Derivation and validation of the QDiabetes algorithm to predict the risk of type 2 diabetes and risk of complications among patients with type 2 diabetes

What is the aim of the study and why is it important?

QDiabetes is a risk prediction algorithm which calculates an individual’s risk of type 2 diabetes taking account of their individual risk factors such as age, sex, ethnicity, clinical values and diagnoses. Type 2 diabetes is identified according to clinical codes recorded on the primary care record or the linked mortality or hospital record. The research describing the derivation and validation of the algorithm has been published in the BMJ.

QDiabetes is used to quantify an individual’s risk of type 2 diabetes in the next 10 years in order to prioritise and inform decisions regarding interventions to lower risk. It is recommended by NICE as the risk score for use in its guidance on diabetes (2012). It is included by Public Health England in its guidance on prevention of diabetes. It is updated regularly to ensure the algorithms remain calibrated to the current population since the incidence of disease and the prevalence of risk factors changes over time. The updates also enable the algorithm to take account of improvements to data quality (for example increases in the completeness of recording of risk factors such as ethnicity or laboratory values) and allow adaptations to any changes which might be required to improve the predictive power of the tool or the population to which is applied.

Chief Investigator

Julia Hippisley-Cox



Location of research


Date on which research approved


Project reference ID


Generic ethics approval reference


Are all data accessed are in anonymised form?


Brief summary of the dataset to be released (including any sensitive data)

Risk factors for diabetes from GP records linked to diagnoses of type 1 and type 2 medication

Implications and Impact

A Public Health England feasibility review of QDiabetes shows it to be more accurate than current approaches for identifying patients at risk of diabetes in NHS Health Checks. It has been independently and externally validated in international populations and compared with other diabetes risk prediction models and shown to have best performancex.

Funding Source

No external funding

Research Team

Professor Julia Hippisley-Cox, Professor Carol Coupland Dr John Robson Dr Peter Brindle


  • Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of the QDScore
    Authors: Hippisley-Cox J, Coupland C, Robson J, Sheikh A, Brindle P
    Ref: BMJ 2009;338:b880
  • Development and validation of QDiabetes-2018 risk prediction algorithm to estimate future risk of type 2 diabetes: cohort study.
    Authors: Hippisley-Cox J, Coupland C
    Ref: BMJ 2017;359 doi: 10.1136/bmj.j5019

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