title: Identifying individuals with a reduced GFR using ambulatory laboratory database surveillance.
abstract: The use of outpatient laboratory databases to identify people with a low GFR may be part of an effective strategy to increase their use of treatments to prevent kidney failure. All renal function data from 17 independent outpatient laboratories in Eastern Ontario were combined to determine the proportion of adults with at least one serum creatinine measurement during a 1-yr period. The detection rates of low GFR were measured using different algorithms, and what proportion of identified low GFR was transient was considered. Canadian census data were used to calculate rates and proportions. Renal function testing was common. Of the 1,090,000 adult residents, 32% of the entire population and 63% of seniors had at least one serum creatinine measured during the study year. Sixteen percent of the population (49% of those with tests performed) had at least one GFR <80 ml/min per 1.73 m2, 5% (16%) had at least one GFR <60 ml/min per 1.73 m2, and 0.6% (1.7%) had at least one GFR <30 ml/min per 1.73 m2. Low GFR were usually not transient: 68% of individuals with subsequent testing at least 30 d later had a similar or worse GFR. Ambulatory laboratory database case finding, particularly in older patients, seems to be a promising method for easily identifying large segments of the population with persistent reductions in GFR. Whether such identification leads to improved health outcomes warrants further study.
authors.affiliation: Division of Nephrology, University of Western Ontario, London Kidney Clinical Research Unit, Room A01, Westminster Tower, London Health Sciences Centre, 800 Commissioners Road East, London, Ontario N6A 4G5, Canada. amit.garg@lhsc.on.ca
authors.forename: Amit X
authors.initials: AX
authors.lastname: Garg
authors.forename: Muhammad
authors.initials: M
authors.lastname: Mamdani
authors.forename: David N
authors.initials: DN
authors.lastname: Juurlink
authors.forename: Carl
authors.initials: C
authors.lastname: van Walraven
mesh_terms: D000465:Algorithms; D003404:Creatinine / Q000097:blood; D016208:Databases, Factual; D005919:Glomerular Filtration Rate; D006801:Humans; D007676:Kidney Failure, Chronic / Q000175:diagnosis* / Q000453:epidemiology / Q000517:prevention & control*; D008403:Mass Screening / Q000379:methods*; D009864:Ontario / Q000453:epidemiology; D010045:Outpatients / Q000706:statistics & numerical data; D012307:Risk Factors
chemical_list: D003404:Creatinine