ABSTRACT
Objectives
Pleural tuberculosis (PlTB) diagnosis is a challenge due to its paucibacillary nature and to the need of invasive procedures. This study aimed to identify easily available variables and build a predictive model for PlTB diagnosis which may allow earlier and affordable alternative strategy to be used in basic health care units.
Methods
An observational cross-sectional study compared PlTB and non-TB patients followed at a tertiary Brazilian hospital between 2010 and 2018. Unconditional logistic regression analysis was performed and a Decision Tree Classifier (DTC) model was validated and applied in additional PlTB patients with empiric diagnosis. The accuracy (Acc), sensitivity (Se), specificity (Sp), positive and negative predictive values were calculated.
Results
From 1,135 TB patients, 160 were considered for analysis (111 confirmed PlTB and 49 unconfirmed PlTB). Indeed, 58 non-TB patients were enrolled as controls. Hyporexia [adjusted odds ratio (aOR) 27.39 (95% CI 6.26 – 119.89)] and cellular/biochemical characteristics on pleural fluid (PF) (polimorphonuclear in two categories: 3-14% aOR 26.22, 95% CI 7.11 – 96.68 and < 3% aOR 28.67, 95% CI 5.51 – 149.25; and protein ≥ 5g/dL aOR 7.24, 95% CI 3.07 – 17.11) were associated with higher risk for TB. The DTC constructed using these variables showed Acc=87.6%, Se=89.2%, Sp=84.5% for PlTB diagnosis and was successfully applied in unconfirmed PlTB patients.
Conclusion
The DTC model showed an excellent performance for PlTB diagnosis and can be considered as an alternative diagnostic strategy by using clinical patterns in association with PF cellular/biochemical characteristics, which were affordable and easily performed in basic health care units.
Keywords:
Pleural tuberculosis; Diagnosis; Decision tree classifier
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