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The identification of risk factors for infectious patients with acute fever and formulation of activation criteria to alert outpatient clinic doctors

Identifieur interne : 002700 ( Main/Exploration ); précédent : 002699; suivant : 002701

The identification of risk factors for infectious patients with acute fever and formulation of activation criteria to alert outpatient clinic doctors

Auteurs : Hongli Xiao ; Haiyu Qi ; Xingwang Li [République populaire de Chine] ; Jing Wang ; Xiaoli Li ; Suxia Ma ; Zongli Diao ; Yan Wang ; Fangfang Sun ; Chenghong Yin [République populaire de Chine]

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RBID : ISTEX:C063972CB5A1B4B5B08BF082C80D581F8E9E6E51

English descriptors

Abstract

Rationale, aims and objectives  Acute fever is the most common clinical symptom for infectious diseases. It is necessary to identify risk factors for infectious patients with acute fever and formulate activation criteria of early warning infectiosity score system (EWIS) to alert outpatient clinic doctors. Methods  Logistic regression analysis was used to determine risk factors for infectious diseases from the clinical data of 758 patients with acute fever. Risk factors were weighted and an EWIS was formulated. A receiver operator characteristic (ROC) analysis of weighted cumulative scores was performed to evaluate the diagnostic accuracy of EWIS, and the Kappa test used to confirm diagnostic reliability. A χ2‐test for trend was applied to determine the relevance between EWIS and incidence of infectious diseases. Results  Risk factors for infections included conjunctival hyperaemia, rash, diarrhoea, increased alanine aminotransferase, splenomegaly and abnormal percentage of peripheral neutrophils (NE%). Risk factors were weighted and tabulated. The areas under the ROC curves of the EWIS was 0.929 and ≥4 points predicted infectious diseases, and the Kappa values were 0.750. As the score increased, the incidence of infectious diseases increased. The areas under the ROC curves of the EWIS predicting on single viral and bacterial infectious diseases were 0.961 and 0.896, and the Kappa values were 0.807 and 0.701, respectively. Conclusions  Risk factors for infections have been identified, quantified and formulated into a table of EWIS that have high diagnostic accuracy and reliability for the early identification of contagious diseases.

Url:
DOI: 10.1111/j.1365-2753.2009.01306.x


Affiliations:


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<div type="abstract" xml:lang="en">Rationale, aims and objectives  Acute fever is the most common clinical symptom for infectious diseases. It is necessary to identify risk factors for infectious patients with acute fever and formulate activation criteria of early warning infectiosity score system (EWIS) to alert outpatient clinic doctors. Methods  Logistic regression analysis was used to determine risk factors for infectious diseases from the clinical data of 758 patients with acute fever. Risk factors were weighted and an EWIS was formulated. A receiver operator characteristic (ROC) analysis of weighted cumulative scores was performed to evaluate the diagnostic accuracy of EWIS, and the Kappa test used to confirm diagnostic reliability. A χ2‐test for trend was applied to determine the relevance between EWIS and incidence of infectious diseases. Results  Risk factors for infections included conjunctival hyperaemia, rash, diarrhoea, increased alanine aminotransferase, splenomegaly and abnormal percentage of peripheral neutrophils (NE%). Risk factors were weighted and tabulated. The areas under the ROC curves of the EWIS was 0.929 and ≥4 points predicted infectious diseases, and the Kappa values were 0.750. As the score increased, the incidence of infectious diseases increased. The areas under the ROC curves of the EWIS predicting on single viral and bacterial infectious diseases were 0.961 and 0.896, and the Kappa values were 0.807 and 0.701, respectively. Conclusions  Risk factors for infections have been identified, quantified and formulated into a table of EWIS that have high diagnostic accuracy and reliability for the early identification of contagious diseases.</div>
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