437-P: Establishing a Simple Decision-Making Model for the Differential Diagnosis of Diabetes Kidney Disease and Nondiabetes Kidney Disease in T2D Patients through Machine Learning

Abstract

Objective: To study the 2-DM patients with renal biopsy, analyze the risk factors of DKD and establish a simple decision model to differentiate DKD and NDKD. Methods: 99 2-DM patients were admitted to hospital in Beijing from December 1, 2018 to December 31, 2021. All of the patients accepted kidney biopsy and biochemical test, fundus photography and OCTA. According to the pathological reports, 99 cases were divided into DKD group and NDKD group. The difference indicators of the two groups were compared. The risk factors of DKD were analyzed by logistic regression. A simple and convenient method to distinguish DKD and NDKD was established through the decision tree model. Results: This study was a cross-sectional study. Compared with DKD and NDKD groups, the ratio of male/female in DKD group was higher than that in NDKD group (P=0.033), DR prevalence rate was significantly higher than that in NDKD group (P<0.001), course DM, FBG, HBAIC, SBP, TC and EGFR were also higher than those in NDKD group, and HGB decreased. Binary logistic regression analysis was conducted to determine whether DKD was combined. After adjusting for the above indicators, age, DR, course of DM, HBA1C and HGB were still independently related to DKD. A decision tree model was established with the training set: the course DM, DR, and HBA1C were risk factors of DKD. The accuracy of prediction classification is 75%, the sensitivity was 46.2%, and the specificity was 88.9%. Conclusion: If the patients with type 2 diabetes kidney injury have poor blood glucose control, no DR but anemia, a renal puncture should be considered for definite diagnosis of NDKD. Disclosure L.Zhang: None. Funding Beijing Science & Technology Development of Traditional Chinese Medicine (JJ-2020-25)