CADOOL: Active Close Contact Investigation of Tuberculosis Through Computer-aided Detection and Stool Xpert MTB/RIF Among People Living in Ethiopia
Study Details
Study Description
Brief Summary
Tuberculosis is the 13th cause of death from all causes, infecting roughly the 25% of the world population, and Ethiopia is listed among the 30 high-burden countries both for TB and for HIV/TB. In recent years, the immediate consequence of the COVID-19 pandemic was a large fall in the number of newly reported TB cases indicators that represent a relevant drawback in the pursue of the 2025 End TB Milestones.
For active case investigation of TB close contacts, WHO recently recommended the use of Computer- aided detection (CAD), a technology that can help chest X-ray interpretation in situations of human resources constrains, and it may be cost-effective in low-resource settings. Also, for tuberculosis diagnosis, widely-available GeneXpert on stool samples showed high diagnostic performances in term of both sensitivity and specificity.
It is important to assess alternative modalities that could improve diagnosis during TB contact investigation in Ethiopia and the other countries where TB represents a crucial burden.
| Condition or Disease | Intervention/Treatment | Phase |
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N/A |
Detailed Description
Despite being a preventable and treatable disease, TB infects roughly 25% of the world population, causing an estimated 1.4 million deaths among HIV-negative people (95% uncertainty interval: 1.3- 1.5 million) and 187 000 deaths (95% UI: 158 000-218 000) among people living with HIV. Globally, an estimated 10.6 million people fell ill with tuberculosis only in 2021, increasing from the 10.1 million cases estimated for 2020, thus reversing a long-lasting decreasing trend. Tuberculosis is a disease of poverty, as 87% of all incident cases of TB were registered in the 30 high-burden countries, mainly low-income countries from WHO regions of South-East Asia and Africa. At this regard, the relationship between TB burden and commonly used indicators of underdevelopment is well established. Widely recognized TB determinants include gross domestic product (GDP) and prevalence of undernourishment, the latter, alone, being accountable for an estimated 2 million new TB cases in 2021. As reported by the World Health Organization, the immediate consequence of the COVID-19 pandemic was a large fall in the number of newly reported TB cases and an estimated increase of incident cases of rifampicin-resistant TB, all indicators that represent a relevant drawback in the pursue of the 2025 End TB Milestones. These gap in TB diagnosis include both people who are diagnosed but not reported to local public health authorities and people who are not diagnosed and thus have not entered in care.
For the diagnosis of active TB, the gold standard technique remains seeking for microbiological evidence of infection. This is classically done by sputum smear microscopy and liquid culture with drug susceptibility testing. Culture is the gold standard for the diagnosis, but its use, however, is hampered by the fact that results are available only after 10-21 days and is thus of little or no help in clinical decision-making. Since its endorsement by the WHO guidelines in 2010, Xpert MTB/RIF (Cepheid, CA, USA), a molecular test that can detect mycobacterial DNA and genetic polymorphisms associated with rifampicin resistance, has entered into diagnostic workup in both high- and low-income countries. A new version of the test has been approved by WHO in 2021, Xpert MTB/RIF Ultra, with a sensitivity approaching the one recorded for culture assays, but with the advantage, like its predecessor, of requiring fewer resources and delivering faster results. Along with microbiological testing, detection of radiological abnormalities through chest X-ray has recently gained a lot of attention, and it is now recommended as the standard screening test. A more detailed description of the diagnostic performances of chest X-ray and digital chest X-ray with computer-aided detection is provided in the next paragraph of this introduction.
