Comparative Efficacy of CUS, CXR and CAD in TB Diagnosis in LMIC

Tuberculosis, Pulmonary Clinical Trial

— CUSTB  

Official title:

Comparative Efficacy of Chest Ultrasound, Chest X-Ray and Computer-aided Diagnostic (CAD) for Tuberculosis Diagnosis in Low-Resource Setting: A Cross-Sectional Study From Ethiopia (CUSTB)

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06409780
• Other study ID #: CUSTBv1
• Status: Not yet recruiting
• Phase: N/A
• Start date: May 5, 2024
• Est. completion date: April 30, 2025

Study information

• Verified date: May 2024
• Source: University of Bari
• Contact: Giacomo Guido, MD
• Phone: 3202842602
• Email: giacguido[@]gmail.com
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

Pulmonary Tuberculosis (TB) remains a significant global health concern, particularly in low- and middle-income countries (LMIC), where resources for healthcare are often limited. While CXR is the standard imaging modality for TB diagnosis, its sensitivity and specificity can vary depending on factors such as the stage of the disease and the quality of the image obtained. This study endeavors to assess the diagnostic precision of Chest Ultrasound (CUS) relative to Chest X-ray (CXR) and CAD score in the detection of Pulmonary Tuberculosis (TB) among both index cases and household contacts.

Description:

BACKGROUND Pulmonary Tuberculosis (TB) remains a significant global health concern, particularly in low- and middle-income countries (LMIC), where resources for healthcare are often limited. Early and accurate diagnosis of TB is crucial for timely initiation of treatment and prevention of transmission, yet it presents challenges due to various factors including the complexity of symptoms and limitations in diagnostic tools. While CXR is the standard imaging modality for TB diagnosis, its sensitivity and specificity can vary depending on factors such as the stage of the disease and the quality of the image obtained. Specifically, in Ethiopia, a recent study on the facilitators of pulmonary tuberculosis diagnosis emphasizes the importance of integrating radiographic screening with symptom-based screening in health facilities, while acknowledging the high cost of this implementation. In recent years, CUS has emerged as a promising adjunctive tool in TB diagnosis, especially in at-risk populations, such as People Living with HIV. Its advantages include portability, lack of radiation exposure, and potential for bedside use, making it particularly valuable in resource-limited settings where access to advanced imaging techniques may be limited. The initial evidence indicates that the use of CUS exhibits high sensitivity in detecting microbiologically confirmed TB among adults. Additionally, Computer-Aided Diagnosis (CAD) systems utilizing artificial intelligence algorithms have shown promise in improving diagnostic accuracy by assisting clinicians in interpreting medical images. However, despite these advancements, limited research has directly compared the diagnostic performance of CUS, CXR, and CAD score in the diagnosis of TB, with no evidence at all in household contacts of index cases. Understanding the comparative effectiveness of these diagnostic modalities is essential for optimizing TB diagnosis strategies and improving patient outcomes, especially in high-risk populations such as household contacts who are at increased risk of TB transmission11. Therefore, this study aims to fill this gap by evaluating the diagnostic accuracy of CUS, CXR, and CAD score in identifying TB among both index cases and household contacts. By employing a cross-sectional design, the study seeks to determine the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of each diagnostic modality, as well as explore potential correlations and discrepancies among them. The findings from this study have the potential to inform clinical practice guidelines and contribute to the development of more effective TB diagnosis and management strategies tailored to the needs of diverse populations and healthcare settings.

