About: Predictive model of diagnosing probable cases of severe acute respiratory syndrome in febrile patients with exposure risk

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Predictive model of diagnosing probable cases of severe acute respiratory syndrome in febrile patients with exposure risk Goto Sponge NotDistinct Permalink

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Predictive model of diagnosing probable cases of severe acute respiratory syndrome in febrile patients with exposure risk
Creator	Chen, Shey-Ying Chen, Wen-Jone Chiang, Wen-Chu Su, Chan-Ping Hsu, Chiung-Yuan Ko, Patrick Chow-In Tsai, Kuang-Chau Yen, Zui-Shen Chen, Fuh-Yuan, Mph Huei, Matthew Ma, Ming Shih, Shyr-Chyr
Source	Elsevier; Medline; PMC
abstract	Abstract Study objective Since the World Health Organization issued a global alert about severe acute respiratory syndrome (SARS) on March 12, 2003, the illness has become a major public health challenge worldwide. The objective of this study is to identify the clinical risk factors of SARS and to develop a scoring system for early diagnosis. Methods The detailed clinical data of all patients presenting to the emergency department (ED) with a temperature higher than 38.0°C (100.3°F), documented at home or at the ED, and risks of exposure to SARS within 14 days were assessed. The diagnosis of probable SARS was made according to the definition of the Centers for Disease Control and Prevention. Items with significant differences among symptoms, signs, and laboratory tests on presentation between SARS and non-SARS groups were determined and used to develop the scoring system. Results Seventy patients were enrolled and 8 were diagnosed as probably having SARS. None of the initially discharged patients or their relatives developed SARS. Compared with the non-SARS group, the SARS group was younger (33.9±15.9 years versus 44±9.8 years; P=.02), had a higher percentage of fever prolonged more than 5 days (87.5% versus 6.5%; P<.01), myalgia (75% versus 27.4%; P=.01), and diarrhea (50% versus 9.7%; P=.02); had less occurrence of cough before or during fever (0% versus 64.5%; P=.01); and had lower absolute lymphocyte (0.9±0.3×109/L versus 1.5±1.1×109/L; P<.01) and platelet counts (144.1±36.3×109/L versus 211.6±78.8×109/L; P=.02). A 4-item symptom score based on the presence of cough before or concomitant with fever, myalgia, diarrhea, and rhinorrhea or sore throat detects SARS with 100% sensitivity and 75.9% specificity; a 6-item clinical score based on lymphopenia (<1.0×109/L), thrombocytopenia (<150×109/L) and the 4 symptom items detects SARS with 100% sensitivity and 86.3% specificity. Conclusion Certain symptoms and laboratory tests indicate higher risk of febrile probable SARS. In nonendemic areas, the febrile patients with recent contact with SARS or travel history to endemic areas could be screened for the probability of SARS by the use of clinical and symptom scores.
has issue date	2004-01-31(xsd:dateTime)
bibo:doi	10.1016/s0196-0644(03)00817-5
bibo:pmid	14707932
has license	els-covid
sha1sum (hex)	106408fe3c8a22b0029fb626b53adccd7d4bfa3a
schema:url	https://doi.org/10.1016/s0196-0644%2803%2900817-5
resource representing a document's title	Predictive model of diagnosing probable cases of severe acute respiratory syndrome in febrile patients with exposure risk
has PubMed Central identifier	PMC7124324
has PubMed identifier	14707932
schema:publication	Annals of Emergency Medicine
resource representing a document's body	covid:106408fe3c8a22b0029fb626b53adccd7d4bfa3a#body_text
is schema:about of	named entity 'D I C' named entity 'DIAGNOSING' named entity 'EXPOSURE' named entity 'RISK' named entity 'CASES' named entity 'PROBABLE' named entity 'FEBRILE' named entity 'PREDICTIVE MODEL' named entity 'MODEL OF' named entity 'L S' named entity 'SEVERE ACUTE RESPIRATORY SYNDROME' named entity 'A N' named entity 'PATIENTS' named entity 'Severe Acute Respiratory Syndrome' named entity 'Severe Acute Respiratory Syndrome' named entity 'Febrile' named entity 'univariate analysis' named entity 'laboratory tests' named entity 'sore throat' named entity 'Rhinorrhea' named entity 'laboratory tests' named entity 'SARS' named entity 'quarantine' named entity 'World Health Organization' named entity 'SARS' named entity 'SARS' named entity 'fever' named entity 'SARS epidemics' named entity 'clinical characteristics' named entity 'mycoplasma pneumonia' named entity 'teaching hospital' named entity 'recall bias' named entity 'SARS' named entity 'diarrhea' named entity 'univariate analysis' named entity 'Severe acute respiratory syndrome' named entity 'febrile' named entity 'emergency physicians' named entity 'lymphopenia' named entity 'polymerase chain reaction' named entity 'SARS' named entity 'lymphopenia' named entity 'fever' named entity 'exact test' named entity 'lymphopenia' named entity 'cough' named entity 'Centers for Disease Control and Prevention' named entity 'endemic' named entity '95% confidence interval' named entity 'emergency physicians' named entity 'thrombocytopenia' named entity 'laboratory tests' named entity 'fever' named entity 'diarrhea' named entity 'SARS' named entity 'SARS' named entity 'overdiagnosis' named entity 'SARS' named entity 'SPSS' named entity 'SARS' named entity 'public health' named entity 'SARS' named entity 'affected areas' named entity 'Canada' named entity 'rhinorrhea' named entity 'lymphopenia' named entity 'SARS' named entity 'airway' named entity 'fever'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software