Diabetic Kidney DiseaseNCT07672639
Development and Validation of a Machine Learning Model for Differentiating Diabetic Kidney Disease and Non-Diabetic Kidney Disease in Type 2 Diabetes
An observational study of Diabetic Kidney Disease and Type 2 Diabetes, run by Beijing Tongren Hospital at 1 site in China.
Trial summary
This multicenter retrospective observational study aims to develop and validate an interpretable machine learning model for differentiating diabetic kidney disease (DKD) from non-diabetic kidney disease (NDKD) in patients with type 2 diabetes mellitus. Read more about this study
- Who is it for?
- Ages 18 to 70 years
- Conditions the study is for
- Diabetic Kidney Disease, Type 2 Diabetes, Chronic Kidney Disease
- How many people
- 2,201 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2019to 1 Dec 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 to 70 years
- All sexes
- Adult, Older adult
Study population: The study population consisted of adults aged 18-70 years with type 2 diabetes mellitus who underwent clinically indicated kidney biopsy at 14 medical centers in China. Participants were identified retrospectively from biopsy records between January 2019 and December 2022. Patients with definitive pathological diagnoses of diabetic kidney disease (DKD) or non-diabetic kidney disease (NDKD) and available clinical data were included in model development. An independent cohort of biopsy-confirmed patients enrolled between January 2022 and December 2024 was used for external validation.
Who may be able to join
- Age 18-70 years
- Diagnosis of type 2 diabetes mellitus according to ADA criteria
- Underwent kidney biopsy
- Definitive pathological diagnosis available
- Availability of required clinical and laboratory data
Who cannot join
- Type 1 diabetes mellitus
- Secondary diabetes
- Missing key clinical data
- Non-diagnostic kidney biopsy
- Incomplete pathological information
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Development and Validation of an Interpretable Machine Learning Model for Noninvasive Differentiation of Diabetic Kidney Disease and Non-Diabetic Kidney Disease in Type 2 Diabetes: A Multicenter Retrospective Cohort Study
This multicenter retrospective observational study aims to develop and validate an interpretable machine learning model for differentiating diabetic kidney disease (DKD) from non-diabetic kidney disease (NDKD) in patients with type 2 diabetes mellitus. Clinical, laboratory, and pathological data from biopsy-confirmed patients were collected from 14 medical centers in China. Multiple machine learning algorithms were evaluated and externally validated. The final model was implemented as a web-based clinical decision support tool.
Read the detailed description
This study retrospectively collected clinical and pathological data from adult patients with type 2 diabetes who underwent kidney biopsy between January 2019 and December 2022 at 14 medical centers in China.
Patients were classified as having diabetic kidney disease (DKD), non-diabetic kidney disease (NDKD), or mixed pathology according to kidney biopsy findings. Demographic characteristics, diabetic complications, laboratory measurements, and renal function parameters were extracted from electronic medical records.
Six machine learning algorithms were trained and compared for discriminating DKD from NDKD. Recursive feature elimination was used for feature selection. The best-performing model was externally validated using an independent cohort enrolled between January 2022 and December 2024. Model interpretability was assessed using SHapley Additive exPlanations (SHAP).
The primary objective was to develop a noninvasive and interpretable diagnostic model capable of distinguishing DKD from NDKD using routinely available clinical variables.
- Medical subject headings
- Diabetic Nephropathies, Diabetes Mellitus, Type 2, Renal Insufficiency, Chronic
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic classification of DKD versus NDKD
Pathological diagnosis based on kidney biopsy findings.
Measured: During procedure
Secondary outcomes (4)
Area under the receiver operating characteristic curve (AUC)
Discriminative performance of the random forest model for distinguishing DKD from NDKD.
Measured: Through study completion (December 2024)
Sensitivity (%)
Sensitivity of the final random forest model for distinguishing DKD from NDKD.
Measured: Through study completion (December 2024)
Specificity (%)
Specificity of the final random forest model for distinguishing DKD from NDKD.
Measured: Through study completion (December 2024)
Accuracy (%)
Accuracy of the final random forest model for distinguishing DKD from NDKD.
