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Positive Predictive Values of International Classification of Diseases, 10th Revision Coding Algorithms to Identify Patients With Autosomal Dominant Polycystic Kidney Disease

BACKGROUND: International Classification of Diseases, 10th Revision codes (ICD-10) for autosomal dominant polycystic kidney disease (ADPKD) is used within several administrative health care databases. It is unknown whether these codes identify patients who meet strict clinical criteria for ADPKD. OB...

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Autores principales: Kalatharan, Vinusha, Pei, York, Clemens, Kristin K., McTavish, Rebecca K., Dixon, Stephanie N., Rochon, Matthew, Nash, Danielle M., Jain, Arsh, Sarma, Sisira, Zaleski, Andrew, Lum, Andrea, Garg, Amit X.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5518965/
https://www.ncbi.nlm.nih.gov/pubmed/28781884
http://dx.doi.org/10.1177/2054358116679130
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author Kalatharan, Vinusha
Pei, York
Clemens, Kristin K.
McTavish, Rebecca K.
Dixon, Stephanie N.
Rochon, Matthew
Nash, Danielle M.
Jain, Arsh
Sarma, Sisira
Zaleski, Andrew
Lum, Andrea
Garg, Amit X.
author_facet Kalatharan, Vinusha
Pei, York
Clemens, Kristin K.
McTavish, Rebecca K.
Dixon, Stephanie N.
Rochon, Matthew
Nash, Danielle M.
Jain, Arsh
Sarma, Sisira
Zaleski, Andrew
Lum, Andrea
Garg, Amit X.
author_sort Kalatharan, Vinusha
collection PubMed
description BACKGROUND: International Classification of Diseases, 10th Revision codes (ICD-10) for autosomal dominant polycystic kidney disease (ADPKD) is used within several administrative health care databases. It is unknown whether these codes identify patients who meet strict clinical criteria for ADPKD. OBJECTIVE: The objective of this study is (1) to determine whether different ICD-10 coding algorithms identify adult patients who meet strict clinical criteria for ADPKD as assessed through medical chart review and (2) to assess the number of patients identified with different ADPKD coding algorithms in Ontario. DESIGN: Validation study of health care database codes, and prevalence. SETTING: Ontario, Canada. PATIENTS: For the chart review, 201 adult patients with hospital encounters between April 1, 2002, and March 31, 2014, assigned either ICD-10 codes Q61.2 or Q61.3. MEASUREMENTS: This study measured positive predictive value of the ICD-10 coding algorithms and the number of Ontarians identified with different coding algorithms. METHODS: We manually reviewed a random sample of medical charts in London, Ontario, Canada, and determined whether or not ADPKD was present according to strict clinical criteria. RESULTS: The presence of either ICD-10 code Q61.2 or Q61.3 in a hospital encounter had a positive predictive value of 85% (95% confidence interval [CI], 79%-89%) and identified 2981 Ontarians (0.02% of the Ontario adult population). The presence of ICD-10 code Q61.2 in a hospital encounter had a positive predictive value of 97% (95% CI, 86%-100%) and identified 394 adults in Ontario (0.003% of the Ontario adult population). LIMITATIONS: (1) We could not calculate other measures of validity; (2) the coding algorithms do not identify patients without hospital encounters; and (3) coding practices may differ between hospitals. CONCLUSIONS: Most patients with ICD-10 code Q61.2 or Q61.3 assigned during their hospital encounters have ADPKD according to the clinical criteria. These codes can be used to assemble cohorts of adult patients with ADPKD and hospital encounters.