In recent years, computer-aided detection (CAD) are artificial- intelligence based software that have been developed with the aim to offer automated interpretation of digital CXR images. In broad terms, CAD programs produce a numerical score that interpret CXR alterations in order to quantify the probability of TB. When diagnostic accuracy is assessed against bacteriologically confirmed TB, CAD software sensitivity ranges from 90% to 92%, and specificity ranges from 23% to 66%, fulfilling thus the minimal sensitivity required by WHO for a screening test. These diagnostic performances were confirmed by several prospective studies conducted in low- and middle-income countries (LMIC), systematic reviews and one patient-level meta-analysis, thus leading to the endorsement of CAD technologies by current WHO consolidated TB guidelines for the screening of individuals aged 15 years or older belonging to selected high-risk populations. Among the CAD software packages commercially available, CAD4TB platform version 6 (Delft Imaging Systems, The Netherlands), developed using deep-learning technologies - a type of machine learning technology based on artificial neural networks - releases results in few seconds and was designed to work on patients from 4 years of age. In a recent study conducted, when compared to a microbiological Xpert reference standard, CAD4TB v7 has been shown to have both high efficiency and be cost-effective in high-burden settings, being able to process 132 images per day at the cost of less than 6 USD per person. Even if the technology is validated for the use in individuals aged 4 years or older, very few data exist on the clinical utility of this technology among children.In recent years, attention has been attracted by Xpert MTB/RIF on stool samples, since mycobacteria- containing sputum may be swallowed and then be available for molecular testing. Similarly to Xpert on sputum samples, stool Xpert is able to detect both the presence of mycobacterial DNA and mutations associated with rifampin resistance. The use of Xpert MTB/RIF on stool samples has been introduced in the 2020 WHO guidelines as a possible initial diagnostic test for children with signs and symptoms of pulmonary TB. However, this recommendation is based on low certainty of evidence. Due to lack of available evidence, no recommendation has been issued so far about the use of Xpert on stool samples in the adult population.
Ethiopia is listed among the 30 high-burden countries both for TB and for HIV/TB, transitioning out, in the last Global Tuberculosis Report, from the list of the high-burden countries for multidrug- resistant or rifampicin resistant TB (MDR-RRTB). Annual TB incidence is 132 cases per 100,000 people (95%CI 92-178), with a case-fatality ratio of 15% and most of the cases attributable to undernourishment. Disease incidence and mortality, in both HIV-positive and HIV-uninfected people, is reducing, almost attaining the End TB milestone of a 35% reduction in TB deaths compared to 2015. However, since the pandemic-related disruption on TB services impacted the number of newly diagnosed TB cases, its influence on mortality and overall disease burden is likely yet to be seen.
As in most of low-resources, high-burden countries, provider-initiated contact investigation is rarely carried out in Ethiopia, although contact tracing and evaluation of all persons who have been incontact with an active case of TB is recommended by the latest National Guidelines . The World Health Organization recently updated its screening guidelines, putting emphasis on the importance of the importance of active, provider-initiated screening of at-risk populations, especially households of index TB patients . Epidemiological data about active TB among household contacts are limited but, when reported, prevalence rates are high . Also, a meta-analysis conducted by Gamtesa et al. found that healthcare-seeking behavior in Ethiopia is low even in patients with showing signs and symptoms of TB.
Provider-initiated screening of selected, high-risk populations is a key strategy in the fight towards TB eradication. According to WHO, this approach should entail systematic identification of people with possible TB disease with tests, examinations or other procedures that can be applied rapidly. In this context, data about the clinical impact of the use of CAD technologies in Ethiopia are lacking. Also, data about diagnostic performances of, respectively, CAD software and stool Xpert MTB/RIF in the pediatric population and adult population are needed.
The study will be carried out in at St. Luke Catholic Hospital, Wolisso, in Ethiopia.
St. Luke Catholic Hospital is the referral Hospital in Southwest Shoa zone, Oromia Region, between Addis Ababa and Jimma covering 400km distance. St. Luke Catholic Hospital serves a population from a catchment area of roughly 1.4 million people. As of today, the hospital bed capacity is 208, while mean outpatient department visits are 350 patients per day .
For the year 2021, in St Luke Hospital 207 patients were diagnosed with pulmonary TB. Of those, 164 patients were treated as inpatients, and 43 were treated as outpatients. Out of these, 5 showed Rifampicin resistance. These MDR cases were referred to the Therapeutic Initiating Centres for second line treatment. Among patients treated in OPD clinic, 30 were followed into the hospital TB clinic.
