Machine-learning tool could aid earlier diagnosis of type 1 diabetes

Speaking at the 2022 Diabetes UK Professional Conference, Julia Townson (Cardiff University, UK) explained that around a quarter of children with type 1 diabetes in the UK are not diagnosed until they are in diabetic ketoacidosis (DKA), with rates unchanged for 25 years despite public health campaigns, highlighting the need for improved tools for early detection. She said that previous research identified different patterns of primary care contact among children who later go on to develop type 1 diabetes versus those who do not, leading the team to hypothesize that primary care data could be used to flag those likely to be diagnosed with the condition. To investigate this, Townson and colleagues used a machine-learning algorithm drawing on 81 pieces of information from electronic health records studied from 2000 to 2016 to produce a single score that indicates the likelihood of being diagnosed with type 1 diabetes. The information used in the tool included flags such as family history, fatigue, urinary tract infections, obesity, and weight loss, as well as data on the frequency of recent primary care contact relative to average contact frequency for each child. The Welsh SAIL/Brecon registry of approximately 35 million primary care contacts for 1 million children (0.21% with type 1 diabetes) was used as the training dataset, and the tool was tested using the English Clinical Practice Research Datalink (CPRD) and Hospital Episode Statistics records involving around 43 million contacts for 1.5 million children (0.10% with type 1 diabetes).