Validity of Clinical Prediction Rules for Isolating Inpatients with Suspected Tuberculosis

A systematic review of decision rules to predict a patient's risk for active pulmonary TB at the time of hospital admission. The review suggested that clinicians can use prediction rules to identify patients with very low risk of infection among those with suspected TB on hospital admission, and thus reduce the isolation of patients without TB.

  • Authors
  • Juan P. Wisnivesky
  • Thomas McGinn
  • Denise Serebrisky
  • Carlton Moore
  • Henry S. Sacks
  • Michael C. Iannuzzi
  • Published
  • J Gen Intern Med

Abstract

Objective

Declining rates of tuberculosis (TB) in the United States has resulted in a low prevalence of the disease among patients placed on respiratory isolation. The purpose of this study is to systematically review decision rules to predict the patient’s risk for active pulmonary TB at the time of admission to the hospital.

Data Sources

We searched MEDLINE (1975 to 2003) supplemented by reference tracking. We included studies that reported the sensitivity and specificity of clinical variables for predicting pulmonary TB, used Mycobacterium TB culture as the reference standard, and included at least 50 patients.

Review Method

Two reviewers independently assessed study quality and abstracted data regarding the sensitivity and specificity of the prediction rules.

Results

Nine studies met inclusion criteria. These studies included 2,194 participants. Most studies found that the presence of TB risk factors, chronic symptoms, positive tuberculin skin test (TST), fever, and upper lobe abnormalities on chest radiograph were associated with TB. Positive TST and a chest radiograph consistent with TB were the predictors showing the strongest association with TB (odds ratio: 5.7 to 13.2 and 2.9 to 31.7, respectively). The sensitivity of the prediction rules for identifying patients with active pulmonary TB varied from 81% to 100%; specificity ranged from 19% to 84%.

Conclusions

Our analysis suggests that clinicians can use prediction rules to identify patients with very low risk of infection among those suspected for TB on admission to the hospital, and thus reduce isolation of patients without TB.

  • Focus
  • Evidence Based Medicine