Diagnostic accuracy of chest ultrasound scan in the diagnosis of childhood tuberculosis
Erem, G.; Otike, C.; Okuja, M.; Ameda, F.; Nalyweyiso, D. I.; Mubuuke, A. G.; Kakinda, M.
Show abstract
Chest Ultrasound Scan (CUS) has been utilized in place of CXR in the diagnosis of adult pneumonia with similar or higher sensitivity and specificity to CXR. However, there is a paucity of data on the use of CUS for the diagnosis of childhood TB. This study aimed to determine the diagnostic accuracy of CUS for childhood TB. This cross-sectional study was conducted at the Mulago National Referral Hospital in Uganda. Eighty children up to 14 years of age with presumptive TB were enrolled. They all had CUS and CXR performed and interpreted independently by radiologists. The radiologist who performed the CXR was blinded to the CUS findings, and vice versa. Radiologists noted whether TB was likely or unlikely. A two-by-two table was developed to compare the absolute number of children as either TB likely or TB unlikely on CXR or CUS. This was used to calculate the sensitivity and specificity of CUS when screening for TB in children, with a correction to accommodate the use of CXR as a reference test. The sensitivity of CUS was 64% (95% CI 48.5%-77.3%), while its specificity was 42.7% (95% CI 25.5%-60.8%). Both the CUS and CXR found 29 children with a likelihood of TB, and 27 children unlikely to have TB. CUS met the sensitivity target set by the WHO TPP for Triage, and it had a sensitivity and specificity comparable to that of CXR.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- High Proportion of RR-TB and mutations conferring RR outside of the RRDR of the rpoB gene detected in GeneXpert MTB/RIF assay positive pulmonary tuberculosis cases, in Addis Ababa, Ethiopia 97%
- Comparison of tests done, and Tuberculosis cases detected by Xpert® MTB/RIF and Xpert® MTB/RIF-Ultra in Uganda 97%
- The Pattern of rpoB gene mutation of Mycobacterium tuberculosis and predictors of rifampicin resistance detected by gene Xpert MTB/Rif in Tanzania 96%
Similar papers in this journal
- Healthcare seeking behavior and delays in case of drug-resistant Tuberculosis patients in Bangladesh: Findings from a cross-sectional survey 96%
- Treatment preferences among people at risk of developing tuberculosis: a discrete choice experiment 95%
- Evaluating the health impact, health-system costs and cost-effectiveness of using TrueNat on stool samples compared to usual care for the diagnosis of paediatric tuberculosis in primary care settings: a modelling analysis 94%
Similar papers in this journal
- Epidemiological And Clinical Characteristics Of COVID-19 Patients In Kenya 95%
- Clinical pneumonia in the hospitalised child in Malawi in the post-pneumococcal conjugate vaccine era: a prospective hospital-based observational study 94%
- Pre-diagnostic loss to follow-up in an active case-finding TB program: a mixed-methods study from rural Bihar, India 94%
Similar papers in this journal
- Patients at risk of pulmonary fibrosis Post Covid-19: Epidemiology, pulmonary sequelaes and humoral response 91%
- Acceptability, willingness to use and preferred distribution models of oral-based HIV self-testing kits among key and priority populations enrolled in HIV pre-exposure prophylaxis clinics in central Uganda. A mixed-methods cross-sectional study 91%
- Serologic SARS-CoV-2 testing in healthcare workers with positive RT-PCR test or Covid-19 related symptoms 89%
Similar papers in this journal
- Detection of new Mycobacterium leprae subtype in Bangladesh by genomic characterization to explore transmission patterns 92%
- Toward Precision Detection of Pyrazinamide Resistance: Critical Concentration Assessment and Rapid Molecular Method Validation 90%
- Antimicrobial resistance profiling and phylogenetic analysis of Neisseria gonorrhoeae clinical isolates from Kenya in a resource-limited setting 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.