Clinical Prognostic Rules for Severe Acute Respiratory Syndrome in Low- and High-Resource Settings | Global Health | JAMA Internal Medicine | JAMA Network
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Original Investigation
July 24, 2006

Clinical Prognostic Rules for Severe Acute Respiratory Syndrome in Low- and High-Resource Settings

Author Affiliations

Author Affiliations: Department of Community Medicine and School of Public Health, the University of Hong Kong (Drs Cowling, Ho, Lam, and Leung and Ms Wong), Health, Welfare, and Food Bureau (Dr Lo), and Centre for Health Protection, Department of Health (Dr Tsang), Government of the Hong Kong Special Administrative Region, Hong Kong; Mount Sinai Hospital, Toronto, Ontario (Dr Muller); and Canadian SARS Research Network, Canada (Drs Muller and Louie).

Arch Intern Med. 2006;166(14):1505-1511. doi:10.1001/archinte.166.14.1505

Background  An accurate prognostic model for patients with severe acute respiratory syndrome (SARS) could provide a practical clinical decision aid. We developed and validated prognostic rules for both high- and low-resource settings based on data available at the time of admission.

Methods  We analyzed data on all 1755 and 291 patients with SARS in Hong Kong (derivation cohort) and Toronto (validation cohort), respectively, using a multivariable logistic scoring method with internal and external validation. Scores were assigned on the basis of patient history in a basic model, and a full model additionally incorporated radiological and laboratory results. The main outcome measure was death.

Results  Predictors for mortality in the basic model included older age, male sex, and the presence of comorbid conditions. Additional predictors in the full model included haziness or infiltrates on chest radiography, less than 95% oxygen saturation on room air, high lactate dehydrogenase level, and high neutrophil and low platelet counts. The basic model had an area under the receiver operating characteristic (ROC) curve of 0.860 in the derivation cohort, which was maintained on external validation with an area under the ROC curve of 0.882. The full model improved discrimination with areas under the ROC curve of 0.877 and 0.892 in the derivation and validation cohorts, respectively.

Conclusion  The model performs well and could be useful in assessing prognosis for patients who are infected with re-emergent SARS.