A clinical prediction rule for diagnosing severe acute respiratory syndrome in the emergency department.

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.