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

BACKGROUND: Large, population-based administrative healthcare databases can be used to identify patients with chronic kidney disease (CKD) when serum creatinine laboratory results are unavailable. We examined the validity of algorithms that used combined hospital encounter and physician claims datab...

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Autores principales: Fleet, Jamie L, Dixon, Stephanie N, Shariff, Salimah Z, Quinn, Robert R, Nash, Danielle M, Harel, Ziv, Garg, Amit X
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3637099/
https://www.ncbi.nlm.nih.gov/pubmed/23560464
http://dx.doi.org/10.1186/1471-2369-14-81
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author Fleet, Jamie L
Dixon, Stephanie N
Shariff, Salimah Z
Quinn, Robert R
Nash, Danielle M
Harel, Ziv
Garg, Amit X
author_facet Fleet, Jamie L
Dixon, Stephanie N
Shariff, Salimah Z
Quinn, Robert R
Nash, Danielle M
Harel, Ziv
Garg, Amit X
author_sort Fleet, Jamie L
collection PubMed
description BACKGROUND: Large, population-based administrative healthcare databases can be used to identify patients with chronic kidney disease (CKD) when serum creatinine laboratory results are unavailable. We examined the validity of algorithms that used combined hospital encounter and physician claims database codes for the detection of CKD in Ontario, Canada. METHODS: We accrued 123,499 patients over the age of 65 from 2007 to 2010. All patients had a baseline serum creatinine value to estimate glomerular filtration rate (eGFR). We developed an algorithm of physician claims and hospital encounter codes to search administrative databases for the presence of CKD. We determined the sensitivity, specificity, positive and negative predictive values of this algorithm to detect our primary threshold of CKD, an eGFR <45 mL/min per 1.73 m(2) (15.4% of patients). We also assessed serum creatinine and eGFR values in patients with and without CKD codes (algorithm positive and negative, respectively). RESULTS: Our algorithm required evidence of at least one of eleven CKD codes and 7.7% of patients were algorithm positive. The sensitivity was 32.7% [95% confidence interval: (95% CI): 32.0 to 33.3%]. Sensitivity was lower in women compared to men (25.7 vs. 43.7%; p <0.001) and in the oldest age category (over 80 vs. 66 to 80; 28.4 vs. 37.6 %; p < 0.001). All specificities were over 94%. The positive and negative predictive values were 65.4% (95% CI: 64.4 to 66.3%) and 88.8% (95% CI: 88.6 to 89.0%), respectively. In algorithm positive patients, the median [interquartile range (IQR)] baseline serum creatinine value was 135 μmol/L (106 to 179 μmol/L) compared to 82 μmol/L (69 to 98 μmol/L) for algorithm negative patients. Corresponding eGFR values were 38 mL/min per 1.73 m(2) (26 to 51 mL/min per 1.73 m(2)) vs. 69 mL/min per 1.73 m(2) (56 to 82 mL/min per 1.73 m(2)), respectively. CONCLUSIONS: Patients with CKD as identified by our database algorithm had distinctly higher baseline serum creatinine values and lower eGFR values than those without such codes. However, because of limited sensitivity, the prevalence of CKD was underestimated.
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spelling pubmed-36370992013-04-27 Detecting chronic kidney disease in population-based administrative databases using an algorithm of hospital encounter and physician claim codes Fleet, Jamie L Dixon, Stephanie N Shariff, Salimah Z Quinn, Robert R Nash, Danielle M Harel, Ziv Garg, Amit X BMC Nephrol Research Article BACKGROUND: Large, population-based administrative healthcare databases can be used to identify patients with chronic kidney disease (CKD) when serum creatinine laboratory results are unavailable. We examined the validity of algorithms that used combined hospital encounter and physician claims database codes for the detection of CKD in Ontario, Canada. METHODS: We accrued 123,499 patients over the age of 65 from 2007 to 2010. All patients had a baseline serum creatinine value to estimate glomerular filtration rate (eGFR). We developed an algorithm of physician claims and hospital encounter codes to search administrative databases for the presence of CKD. We determined the sensitivity, specificity, positive and negative predictive values of this algorithm to detect our primary threshold of CKD, an eGFR <45 mL/min per 1.73 m(2) (15.4% of patients). We also assessed serum creatinine and eGFR values in patients with and without CKD codes (algorithm positive and negative, respectively). RESULTS: Our algorithm required evidence of at least one of eleven CKD codes and 7.7% of patients were algorithm positive. The sensitivity was 32.7% [95% confidence interval: (95% CI): 32.0 to 33.3%]. Sensitivity was lower in women compared to men (25.7 vs. 43.7%; p <0.001) and in the oldest age category (over 80 vs. 66 to 80; 28.4 vs. 37.6 %; p < 0.001). All specificities were over 94%. The positive and negative predictive values were 65.4% (95% CI: 64.4 to 66.3%) and 88.8% (95% CI: 88.6 to 89.0%), respectively. In algorithm positive patients, the median [interquartile range (IQR)] baseline serum creatinine value was 135 μmol/L (106 to 179 μmol/L) compared to 82 μmol/L (69 to 98 μmol/L) for algorithm negative patients. Corresponding eGFR values were 38 mL/min per 1.73 m(2) (26 to 51 mL/min per 1.73 m(2)) vs. 69 mL/min per 1.73 m(2) (56 to 82 mL/min per 1.73 m(2)), respectively. CONCLUSIONS: Patients with CKD as identified by our database algorithm had distinctly higher baseline serum creatinine values and lower eGFR values than those without such codes. However, because of limited sensitivity, the prevalence of CKD was underestimated. BioMed Central 2013-04-05 /pmc/articles/PMC3637099/ /pubmed/23560464 http://dx.doi.org/10.1186/1471-2369-14-81 Text en Copyright © 2013 Fleet et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Fleet, Jamie L
Dixon, Stephanie N
Shariff, Salimah Z
Quinn, Robert R
Nash, Danielle M
Harel, Ziv
Garg, Amit X
Detecting chronic kidney disease in population-based administrative databases using an algorithm of hospital encounter and physician claim codes
title Detecting chronic kidney disease in population-based administrative databases using an algorithm of hospital encounter and physician claim codes
title_full Detecting chronic kidney disease in population-based administrative databases using an algorithm of hospital encounter and physician claim codes
title_fullStr Detecting chronic kidney disease in population-based administrative databases using an algorithm of hospital encounter and physician claim codes
title_full_unstemmed Detecting chronic kidney disease in population-based administrative databases using an algorithm of hospital encounter and physician claim codes
title_short Detecting chronic kidney disease in population-based administrative databases using an algorithm of hospital encounter and physician claim codes
title_sort detecting chronic kidney disease in population-based administrative databases using an algorithm of hospital encounter and physician claim codes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3637099/
https://www.ncbi.nlm.nih.gov/pubmed/23560464
http://dx.doi.org/10.1186/1471-2369-14-81
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