Predictors for identifying the most infectious pulmonary tuberculosis patient

BACKGROUND/PURPOSE

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.

The validity of medical history, classic symptoms, and chest radiographs in predicting pulmonary tuberculosis: derivation of a pulmonary tuberculosis prediction model

STUDY OBJECTIVE

To improve the respiratory isolation policy for patients with suspected pulmonary tuberculosis (TB).

DESIGN

Prospective, descriptive, French multicenter study.

SETTING

Emergence of nosocomial outbreaks of TB.

PATIENTS

All consecutive patients admitted with suspicion of pulmonary TB.

MEASUREMENTS AND RESULTS

Medical history, social factors, symptoms, and chest radiograph (CXR) pattern (symptoms and CXR both scored as typical of pulmonary TB, compatible, negative, or atypical) were obtained on admission. Serial morning sputa were collected. Of the 211 patients, 47 (22.3%) had culture-proven pulmonary TB, including 31 (14.7%) with a positive smear. Mean age was 46.2 years; 52 patients were HIV positive (24.6%). The sensitivity of the respiratory isolation policy was 71.4%, specificity was 51.7%, negative predictive value (NPV) was 88.2%, and positive predictive value (PPV) was 26.3%. On univariate analysis, predictive factors of culture-proven pulmonary TB were CXR (p < 0.00001), symptoms (p = 0.0004), age (mean, 40.8 years for TB patients vs 47.5 years for non-TB patients; p = 0.04), absence of HIV infection (89.4% vs 71.3%; p = 0.01), immigrant status (72% vs 55%; p = 0.03), and bacillus Calmette-Guérin status (p = 0.025). On multivariate analysis, CXR pattern (p < 0.00001), HIV infection (p = 0.002), and symptoms (p = 0.009) remained independently predictive. Based on these data, a model was proposed using a receiver operating characteristics curve. In the derivation cohort, the sensitivity and NPV of the model in detecting smear-positive pulmonary TB would have been 100%. The specificity and PPV would have been 48.4% and 25%, respectively. The model performed less well when evaluated on two retrospective groups, but its sensitivity remained above that of the current respiratory isolation policy (91.1% and 82.4% for the retrospective groups vs 71.1% for the current policy).

CONCLUSIONS

Improved interpretation of clinical and radiologic data available on patient admission could improve adequacy of respiratory isolation. A prediction model is proposed.