Open Access Highly Accessed Research article

Detecting chronic kidney disease in population-based administrative databases using an algorithm of hospital encounter and physician claim codes

Jamie L Fleet1, Stephanie N Dixon123, Salimah Z Shariff13, Robert R Quinn4, Danielle M Nash1, Ziv Harel5 and Amit X Garg1236*

Author Affiliations

1 Department of Medicine, Division of Nephrology, Western University, London, Canada

2 Department of Epidemiology & Biostatistics, Western University, London, Canada

3 Institute for Clinical Evaluative Sciences, Ontario, Canada

4 Departments of Medicine & Community Health Sciences, University of Calgary, Calgary, Canada

5 Division of Nephrology, University of Toronto, Toronto, Canada

6 London Kidney Clinical Research Unit, Room ELL-101, Westminster, London Health Sciences Centre, 800 Commissioners Road East, London, Ontario, N6A 4G5, Canada

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BMC Nephrology 2013, 14:81  doi:10.1186/1471-2369-14-81

Published: 5 April 2013

Additional files

Additional file 1: Table S1:

Standards for the reporting of diagnostic accuracy studies checklist. Table S2. List of all 55 potential chronic kidney disease codes and performance in detecting an estimated glomerular filtration rate of < 45 mL/min per 1.73 m2. All potential codes were reviewed by two nephrologists to identify any potentially relevant renal codes. The final list consisted of 11 of these codes.

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