Serena Low1, Su Chi Lim2, Xiao Zhang3, Shiyi Zhou4, Lee Ying Yeoh5, Yan Lun Liu6, Subramaniam Tavintharan7, Chee Fang Sum8. 1. Khoo Teck Puat Hospital, Clinical Research Unit, 90 Yishun Central, Singapore 768828, Singapore. Electronic address: low.serena.km@alexandrahealth.com.sg. 2. Khoo Teck Puat Hospital, Diabetes Centre, 90 Yishun Central, Singapore 768828, Singapore. Electronic address: lim.su.chi@alexandrahealth.com.sg. 3. Khoo Teck Puat Hospital, Clinical Research Unit, 90 Yishun Central, Singapore 768828, Singapore. Electronic address: zhang.xiao@alexandrahealth.com.sg. 4. Khoo Teck Puat Hospital, Clinical Research Unit, 90 Yishun Central, Singapore 768828, Singapore. Electronic address: zhoushiyi@genomics.cn. 5. Khoo Teck Puat Hospital, Department of General Medicine, 90 Yishun Central, Singapore 768828, Singapore. Electronic address: yeoh.lee.ying@alexandrahealth.com.sg. 6. Khoo Teck Puat Hospital, Department of General Medicine, 90 Yishun Central, Singapore 768828, Singapore. Electronic address: liu.allen.yl@alexandrahealth.com.sg. 7. Khoo Teck Puat Hospital, Diabetes Centre, 90 Yishun Central, Singapore 768828, Singapore. Electronic address: subramaniam.tavintharan@alexandrahealth.com.sg. 8. Khoo Teck Puat Hospital, Diabetes Centre, 90 Yishun Central, Singapore 768828, Singapore. Electronic address: sum.chee.fang@alexandrahealth.com.sg.
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 5.5years, 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 χ2 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.
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 5.5years, 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 χ2 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.
Authors: Nakib Hayat Chowdhury; Mamun Bin Ibne Reaz; Fahmida Haque; Shamim Ahmad; Sawal Hamid Md Ali; Ahmad Ashrif A Bakar; Mohammad Arif Sobhan Bhuiyan Journal: Diagnostics (Basel) Date: 2021-12-03