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Infect Control Hosp Epidemiol. 2008 Oct;29(10):927-32. doi: 10.1086/590667.

Derivation and validation of a clinical prediction score for isolation of inpatients with suspected pulmonary tuberculosis.

Author information

1
Division of Pulmonary and Critical Care Medicine, University of California, Davis Health System, Sacramento, California 95817, USA.

Abstract

BACKGROUND:

The use of a clinical prediction score to improve the practice of instituting airborne-transmission precautions in patients with suspected tuberculosis holds promise for increasing appropriate isolation and decreasing unnecessary isolation. The objective of this study was to derive and validate a clinical prediction score for patients with suspected tuberculosis.

METHODS:

We used a case-control study design to evaluate differences between patients with a diagnosis of tuberculosis and those placed under airborne precautions who had negative culture results. We developed risk scores based on a multivariable analysis of independently significant factors associated with tuberculosis. Subsequently, we evaluated the sensitivity and specificity of the score in a separate (validation) cohort of patients.

RESULTS:

Within our population, we found 4 clinical factors associated with tuberculosis: chronic symptoms (odds ratio [OR], 10.2 [95% confidence interval {CI}, 2.95-35.4]), upper lobe disease on chest radiograph (OR, 5.27 [95% CI, 1.6-17.23]), foreign-born status (OR, 7.01 [95% CI, 2.1-23.8]), and immunocompromised state other than human immunodeficiency virus infection (OR, 8.14 [95% CI, 2.08-31.8]). Shortness of breath (OR, 0.13 [95% CI, 0.04-0.45]) was found to be associated with non-tuberculosis diagnoses and considered a negative predictor in the model. Using a cut-off point to maximize sensitivity, we applied the prediction rule to the validation cohort, resulting in a sensitivity of 97% and a specificity of 42%.

CONCLUSION:

The tuberculosis prediction rule derived from our patient population could improve utilization of airborne precautions. Clinical prediction rules continue to show their utility for improvement in isolation practices in different demographic areas.

PMID:
18785863
DOI:
10.1086/590667
[Indexed for MEDLINE]

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