Development and Validation of DM and Pre-DM Risk Prediction Model

Many DM and pre-DM remain undiagnosed. The aim is to develop and validate a risk prediction function to detect DM and pre-DM in Chinese adults aged 18-84 in primary care (PC). The objectives are to: 1. Develop a risk prediction function using non-laboratory parameters to predict DM and pre-DM from the data of the HK Population Health Survey 2014/2015 2. Develop a risk scoring algorithm and determine the cut-off score 3. Validate the risk prediction function and determine its sensitivity in predicting DM and pre-DM in PC Hypothesis to be tested: The prediction function developed from the Population Health Survey (PHS) 2014/2015 is valid and sensitive in PC. Design and subjects: We will develop a risk prediction function for DM and pre-DM using data of 1,857 subjects from the PHS 2014/2015. We will recruit 1014 Chinese adults aged 18-84 from PC clinics to validate the risk prediction function. Each subject will complete an assessment on the relevant risk factors and have a blood test on OGTT and HbA1c on recruitment and at 12 months. Main outcome measures: The area under the Receiver operating characteristic (ROC) curve, sensitivity and specificity of the prediction function. Data analysis and expected results: Machine learning and Logistic regressions will be used to develop the best model. ROC curve will be used to determine the cut-off score. Sensitivity and specificity will be determined by descriptive statistics. A new HK Chinese general population specific risk prediction function will enable early case finding and intervention to prevent DM and DM complications in PC.