STUDY DESIGN AND METHODS This study will employ a cross-sectional design to compare the diagnostic accuracy of CUS with CXR and CAD score in identifying TB among index cases and household contacts. The study will last 12 months at the end of which data analysis will be performed by the research team. No interference with the routinary activities related with the admission and the care of the patient is expected in the research. The participants will be enrolled at the admission in Outpatient Department (OPD) or Medical Ward, after the inclusion criteria are spontaneously encountered thanks to independent clinician evaluation. Vital parameters and clinical signs will be recorded CXR will be performed as standard of care with capture images in the posteroanterior view, ensuring adequate visualization of the chest area. Position the participant in an upright or standing position, facing the X-ray machine. The acquired chest X-ray images will be transferred to the CAD software platform, which may highlight regions of interest and assess a score according to the findings. During CUS, the participant will stay in a supine or seated position, exposing the chest area for ultrasound examination. A thin layer of ultrasound gel will be applied to the skin to facilitate acoustic coupling and improve image quality. STATISTICAL ANALYSIS Two interim analyses are planned at 3 and 6 months of enrollment to verify the assumptions about the sensitivity and specificity of CUS, CXR and CAD in diagnosing TB. The interim analyses will not include any formal statistical testing. Categorical data will be summarized as absolute and relative frequencies. Numerical data will be summarized using mean and standard deviation (SD), or median and interquartile range (IQR). In accuracy investigation, the standard measures will be calculated (sensitivity, specificity, positive predictive value, negative predictive value). The non-inferiority hypothesis will be testes using a RMLE-based score test according to Liu et al.12 Adjustment for multiple testing will be performed according to Benjamini- Hochberg procedure. Concordance between CUS and CXR, and between CUS and CAD will be assessed using Cohen's kappa and Gwet's AC1. Comparisons between variables will be performed with exploratory purpose using Pearson's or Spearman's correlation coefficients, Student's t-test, paired Student's ttest, Mann-Whitney test, Wilcoxon test, Chi Square test, or Fisher's test, as appropriate. Estimates will be reported with 95% confidence intervals were appropriate. Statistical significance will be set at 5%. The statistical analysis will be carried out using R 4.3 (R Foundation for Statistical Computing, Vienna, Austria).

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 136
• Est. completion date: April 30, 2025
• Est. primary completion date: May 5, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 5 Years and older

Eligibility

Inclusion Criteria:

• Subjects older than 5 years old.

• Capacity to provide informed consensus.

• Condition requiring a diagnosis of pulmonary tuberculosis within 7 days, with either microbiologically or radiologically criteria (index case), or being the household contact reported by an index case.

Exclusion Criteria:

• Exposure to any antitubercular treatment prior 7 days than the enrollment after the initiation of clinic

• Withdraw of the informed consent

Related Conditions & MeSH terms

• Tuberculosis
• Tuberculosis, Pulmonary

Intervention

Diagnostic Test:
• Comparative analysis of CUS, CAD and CXR in pulmonary TB
The strumental analysis of pulmonary TB signs with chest ultrasound, chest x-ray and CAD will be compared in two different settings: index cases and household contacts

Locations

Country	Name	City	State
n/a

Sponsors (2)

Lead Sponsor	Collaborator
University of Bari	Doctors with Africa - CUAMM

References & Publications (11)

Abraham Y, Manyazewal T, Amdemariam Z, Petros H, Ayenadis F, Mekonen H, Workneh F. Facilitators and barriers to implementing chest radiography in tuberculosis systematic screening of clinically high-risk groups in Ethiopia: A qualitative study. SAGE Open Med. 2024 Feb 19;12:20503121241233232. doi: 10.1177/20503121241233232. eCollection 2024. — View Citation

Abuzerr S, Zinszer K. Computer-aided diagnostic accuracy of pulmonary tuberculosis on chest radiography among lower respiratory tract symptoms patients. Front Public Health. 2023 Oct 27;11:1254658. doi: 10.3389/fpubh.2023.1254658. eCollection 2023. — View Citation

Acharya V, Dhiman G, Prakasha K, Bahadur P, Choraria A, M S, J S, Prabhu S, Chadaga K, Viriyasitavat W, Kautish S. AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model. Comput Intell Neurosci. 2022 Oct 3;2022:2399428. doi: 10.1155/2022/2399428. eCollection 2022. — View Citation

Arega B, Tilahun K, Minda A, Agunie A, Mengistu G. Prevalence rate of undiagnosed tuberculosis in the community in Ethiopia from 2001 to 2014: systematic review and meta-analysis. Arch Public Health. 2019 Jul 11;77:33. doi: 10.1186/s13690-019-0360-2. eCollection 2019. — View Citation

