title: Implementation of the automated Leicester Practice Risk Score in two diabetes prevention trials provides a high yield of people with abnormal glucose tolerance.
abstract: OBJECTIVE
  The Leicester Practice Risk Score (LPRS) is a tool for identifying those at high risk of either impaired glucose regulation (IGR), defined as impaired glucose tolerance and/or impaired fasting glucose, or type 2 diabetes from routine primary care data. The aim of this study was to determine the yield from the LPRS when applied in two diabetes prevention trials.
  
  
  METHODS
  Let's Prevent Diabetes (LPD) and Walking Away from Diabetes (WAD) studies used the LPRS to identify people at risk of IGR or type 2 diabetes from 54 general practices. The top 10% at risk within each practice were invited for screening using a 75 g OGTT. The response rate to the invitation and the prevalence of IGR and/or type 2 diabetes in each study were calculated.
  
  
  RESULTS
  Of those invited 19.2% (n = 3,449) in LPD and 22.1% (n = 833) in WAD attended. Of those screened for LPD 25.5% (95% CI 24.1, 27.0) had IGR and 4.5% (95% CI 3.8, 5.2) had type 2 diabetes, giving a prevalence of any abnormal glucose tolerance of 30.1% (95% CI 28.5, 31.6). Comparable rates were seen for the WAD study: IGR 26.5% (95% CI 23.5, 29.5), type 2 diabetes 3.0% (95% CI 1.8, 4.2) and IGR/type 2 diabetes 29.5% (95% CI 26.4, 32.6).
  
  
  CONCLUSIONS
  Using the LPRS identifies a high yield of people with abnormal glucose tolerance, significantly higher than those seen in a population screening programme in the same locality. The LPRS is an inexpensive and simple way of targeting screening programmes at those with the highest risk.
authors.affiliation: Department of Health Sciences, University of Leicester, Leicester Diabetes Centre (Broadleaf), Leicester General Hospital, Gwendolen Road, Leicester, UK. lg48@le.ac.uk
authors.forename: L J
authors.initials: LJ
authors.lastname: Gray
authors.forename: K
authors.initials: K
authors.lastname: Khunti
authors.forename: C
authors.initials: C
authors.lastname: Edwardson
authors.forename: S
authors.initials: S
authors.lastname: Goldby
authors.forename: J
authors.initials: J
authors.lastname: Henson
authors.forename: D H
authors.initials: DH
authors.lastname: Morris
authors.forename: D
authors.initials: D
authors.lastname: Sheppard
authors.forename: D
authors.initials: D
authors.lastname: Webb
authors.forename: S
authors.initials: S
authors.lastname: Williams
authors.forename: T
authors.initials: T
authors.lastname: Yates
authors.forename: M J
authors.initials: MJ
authors.lastname: Davies
mesh_terms: D000328:Adult; D000368:Aged; D001786:Blood Glucose / Q000378:metabolism; D005260:Female; D018149:Glucose Intolerance; D005951:Glucose Tolerance Test; D006442:Glycated Hemoglobin / Q000378:metabolism; D006292:Health Priorities; D006293:Health Promotion; D006801:Humans; D008297:Male; D008403:Mass Screening / Q000379:methods*; D008875:Middle Aged; D011236:Prediabetic State / Q000097:blood / Q000453:epidemiology / Q000517:prevention & control*; D015995:Prevalence; D018570:Risk Assessment; D012307:Risk Factors; D006113:United Kingdom / Q000453:epidemiology
chemical_list: D001786:Blood Glucose; D006442:Glycated Hemoglobin; C517652:hemoglobin A1c protein, human