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. 2009 Mar 17;338:b880.
doi: 10.1136/bmj.b880.

Predicting Risk of Type 2 Diabetes in England and Wales: Prospective Derivation and Validation of QDScore

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Free PMC article

Predicting Risk of Type 2 Diabetes in England and Wales: Prospective Derivation and Validation of QDScore

Julia Hippisley-Cox et al. BMJ. .
Free PMC article

Abstract

Objective: To develop and validate a new diabetes risk algorithm (the QDScore) for estimating 10 year risk of acquiring diagnosed type 2 diabetes over a 10 year time period in an ethnically and socioeconomically diverse population.

Design: Prospective open cohort study using routinely collected data from 355 general practices in England and Wales to develop the score and from 176 separate practices to validate the score.

Participants: 2 540 753 patients aged 25-79 in the derivation cohort, who contributed 16 436 135 person years of observation and of whom 78 081 had an incident diagnosis of type 2 diabetes; 1 232 832 patients (7 643 037 person years) in the validation cohort, with 37 535 incident cases of type 2 diabetes.

Outcome measures: A Cox proportional hazards model was used to estimate effects of risk factors in the derivation cohort and to derive a risk equation in men and women. The predictive variables examined and included in the final model were self assigned ethnicity, age, sex, body mass index, smoking status, family history of diabetes, Townsend deprivation score, treated hypertension, cardiovascular disease, and current use of corticosteroids; the outcome of interest was incident diabetes recorded in general practice records. Measures of calibration and discrimination were calculated in the validation cohort.

Results: A fourfold to fivefold variation in risk of type 2 diabetes existed between different ethnic groups. Compared with the white reference group, the adjusted hazard ratio was 4.07 (95% confidence interval 3.24 to 5.11) for Bangladeshi women, 4.53 (3.67 to 5.59) for Bangladeshi men, 2.15 (1.84 to 2.52) for Pakistani women, and 2.54 (2.20 to 2.93) for Pakistani men. Pakistani and Bangladeshi men had significantly higher hazard ratios than Indian men. Black African men and Chinese women had an increased risk compared with the corresponding white reference group. In the validation dataset, the model explained 51.53% (95% confidence interval 50.90 to 52.16) of the variation in women and 48.16% (47.52 to 48.80) of that in men. The risk score showed good discrimination, with a D statistic of 2.11 (95% confidence interval 2.08 to 2.14) in women and 1.97 (1.95 to 2.00) in men. The model was well calibrated.

Conclusions: The QDScore is the first risk prediction algorithm to estimate the 10 year risk of diabetes on the basis of a prospective cohort study and including both social deprivation and ethnicity. The algorithm does not need laboratory tests and can be used in clinical settings and also by the public through a simple web calculator (www.qdscore.org).

Conflict of interest statement

Competing interests: JH-C is co-director of QResearch, a not for profit organisation, which is a joint partnership between the University of Nottingham and EMIS. JH-C is also director of ClinRisk Ltd, which produces software to ensure the reliable and updatable implementation of clinical risk algorithms within clinical computer systems to help to improve patients’ care. EMIS is the leading supplier of information technology for 60% of UK general practices and may implement the QDScore within its clinical computer system. AS chairs the Equality and Diversity Forum of the National Clinical Assessment Service and is co-investigator on an MRC/NPRI funded randomised controlled trial aiming to prevent onset of type 2 diabetes in South Asians in the UK; he is also a co-investigator on the MRC Edinburgh Translational Medicine Methodology Hub. QResearch does analyses for the Department of Health and other government organisations. All research using QResearch is peer reviewed and published. This work and any views expressed within it are solely those of the co-authors and not of any affiliated bodies or organisations.

Figures

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Fig 1 Graphical representation of age interactions for men and women for risk of type 2 diabetes
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Fig 2  QDScore predicted and observed risk of diabetes by 10th of predicted risk

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