Lung and abdominal ultrasound accuracy for tuberculosis: An Indian prospective cohort study.

backgroundTuberculosis (TB) diagnosis remains a challenge, particularly in low-resource settings. Point-of-care ultrasound (POCUS) has shown promise, but most studies focus on HIV-infected populations. In the case of TB, data on lung ultrasound (LUS) are sparse. Therefore, this study evaluates the diagnostic accuracy of lung and abdominal ultrasound for TB diagnosis in an Indian tertiary care hospital.

methodsWe prospectively enrolled adults with presumed TB and performed comprehensive ultrasound assessments. Accuracy of individual and combined sonographic findings was evaluated against a robust reference standard (mycobacterial culture and PCR). Comparators were C-reactive protein at a cut-off of 5mg/l and chest x-ray (CXR). A multivariable model incorporating clinical and ultrasound findings was explored using generalized mixed models and a random forest approach. (Trial registry DRKS00026636).

findingsAmong 541 participants, 102 (19%) were diagnosed with TB and 1% were HIV-positive. The "Focused Assessment with Sonography for HIV-associated TB" (FASH) demonstrated moderate sensitivity (51%) and specificity (70%). Consolidations <1 cm on LUS showed high sensitivity (93%) but low specificity (16%) and were also seen in non-TB lung infections and other conditions like bronchial asthma and COPD. Accuracy of larger (≥1 cm) consolidations (72% sensitive, 55% specific) on LUS was comparable with CXR suggesting possible TB (81% sensitive, 58% specific). Predictive modeling suggests moderate diagnostic performance (AUC = 0.79).

interpretationIn our study, POCUS did not meet WHO targets for a stand-alone facility-based screening test. Nevertheless, diagnostic accuracy for some findings is comparable to CXR and could be integrated into diagnostic algorithms to improve TB screening where CXR cannot reach. Future research should explore artificial intelligence to enhance TB-POCUS accuracy and accessibility, as was previously reported for CXR. RESEARCH IN CONTEXT: Prior to this study, lung ultrasound (LUS) for TB had been assessed in only a few studies, limited by uncertain sonographic characterization of TB-related findings, lack of consistent terminology, and small numbers of participants with confirmed non-TB diagnoses to determine specificity for TB. Studies evaluating Focused assessment with sonography for HIV-associated tuberculosis (FASH) almost exclusively included HIV-infected individuals and demonstrated moderate sensitivity and specificity. However, varying study designs and reference standards limit broader generalization of their findings. Our prospective study from a TB-endemic setting (India) recruited 541 predominantly HIV-negative participants with presumed TB. This is the largest cohort to date assessing LUS, FASH, and additional ultrasound findings for TB diagnosis. Our study demonstrates that no single ultrasound finding alone, or even in combination, reaches the accuracy targets of the target product profile for a facility-based screening test (triage) proposed by WHO. FASH accuracy in our study aligned with previously reported data in HIV-negative participants but was less specific in HIV-positive participants. The accuracy of additional ultrasound items of LUS and FASH was comparable to chest x-ray (CXR). In summary, this study demonstrates accuracy of ultrasound for TB diagnosis, backed by a robust study design and using a comprehensive reference standard and CXR comparator for LUS. Modelling suggests that an algorithmic approach combining ultrasound and clinical findings may be of highest value to inform risk of TB and guide further testing to confirm the diagnosis of TB. Other use cases of POCUS, which may aid clinical decision making in the assessment of disease severity, sampling strategy, and monitoring, should be evaluated by future studies. These should also focus on the accuracy of POCUS in people living with HIV and children, as well as evaluate POCUS more broadly as part of a diagnostic algorithm and by using artificial intelligence to improve the yield of TB-POCUS.