A Clinical Prediction Rule for Diagnosing Severe Acute Respiratory Syndrome in the Emergency Department

7 September 2004 | Volume 141 Issue 5 | Pages 333-342

Background: Accurate, objective models of triage for patients with suspected severe acute respiratory syndrome (SARS) could assess risks and improve decisions about isolation and inpatient treatment.

Objective: To develop and validate a clinical prediction rule for identifying patients with SARS in an emergency department setting.

Design: Retrospective analysis using a 2-step coefficient-based multivariable logistic regression scoring method with internal validation by bootstrapping.

Setting: 2 hospitals in Hong Kong.

Participants: 1274 consecutive patients from 1 hospital and 1375 consecutive patients from another hospital.

Measurements: Points were assigned on the basis of history, physical examination, and simple investigations obtained at presentation. The outcome measure was a final diagnosis of SARS, as confirmed by World Health Organization laboratory criteria.

Results: Predictors for SARS on the basis of history (step 1) included previous contact with a patient with SARS and the presence of fever, myalgia, and malaise. Age 65 years and older and younger than 18 years and the presence of sputum, abdominal pain, sore throat, and rhinorrhea were inversely related to having SARS. In step 2, haziness or pneumonic consolidation on chest radiographs and low lymphocyte and platelet counts, in addition to a positive contact history and fever were associated with a higher probability of SARS. A high neutrophil count, the extremes of age, and sputum production were associated with a lower probability of SARS. In the derivation sample, the observed incidence of SARS was 4.4% for those assigned to the low-risk group (in steps 1 or 2); in the high-risk group, incidence of SARS was 21.0% for quartile 1, 39.5% for quartile 2, 61.2% for quartile 3, and 79.7% for quartile 4. This prediction rule achieved an optimism-corrected sensitivity of 0.90, a specificity of 0.62, and an area under the receiver-operating characteristic curve of 0.85.

Limitations: The prediction rule may not apply to isolated cases occurring during an interepidemic period. Generalizability of the findings should be confirmed in other SARS-affected countries and should be prospectively validated if SARS returns.

Conclusions: Our findings suggest that a simple model that uses clinical data at the time of presentation to an emergency department during an acute outbreak predicted the incidence of SARS and provided good diagnostic utility.

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Editors' Notes Context Which characteristics distinguish patients with severe acute respiratory syndrome (SARS)? Contribution These investigators developed a prediction rule for SARS by using data from 2649 consecutive patients seen at 2 Hong Kong triage clinics during the 2003 SARS epidemic. The following characteristics increased the likelihood of SARS: previous contact with a patient with SARS, fever, myalgia, malaise, abnormal chest radiograph, and abnormal lymphocyte and low platelet counts. Age 65 years and older or younger than 18 years, productive sputum, abdominal pain, sore throat, rhinorrhea, and high neutrophil count decreased the likelihood of SARS. Cautions Findings probably are not applicable to isolated cases occurring during an interepidemic period. –The Editors

 

Author and Article Information

From University of Hong Kong, Prince of Wales Hospital and Chinese University of Hong Kong, United Christian Hospital, Pamela Youde Nethersole Eastern Hospital, and Hong Kong Hospital Authority, Hong Kong, China.

Note: Drs. Leung and Rainer contributed equally to this article.

Acknowledgments: The authors thank their colleagues in the Accident and Emergency Departments of Pamela Youde Nethersole Eastern Hospital, Prince of Wales Hospital, and United Christian Hospital. They also thank Dr. Wong Wing Nam for data capture, entry, and cleaning; staff in the Health Informatics, Information Technology, Medical Services Development, and Statistics Divisions of the Hospital Authority Head Office for collating and processing multiple data sources used in this analysis; and Keith Tin and Marie Chi for expert technical assistance in the preparation of the manuscript.

Grant Support: By the Research Fund for the Control of Infectious Disease, Government of the Hong Kong Special Administrative Region (grant 01030362 and a Special Commissioned Project Grant to the University of Hong Kong) and by the University of Hong Kong SARS Research Fund.

Potential Financial Conflicts of Interest: None disclosed.

Requests for Single Reprints: Timothy H. Rainer, MD, MRCP, Accident and Emergency Medicine Academic Unit, Prince of Wales Hospital, Chinese University of Hong Kong, Shatin, Hong Kong, China; e-mail, b875722{at}mailserv.cuhk.edu.hk.

Current Author Addresses: Drs. Leung, O.L Wong, Hedley, and Lam: Department of Community Medicine and School of Public Health, University of Hong Kong, 21 Sassoon Road, Pokfulam, Hong Kong, China.

Dr. Rainer: Accident and Emergency Medicine Academic Unit, Trauma and Emergency Centre, Prince of Wales Hospital, 30–32 Ngan Shing Street, Chinese University of Hong Kong, Shatin, Hong Kong, China.

Dr. Lau: Accident and Emergency Department, United Christian Hospital, Kwun Tong, 130 Hip-Wo Street, Kowloon, Hong Kong, China.

Drs. Tong and T.-W. Wong: Accident and Emergency Department, Pamela Youde Nethersole Eastern Hospital, Chai Wan, Hong Kong, China.

Dr. Kong: Health Informatics Section, Hong Kong Hospital Authority, 147 Argyle Street, Kowloon, Hong Kong, China.

Author Contributions: Conception and design: G.M. Leung, T.H. Rainer, T.-W. Wong, J.H.B. Kong, A.J. Hedley, T.-H. Lam.

Analysis and interpretation of the data: G.M. Leung, T.H. Rainer, I.O.L. Wong.

Drafting of the article: G.M. Leung.

Critical revision of the article for important intellectual content: I.O.L. Wong, A. Tong, T.-W. Wong, J.H.B. Kong, A.J. Hedley, T.-H. Lam.

Final approval of the article: G.M. Leung, T.H. Rainer, F.-L. Lau, T.-W. Wong, J.H.B. Kong, A.J. Hedley, T.-H. Lam.

Provision of study materials or patients: T.H. Rainer, F.-L. Lau.

Statistical expertise: I.O.L. Wong.

Obtaining of funding: G.M. Leung.

Administrative, technical, or logistic support: A. Tong, T.-W. Wong, J.H.B. Kong.

Collection and assembly of data: T.H. Rainer, F.-L. Lau, A. Tong.

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Vadiation of A SARS Prediction Rule

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Annals Online, 19 Oct 2004 [Full text]