Algorithm for the diagnosis of smear-negative pulmonary tuberculosis in high-incidence resource-constrained settings

Abstract

Objectives: Diagnosis of smear-negative pulmonary tuberculosis (SNPT) remains a challenge, particularly in resource-constrained settings. We evaluated a diagnostic algorithm that combines affordable laboratory tools and a clinical prediction rule (CPR). Methods: We derived, based on published evidence, a diagnostic algorithm for SNPT. Sputum concentration constitutes its first step. In suspects with negative results, SNPT probability is classified with a CPR as low (excluded), high (confirmed) or intermediate. For intermediate patients, sputum Middlebrook 7H9 liquid culture is performed, and they are assessed after 2 weeks. If clinically deteriorated, with still negative liquid culture, bronchoscopy is offered. Otherwise, results of Middlebrook 7H9 culture are awaited. We prospectively evaluated this algorithm against a reference standard of solid and liquid cultures in two reference hospitals in Lima, Peru. Results: 670 SNPT suspects were included from September 2005 to March 2008. The prevalence of SNPT was 27% according to the reference standard. The algorithm's overall accuracy was 0.94 (95% CI 0.91–0.95), its sensitivity was 0.88 (95% CI 0.82–0.92) and its specificity, 0.96 (95% CI 0.94–0.98). Sputum concentration, the CPR, Middlebrook 7H9 sputum culture and bronchoscopic samples defined a diagnosis of SNPT according to the algorithm in 57 (37%), 25 (16%), 63 (41%) and 8(5%) of patients, respectively. 65% of patients were diagnosed within 3 weeks. Conclusions: The algorithm was accurate for SNPT diagnosis. Sputum concentration, CPR and selective Middlebrook 7H9 culture are essential components.