QDiabetes risk model updated
medwireNews: The QDiabetes risk assessment tool for type 2 diabetes has been updated with an expanded range of risk factors and additional models that incorporate blood glucose tests.
The existing model is based on variables including age, ethnicity, deprivation, BMI, family history of diabetes, and cardiovascular disease.
It now also incorporates atypical antipsychotics, statins, schizophrenia or bipolar affective disorder, learning disability, gestational diabetes, and polycystic ovary syndrome. The researchers found these variables to be associated with risk increases for diabetes ranging from 26% for schizophrenia or bipolar affective disorder or learning disability in men to 359% for gestational diabetes in women.
The previous omission of these well-established diabetes risk factors would cause QDiabetes to underestimate risk in affected patients, say the researchers – Julia Hippisley-Cox and Carol Coupland, both from the University of Nottingham in the UK.
“The inclusion of these new risk factors will help ensure more accurate estimation of the level of risk in the affected population to improve information for individual patients and for surveillance strategies,” they write in The BMJ.
The team developed the updated tool using data from 8,186,705 people without diabetes registered at 1457 English primary care practices in the QResearch database. During a median 3.9 years of follow-up, 178,314 people developed diabetes.
Because people identified as high-risk patients using QDiabetes are then advised to have a blood glucose test, the researchers created an additional two QDiabetes models that include fasting blood glucose and glycated hemoglobin (HbA1c), to help healthcare providers give their patients more accurate estimates of their personal diabetes risk.
Of the two models, they found the one including fasting blood glucose to be the most accurate. In an additional 2,629,940 people from 363 primary care practices in the validation cohort, this model explained 63.3% of the variation in time to diabetes diagnosis in women and 58.4% in men, whereas the model including HbA1c explained a corresponding 60.3% and 55.5%.
The researchers therefore suggest using the updated basic QDiabetes model to identify high-risk patients, followed by the models incorporating blood glucose tests to refine risk estimates – ideally using fasting blood glucose, since this was more accurate.
They add: “Although the new models are more complex than the existing models, this is unlikely to affect the uptake of the new models as they are all designed to be calculated automatically based on information recorded in the electronic patient record.”
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