Chronic Kidney Disease, Diabetic Kidney Disease, Type 2 Diabetes
Conditions
Brief 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.
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.
Interventions
None listed
Sponsors
Beijing Tongren Hospital
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 70 Years
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic classification of DKD versus NDKD | During procedure | Pathological diagnosis based on kidney biopsy findings. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Area under the receiver operating characteristic curve (AUC) | Through study completion (December 2024) | Discriminative performance of the random forest model for distinguishing DKD from NDKD. |
| Sensitivity (%) | Through study completion (December 2024) | Sensitivity of the final random forest model for distinguishing DKD from NDKD. |
| Specificity (%) | Through study completion (December 2024) | Specificity of the final random forest model for distinguishing DKD from NDKD. |
| Accuracy (%) | Through study completion (December 2024) | Accuracy of the final random forest model for distinguishing DKD from NDKD. |
Countries
China
Outcome results
None listed