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Identifying patients with chronic kidney disease from general practice computer records
Simon de Lusignan1, Tom Chan, Paul Stevens
1Primary Care Informatics, Department of Community Health Sciences, Hunter Wing, St George's Hospital Medical School, London SW17 0RE. slusigna@sghms.ac.uk
Family Practice
|April 9, 2005
Summary
Computer searches can identify patients with chronic kidney disease (CKD) and associated conditions. Early detection and intervention are crucial, as CKD is linked to increased mortality and end-stage renal disease.
Area of Science:
- Nephrology
- Public Health
- Health Informatics
Background:
- Chronic kidney disease (CKD) is a significant predictor of end-stage renal disease and mortality.
- The NEOERICA project investigated identifying undiagnosed CKD in general practitioner (GP) computer records for early intervention.
Purpose of the Study:
- To assess the feasibility of identifying patients with undiagnosed CKD using GP computer records.
- To determine the prevalence of Stage 3-5 CKD and associated comorbidities in a UK population.
Main Methods:
- Utilized the simplified Modification of Diet in Renal Disease (MDRD) equation to estimate glomerular filtration rate (GFR).
- Extracted data on serum creatinine (SCr), CKD stage, comorbidities, and risk factors from GP records across 12 practices.
- Analyzed prescription data for potentially nephrotoxic drugs and those requiring caution in renal impairment.
Main Results:
- 25.7% of patients had recorded SCr, with 18.9% having GFR <60 ml/min/1.73 m2 (Stage 3-5 CKD), representing 4.9% of the total population.
- Only 3.6% of identified CKD patients were formally recorded as having renal disease.
- A high prevalence of circulatory diseases (74.6%) and concerning medication prescribing patterns were observed in CKD patients.
Conclusions:
- GP computer databases are effective for identifying patients with CKD, their comorbidities, and treatments.
- The prevalence of Stage 3-5 CKD aligns with previous US findings.
- Low recording rates of diagnosed CKD highlight significant opportunities for improved detection and early intervention strategies.