The validity of searching routinely collected general practice computer data to identify patients with chronic kidney disease (CKD): a manual review of 500 medical records.

BACKGROUND We conducted a search of 12 practices' routinely collected computer data in three localities across the UK and found that 4.9% of the registered population had an estimated glomerular filtration rate (GFR) of <60 ml/min/1.73 m(2) (equivalent to stages 3-5 CKD). Only 3.6% of these were known to have renal disease. Although UK general practice is computerized, important clinical data might be recorded in letters or free-text computer entries and might therefore be invisible to the standard computer search tools. We therefore manually searched through all the records of patients with stages 3-5 CKD in one practice, to test the validity of the computer generated diagnosis and to see if other relevant information was missed by the computer search. METHODS We identified 492 people with stages 3-5 CKD using computer searching and then manually searched their computer records and written notes for any missed data. The dataset included cardiovascular morbidities and risk factors including diabetes; drugs which may impair renal function; known renal disease; and terminal diagnoses and dementia. RESULTS The manual searches only added four renal diagnoses to the 36 already identified. Although heart failure and stroke appear to be over-estimated by computer searches, other cardiovascular diagnoses were reliably recorded. Cardiovascular risk factors and drug recording is a strength of general practice computer data. It is complete and contemporary, though most patients had scope to have their cardiovascular risk reduced further. Eighty-four percent had a haemoglobin estimation, and a higher proportion with reduced renal function were anaemic (P<0.001). Testing for proteinuria was less well recorded; negative stick tests were not recorded. Clinical diagnoses of prostatism and bladder outflow problems made these data hard to interpret. CONCLUSIONS Automated searching of general practice computer records could provide a reliable and valid way of identifying people with stages 3-5 CKD who could benefit from interventions readily available in primary care.

[1]  D L Crombie,et al.  The problem of diagnostic variability in general practice. , 1992, Journal of epidemiology and community health.

[2]  A. Levey,et al.  A More Accurate Method To Estimate Glomerular Filtration Rate from Serum Creatinine: A New Prediction Equation , 1999, Annals of Internal Medicine.

[3]  J. Cameron,et al.  European best practice guidelines for the management of anaemia in patients with chronic renal failure. , 1999, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[4]  Simon de Lusignan,et al.  A Survey to Identify the Clinical Coding and Classification Systems Currently in Use Across Europe , 2001, MedInfo.

[5]  F. Locatelli,et al.  The importance of early detection of chronic kidney disease. , 2002, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[6]  K Doqi,et al.  clinical practice guidelines for chronic kidney disease : evaluation, classification, and stratification , 2002 .

[7]  Simon de Lusignan,et al.  Paperless practices: a report from a research network , 2002 .

[8]  P. Roderick,et al.  A population-based study of the incidence and outcomes of diagnosed chronic kidney disease. , 2003, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[9]  G. Eknoyan,et al.  Prevalence of chronic kidney disease and decreased kidney function in the adult US population: Third National Health and Nutrition Examination Survey. , 2003, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[10]  T. Nickolas,et al.  Awareness of kidney disease in the US population: findings from the National Health and Nutrition Examination Survey (NHANES) 1999 to 2000. , 2004, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[11]  Tom Chan,et al.  Problems with primary care data quality: osteoporosis as an exemplar. , 2004, Informatics in primary care.

[12]  P. Stevens,et al.  Unreferred chronic kidney disease: a longitudinal study. , 2004, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[13]  D. Bates,et al.  Comprehensive computerised primary care records are an essential component of any national health information strategy: report from an international consensus conference. , 2004, Informatics in primary care.

[14]  Tom Chan,et al.  Identifying patients with chronic kidney disease from general practice computer records. , 2005, Family practice.

[15]  Simon de Lusignan,et al.  Codes, classifications, terminologies and nomenclatures: definition, development and application in practice. , 2005 .

[16]  S. de Lusignan Codes, classifications, terminologies and nomenclatures: definition, development and application in practice. , 2005, Informatics in primary care.

[17]  C. Weel,et al.  The use of routinely collected computer data for research in primary care: opportunities and challenges. , 2006, Family practice.