Bobbio F, Di Gennaro F, Marotta C, Kok J, Akec G, Norbis L, Monno L, Saracino A, Mazzucco W, Lunardi M. Focused ultrasound to diagnose HIV-associated tuberculosis (FASH) in the extremely resource-limited setting of South Sudan: a cross-sectional study. BMJ Open. 2019 Apr 2;9(4):e027179. doi: 10.1136/bmjopen-2018-027179. — View Citation

Cozzi D, Bartolucci M, Giannelli F, Cavigli E, Campolmi I, Rinaldi F, Miele V. Parenchymal Cavitations in Pulmonary Tuberculosis: Comparison between Lung Ultrasound, Chest X-ray and Computed Tomography. Diagnostics (Basel). 2024 Feb 29;14(5):522. doi: 10.3390/diagnostics14050522. — View Citation

Danchuk SN, Solomon OE, Kohl TA, Dreyer V, Barilar I, Utpatel C, Niemann S, Soolingen DV, Anthony R, van Ingen J, Michael JS, Behr MA. Challenging the gold standard: the limitations of molecular assays for detection of Mycobacterium tuberculosis heteroresistance. Thorax. 2024 Jan 29:thorax-2023-220202. doi: 10.1136/thorax-2023-220202. Online ahead of print. — View Citation

Fentress M, Henwood PC, Maharaj P, Mitha M, Khan D, Caligiuri P, Karat AS, Olivier S, Edwards A, Ramjit D, Ngcobo N, Wong EB, Grant AD. High sensitivity of ultrasound for the diagnosis of tuberculosis in adults in South Africa: A proof-of-concept study. PLOS Glob Public Health. 2022 Oct 6;2(10):e0000800. doi: 10.1371/journal.pgph.0000800. eCollection 2022. — View Citation

Fentress M, Ugarte-Gil C, Cervantes M, Rivas D, Moore D, Caliguiri P, Bergman K, Noazin S, Padovani A, Gilman RH. Lung Ultrasound Findings Compared with Chest X-Ray Findings in Known Pulmonary Tuberculosis Patients: A Cross-Sectional Study in Lima, Peru. Am J Trop Med Hyg. 2020 Nov;103(5):1827-1833. doi: 10.4269/ajtmh.20-0542. — View Citation

Rea G, Sperandeo M, Lieto R, Bocchino M, Quarato CMI, Feragalli B, Valente T, Scioscia G, Giuffreda E, Foschino Barbaro MP, Lacedonia D. Chest Imaging in the Diagnosis and Management of Pulmonary Tuberculosis: The Complementary Role of Thoraci Ultrasound. Front Med (Lausanne). 2021 Dec 10;8:753821. doi: 10.3389/fmed.2021.753821. eCollection 2021. — View Citation

Suttels V, Du Toit JD, Fiogbe AA, Wachinou AP, Guendehou B, Alovokpinhou F, Toukoui P, Hada AR, Sefou F, Vinasse P, Makpemikpa G, Capo-Chichi D, Garcia E, Brahier T, Keitel K, Ouattara K, Cissoko Y, Beye SA, Mans PA, Agodokpessi G, Boillat-Blanco N, Hartley MA. Point-of-care ultrasound for tuberculosis management in Sub-Saharan Africa-a balanced SWOT analysis. Int J Infect Dis. 2022 Oct;123:46-51. doi: 10.1016/j.ijid.2022.07.009. Epub 2022 Jul 8. — View Citation

* Note: There are 11 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Specificity of CUS in TB diagnosis	Specificity of CUS in diagnosing TB compared to CXR and CAD score among index cases and household contacts	1 year
Primary	Sensitivity of CUS in TB diagnosis	Sensitivity of CUS in diagnosing TB compared to CXR and CAD score among index cases and household contacts	1 year
Secondary	Positive predictive value of CUS in TB diagnosis	Positive Predictive Value (PPV) assessment of CUS in TB diagnosis	1 year
Secondary	Negative predictive value of CUS in TB diagnosis	Negative Predictive Value (NPV) assessment of CUS in TB diagnosis	1 year
Secondary	Concordance of CUS, CXR and CAD in TB	Concordance between CUS, CXR, and CAD score in identifying TB cases	1 year