Derivation and validation of the QKidney algorithm to predict the risk of moderate and severe kidney disease

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

QKidney is a risk prediction algorithm which calculates an individual’s risk of moderate or severe kidney disease taking account of their individual risk factors such as age, sex, ethnicity, clinical values and diagnoses. Moderate or severe kidney disease 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 BMC.

QKidney is used to quantify an individual’s risk of moderate or severe kidney disease in the next 5 years in order to prioritise and inform decisions regarding interventions to lower risk. It was developed at the request of the National Tsar for Kidney Disease. 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 also allows 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 kidney disease linked to outcomes (development of stage 3-5 kidney failure) on GP, hospital or mortality records

Funding Source

No external funding

Research Team

Professor Julia Hippisley-Cox, Professor Carol Coupland


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