Journal of the Formosan Medical Association (Jan 2008)

Predictors for Identifying the Most Infectious Pulmonary Tuberculosis Patient

  • Chuan-Sheng Wang,
  • Huang-Chi Chen,
  • Inn-Wen Chong,
  • Jhi-Jhu Hwang,
  • Ming-Shyan Huang

DOI
https://doi.org/10.1016/S0929-6646(08)60003-0
Journal volume & issue
Vol. 107, no. 1
pp. 13 – 20

Abstract

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Clinicians need to decide whether to begin isolation and empiric therapy for patients suspected of having infectious tuberculosis (TB). This study aimed to identify the demographic, clinical and radiographic characteristics of acid-fast bacilli (AFB) smear-positive patients and to create a smear-positive TB prediction rule, which clinicians may use to predict risk. Methods: This was a retrospective study involving 105 patients with AFB smear-positive TB and 52 patients with AFB smear-negative TB at Kaohsiung Municipal Hsiao-Kang Hospital in southern Taiwan from August 1, 2003 to July 31, 2006. All of the patients had at least one sputum culture that was positive for Mycobacterium tuberculosis. Demographic, clinical and radiographic data of patients with AFB smear-positive TB were compared to those of patients with AFB smear-negative TB. Results: On univariate analysis, young age (p = 0.033), alcoholism (p = 0.036), weight loss (p = 0.003), fever (p = 0.018), consolidation (p = 0.001), infiltration (p = 0.012), cavitary pattern (p = 0.005), right upper lung field (p < 0.001) and left upper lung field (p = 0.001) lesions on chest radiographs were found to be predictive of smear-positive TB patients. In contrast, end-stage renal disease (p = 0.035) and normal chest radiograph (p = 0.006) were predictive of smear-negative TB patients. On multivariate analysis, age less than 65 years (p = 0.004), fever (p = 0.004), right upper lung field (p = 0.044), left upper lung field (p = 0.041), consolidation (p = 0.018) and cavitary (p = 0.049) lesions on chest radiograph were independently associated with an increased risk of an AFB positive smear finding. The smear-positive TB prediction model was created based on the results of the multivariate analysis that had an area of 0.788 under the receiver operating characteristic curve. Conclusion: The smear-positive TB prediction model may help clinicians decide if a patient with pending sputum smear results should first be placed in isolation and empiric anti-tuberculous therapy started.

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