End-stage Renal DiseaseNCT06775067
Incremental Dialysis Decision Model Based on Expert-Guided Machine Learning
An observational study of End-stage Renal Disease, run by Huashan Hospital at 1 site in China.
Trial summary
This observational prospective study combined clinical expert knowledge with machine learning to develop and validate a predictive model for incremental hemodialysis decision-making. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- End-stage Renal Disease
- How many people
- 175 participants
- Where
- 1 siteChina
- Study dates
- 12 Apr 2010to 28 Jun 2024
- 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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Hemodialysis patients with residual renal function admitted to dialysis centers
Who may be able to join
- New hemodialysis patients (Apr 2010-Jun 2024), started within 3 months, including transfers.
- Age ≥18, stable hemodialysis >6 months.
Who cannot join
- Incomplete/unreliable data.
- Twice-weekly palliative dialysis.
- No baseline urine output or ≤200 mL/24h.
- Liver disease, heart failure, or severe comorbidities.
Trial locations and contacts
1 site in 1 country.
China1 site
Huashan hospital, Fudan university
Shanghai, Shanghai Municipality, 200040
About this study
Official title: Machine Learning Based on Expert Knowledge to Build and Validate a Decision Model for Incremental Dialysis
This observational prospective study combined clinical expert knowledge with machine learning to develop and validate a predictive model for incremental hemodialysis decision-making. The aim of the predictive model is to assist clinicians in developing individualized incremental dialysis treatment plans to optimize patient outcomes.
Read the detailed description
By collecting patients' clinical and biochemical parameters and combining them with experts' judgments of dialysis timing and frequency, the model can dynamically assess patients' risk of needing to increase the frequency of dialysis, thus assisting physicians in formulating individualized incremental dialysis regimens to optimize dialysis outcomes and improve patients' prognosis.
- Conditions
- End-stage Renal Disease
- Keywords
- Machine Learning, Expert Knowledge, Interpretability, Incremental Hemodialysis, Decision Model
- Medical subject headings
- 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 |
|---|---|---|
| Huashan Hospital Hemodialysis Cohort | This is a single-center prospective cohort study that included 175 patients with end-stage renal disease (ESKD) who received maintenance hemodialysis at the hemodialysis center of Huashan Hospital from April 2010 to June 2024. The ESKD patient population was comprised of 175 cases in total. All patients retained some residual kidney function (RKF), and their dialysis records and regular laboratory test results were integrated as input features for the machine learning model. The primary objective of the model was twofold: first, to integrate expert knowledge with machine learning to predict when a switch from lower frequency incremental dialysis (I-HD) to higher frequency dialysis should be made; and second, to identify key variables affecting the risk of adverse outcomes over a two-year period. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Number (Proportion) of Participants Who Experience an Incremental Dialysis Event, Assessed Monthly
An incremental dialysis event is defined as an increase in a patient's dialysis frequency (e.g., from 1 session per week to 2 sessions per week, or from 2 to 3 sessions per week, etc.) due to clinical considerations such as decreased residual renal function, fluid overload, or other physician-determined criteria. At each monthly visit (up to 5 years from enrollment), investigators will record whether each participant experiences an incremental event. We will quantify the primary outcome as the number and proportion of participants who transition to a higher dialysis frequency per month, as well as the cumulative incidence over time.
Measured: Baseline and monthly visits from enrollment until incremental dialysis event, death, transfer, or up to 5 years (whichever occurs first)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Jan 2025
- Study start12 Apr 2010
- Primary completion28 Jun 2024
- Study completion28 Jun 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for End-stage Renal DiseaseStudies of the same condition, open and under way.
- More trials run by Huashan HospitalThe sponsor's other registered studies.
- 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
- Prospective
- Enrolment
- 175 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
- NCT06775067
- Sponsor's protocol ID
- KY2019-585
- Status last verified
- Jan 2025
- First submitted
- 5 Jan 2025
- Last updated
- 14 Jan 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 14 Jan 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine Learning Based on Expert Knowledge to Build and Validate a Decision Model for Incremental Dialysis
- ClinicalTrials.gov ID
- NCT06775067
- Acronym / sponsor ID
- KY2019-585
- Conditions
- End-stage Renal Disease
- Keywords
- Machine Learning, Expert Knowledge, Interpretability, Incremental Hemodialysis, Decision Model
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 175 participants (actual)
- Lead sponsor
- Huashan HospitalUniversity, hospital or other
- Study start
- 12 Apr 2010 (actual)
- Primary completion
- 28 Jun 2024 (actual)
- Study completion
- 28 Jun 2024 (actual)
- First posted
- 14 Jan 2025 (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.
This observational prospective study combined clinical expert knowledge with machine learning to develop and validate a predictive model for incremental hemodialysis decision-making. The aim of the predictive model is to assist clinicians in developing individualized incremental dialysis treatment plans to optimize patient outcomes.
Detailed description
By collecting patients' clinical and biochemical parameters and combining them with experts' judgments of dialysis timing and frequency, the model can dynamically assess patients' risk of needing to increase the frequency of dialysis, thus assisting physicians in formulating individualized incremental dialysis regimens to optimize dialysis outcomes and improve patients' prognosis.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Huashan Hospital Hemodialysis Cohort | – | –This is a single-center prospective cohort study that included 175 patients with end-stage renal disease (ESKD) who received maintenance hemodialysis at the hemodialysis center of Huashan Hospital from April 2010 to June 2024. The ESKD patient population was comprised of 175 cases in total. All patients retained some residual kidney function (RKF), and their dialysis records and regular laboratory test results were integrated as input features for the machine learning model. The primary objective of the model was twofold: first, to integrate expert knowledge with machine learning to predict when a switch from lower frequency incremental dialysis (I-HD) to higher frequency dialysis should be made; and second, to identify key variables affecting the risk of adverse outcomes over a two-year period. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Hemodialysis patients with residual renal function admitted to dialysis centers
Key inclusion criteria (2 listed)
- New hemodialysis patients (Apr 2010-Jun 2024), started within 3 months, including transfers.
- Age ≥18, stable hemodialysis >6 months.
Key exclusion criteria (4 listed)
- Incomplete/unreliable data.
- Twice-weekly palliative dialysis.
- No baseline urine output or ≤200 mL/24h.
- Liver disease, heart failure, or severe comorbidities.
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
Number (Proportion) of Participants Who Experience an Incremental Dialysis Event, Assessed Monthly
Time frame: Baseline and monthly visits from enrollment until incremental dialysis event, death, transfer, or up to 5 years (whichever occurs first)
An incremental dialysis event is defined as an increase in a patient's dialysis frequency (e.g., from 1 session per week to 2 sessions per week, or from 2 to 3 sessions per week, etc.) due to clinical considerations such as decreased residual renal function, fluid overload, or other physician-determined criteria. At each monthly visit (up to 5 years from enrollment), investigators will record whether each participant experiences an incremental event. We will quantify the primary outcome as the number and proportion of participants who transition to a higher dialysis frequency per month, as well as the cumulative incidence over time.
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 14 Jan 2025.
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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 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 2 inclusion and 4 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 14 Jan 2025. 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?
Number (Proportion) of Participants Who Experience an Incremental Dialysis Event, Assessed Monthly (time frame: Baseline and monthly visits from enrollment until incremental dialysis event, death, transfer, or up to 5 years (whichever occurs first)). 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 14 Jan 2025.