New Online Test Estimates 10-Year Risk for Diabetes

The QDScore, which includes both social deprivation and ethnicity, is the first risk prediction algorithm to estimate the 10-year risk for diabetes.

The QDScore, which includes both social deprivation and ethnicity, is the first risk prediction algorithm to estimate the 10-year risk for diabetes.

“Although several algorithms for predicting the risk of Type 2 diabetes have been developed, no widely accepted diabetes risk prediction score has been developed and validated for use in routine clinical practice,” write Julia Hippisley-Cox, from University Park, Nottingham, United Kingdom, and colleagues. “Previous studies have been limited by size, and some have performed inadequately when tested in ethnically diverse populations. A new diabetes risk prediction tool with appropriate weightings for both social deprivation and ethnicity is needed given the prevalence of Type 2 diabetes, particularly among minority ethnic communities, appreciable numbers of whom remain without a diagnosis for long periods of time.”

The goal of this study was to develop and validate the QDScore for estimating the 10-year risk of acquiring diagnosed Type 2 diabetes during a 10-year period, with the use of routinely collected data from an ethnically and socioeconomically diverse population. The derivation cohort, obtained from 355 general practices in England and Wales, consisted of 2,540,753 patients who were observed for 16,436,135 person-years. Age range was 25 to 79 years; 78,081 had an incident diagnosis of Type 2 diabetes. The validation cohort, obtained from 176 separate practices, consisted of 1,232,832 patients (7,643,037 person-years) with 37,535 incident cases of Type 2 diabetes.

Effects of risk factors in the derivation cohort were estimated, and a risk equation in men and women was derived with use of a Cox proportional hazards model. In the final model, predictive variables 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 primary endpoint of the study was incident diabetes, as recorded in general practice records. The validation cohort was used to determine measures of calibration and discrimination.

Different ethnic groups had 4-fold to 5-fold variation in the risk for Type 2 diabetes. The algorithm explained 51.53% of the variation in women and 48.16% of that in men, when tested in the validation dataset. The model was well calibrated, and the risk score showed good discrimination. D statistic was 2.11 in women and 1.97 in men.

“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 study authors write. “The algorithm does not need laboratory tests and can be used in clinical settings and also by the public through a simple web calculator.” See this week’s Tool For Your Practice.

“This algorithm to predict risk of Type 2 diabetes has the unique advantage of including both ethnicity and social deprivation, can be derived without laboratory measurements, and thus is suitable for use both in clinical settings and for self assessment,” the study authors conclude. “The QDScore could be used to identify patients at high risk of diabetes who might benefit from interventions to reduce their risk.”

In an accompanying editorial, Peter E.H. Schwarz, from the Technical University Dresden in Dresden, Germany, and colleagues note that the QDScore is a useful computer-based screening tool.

“Incorporation of the QDScore into practice computer programs would not increase doctors’ daily workload, but it would be useful because doctors will not always know the complete medical history of their patients and will not identify all people at increased risk of diabetes,” the editorialists conclude. “However, accurate and standardized risk stratification is a challenge, and follow-up studies are needed to assess the success of the QDScore.”
See this week’s Tool For Your Practice

Practice Pearls

• In a previous study, the presence of obesity in a friend, a sibling, or a spouse was associated with an increased risk for incident obesity. However, obesity in a neighbor was not associated with an increase in the risk for incident obesity.
• The QDScore consists of demographic and disease factors but not laboratory data. In the current study, it successfully predicted the 10-year risk for incident Type 2 diabetes.

BMJ. Published online March 18, 2009.

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