Novel methodology to predict hypoglycaemia rates with basal insulin in real-world populations

Novel methodology to predict hypoglycaemia rates with basal insulin in real-world populations

January 25, 2020 1 By Jose Scott


People with diabetes who require basal insulin
to achieve blood glucose control can be at risk of hypoglycaemia , where blood glucose
levels drop too low. In randomised clinical trials (or RCTs), use of second-generation basal insulin analogues, such as insulin glargine 300 units/mL (known as Glargine 300) and insulin degludec, results in similar glycated haemoglobin reductions compared with first-generation basal insulin
analogues, such as glargine 100 and insulin detemir, but with less hypoglycaemia. However, it is not known whether these results
translate directly to routine clinical practice, as RCTs often apply strict inclusion and exclusion
criteria, meaning that they may not be generalisable to real-life situations. Electronic medical
records are a source of rich real-world data, but using them to make comparisons between
different treatments can be difficult because results might be biased by confounding data,
something that the randomisation in RCTs is designed to minimise. In order to make the most of large amounts
of data, such as those from electronic medical records, computers can be programmed to model
complex data relationships and can even ‘learn’ from the data to make predictions. This is a process called ‘machine learning’. The LIGHTNING study uses advanced methods, including machine learning, to predict hypoglycaemia
rates in people with type 2 diabetes using first-and second-generation basal insulin
analogues, by analysing electronic medical records. Factors that contribute to hypoglycaemia
rates in patients using a particular basal insulin are first modelled using part of the
dataset called the training dataset. This basal-insulin-specific model is then
applied to the rest of the dataset (called the ‘test dataset’) to see if it accurately
predicts hypoglycaemia rates in this subset. Changes are made to the model to improve prediction
accuracy, before the model is applied to the test dataset again, then changed again, then
tested again, and so on, until prediction is optimized. At that point, the optimized model is applied
to the full dataset, irrespective of what treatment the patients were using, to give
a prediction of what hypoglycaemia rates would be if all patients were using the basal insulin
being modelled. This whole process is repeated many times
for a particular basal insulin (to obtain estimates of variability), and
each basal-insulin-specific hypoglycaemia prediction model is generated in the same
way. Real-world data are an important adjunct to RCTs, and it is hoped that the predictive
models generated from the LIGHTNING study will aid clinical decision making.