Tuberculosis, PulmonaryCUSTBNCT06409780
Comparative Efficacy of CUS, CXR and CAD in TB Diagnosis in LMIC
A study testing Comparative analysis of CUS, CAD and CXR in pulmonary TB in people with Tuberculosis, Pulmonary, run by University of Bari.
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. Read more about this study
- Who is it for?
- Ages 5 years and olderHealthy volunteers can take part
- Conditions the study is for
- Tuberculosis, Pulmonary
- What is being tested?
- Comparative analysis of CUS, CAD and CXR in pulmonary TB
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 136 participants
- Where
- No sites listed yet
- Study dates
- 5 May 2024to 30 Apr 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 5 years and older
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- Exposure to any antitubercular treatment prior 7 days than the enrollment after the initiation of clinic
- Withdraw of the informed consent
Trial locations and contacts
No sites are listed yet.
About this study
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)
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.
Read the detailed 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).
- Conditions
- Tuberculosis, Pulmonary
- Being tested
- Comparative analysis of CUS, CAD and CXR in pulmonary TB
- Keywords
- Tuberculosis, Chest Ultrasound, Chest X-Ray, CAD, LMIC
- Medical subject headings
- Tuberculosis, Pulmonary, Tuberculosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Comparative analysis of CUS, CAD and CXR in pulmonary TBDiagnostic test
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
| Study group | Role | What they receive |
|---|---|---|
| Diagnosis of Pulmonary Tuberculosis in index case | Experimental | CUS, CXR and CAD analysis will be performed in all patients with new diagnosis of pulmonary tuberculosis according to microbiological criteria Receives: Diagnostic Test: Comparative analysis of CUS, CAD and CXR in pulmonary TB |
| Screening of Pulmonary Tuberculosis in household contacts | Experimental | CUS, CXR and CAD analysis will be performed in all participants who are household contacts of an index case Receives: Diagnostic Test: Comparative analysis of CUS, CAD and CXR in pulmonary TB |
What is being measured
The primary outcome is the main question the study is designed to answer.
Specificity of CUS in TB diagnosis
Specificity of CUS in diagnosing TB compared to CXR and CAD score among index cases and household contacts
Measured: 1 year
Sensitivity of CUS in TB diagnosis
Sensitivity of CUS in diagnosing TB compared to CXR and CAD score among index cases and household contacts
Measured: 1 year
Secondary outcomes (3)
Positive predictive value of CUS in TB diagnosis
Positive Predictive Value (PPV) assessment of CUS in TB diagnosis
Measured: 1 year
Negative predictive value of CUS in TB diagnosis
Negative Predictive Value (NPV) assessment of CUS in TB diagnosis
Measured: 1 year
Concordance of CUS, CXR and CAD in TB
Concordance between CUS, CXR, and CAD score in identifying TB cases
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 May 2024
- Study start5 May 2024 (estimated)
- Primary completion5 May 2024 (estimated)
- Study completion30 Apr 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (12)Papers the sponsor has linked to this study.
- Other trials for Tuberculosis, PulmonaryStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 136 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Bari
- Sponsor type
- University, hospital or other
- Collaborators
- Doctors with Africa - CUAMM
- Responsible party
- Principal investigator, Francesco Di Gennaro, University of Bari
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06409780
- Sponsor's protocol ID
- CUSTBv1
- Acronym
- CUSTB
- Status last verified
- May 2024
- First submitted
- 2 May 2024
- Last updated
- 10 May 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Data of individual participants will be available under formal request after dissemination
Publications
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.
PubMed 31333842Danchuk 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 Jun 14;79(7):670-675. doi: 10.1136/thorax-2023-220202.
PubMed 38286614Acharya 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.
PubMed 36225551Abraham 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.
PubMed 38379811Bobbio 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.
PubMed 30944140Suttels 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.
PubMed 35811083Abuzerr 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.
PubMed 37965525Fentress 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.
PubMed 32815504Fentress 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.
PubMed 36962607Cozzi 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.
PubMed 38472994Rea 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.
PubMed 34957142Guido G, Nigussa W, Cotugno S, Kenate Sori B, Bobbio FA, Gulo B, Pisani L, Manenti F, Miressa M, Cavallin F, Abata S, Segala FV, Reta A, Tulome O, Putoto G, Iatta R, Tuttolomondo A, Veronese N, Barbagallo M, Saracino A, Di Gennaro F. Comparative effectiveness of chest ultrasound, chest X-ray and computer-aided diagnostic (CAD) for tuberculosis diagnosis in low-resource setting: study protocol for a cross-sectional study from Ethiopia. Front Public Health. 2024 Nov 28;12:1476866. doi: 10.3389/fpubh.2024.1476866. eCollection 2024.
