Abstract:

More than 650 million people suffer from prediabetes worldwide and the prevalence is increasing rapidly. A large part of these people will eventually develop microvascular and macrovascular complications generating a large economic burden on society. To prevent or delay onset of these complications, both lifestyle and pharmacological interventions are necessary. However, treatment tools or guidelines specifically for prevention of complications for this group does not exist in general practice. To address this challenge, the project seeks to improve the management of people with prediabetes by developing a decision support system to be implemented at the general practitioner. Based on a prediction of the personalized risk of micro- or macrovascular complications and a risk stratification, individuals with high- risk profiles will be identified. Additionally, different scenarios with lifestyle and pharmacological interventions will be simulated. This novel prediabetes risk engine tool will support informed treatment and early prevention strategies at the general practitioner, aiming to prevent or delay the onset of complications. Data from clinical studies and Danish national registers will be analyzed using data science techniques to identify patterns which are important for prediction of diabetes-related complications. Additionally, Artificial Intelligence including machine learning methodology will be used to develop the prediction model. No studies regarding prediabetes have investigated development and implementation of a flexible model allowing usage with only a limited amount of clinical data, and with a possibility of entering further data to increase precision of the risk estimate. Therefore, this project will focus on development of a flexible predictive model aimed at estimating the personalized risk of micro- or macrovascular complications among individuals with pre-diabetes.