Doctors have developed a computerised test for
predicting the risk of type 2 diabetes based on information in
patients' electronic health records, theBritish Medical
Journalreported 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 bmj.com.
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.