PubMed 39691654
Study at a glance
From the registry record, last updated by the sponsor on 10 May 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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)
- ClinicalTrials.gov ID
- NCT06409780
- Acronym / sponsor ID
- CUSTB / CUSTBv1
- Conditions
- Tuberculosis, Pulmonary
- Keywords
- Tuberculosis, Chest Ultrasound, Chest X-Ray, CAD, LMIC
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 136 participants (estimated)
- Lead sponsor
- University of BariUniversity, hospital or other
- Collaborators
- Doctors with Africa - CUAMM
- Study start
- 5 May 2024 (estimated)
- Primary completion
- 5 May 2024 (estimated)
- Study completion
- 30 Apr 2025 (estimated)
- First posted
- 10 May 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed 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).
Study design
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Diagnosis of Pulmonary Tuberculosis in index case | Experimental | Diagnostic Test: Comparative analysis of CUS, CAD and CXR in pulmonary TBCUS, CXR and CAD analysis will be performed in all patients with new diagnosis of pulmonary tuberculosis according to microbiological criteria |
| Screening of Pulmonary Tuberculosis in household contacts | Experimental | Diagnostic Test: Comparative analysis of CUS, CAD and CXR in pulmonary TBCUS, CXR and CAD analysis will be performed in all participants who are household contacts of an index case |
Who may qualify
- Ages
- 5 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (2 listed)
- Exposure to any antitubercular treatment prior 7 days than the enrollment after the initiation of clinic
- Withdraw of the informed consent
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Comparative analysis of CUS, CAD and CXR in pulmonary TBThe 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 | Diagnostic test | Diagnosis of Pulmonary Tuberculosis in index case, Screening of Pulmonary Tuberculosis in household contacts |
Primary outcome
Specificity of CUS in TB diagnosis
Time frame: 1 year
Specificity of CUS in diagnosing TB compared to CXR and CAD score among index cases and household contacts
Sensitivity of CUS in TB diagnosis
Time frame: 1 year
Sensitivity of CUS in diagnosing TB compared to CXR and CAD score among index cases and household contacts
3 secondary outcomes
Positive predictive value of CUS in TB diagnosis
Time frame: 1 year
Negative predictive value of CUS in TB diagnosis
Time frame: 1 year
Concordance of CUS, CXR and CAD in TB
Time frame: 1 year
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 May 2024.
- Background: PubMed 31333842Arega 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.
- Background: PubMed 38286614Danchuk 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 Jun 14;79(7):670-675. doi: 10.1136/thorax-2023-220202.
- Background: PubMed 36225551Acharya 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.
- Background: PubMed 38379811Abraham 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.
- Background: PubMed 30944140Bobbio 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.
- Background: PubMed 35811083Suttels 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.
- Background: PubMed 37965525Abuzerr 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.
- Background: PubMed 32815504Fentress 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.
- Background: PubMed 36962607Fentress 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.
- Background: PubMed 38472994Cozzi 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.
- Background: PubMed 34957142Rea 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.
- Derived from this study: PubMed 39691654Guido G, Nigussa W, Cotugno S, Kenate Sori B, Bobbio FA, Gulo B, Pisani L, Manenti F, Miressa M, Cavallin F, Abata S, Segala FV, Reta A, Tulome O, Putoto G, Iatta R, Tuttolomondo A, Veronese N, Barbagallo M, Saracino A, Di Gennaro F. Comparative effectiveness of chest ultrasound, chest X-ray and computer-aided diagnostic (CAD) for tuberculosis diagnosis in low-resource setting: study protocol for a cross-sectional study from Ethiopia. Front Public Health. 2024 Nov 28;12:1476866. doi: 10.3389/fpubh.2024.1476866. eCollection 2024.
- Individual participant dataSharing planned: Yes. Data of individual participants will be available under formal request after dissemination
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Questions about this trial
What is this clinical trial studying?
A study testing Comparative analysis of CUS, CAD and CXR in pulmonary TB in people with Tuberculosis, Pulmonary, run by University of Bari.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 10 May 2024. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Giacomo Guido, MD (3202842602 ext. +39, giacguido@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Specificity of CUS in TB diagnosis (time frame: 1 year). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06409780, which also lists 12 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 10 May 2024.