A prediction model on incident ESKD among individuals with T2D and CKD

Abstract

BackgroundDiabetes is the leading cause of end-stage kidney disease (ESKD). This study aimed to predict incident ESKD among individuals with T2D and CKD. MethodThe Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial data were split into a training set and a validation set by a ratio of 7:3. A dynamic time-varying Cox model was fit to predict the development of incident ESKD. Significant predictors were identified from a list of candidate variables, including demographic characteristics, physical exam results, laboratory results, medical history, drug information, and healthcare utilization. Model performance was evaluated by Brier score and C statistics. Decomposition analysis was conducted to assess the variable importance. Patient-level data from Harmony Outcome clinical trial and CRIC study were used for external validation. ResultsA total of 6982 diabetes patients with CKD were used for model development, with a median follow-up of four years and 312 ESKD events. The significant predictors for the final model were female sex, race, smoking status, age at T2D diagnosis, SBP, HR, HbA1c, estimated glomerular filtration rate (eGFR), urine albumin-creatinine ratio (UACR), retinopathy event occurring in last year, antihypertensive drug use, and an interaction term between SBP and female. The model demonstrated good performance in discrimination (C-statistic 0.764 [95 % CI 0.763–0.811]) and calibration (Brier Score 0.0083 [95 % CI 0.0063–0.0108]). The top 3 most important predictors in the prediction model were eGFR, retinopathy event, and UACR. Acceptable discrimination (C-statistic: 0.701 [95 % CI 0.665–0.716]; 0.86 [95 % CI 0.847–0.872]) and calibration (Brier Score: 0.0794 [95 % CI 0.0733–0.1022]; 0.0476 [95 % CI 0.0440, 0.0506]) were demonstrated in the Harmony Outcome and CRIC data, respectively. ConclusionThe dynamic risk prediction of incident ESKD among individuals with T2D can be a useful tool to support better disease management to lower the risk of developing ESKD.