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NCT Number: NCT07672639

Development and Validation of a Machine Learning Model for Differentiating Diabetic Kidney Disease and Non-Diabetic Kidney Disease in Type 2 Diabetes

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.

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Key information

Conditions

Diabetic Kidney Disease Chronic Disease Chronic Kidney Disease Diabetes Complications Diabetes Mellitus Diabetes Mellitus, Type 2 Diabetic Nephropathies Disease Attributes Endocrine System Diseases Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Glucose Metabolism Disorders Kidney Diseases Male Urogenital Diseases Metabolic Diseases Nutritional and Metabolic Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms Renal Insufficiency Renal Insufficiency, Chronic Type 2 Diabetes Urogenital Diseases Urologic Diseases

Age range

18 year–70 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Beijing Tongren Hospital

Beijing, Beijing Municipality, 100730, China

About this study

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.

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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

Exclusion criteria

  • Type 1 diabetes mellitus
  • Secondary diabetes
  • Missing key clinical data
  • Non-diagnostic kidney biopsy
  • Incomplete pathological information

Treatment and study plan

Primary outcomes

  1. Diagnostic classification of DKD versus NDKD

    Time frame: During procedure

    Pathological diagnosis based on kidney biopsy findings.

Secondary outcomes

  1. Area under the receiver operating characteristic curve (AUC)

    Time frame: Through study completion (December 2024)

    Discriminative performance of the random forest model for distinguishing DKD from NDKD.

  2. Sensitivity (%)

    Time frame: Through study completion (December 2024)

    Sensitivity of the final random forest model for distinguishing DKD from NDKD.

  3. Specificity (%)

    Time frame: Through study completion (December 2024)

    Specificity of the final random forest model for distinguishing DKD from NDKD.

  4. Accuracy (%)

    Time frame: Through study completion (December 2024)

    Accuracy of the final random forest model for distinguishing DKD from NDKD.

Sponsors and collaborators

Lead sponsor

Beijing Tongren Hospital

Other

Registry information

Official study 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

Important dates

Study start
2019
Primary completion
2022
Study completion
2024
First posted
Jun 29, 2026
Registry last updated
Jun 29, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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