Measured: Through study completion (December 2024)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Jun 2026
- Study start1 Jan 2019
- Primary completion1 Dec 2022
- Study completion1 Dec 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Diabetic Kidney DiseaseStudies 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
- 2,201 participants (actual)
Sponsor and investigators
- Lead sponsor
- Beijing Tongren Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07672639
- Sponsor's protocol ID
- TREC2022-KY126
- Status last verified
- Jun 2026
- First submitted
- 15 Jun 2026
- Last updated
- 29 Jun 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 29 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of an Interpretable Machine Learning Model for Noninvasive Differentiation of Diabetic Kidney Disease and Non-Diabetic Kidney Disease in Type 2 Diabetes: A Multicenter Retrospective Cohort Study
- ClinicalTrials.gov ID
- NCT07672639
- Acronym / sponsor ID
- TREC2022-KY126
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 2,201 participants (actual)
- Lead sponsor
- Beijing Tongren HospitalUniversity, hospital or other
- Study start
- 1 Jan 2019 (actual)
- Primary completion
- 1 Dec 2022 (actual)
- Study completion
- 1 Dec 2024 (actual)
- First posted
- 29 Jun 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.
This multicenter retrospective observational study aims to develop and validate an interpretable machine learning model for differentiating diabetic kidney disease (DKD) from non-diabetic kidney disease (NDKD) in patients with type 2 diabetes mellitus. Clinical, laboratory, and pathological data from biopsy-confirmed patients were collected from 14 medical centers in China. Multiple machine learning algorithms were evaluated and externally validated. The final model was implemented as a web-based clinical decision support tool.
Detailed description
This study retrospectively collected clinical and pathological data from adult patients with type 2 diabetes who underwent kidney biopsy between January 2019 and December 2022 at 14 medical centers in China.
Patients were classified as having diabetic kidney disease (DKD), non-diabetic kidney disease (NDKD), or mixed pathology according to kidney biopsy findings. Demographic characteristics, diabetic complications, laboratory measurements, and renal function parameters were extracted from electronic medical records.
Six machine learning algorithms were trained and compared for discriminating DKD from NDKD. Recursive feature elimination was used for feature selection. The best-performing model was externally validated using an independent cohort enrolled between January 2022 and December 2024. Model interpretability was assessed using SHapley Additive exPlanations (SHAP).
The primary objective was to develop a noninvasive and interpretable diagnostic model capable of distinguishing DKD from NDKD using routinely available clinical variables.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 to 70 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- The study population consisted of adults aged 18-70 years with type 2 diabetes mellitus who underwent clinically indicated kidney biopsy at 14 medical centers in China. Participants were identified retrospectively from biopsy records between January 2019 and December 2022. Patients with definitive pathological diagnoses of diabetic kidney disease (DKD) or non-diabetic kidney disease (NDKD) and available clinical data were included in model development. An independent cohort of biopsy-confirmed patients enrolled between January 2022 and December 2024 was used for external validation.
Key inclusion criteria (5 listed)
- Age 18-70 years
- Diagnosis of type 2 diabetes mellitus according to ADA criteria
- Underwent kidney biopsy
- Definitive pathological diagnosis available
- Availability of required clinical and laboratory data
Key exclusion criteria (5 listed)
- Type 1 diabetes mellitus
- Secondary diabetes
- Missing key clinical data
- Non-diagnostic kidney biopsy
- Incomplete pathological information
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
Diagnostic classification of DKD versus NDKD
Time frame: During procedure
Pathological diagnosis based on kidney biopsy findings.
4 secondary outcomes
Area under the receiver operating characteristic curve (AUC)
Time frame: Through study completion (December 2024)
Sensitivity (%)
Time frame: Through study completion (December 2024)
Specificity (%)
Time frame: Through study completion (December 2024)
Accuracy (%)
Time frame: Through study completion (December 2024)
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 29 Jun 2026.
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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.
The record lists no contact. Use the official record to reach the sponsor.
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 Diabetic Kidney Disease and Type 2 Diabetes, run by Beijing Tongren 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 5 inclusion and 5 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 29 Jun 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?
Diagnostic classification of DKD versus NDKD (time frame: During procedure). 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 29 Jun 2026.