Features discriminating SARS from other severe viral respiratory tract infections - PubMed (original) (raw)

doi: 10.1007/s10096-006-0246-4.

N Lee, M Ip, A P Galvani, G E Antonio, K T Wong, D P N Chan, A W H Ng, K K Shing, S S L Chau, P Mak, P K S Chan, A T Ahuja, D S Hui, J J Y Sung

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Features discriminating SARS from other severe viral respiratory tract infections

T H Rainer et al. Eur J Clin Microbiol Infect Dis. 2007 Feb.

Abstract

This study investigated the discriminatory features of severe acute respiratory syndrome (SARS) and severe non-SARS community-acquired viral respiratory infection (requiring hospitalization) in an emergency department in Hong Kong. In a case-control study, clinical, laboratory and radiological data from 322 patients with laboratory-confirmed SARS from the 2003 SARS outbreak were compared with the data of 253 non-SARS adult patients with confirmed viral respiratory tract infection from 2004 in order to identify discriminatory features. Among the non-SARS patients, 235 (93%) were diagnosed as having influenza infections (primarily H3N2 subtype) and 77 (30%) had radiological evidence of pneumonia. In the early phase of the illness and after adjusting for baseline characteristics, SARS patients were less likely to have lower respiratory symptoms (e.g. sputum production, shortness of breath, chest pain) and more likely to have myalgia (p < 0.001). SARS patients had lower mean leukocyte and neutrophil counts (p < 0.0001) and more commonly had "ground-glass" radiological changes with no pleural effusion. Despite having a younger average age, SARS patients had a more aggressive respiratory course requiring admission to the ICU and a higher mortality rate. The area under the receiver operator characteristic curve for predicting SARS when all variables were considered was 0.983. Using a cutoff score of >99, the sensitivity was 89.1% (95%CI 82.0-94.0) and the specificity was 98.0% (95%CI 95.4-99.3). The area under the receiver operator characteristic curve for predicting SARS when all variables except radiological change were considered was 0.933. Using a cutoff score of >8, the sensitivity was 80.7% (95%CI 72.4-87.3) and the specificity was 94.5% (95%CI 90.9-96.9). Certain clinical manifestations and laboratory changes may help to distinguish SARS from other influenza-like illnesses. Scoring systems may help identify patients who should receive more specific tests for influenza or SARS.

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Figures

Fig. 1

Fig. 1

Receiver operating characteristic curve for predicting SARS, including all significant variables

Fig. 2

Fig. 2

Receiver operating characteristic curve for predicting SARS, including all significant variables except radiographic appearance

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