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Validating International Classification of Disease 10(th) Revision algorithms for identifying influenza and respiratory syncytial virus hospitalizations

OBJECTIVE: Routinely collected health administrative data can be used to efficiently assess disease burden in large populations, but it is important to evaluate the validity of these data. The objective of this study was to develop and validate International Classification of Disease 10(th) revision...

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Autores principales: Hamilton, Mackenzie A., Calzavara, Andrew, Emerson, Scott D., Djebli, Mohamed, Sundaram, Maria E., Chan, Adrienne K., Kustra, Rafal, Baral, Stefan D., Mishra, Sharmistha, Kwong, Jeffrey C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7790248/
https://www.ncbi.nlm.nih.gov/pubmed/33411792
http://dx.doi.org/10.1371/journal.pone.0244746
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author Hamilton, Mackenzie A.
Calzavara, Andrew
Emerson, Scott D.
Djebli, Mohamed
Sundaram, Maria E.
Chan, Adrienne K.
Kustra, Rafal
Baral, Stefan D.
Mishra, Sharmistha
Kwong, Jeffrey C.
author_facet Hamilton, Mackenzie A.
Calzavara, Andrew
Emerson, Scott D.
Djebli, Mohamed
Sundaram, Maria E.
Chan, Adrienne K.
Kustra, Rafal
Baral, Stefan D.
Mishra, Sharmistha
Kwong, Jeffrey C.
author_sort Hamilton, Mackenzie A.
collection PubMed
description OBJECTIVE: Routinely collected health administrative data can be used to efficiently assess disease burden in large populations, but it is important to evaluate the validity of these data. The objective of this study was to develop and validate International Classification of Disease 10(th) revision (ICD -10) algorithms that identify laboratory-confirmed influenza or laboratory-confirmed respiratory syncytial virus (RSV) hospitalizations using population-based health administrative data from Ontario, Canada. STUDY DESIGN AND SETTING: Influenza and RSV laboratory data from the 2014–15, 2015–16, 2016–17 and 2017–18 respiratory virus seasons were obtained from the Ontario Laboratories Information System (OLIS) and were linked to hospital discharge abstract data to generate influenza and RSV reference cohorts. These reference cohorts were used to assess the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the ICD-10 algorithms. To minimize misclassification in future studies, we prioritized specificity and PPV in selecting top-performing algorithms. RESULTS: 83,638 and 61,117 hospitalized patients were included in the influenza and RSV reference cohorts, respectively. The best influenza algorithm had a sensitivity of 73% (95% CI 72% to 74%), specificity of 99% (95% CI 99% to 99%), PPV of 94% (95% CI 94% to 95%), and NPV of 94% (95% CI 94% to 95%). The best RSV algorithm had a sensitivity of 69% (95% CI 68% to 70%), specificity of 99% (95% CI 99% to 99%), PPV of 91% (95% CI 90% to 91%) and NPV of 97% (95% CI 97% to 97%). CONCLUSION: We identified two highly specific algorithms that best ascertain patients hospitalized with influenza or RSV. These algorithms may be applied to hospitalized patients if data on laboratory tests are not available, and will thereby improve the power of future epidemiologic studies of influenza, RSV, and potentially other severe acute respiratory infections.
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spelling pubmed-77902482021-01-14 Validating International Classification of Disease 10(th) Revision algorithms for identifying influenza and respiratory syncytial virus hospitalizations Hamilton, Mackenzie A. Calzavara, Andrew Emerson, Scott D. Djebli, Mohamed Sundaram, Maria E. Chan, Adrienne K. Kustra, Rafal Baral, Stefan D. Mishra, Sharmistha Kwong, Jeffrey C. PLoS One Research Article OBJECTIVE: Routinely collected health administrative data can be used to efficiently assess disease burden in large populations, but it is important to evaluate the validity of these data. The objective of this study was to develop and validate International Classification of Disease 10(th) revision (ICD -10) algorithms that identify laboratory-confirmed influenza or laboratory-confirmed respiratory syncytial virus (RSV) hospitalizations using population-based health administrative data from Ontario, Canada. STUDY DESIGN AND SETTING: Influenza and RSV laboratory data from the 2014–15, 2015–16, 2016–17 and 2017–18 respiratory virus seasons were obtained from the Ontario Laboratories Information System (OLIS) and were linked to hospital discharge abstract data to generate influenza and RSV reference cohorts. These reference cohorts were used to assess the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the ICD-10 algorithms. To minimize misclassification in future studies, we prioritized specificity and PPV in selecting top-performing algorithms. RESULTS: 83,638 and 61,117 hospitalized patients were included in the influenza and RSV reference cohorts, respectively. The best influenza algorithm had a sensitivity of 73% (95% CI 72% to 74%), specificity of 99% (95% CI 99% to 99%), PPV of 94% (95% CI 94% to 95%), and NPV of 94% (95% CI 94% to 95%). The best RSV algorithm had a sensitivity of 69% (95% CI 68% to 70%), specificity of 99% (95% CI 99% to 99%), PPV of 91% (95% CI 90% to 91%) and NPV of 97% (95% CI 97% to 97%). CONCLUSION: We identified two highly specific algorithms that best ascertain patients hospitalized with influenza or RSV. These algorithms may be applied to hospitalized patients if data on laboratory tests are not available, and will thereby improve the power of future epidemiologic studies of influenza, RSV, and potentially other severe acute respiratory infections. Public Library of Science 2021-01-07 /pmc/articles/PMC7790248/ /pubmed/33411792 http://dx.doi.org/10.1371/journal.pone.0244746 Text en © 2021 Hamilton et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Hamilton, Mackenzie A.
Calzavara, Andrew
Emerson, Scott D.
Djebli, Mohamed
Sundaram, Maria E.
Chan, Adrienne K.
Kustra, Rafal
Baral, Stefan D.
Mishra, Sharmistha
Kwong, Jeffrey C.
Validating International Classification of Disease 10(th) Revision algorithms for identifying influenza and respiratory syncytial virus hospitalizations
title Validating International Classification of Disease 10(th) Revision algorithms for identifying influenza and respiratory syncytial virus hospitalizations
title_full Validating International Classification of Disease 10(th) Revision algorithms for identifying influenza and respiratory syncytial virus hospitalizations
title_fullStr Validating International Classification of Disease 10(th) Revision algorithms for identifying influenza and respiratory syncytial virus hospitalizations
title_full_unstemmed Validating International Classification of Disease 10(th) Revision algorithms for identifying influenza and respiratory syncytial virus hospitalizations
title_short Validating International Classification of Disease 10(th) Revision algorithms for identifying influenza and respiratory syncytial virus hospitalizations
title_sort validating international classification of disease 10(th) revision algorithms for identifying influenza and respiratory syncytial virus hospitalizations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7790248/
https://www.ncbi.nlm.nih.gov/pubmed/33411792
http://dx.doi.org/10.1371/journal.pone.0244746
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