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

Conditions: Diabetic Kidney Disease, Type 2 Diabetes, Chronic Kidney Disease

Sex: All

Ages: 18 Years – 70 Years

Enrollment: 2201

Sponsor: Beijing Tongren Hospital

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. 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.

Eligibility Criteria

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

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View on ClinicalTrials.gov

Source: ClinicalTrials.gov (NCT07672639). StuddyBuddy aggregates publicly available trial information.