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A clinical prediction rule for diagnosing human infections with avian influenza A(H7N9) in a hospital emergency department setting

BACKGROUND: Human infections with avian influenza A(H7N9) virus are associated with severe illness and high mortality. To better inform triage decisions of hospitalization and management, we developed a clinical prediction rule for diagnosing patients with A(H7N9) and determined its predictive perfo...

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Autores principales: Liao, Qiaohong, Ip, Dennis K M, Tsang, Tim K, Cao, Bin, Jiang, Hui, Liu, Fengfeng, Zheng, Jiandong, Peng, Zhibin, Wu, Peng, Huai, Yang, Lau, Eric H Y, Feng, Luzhao, Leung, Gabriel M, Yu, Hongjie, Cowling, Benjamin J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4243192/
https://www.ncbi.nlm.nih.gov/pubmed/25091477
http://dx.doi.org/10.1186/s12916-014-0127-0
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author Liao, Qiaohong
Ip, Dennis K M
Tsang, Tim K
Cao, Bin
Jiang, Hui
Liu, Fengfeng
Zheng, Jiandong
Peng, Zhibin
Wu, Peng
Huai, Yang
Lau, Eric H Y
Feng, Luzhao
Leung, Gabriel M
Yu, Hongjie
Cowling, Benjamin J
author_facet Liao, Qiaohong
Ip, Dennis K M
Tsang, Tim K
Cao, Bin
Jiang, Hui
Liu, Fengfeng
Zheng, Jiandong
Peng, Zhibin
Wu, Peng
Huai, Yang
Lau, Eric H Y
Feng, Luzhao
Leung, Gabriel M
Yu, Hongjie
Cowling, Benjamin J
author_sort Liao, Qiaohong
collection PubMed
description BACKGROUND: Human infections with avian influenza A(H7N9) virus are associated with severe illness and high mortality. To better inform triage decisions of hospitalization and management, we developed a clinical prediction rule for diagnosing patients with A(H7N9) and determined its predictive performance. METHODS: Clinical details on presentation of adult patients hospitalized with either A(H7N9)(n = 121) in China from March to May 2013 or other causes of acute respiratory infections (n = 2,603) in Jingzhou City, China from January 2010 through September 2012 were analyzed. A clinical prediction rule was developed using a two-step coefficient-based multivariable logistic regression scoring method and evaluated with internal validation by bootstrapping. RESULTS: In step 1, predictors for A(H7N9) included male sex, poultry exposure history, and fever, haemoptysis, or shortness of breath on history and physical examination. In step 2, haziness or pneumonic consolidation on chest radiographs and leukopenia were also associated with a higher probability of A(H7N9). The observed risk of A(H7N9) was 0.3% for those assigned to the low-risk group and 2.5%, 4.3%, and 44.0% for tertiles 1 through 3, respectively, in the high-risk group. This prediction rule achieved good model performance, with an optimism-corrected sensitivity of 0.93, a specificity of 0.80, and an area under the receiver-operating characteristic curve of 0.96. CONCLUSIONS: A simple decision rule based on data readily obtainable in the setting of patients’ first clinical presentations from the first wave of the A/H7N9 epidemic in China has been developed. This prediction rule has achieved good model performance in predicting their risk of A(H7N9) infection and should be useful in guiding important clinical and public health decisions in a timely and objective manner. Data to be gathered with its use in the current evolving second wave of the A/H7N9 epidemic in China will help to inform its performance in the field and contribute to its further refinement. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12916-014-0127-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-42431922014-11-26 A clinical prediction rule for diagnosing human infections with avian influenza A(H7N9) in a hospital emergency department setting Liao, Qiaohong Ip, Dennis K M Tsang, Tim K Cao, Bin Jiang, Hui Liu, Fengfeng Zheng, Jiandong Peng, Zhibin Wu, Peng Huai, Yang Lau, Eric H Y Feng, Luzhao Leung, Gabriel M Yu, Hongjie Cowling, Benjamin J BMC Med Research Article BACKGROUND: Human infections with avian influenza A(H7N9) virus are associated with severe illness and high mortality. To better inform triage decisions of hospitalization and management, we developed a clinical prediction rule for diagnosing patients with A(H7N9) and determined its predictive performance. METHODS: Clinical details on presentation of adult patients hospitalized with either A(H7N9)(n = 121) in China from March to May 2013 or other causes of acute respiratory infections (n = 2,603) in Jingzhou City, China from January 2010 through September 2012 were analyzed. A clinical prediction rule was developed using a two-step coefficient-based multivariable logistic regression scoring method and evaluated with internal validation by bootstrapping. RESULTS: In step 1, predictors for A(H7N9) included male sex, poultry exposure history, and fever, haemoptysis, or shortness of breath on history and physical examination. In step 2, haziness or pneumonic consolidation on chest radiographs and leukopenia were also associated with a higher probability of A(H7N9). The observed risk of A(H7N9) was 0.3% for those assigned to the low-risk group and 2.5%, 4.3%, and 44.0% for tertiles 1 through 3, respectively, in the high-risk group. This prediction rule achieved good model performance, with an optimism-corrected sensitivity of 0.93, a specificity of 0.80, and an area under the receiver-operating characteristic curve of 0.96. CONCLUSIONS: A simple decision rule based on data readily obtainable in the setting of patients’ first clinical presentations from the first wave of the A/H7N9 epidemic in China has been developed. This prediction rule has achieved good model performance in predicting their risk of A(H7N9) infection and should be useful in guiding important clinical and public health decisions in a timely and objective manner. Data to be gathered with its use in the current evolving second wave of the A/H7N9 epidemic in China will help to inform its performance in the field and contribute to its further refinement. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12916-014-0127-0) contains supplementary material, which is available to authorized users. BioMed Central 2014-08-05 /pmc/articles/PMC4243192/ /pubmed/25091477 http://dx.doi.org/10.1186/s12916-014-0127-0 Text en © Liao et al.; licensee BioMed Central Ltd 2014 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 work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Liao, Qiaohong
Ip, Dennis K M
Tsang, Tim K
Cao, Bin
Jiang, Hui
Liu, Fengfeng
Zheng, Jiandong
Peng, Zhibin
Wu, Peng
Huai, Yang
Lau, Eric H Y
Feng, Luzhao
Leung, Gabriel M
Yu, Hongjie
Cowling, Benjamin J
A clinical prediction rule for diagnosing human infections with avian influenza A(H7N9) in a hospital emergency department setting
title A clinical prediction rule for diagnosing human infections with avian influenza A(H7N9) in a hospital emergency department setting
title_full A clinical prediction rule for diagnosing human infections with avian influenza A(H7N9) in a hospital emergency department setting
title_fullStr A clinical prediction rule for diagnosing human infections with avian influenza A(H7N9) in a hospital emergency department setting
title_full_unstemmed A clinical prediction rule for diagnosing human infections with avian influenza A(H7N9) in a hospital emergency department setting
title_short A clinical prediction rule for diagnosing human infections with avian influenza A(H7N9) in a hospital emergency department setting
title_sort clinical prediction rule for diagnosing human infections with avian influenza a(h7n9) in a hospital emergency department setting
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4243192/
https://www.ncbi.nlm.nih.gov/pubmed/25091477
http://dx.doi.org/10.1186/s12916-014-0127-0
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