Computerised self-test for type 2 diabetes

Doctors have developed a computerised test for predicting the risk of type 2 diabetes based on information in patients' electronic health records…

Doctors have developed a computerised test for predicting the risk of type 2 diabetes based on information in patients' electronic health records, the British Medical Journal reported this morning.

The computerised test could identify people at high risk of diabetes and allow doctors to intervene before they develop the disease, said a study published on

The score uses information from their electronic health records, or which patients themselves are likely to know. It does not require laboratory tests, so it can be used in routine clinical practice, by national screening programmes, and also by the public.

Type 2 diabetes has increased rapidly worldwide due to ageing populations, poor diet, and obesity, the BMJ said. Early detection is crucial, yet there is no widely accepted risk prediction score in use.

Researchers from the universities of Nottingham, Edinburgh, Queen Mary's and NHS Bristol, analysed the health records of over 2.5 million patients registered at 355 general practices across England and Wales over a period of 10 years to March 2008. All participants were aged between 25 and 79 and were free of diabetes at the start of the study.

They identified patients with type 2 diabetes during the study period from the general practice computer records. They found that after adjusting for all other variables, the risk of being diagnosed as having type 2 diabetes in both men and women was significantly associated with age, sex, ethnicity, body mass index, smoking status, family history of diabetes, social deprivation, treated high blood pressure, heart disease and use of corticosteroids.

They used this to develop and validate a new diabetes risk algorithm (the QDScore) to estimate the risk of acquiring type 2 diabetes over a 10 year period, using the QResearch database.

The team then tested the QDScore by comparing the predicted risk and the observed risk at 10 years in a further 1.2 million patients from a separate sample of practices. This showed the score to be highly accurate.

The QDScore also performed well when compared with another diabetes risk algorithm, known as the Cambridge risk score.

The QDScore is the first risk prediction algorithm to estimate the 10 year risk of diabetes using both ethnicity and social deprivation, say the authors.

Incorporation of the QDScore into practice computer programmes would not increase doctors' daily workload, the authors say. But they say computer access is essential, which may be difficult for people in developing countries.

Several organisations have recommended the use of a prediction algorithm in primary care in Europe and the QDScore will be a useful tool to help achieve these goals, they write. However, they suggest that follow-up studies are needed to assess the success of the QDScore.

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