Development and validation of a predictive model for Chronic Kidney Disease progression in Type 2 Diabetes Mellitus based on a 13-year study in Singapore. (January 2017) - Low, Serena; Lim, Su Chi; Zhang, Xiao; Zhou, Shiyi; Yeoh, Lee Ying; Liu, Yan Lun; Tavintharan, Subramaniam; Sum, Chee Fang
Highlights: This study addresses gaps on model prediction for Asians with Type 2 Diabetes. We developed a predictive model for Chronic Kidney Disease progression. The model uses routinely available clinical data for prediction. The model can be a practical tool for implementation in clinical practice. Abstract: Aims: This study aims to develop and validate a predictive model for Chronic Kidney Disease (CKD) progression in Type 2 Diabetes Mellitus (T2DM). Methods: We conducted a prospective study on 1582 patients with T2DM from a Diabetes Centre in regional hospital in 2002–2014. CKD progression was defined as deterioration across eGFR categories with ⩾25% drop from baseline. The dataset was randomly split into development (70%) and validation (30%) datasets. Stepwise multivariable logistic regression was used to identify baseline predictors for model development. Model performance in the two datasets was assessed. Results: During median follow-up of 5.5 years, 679 (42.9%) had CKD progression. Progression occurred in 467 (42.2%) and 212 patients (44.6%) in development and validation datasets respectively. Systolic blood pressure, HbA1c, estimated glomerular filtration rate and urinary albumin-to-creatinine ratio were associated with progression. Areas under receiving-operating-characteristics curve for the training and test datasets were 0.80 (95%CI, 0.77–0.83) and 0.83 (95%CI, 0.79–0.87). Observed and predicted probabilities by quintiles were not statistically different with Hosmer-Lemeshow χ 2 0.65 ( p  = 0.986) and 1.36 ( p  = 0.928) in the two datasets. Sensitivity and specificity were 71.4% and 72.2% in development dataset, and 75.6% and 72.3% in the validation dataset. Conclusions: A model using routinely available clinical measurements can accurately predict CKD progression in T2DM.