High-resolution CT for identify patients with smear-positive, active pulmonary tuberculosis

Abstract

Purpose: This study evaluates the use of high-resolution computed tomography (HRCT) to differentiate smear-positive, active pulmonary tuberculosis (PTB) from other pulmonary infections in the emergency room (ER) setting.

Methods: One hundred and eighty-three patients diagnosed with pulmonary infections in an ER were divided into an acid fast bacillus (AFB) smear-positive, active PTB group (G1=84) and a non-AFB smear-positive, pulmonary infection group (G2=99). HRCT images from a 64-Multidetector CT were analyzed, retrospectively, for the morphology, number, and segmental distribution of pulmonary lesions.

Results: Utilizing multivariate analysis, five variables were found to be independent risk factors predictive of G1: (1) consolidation involving the apex segment of right upper lobe, posterior segment of the right upper lobe, or apico-posterior segment of the left upper lobe; (2) consolidation involving the superior segment of the right or left lower lobe; (3) presence of a cavitary lesion; (4) presence of clusters of nodules; (5) absence of centrilobular nodules. A G1 prediction score was generated based on these 5 criteria to help differentiate G1 from G2. The area under the receiver operating characteristic (ROC) curve was 0.96 ± 0.012 in our prediction model. With an ideal cut-off point score of 3, the specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) are 90.9%, 96.4%, 90.0% and 96.8%, respectively.

Conclusion: The use of this AFB smear-positive, active PTB prediction model based on 5 key HRCT findings may help ER physicians determine whether or not isolation is required while awaiting serial sputum smear results in high risk patients.

Fig. 1

An 81-year-old woman with smear-positive, active PTB presenting with hemoptysis and pleural effusion. The score of this case is 3. (A) Axial HRCT shows clusters of nodules (black arrowhead) with spiculated margins, peribroncho-arterial distribution in the right upper lobe, and pleural effusion (black arrow). (B) Four months later, axial HRCT shows regression of clusters of nodules (black arrowhead) after tuberculosis treatment. (C) Eight months later, axial HRCT shows residual nodules (black arrowhead). (D) Histologic specimen (biopsies of the right upper lobe via video-assisted thoracoscopy, H&E stain, 40×) photomicrograph shows more concentrated granulomas at the center of the nodule clusters and granulomatous inflammation with peribroncho-arterial distribution (C, white arrow) and a small granulomata (C, white arrowhead) at the periphery of the large nodules. (E) Histologic specimen (biopsies of the right upper lobe via video-assisted thoracoscopy, H&E stain, 40×) photomicrograph shows large tuberculous nodules (C, white arrow) produced by numerous small nodules and a small granulomata (C, white arrowhead) at the periphery of the large nodules. Peripheral low attenuation spots on HRCT correspond to spaces between partially coalescent small nodules (C = clusters of nodules; B = bronchus; A = artery).

Fig. 2

A 66-year-old-male with mycoplasma pneumonia. (A) Coronal HRCT shows centrilobular nodules (white arrowhead) in right middle lobe. (B) Two months later, after antibiotic treatment, coronal HRCT shows the centrilobular nodules disappear (white arrowhead). The score of this case is −1.

Fig. 3

A receiver operating characteristic (ROC) curve plots the false positive rate against the true positive rate for each possible cutoff for a diagnostic test. The area under this curve generated from the multiple logistic regression model with a 95% CI is 0.968 [0.945–0.990]. The best cut-off probability is 0.68 and the cutoff point of relevance is 3. The specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) are 90.9%, 96.4%, 90.0%, and 96.8%, respectively.