Summary
About 50-65% of children suffering from tuberculosis (TB) across all age groups are never detected. This contributes to the 250,000 deaths from TB in children annually, making the disease one of the top ten killers of children under the age of five. To improve TB detection in children and reduce TB-related morbidity and mortality, novel diagnostic tests are urgently needed. The Find-TB project aims to address this urgent need by improving case finding at all levels of care in resource-limited, high TB burden settings through the development and validation of an innovative, digital TB triage tool that provides individualized TB disease risk predictions coupled with clinical recommendations to guide next steps of care. The project will capitalize on digital and scientific innovations that have been successfully applied to the COVID-19 pandemic.
Starting with large, existing geographically and demographically representative data sets, my team and I will develop and validate two predictive models using highly innovative data science methods (machine learning and Bayesian techniques). An app for use by health personnel at all levels of the health care system will be developed for the best performing model. To maximize the app’s implementation potential, I will engage key stakeholders to assess preferences, acceptability, and feasibility prior to and throughout app development. Furthermore, I will apply human-centered design principles. We will then evaluate the design-locked digital tool in a real-world triage use case in a prospective, cross-sectional diagnostic accuracy study. A cost-effectiveness analysis of population level impact will round out a dossier that will inform a WHO review.
This efficient, effective, and scalable digital tool for TB triage of children in resource-limited settings is expected to improve TB diagnosis on an individual-level and thus TB-related morbidity and mortality and on population-level is likely to improve TB control.
Starting with large, existing geographically and demographically representative data sets, my team and I will develop and validate two predictive models using highly innovative data science methods (machine learning and Bayesian techniques). An app for use by health personnel at all levels of the health care system will be developed for the best performing model. To maximize the app’s implementation potential, I will engage key stakeholders to assess preferences, acceptability, and feasibility prior to and throughout app development. Furthermore, I will apply human-centered design principles. We will then evaluate the design-locked digital tool in a real-world triage use case in a prospective, cross-sectional diagnostic accuracy study. A cost-effectiveness analysis of population level impact will round out a dossier that will inform a WHO review.
This efficient, effective, and scalable digital tool for TB triage of children in resource-limited settings is expected to improve TB diagnosis on an individual-level and thus TB-related morbidity and mortality and on population-level is likely to improve TB control.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101086010 |
| Start date: | 01-01-2024 |
| End date: | 31-12-2028 |
| Total budget - Public funding: | 1 999 788,00 Euro - 1 999 788,00 Euro |
Cordis data
Original description
About 50-65% of children suffering from tuberculosis (TB) across all age groups are never detected. This contributes to the 250,000 deaths from TB in children annually, making the disease one of the top ten killers of children under the age of five. To improve TB detection in children and reduce TB-related morbidity and mortality, novel diagnostic tests are urgently needed. The Find-TB project aims to address this urgent need by improving case finding at all levels of care in resource-limited, high TB burden settings through the development and validation of an innovative, digital TB triage tool that provides individualized TB disease risk predictions coupled with clinical recommendations to guide next steps of care. The project will capitalize on digital and scientific innovations that have been successfully applied to the COVID-19 pandemic.Starting with large, existing geographically and demographically representative data sets, my team and I will develop and validate two predictive models using highly innovative data science methods (machine learning and Bayesian techniques). An app for use by health personnel at all levels of the health care system will be developed for the best performing model. To maximize the app’s implementation potential, I will engage key stakeholders to assess preferences, acceptability, and feasibility prior to and throughout app development. Furthermore, I will apply human-centered design principles. We will then evaluate the design-locked digital tool in a real-world triage use case in a prospective, cross-sectional diagnostic accuracy study. A cost-effectiveness analysis of population level impact will round out a dossier that will inform a WHO review.
This efficient, effective, and scalable digital tool for TB triage of children in resource-limited settings is expected to improve TB diagnosis on an individual-level and thus TB-related morbidity and mortality and on population-level is likely to improve TB control.
Status
SIGNEDCall topic
ERC-2022-COGUpdate Date
12-03-2024
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