Routine TB screening in St. Luke Catholic Hospital is based on patient-initiated pathway with assessment of signs and symptoms suggestive of TB. Possible TB cases are identified through a clinical visit that may include chest X-ray evaluation. When performed, chest X-ray images are evaluated by on-duty clinicians in digital format.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Other: ARM 1: without computer-aided detection (CAD) Diagnosis of tuberculosis without the aid of CAD |
Diagnostic Test: Diagnosis of Tuberculosis with computer-aided detection (CAD) software
After enrolment, each subject will be assessed according to ARM 1 (assessment by clinician #1 without CAD) or ARM 2 (assessment by clinician #1 with CAD). Allocation to sequence ARM 1 or ARM 2 will be performed using a computer-generated random assignment list (with a 1:1 ratio), and assignments will be included in sealed opaque envelopes sequentially numbered.
Clinicians cannot be masked to the assessment method (with or without CAD) and cannot be masked to the further assessment (referral for microbiological diagnosis with stool and sputum Xpert) due to care process. However, the statistician will be masked to the assessment during data analysis.
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| Other: ARM 2: with computer-aided detection (CAD) Diagnosis of tuberculosis with the aid of CAD |
Diagnostic Test: Diagnosis of Tuberculosis with computer-aided detection (CAD) software
After enrolment, each subject will be assessed according to ARM 1 (assessment by clinician #1 without CAD) or ARM 2 (assessment by clinician #1 with CAD). Allocation to sequence ARM 1 or ARM 2 will be performed using a computer-generated random assignment list (with a 1:1 ratio), and assignments will be included in sealed opaque envelopes sequentially numbered.
Clinicians cannot be masked to the assessment method (with or without CAD) and cannot be masked to the further assessment (referral for microbiological diagnosis with stool and sputum Xpert) due to care process. However, the statistician will be masked to the assessment during data analysis.
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Outcome Measures
Primary Outcome Measures
- CAD4TB and Stool GeneXpert Accurancy rate for Diagnosis of TB [through study completion, an average of 1 year]
The common accuracy metrics (sensitivity, specificity, positive and negative predictive values) when assessing the accuracy of CAD4TB compared to stool Xpert MTB/RIF.
Secondary Outcome Measures
- Stool GeneXpert and Sputum GeneXpert concordance [12 weeks]
The concordance between stool GeneXpert and sputum GeneXpert.
- Concordance beetwen CAD4TB and Sputum MTB/RIF [through study completion, an average of 1 year]
The common accuracy metrics (sensitivity, specificity, positive and negative predictive values) when assessing the accuracy of CAD4TB compared to Xpert MTB/RIF
- TB incidence rate [through study completion, an average of 1 year]
The incidence of new TB cases among participants during the study period.
Other Outcome Measures
- Clinical TB diagnosis rate [12 weeks]
The additional positive cases when information from CAD4TB is available to the clinician.
- Tuberculosis False positive rate [through study completion, an average of 1 year]
The false positive cases when information from CAD is available to the clinician
- Discordant diagnosis case rate [12 weeks]
The number and type of discordant cases in CAD4TB vs. clinician CXR evaluation.
Eligibility Criteria
Criteria
Inclusion Criteria:
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All patients diagnosed with pulmonary tuberculosis and at least one sputum sample positive for acid- fast bacilli on sputum smear or M. tuberculosis on sputum Xpert or smear will be eligible for inclusion as index cases.
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Index cases will be recruited both among hospitalized patients and patients followed in OPD clinics for completion of TB treatment.
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All household contacts of at least 4 years of age will be eligible for inclusion if they lived in the same dwelling as the index patient during the two months prior to the diagnosis of the index patient.
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Pregnant women are eligible for the inclusion but, to minimize radiation exposure risk to the fetus, chest X-ray will not be offered.
Exclusion Criteria:
- Household contacts already receiving treatment for active or latent tuberculosis will be excluded from the study.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | St Luke Hospital | Wolisso, Oromia | Ethiopia |
Sponsors and Collaborators
- Doctors with Africa - CUAMM
- University of Bari
- Armauer Hansen Research Institute, Ethiopia
Investigators
- Principal Investigator: Worku Nigussa, MD, Doctors with Africa - CUAMM
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- University of Bari