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

TitleA clinical prediction rule for diagnosing human infections with avian influenza A(H7N9) in a hospital emergency department setting
Authors
KeywordsAvian influenza A (H7N9)
Clinical prediction rule
Clinical diagnosis
Hospital emergency setting
Issue Date2014
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcmed/
Citation
BMC Medicine, 2014, v. 12, article 127, p. 1-9 How to Cite?
AbstractBACKGROUND: 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.
Persistent Identifierhttp://hdl.handle.net/10722/205464
ISSN
2015 Impact Factor: 8.005
2015 SCImago Journal Rankings: 3.415
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLiao, Qen_US
dc.contributor.authorIp, DKMen_US
dc.contributor.authorTsang, TKLen_US
dc.contributor.authorCao, Ben_US
dc.contributor.authorJiang, Hen_US
dc.contributor.authorLiu, Fen_US
dc.contributor.authorZheng, Jen_US
dc.contributor.authorPeng, Zen_US
dc.contributor.authorWu, Pen_US
dc.contributor.authorHuai, Yen_US
dc.contributor.authorLau, EHYen_US
dc.contributor.authorFeng, Len_US
dc.contributor.authorLeung, GMen_US
dc.contributor.authorYu, Hen_US
dc.contributor.authorCowling, BJen_US
dc.date.accessioned2014-09-20T02:36:18Z-
dc.date.available2014-09-20T02:36:18Z-
dc.date.issued2014en_US
dc.identifier.citationBMC Medicine, 2014, v. 12, article 127, p. 1-9en_US
dc.identifier.issn1741-7015-
dc.identifier.urihttp://hdl.handle.net/10722/205464-
dc.description.abstractBACKGROUND: 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.-
dc.languageengen_US
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcmed/-
dc.relation.ispartofBMC Medicineen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectAvian influenza A (H7N9)-
dc.subjectClinical prediction rule-
dc.subjectClinical diagnosis-
dc.subjectHospital emergency setting-
dc.titleA clinical prediction rule for diagnosing human infections with avian influenza A(H7N9) in a hospital emergency department settingen_US
dc.typeArticleen_US
dc.identifier.emailIp, DKM: dkmip@hku.hken_US
dc.identifier.emailWu, P: pengwu@hku.hken_US
dc.identifier.emailLau, EHY: ehylau@hku.hken_US
dc.identifier.emailLeung, GM: gmleung@hku.hken_US
dc.identifier.emailCowling, BJ: bcowling@hku.hken_US
dc.identifier.authorityIp, DKM=rp00256en_US
dc.identifier.authorityLau, EHY=rp01349en_US
dc.identifier.authorityLeung, GM=rp00460en_US
dc.identifier.authorityCowling, BJ=rp01326en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/s12916-014-0127-0en_US
dc.identifier.pmid25091477-
dc.identifier.pmcidPMC4243192-
dc.identifier.scopuseid_2-s2.0-84906775770-
dc.identifier.hkuros235437en_US
dc.identifier.volume12en_US
dc.identifier.spage1en_US
dc.identifier.epage9en_US
dc.identifier.isiWOS:000340646900001-
dc.publisher.placeUnited Kingdom-
dc.customcontrol.immutablesml 150123-

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