title: Identifying patients with chronic kidney disease from general practice computer records.
abstract: BACKGROUND
  Chronic kidney disease (CKD) is an important predictor of end-stage renal disease, as well as a marker of increased mortality. The New Opportunities for Early Renal Intervention by Computerised Assessment (NEOERICA) project aimed to assess whether people with undiagnosed CKD who might benefit from early intervention could be identified from GP computer records.
  
  
  METHODS
  The simplified Modification of Diet in Renal Disease (MDRD) equation was used to estimate glomerular filtration rate (GFR) and determine stage of CKD in patients from 12 practices in Surrey, Kent and Greater Manchester with SCr recorded in their notes. Further data were extracted on associated co-morbidities and potentially modifiable risk factors.
  
  
  RESULTS
  One quarter (25.7%; 28,862/112,215) had an SCr recorded and one in five (18.9%) of them had a GFR <60 ml/min/1.73 m2 (equivalent to Stage 3-5 CKD), representing 4.9% of the population. Only 3.6% of these were recorded as having renal disease. Three-quarters (74.6%; 4075/5449) of those with Stage 3-5 CKD had one or more circulatory diseases; 346 were prescribed potentially nephrotoxic drugs and over 4000 prescriptions were issued for drugs recommended to be used with caution in renal impairment.
  
  
  CONCLUSIONS
  Patients with CKD can be identified by searching GP computer databases; along with associated co-morbidities and treatment. Results revealed a similar rate of Stage 3-5 CKD to that found previously in the USA. The very low rate of recording of renal disease in patients found to have CKD indicates scope for improving detection and early intervention.
authors.affiliation: Primary Care Informatics, Department of Community Health Sciences, Hunter Wing, St George's Hospital Medical School, London SW17 0RE. slusigna@sghms.ac.uk
authors.forename: Simon
authors.initials: S
authors.lastname: de Lusignan
authors.forename: Tom
authors.initials: T
authors.lastname: Chan
authors.forename: Paul
authors.initials: P
authors.lastname: Stevens
authors.forename: Donal
authors.initials: D
authors.lastname: O'Donoghue
authors.forename: Nigel
authors.initials: N
authors.lastname: Hague
authors.forename: Billy
authors.initials: B
authors.lastname: Dzregah
authors.forename: Jeremy
authors.initials: J
authors.lastname: Van Vlymen
authors.forename: Mel
authors.initials: M
authors.lastname: Walker
authors.forename: Sean
authors.initials: S
authors.lastname: Hilton
mesh_terms: D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D004739:England / Q000453:epidemiology; D005194:Family Practice / Q000458:organization & administration* / Q000706:statistics & numerical data; D005240:Feasibility Studies; D005260:Female; D005919:Glomerular Filtration Rate; D006801:Humans; D007676:Kidney Failure, Chronic / Q000175:diagnosis / Q000453:epidemiology* / Q000503:physiopathology; D008297:Male; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data*; D008875:Middle Aged; D015995:Prevalence; D012307:Risk Factors; D017678:Sex Distribution