Abstract: SA-PO298
Establishment and Validation of a Risk Prediction Model for Kidney Prognosis of People with Type 2 Diabetic Kidney Disease Diagnosed by Kidney Biopsy
Session Information
- Diabetic Kidney Disease: Clinical Pathology, Diagnostic and Treatment Advances
October 26, 2024 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Diabetic Kidney Disease
- 702 Diabetic Kidney Disease: Clinical
Authors
- Wang, Wenjian, Department of Nephrology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
- Chen, Xiaojie, Department of Nephrology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China
Background
Diabetic kidney disease (DKD) is the most common cause of end-stage renal disease
(ESRD) and is associated with increased morbidity and mortality in patients with diabetes.
Identification of risk factors associates with renal progression(50%eGFR decline and/or progressed to ESRD) of DKD is expected to result in early detection and intervention and improve prognosis.
Methods
Between January 2010 and December 2023, a total of 186 Chinese patients with T2DM and DKD confirmed by renal biopsy were enrolled and followed up for at least 90 days.
Three machine learning algorithms (Logistic Regression, LASSO and stepwise AIC) were used to identify the critical clinical and pathological features and to build a risk prediction model for renal prognosis.
Results
There were 107 renal outcome events (57.53%) during the 2.27 year median follow
up. The stepwise AIC algorithm showed the best performance at predicting progression to ESRD, showing the highest AUC (0.88) . The stepwise AIC algorithm identified eight major factors:
HDL-C, gender, hemoglobin (Hb), 24-hour urine urinary total protein, 24-hour urine urea nitrogen, baseline estimated glomerular filtration rate, age and renal hyalinosis of renal arterioles.
Conclusion
Our model can efficiently predict the incident renal prognosis in DKD patients. Compared with the previous models, the importance of 24-hour urine urea nitrogen, Hb, and renal hyalinosis of renal arterioles were highlighted in the current model.
Funding
- Government Support – Non-U.S.