Background An accurate point-of-care test for tuberculosis (TB) in children remains an elusive goal. Recent evaluation of a novel point-of-care urinary lipoarabinomannan test, Fujifilm SILVAMP Tuberculosis Lipoarabinomannan (FujiLAM), in adults living with human immunodeficiency virus (HIV) showed significantly superior sensitivity than the current Alere Determine Tuberculosis Lipoarabinomannan test (AlereLAM). We therefore compared the accuracy of FujiLAM and AlereLAM in children with suspected TB. Methods Children hospitalized with suspected TB in Cape Town, South Africa, were enrolled (consecutive admissions plus enrichment for a group of children living with HIV and with TB), their urine was collected and biobanked, and their sputum was tested with mycobacterial culture and Xpert MTB/RIF or Xpert MTB/RIF Ultra. Biobanked urine was subsequently batch tested with FujiLAM and AlereLAM. Children were categorized as having microbiologically confirmed TB, unconfirmed TB (clinically diagnosed), or unlikely TB. Results A total of 204 children were enrolled and had valid results from both index tests, as well as sputum microbiological testing. Compared to a microbiological reference standard, the sensitivity of FujiLAM and AlereLAM was similar (42% and 50%, respectively), but lower than that of Xpert MTB/RIF of sputum (74%). The sensitivity of FujiLAM was higher in children living with HIV (60%) and malnourished children (62%). The specificity of FujiLAM was substantially higher than that of AlereLAM (92% vs 66%, respectively). The specificity of both tests was higher in children 2 years or older (FujiLAM, 96%; AlereLAM, 72%). Conclusions The high specificity of FujiLAM suggests utility as a “rule-in” test for children with a high pretest probability of TB, including hospitalized children living with HIV or with malnutrition.
Pediatric tuberculosis (TB) remains a global health crisis. Despite progress, pediatric patients remain difficult to diagnose, with approximately half of all childhood TB patients lacking bacterial confirmation. In this pilot study (n = 31), we identify a 4-compound breathprint and subsequent machine learning model that accurately classifies children with confirmed TB (n = 10) from children with another lower respiratory tract infection (LRTI) (n = 10) with a sensitivity of 80% and specificity of 100% observed across cross validation folds. Importantly, we demonstrate that the breathprint identified an additional nine of eleven patients who had unconfirmed clinical TB and whose symptoms improved while treated for TB. While more work is necessary to validate the utility of using patient breath to diagnose pediatric TB, it shows promise as a triage instrument or paired as part of an aggregate diagnostic scheme.
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