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spelling pubmed-55189652017-08-04 Positive Predictive Values of International Classification of Diseases, 10th Revision Coding Algorithms to Identify Patients With Autosomal Dominant Polycystic Kidney Disease Kalatharan, Vinusha Pei, York Clemens, Kristin K. McTavish, Rebecca K. Dixon, Stephanie N. Rochon, Matthew Nash, Danielle M. Jain, Arsh Sarma, Sisira Zaleski, Andrew Lum, Andrea Garg, Amit X. Can J Kidney Health Dis Original Research Article BACKGROUND: International Classification of Diseases, 10th Revision codes (ICD-10) for autosomal dominant polycystic kidney disease (ADPKD) is used within several administrative health care databases. It is unknown whether these codes identify patients who meet strict clinical criteria for ADPKD. OBJECTIVE: The objective of this study is (1) to determine whether different ICD-10 coding algorithms identify adult patients who meet strict clinical criteria for ADPKD as assessed through medical chart review and (2) to assess the number of patients identified with different ADPKD coding algorithms in Ontario. DESIGN: Validation study of health care database codes, and prevalence. SETTING: Ontario, Canada. PATIENTS: For the chart review, 201 adult patients with hospital encounters between April 1, 2002, and March 31, 2014, assigned either ICD-10 codes Q61.2 or Q61.3. MEASUREMENTS: This study measured positive predictive value of the ICD-10 coding algorithms and the number of Ontarians identified with different coding algorithms. METHODS: We manually reviewed a random sample of medical charts in London, Ontario, Canada, and determined whether or not ADPKD was present according to strict clinical criteria. RESULTS: The presence of either ICD-10 code Q61.2 or Q61.3 in a hospital encounter had a positive predictive value of 85% (95% confidence interval [CI], 79%-89%) and identified 2981 Ontarians (0.02% of the Ontario adult population). The presence of ICD-10 code Q61.2 in a hospital encounter had a positive predictive value of 97% (95% CI, 86%-100%) and identified 394 adults in Ontario (0.003% of the Ontario adult population). LIMITATIONS: (1) We could not calculate other measures of validity; (2) the coding algorithms do not identify patients without hospital encounters; and (3) coding practices may differ between hospitals. CONCLUSIONS: Most patients with ICD-10 code Q61.2 or Q61.3 assigned during their hospital encounters have ADPKD according to the clinical criteria. These codes can be used to assemble cohorts of adult patients with ADPKD and hospital encounters. SAGE Publications 2016-12-14 /pmc/articles/PMC5518965/ /pubmed/28781884 http://dx.doi.org/10.1177/2054358116679130 Text en © The Author(s) 2016 http://creativecommons.org/licenses/by-nc/3.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License (http://www.creativecommons.org/licenses/by-nc/3.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page(https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research Article
Kalatharan, Vinusha
Pei, York
Clemens, Kristin K.
McTavish, Rebecca K.
Dixon, Stephanie N.
Rochon, Matthew
Nash, Danielle M.
Jain, Arsh
Sarma, Sisira
Zaleski, Andrew
Lum, Andrea
Garg, Amit X.
Positive Predictive Values of International Classification of Diseases, 10th Revision Coding Algorithms to Identify Patients With Autosomal Dominant Polycystic Kidney Disease
title Positive Predictive Values of International Classification of Diseases, 10th Revision Coding Algorithms to Identify Patients With Autosomal Dominant Polycystic Kidney Disease
title_full Positive Predictive Values of International Classification of Diseases, 10th Revision Coding Algorithms to Identify Patients With Autosomal Dominant Polycystic Kidney Disease
title_fullStr Positive Predictive Values of International Classification of Diseases, 10th Revision Coding Algorithms to Identify Patients With Autosomal Dominant Polycystic Kidney Disease
title_full_unstemmed Positive Predictive Values of International Classification of Diseases, 10th Revision Coding Algorithms to Identify Patients With Autosomal Dominant Polycystic Kidney Disease
title_short Positive Predictive Values of International Classification of Diseases, 10th Revision Coding Algorithms to Identify Patients With Autosomal Dominant Polycystic Kidney Disease
title_sort positive predictive values of international classification of diseases, 10th revision coding algorithms to identify patients with autosomal dominant polycystic kidney disease
topic Original Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5518965/
https://www.ncbi.nlm.nih.gov/pubmed/28781884
http://dx.doi.org/10.1177/2054358116679130
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