Low tuberculosis treatment initiation after positive tuberculosis lipoarabinomannan results.

A positive urine TB-LAM result should lead to TB treatment initiation. TB treatment uptake was low after a positive TB-LAM but negative Xpert test. A lack of trust in TB-LAM results by clinicians and false-positive results may contribute to these results. https://bit.ly/3VGHekC.

Implementation of the advanced HIV disease care package with point-of-care CD4 testing during tuberculosis case finding: A mixed-methods evaluation.

During TB-case finding, we assessed the feasibility of implementing the advanced HIV disease (AHD) care package, including VISITECT CD4 Advanced Disease (VISITECT), a semiquantitative test to identify a CD4≤200cells/µl. Adult participants with tuberculosis symptoms, recruited near-facility in Lesotho and South-Africa between 2021-2022, were offered HIV testing (capillary blood), Xpert MTB/RIF and Ultra, and MGIT culture (sputum). People living with HIV (PLHIV) were offered VISITECT (venous blood) and Alere tuberculosis-lipoarabinomannan (AlereLAM, urine) testing. AHD was defined as a CD4≤200cells/µl on VISITECT or a positive tuberculosis test. A CD4≤200cells/µl on VISITECT triggered Immy cryptococcal antigen (Immy CrAg, plasma) testing. Participants were referred with test results. To evaluate feasibility, we assessed i) acceptability and ii) intervention delivery of point-of-care diagnostics among study staff using questionnaires and group discussions, iii) process compliance, and iv) early effectiveness (12-week survival and treatment status) in PLHIV. Predictors for 12-week survival were assessed with logistic regression. Thematic content analysis and triangulation were performed. Among PLHIV (N = 676, 48.6% of 1392 participants), 7.8% were newly diagnosed, 81.8% on ART, and 10.4% knew their HIV status but were not on ART. Among 676 PLHIV, 41.7% had AHD, 29.9% a CD4≤200cells/µl and 20.6% a tuberculosis diagnosis. Among 200 PLHIV tested with Immy CrAg, 4.0% were positive. The procedures were acceptable for study staff, despite intervention delivery challenges related to supply and the long procedural duration (median: 73 minutes). At 12 weeks, among 276 PLHIV with AHD and 328 without, 3.3% and 0.9% had died, 84.8% and 92.1% were alive and 12.0% and 7.0% had an unknown status, respectively. Neither AHD nor tuberculosis status were associated with survival. Implementing AHD care package diagnostics was feasible during tuberculosis-case finding. AHD was prevalent, and not associated with survival, which is likely explained by the low specificity of VISITECT. Challenges with CD4 testing and preventive treatment uptake require addressing.

COVID-19 screening in low resource settings using artificial intelligence for chest radiographs and point-of-care blood tests.

Artificial intelligence (AI) systems for detection of COVID-19 using chest X-Ray (CXR) imaging and point-of-care blood tests were applied to data from four low resource African settings. The performance of these systems to detect COVID-19 using various input data was analysed and compared with antigen-based rapid diagnostic tests. Participants were tested using the gold standard of RT-PCR test (nasopharyngeal swab) to determine whether they were infected with SARS-CoV-2. A total of 3737 (260 RT-PCR positive) participants were included. In our cohort, AI for CXR images was a poor predictor of COVID-19 (AUC = 0.60), since the majority of positive cases had mild symptoms and no visible pneumonia in the lungs. AI systems using differential white blood cell counts (WBC), or a combination of WBC and C-Reactive Protein (CRP) both achieved an AUC of 0.74 with a suggested optimal cut-off point at 83% sensitivity and 63% specificity. The antigen-RDT tests in this trial obtained 65% sensitivity at 98% specificity. This study is the first to validate AI tools for COVID-19 detection in an African setting. It demonstrates that screening for COVID-19 using AI with point-of-care blood tests is feasible and can operate at a higher sensitivity level than antigen testing.

Incidental radiological findings during clinical tuberculosis screening in Lesotho and South Africa: a case series.

BACKGROUND: Chest X-ray offers high sensitivity and acceptable specificity as a tuberculosis screening tool, but in areas with a high burden of tuberculosis, there is often a lack of radiological expertise to interpret chest X-ray. Computer-aided detection systems based on artificial intelligence are therefore increasingly used to screen for tuberculosis-related abnormalities on digital chest radiographies. The CAD4TB software has previously been shown to demonstrate high sensitivity for chest X-ray tuberculosis-related abnormalities, but it is not yet calibrated for the detection of non-tuberculosis abnormalities. When screening for tuberculosis, users of computer-aided detection need to be aware that other chest pathologies are likely to be as prevalent as, or more prevalent than, active tuberculosis. However, non--tuberculosis chest X-ray abnormalities detected during chest X-ray screening for tuberculosis remain poorly characterized in the sub-Saharan African setting, with only minimal literature. CASE PRESENTATION: In this case series, we report on four cases with non-tuberculosis abnormalities detected on CXR in TB TRIAGE + ACCURACY (ClinicalTrials.gov Identifier: NCT04666311), a study in adult presumptive tuberculosis cases at health facilities in Lesotho and South Africa to determine the diagnostic accuracy of two potential tuberculosis triage tests: computer-aided detection (CAD4TB v7, Delft, the Netherlands) and C-reactive protein (Alere Afinion, USA). The four Black African participants presented with the following chest X-ray abnormalities: a 59-year-old woman with pulmonary arteriovenous malformation, a 28-year-old man with pneumothorax, a 20-year-old man with massive bronchiectasis, and a 47-year-old woman with aspergilloma. CONCLUSIONS: Solely using chest X-ray computer-aided detection systems based on artificial intelligence as a tuberculosis screening strategy in sub-Saharan Africa comes with benefits, but also risks. Due to the limitation of CAD4TB for non-tuberculosis-abnormality identification, the computer-aided detection software may miss significant chest X-ray abnormalities that require treatment, as exemplified in our four cases. Increased data collection, characterization of non-tuberculosis anomalies and research on the implications of these diseases for individuals and health systems in sub-Saharan Africa is needed to help improve existing artificial intelligence software programs and their use in countries with high tuberculosis burden.