First submitted
- 8 Mar 2026
- Last updated
- 17 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 17 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Research and Validation of a Big Data-Driven Intelligent Decision-Making System for Hemodialysis
- ClinicalTrials.gov ID
- NCT07466329
- Acronym / sponsor ID
- KY2025-1424
- Conditions
- ESRD (End Stage Renal Disease)
- Keywords
- End-Stage Renal Disease, Maintenance Hemodialysis
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 778 participants (actual)
- Lead sponsor
- Huashan HospitalUniversity, hospital or other
- Study start
- 1 Jan 2011 (actual)
- Primary completion
- 30 Sep 2025 (actual)
- Study completion
- 30 Sep 2025 (actual)
- First posted
- 12 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Objectives and Scope:This observational study aims to leverage real-world data from Huashan Hospital to develop an AI-driven intelligent decision-making system for assessing dialysis adequacy in maintenance hemodialysis (MHD) patients, and to analyze early warning factors contributing to inadequate dialysis.
Core Research Question:Can an AI-based early warning and diagnostic model, built on multidimensional big data, identify the risk of inadequate hemodialysis at an ultra-early stage and accurately diagnose composite complications such as cardiovascular and cerebrovascular diseases? Methodology:The study will conduct a retrospective analysis of adult MHD patients treated at Huashan Hospital between January 2011 and September 2025. The dataset encompasses multidimensional variables, including sociodemographics, treatment parameters, laboratory indicators, metabolomics, and physical functions. Utilizing Dynamic Network Biomarkers (DNB) technology to screen for early warning markers, combined with artificial intelligence algorithms such as Neural Networks and Support Vector Machines (SVM), the study will construct two primary models: "Ultra-early Warning" and "Disease State Diagnosis." These models are designed to provide clinical decision support for precise interventions.
Detailed description
Research Background：End-stage renal disease (ESRD) represents the terminal stage of chronic kidney disease (CKD) progression. By 2020, the global ESRD population exceeded 12 million, with China accounting for nearly 30%, the highest in the world. Renal replacement therapy (RRT), including hemodialysis (HD), peritoneal dialysis (PD), and kidney transplantation, is the primary treatment for ESRD. Over 3.5 million patients worldwide receive maintenance dialysis, 90% of whom undergo HD. According to the Chinese National Renal Data System (CNRDS), the total number of dialysis patients in China approached 1 million in 2022, with maintenance hemodialysis (MHD) patients reaching 840,000-a 3.5-fold increase from 2012. Addressing the rapid growth in dialysis demand by improving medical quality and promoting social reintegration has become a global healthcare priority.Dialysis adequacy is a critical survival indicator for MHD patients. Currently, clinical practice relies on the Urea Reduction Ratio (URR) and Kt/V to assess adequacy. However, the 2002 HEMO study demonstrated that high-flux dialysis based on Kt/V did not improve survival rates. Consequently, existing metrics are criticized for failing to reflect the clearance of middle-molecule toxins and lacking a direct correlation with clinical outcomes, quality of life, and long-term prognosis. Identifying which indicators and computational models best assess dialysis adequacy remains an unresolved challenge in nephrology.Recently, advancements in Artificial Intelligence (AI) have offered new research avenues for assessing dialysis adequacy. Since 2005, studies using Artificial Neural Networks (ANN) and various machine learning (ML) models-including Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGBoost)-have demonstrated superior predictive performance (AUROC up to 0.874) compared to traditional linear regression and formulas (e.g., Smye, Daugirdas). Despite this progress, existing studies often lack comprehensive variables such as dietary nutrition, neuropsychiatric status, and physical function. Furthermore, most current models are "diagnostic" in nature-identifying differences between stable "normal" and "diseased" states-making them suitable for diagnosis but insufficient for early intervention.Therefore, this retrospective study leverages real-world data (RWD) from Huashan Hospital to identify early warning factors for inadequate dialysis. The investigators aim to construct an "Ultra-early AI Warning Model" and an "AI Diagnostic Model" to form an Intelligent Decision-Making System for Hemodialysis, providing precise clinical intervention recommendations.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients undergoing hemodialysis at this center from January 2011 to September 2025. | – | –Information will be retrospectively collected from patients who underwent hemodialysis at this center between January 2011 and September 2025. Data are primarily sourced from electronic information systems, including the Hemodialysis Electronic Management System, the hospital Health Information System (HIS), and the Inpatient Medical Record System. The dataset encompasses personal information, laboratory results, diagnostic data, medical orders, and nutritional status. |
Who may qualify
- Ages
- 18 to 90 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of patients receiving maintenance hemodialysis at the Hemodialysis Center of Huashan Hospital between January 2011 and September 2025. Eligibility criteria require patients to be aged 18-90 years with a dialysis vintage of at least 3 months. Additionally, participants must have comprehensive clinical and follow-up data maintained within the center's electronic medical record system.
Key inclusion criteria (3 listed)
- Patients undergoing long-term maintenance hemodialysis with a dialysis vintage of at least 3 months.
- Aged between 18 and 90 years.
- Possess relatively comprehensive hemodialysis records maintained within this center.
Key exclusion criteria (3 listed)
- Patients with significantly incomplete dialysis-related data.
- Patients with poor compliance during dialysis or those receiving palliative dialysis.
- Other conditions deemed unsuitable by the investigator.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Cardiovascular and Cerebrovascular Diseases (CCVD)
Time frame: 15 years (From January 2011 to September 2025)
Clinicians diagnose these conditions based on the American Heart Association (AHA) professional guidelines and diagnostic criteria.
1 secondary outcome
Composite Complications
Time frame: 15 years (From January 2011 to September 2025)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Mar 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Jing Chen principal investigator
Huashan Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of ESRD (End Stage Renal Disease), run by Huashan Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 17 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Cardiovascular and Cerebrovascular Diseases (CCVD) (time frame: 15 years (From January 2011 to September 2025)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Mar 2026.