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At the heart of general practice since 1960

Toolbox - QDiabetes

Professor Julia Hippisley-Cox offers her potted guide on how and when to use this tool for identifying patients at high risk of type 2 diabetes

 

What is QDiabetes

QDiabetes is a validated tool which predicts a patient’s absolute risk of developing type 2 diabetes within the next 10 years.  There is good evidence that diabetes can be prevented with targeted interventions such as weight loss, exercise and lifestyle changes.

QDiabetes is available online and on the app store, or integrated into GP computer systems.

To calculate a patient’s QDiabetes score you need to know: age, sex, ethnicity, deprivation score, smoking status, family history of diabetes, history of cardiovascular disease, hypertension on treatment, regular steroid use, and body mass index.

When to use it

QDiabetes can be used in any patient aged 25-84 without an existing diagnosis of type 2 diabetes.

How to use it

QDiabetes is based on variables which the patient is likely to know or information which is recorded in the GP practice computer.

You can go online and enter information into the web calculator. This will generate a patient’s 10-year risk of diabetes. In the example below the patient has a 55% risk of developing type 2 diabetes, which is four-times higher than the average risk for a similar person of the same age, sex and ethnic group. This can be useful for explaining risk to an individual patient.

You can also use the QIntervention tool. This combines QRISK2 with QDiabetes to give a combined vascular risk. You can also use the ‘what if’s to  demonstrate to patients how their risk would change with interventions such as weight loss

QDiabetes is available for all GP system suppliers to integrate as with QRISK2 - many of the questions overlap and so it is possible to combine QDiabetes and QRISK2 into a vascular risk assessment.  It has already been integrated into EMIS Web as a calculator tool which can be used to risk assess patients within the consultation using data already recorded in the clinical system. This will store the result into the clinical record so that it can be used for analyses and reporting in the practice. It can also be used as a batch processing tool which allows you to risk score every patient and generate a list of high-risk patients for further assessment and intervention.

What constitutes high risk?

There is no absolute definition of high risk, but as a rule of thumb, 10% of the population will have a 10% plus risk of developing diabetes over the next 10 years.

Some practices risk score their populations and then order the list so that those at highest risk are at the top of the list. You can then call in the top ‘X’ number of patients for further assessment depending on practice workload.

The evidence

It has been formally validated on two separate populations of patients1 2 – one from EMIS practices and one from Vision practices. The results show it accurately predicts the level of risk and also distinguishes well between those at risk. One of the validations was conducted by a fully independent team from Oxford University. This is the ‘gold standard’ for validation of risk prediction tools1.

QDiabetes has been recommended in the NICE guidelines on the identification of patients at risk of diabetes published in July 2012.

 

Professor Julia Hippisley-Cox is a professor of clinical epidemiology and general practice in the division of primary care at the University of Nottingham and a sessional GP in the city. Professor Hippisley-Cox was co-founder of the QResearch database and developed QDiabetes and QRisk, and is medical director of CliRisk Ltd which makes open and closed source software to implement risk algorithms in clinical practice

 

References

1. Collins GS, Altman DG. External validation of the QDScore for predicting the 10-year risk of developing Type 2 diabetes. Diabetic Medicine 2011;28:599-607.

2. Hippisley-Cox J, Coupland C, Robson J, Sheikh A, Brindle P. Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. BMJ 2009;338:b880-.

 

 

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