A calculator that combines health data and blood test results will help speed up diagnosing whether a person has type 1 or type 2 diabetes, researchers have said.

A team from the University of Exeter developed the model to help prevent people from being wrongly diagnosed. The calculator is called the T1DT2D Prediction Model and is currently available online in ‘beta format’.

The calculator has been tested on more than 2,000 people and shown to be effective. The calculator is designed to be used by adults between the ages of 18 and 50 years old.

Lead researcher Dr Angus Jones, of the University of Exeter Medical School, said: “The right diagnosis in diabetes is absolutely crucial to getting the best outcomes for patients, as treatment is very different in different types of diabetes.

“However, in some people it can be very difficult to know what type of diabetes they have. Our new calculator can help clinicians by combining different features to give them the probability a person will have type 1 diabetes and assess whether additional tests are likely to be helpful.”

In May this year, Dr Jones published research showing that nearly 40 per cent of adults with type 1 diabetes were initially given the wrong diagnosis and treated for type 2 diabetes instead.

A wrong diagnosis can lead to people being treated with the wrong medication for up to several years, which can lead to a greater risk of diabetes complications developing. The new calculator therefore helps to prevent this situation from occurring.

The new calculator follows the success of another calculator developed by the university, one that determines the likelihood that someone diagnosed with diabetes may have a rarer form of diabetes called maturity onset diabetes of the young (MODY).

The MODY Probability Calculator has already been used by more than 100,000 people and more than 9,000 people have downloaded the Diabetes Diagnostics phone app that includes the MODY calculator.

The new type 1 diabetes probability calculator will be added to the Diabetes Diagnostics app.

The research team from Exeter have published the methodology of developing and validating the model in the BMJ Open journal.

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