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

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About: A clinical prediction rule for diagnosing human infections with avian influenza A(H7N9) in a hospital emergency department setting Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	A clinical prediction rule for diagnosing human infections with avian influenza A(H7N9) in a hospital emergency department setting
Creator	Cao, Bin Wu, Peng Yu, Hongjie Feng, Luzhao Huai, Yang Jiang, Hui Peng, Zhibin Zheng, Jiandong Liao, Qiaohong Leung, Gabriel Liu, Fengfeng Cowling, Benjamin Ip, Dennis Lau, Eric Tsang, Tim
Source	Medline; PMC
abstract	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.
has issue date	2014-08-05(xsd:dateTime)
bibo:doi	10.1186/s12916-014-0127-0
bibo:pmid	25091477
has license	cc-by
sha1sum (hex)	3a4cf1cc2f583dea77288aa7e114c117a00660b2
schema:url	https://doi.org/10.1186/s12916-014-0127-0
resource representing a document's title	A clinical prediction rule for diagnosing human infections with avian influenza A(H7N9) in a hospital emergency department setting
has PubMed Central identifier	PMC4243192
has PubMed identifier	25091477
schema:publication	BMC Med
resource representing a document's body	covid:3a4cf1cc2f583dea77288aa7e114c117a00660b2#body_text
is schema:about of	named entity 'high' named entity 'January 2010' named entity 'bootstrapping' named entity 'male' named entity 'validation' covid:arg/3a4cf1cc2f583dea77288aa7e114c117a00660b2 named entity 'sensitivity' named entity 'March' named entity 'developed' named entity 'This' named entity 'infection' named entity 'developed' named entity 'Results' named entity 'refinement' named entity 'presentation' named entity 'predicting' named entity 'breath' named entity 'group' named entity 'inform' named entity 'details' named entity 'H7N9' named entity 'Human' named entity 'manner' named entity 'two-step' named entity 'radiographs' named entity 'current' named entity 'management' named entity 'H7N9' named entity 'epidemic' named entity 'virus' named entity 'Jingzhou City' named entity 'physical examination' named entity 'public health' named entity 'logistic regression' named entity 'infection' named entity 'epidemic' named entity 'China' named entity 'haemoptysis' named entity 'H7N9' named entity 'clinical prediction rule' named entity 'hospitalized' named entity 'infection' named entity 'risk group' named entity 'ARI' named entity 'H7N9' named entity 'H7N9' named entity 'biochemistry' named entity 'respiratory illness' named entity 'shortness of breath' named entity 'H7N9' named entity 'H7N9' named entity 'abnormal breath sounds' named entity 'ROC curve' named entity 'severe acute respiratory infection' named entity 'infection' named entity 'antiviral treatment' named entity 'control patients' named entity 'WBC' named entity 'respiratory symptoms' named entity 'poultry' named entity 'risk stratification' named entity 'epidemics' named entity 'H7N9' named entity 'H7N9' named entity 'clinical decision-making' named entity 'virus' named entity 'collection of data'

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