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The BreathForDiagnosis consortium combines expertise from academia, NGOs, and industry. It includes experts in the development of diagnostics, partners with extensive expertise in implementation science, including mixed-methods research approaches, cost-effectiveness, and modelling. The studies will take place in four countries: South Africa, Germany, Italy, and Romania.


Respiratory infections resulted in over seven million deaths in 2020 and were responsible for seven out of nine pandemics in the 20th century. Aerosol transmission of respiratory infections has been largely underestimated, with the COVID-19 pandemic highlighting the limitations of traditional views of droplet and fomite transmission. Moreover, respiratory infections such as tuberculosis (TB) and COVID-19 have a propensity to disproportionately affect underprivileged populations and those with low socioeconomic status, including vulnerable populations. Improving early detection of respiratory infections is key to improving both individual and public health.


Current diagnostic sampling methods for respiratory infections are flawed. Sputum is often difficult to obtain and transport and has suboptimal sensitivity for many respiratory pathogens. Production of sputum varies according to pathogen and individual immune response, and methods to induce sputum are resource consuming and not scalable. Based on current evidence, exhaled breath aerosol (XBA) is a non-invasive, easy to collect sample for detection of respiratory infections.


The overall aim of the BreathForDiagnosis project is to establish XBA as a novel, non-invasive sample type using innovative, scalable point-of-care sampling devices and to generate evidence for diagnosis, screening, and antimicrobial resistance detection use cases. Our project will go beyond the current state-of-the-art for breath sampling and diagnosis by optimising, validating, and testing two simplified, fast, and scalable XBA sampling devices. The two sampling devices are shown on the right.

R2D2 TB Network
Rapid Research in Diagnostics Development for TB Network

This R2D2 TB Network aims to ensure faster, simpler, cheaper, and more accurate TB diagnostics by providing a transparent process for evaluating, identifying, and advancing TB diagnostic methods. This network connects highly experienced clinical study sites in over 10 countries with experts in TB care, diagnostic development, laboratory medicine, epidemiology, health economics, technology assessment and mathematical modeling. Central aims of this network is the provision of access to international networks of TB diagnostic trial sites, establishing partnerships between diagnostic developers, facilitating high quality data collection on TB diagnostic performance as well as helping to advance TB diagnostics with economic and impact modeling. Claudia is a PI on this project together with Adithya Cattamanchi and Payam Nahid.

Abdominal and Lung Point Of Care UltraSound for TB

This is a prospective, cross-sectional multicentre cohort study in which the accuracy and the diagnostic yield of Tuberculosis-focused point-of-care ultrasound (POCUS) and image interpretation with artificial intelligence (AI) will be assessed in inpatients and outpatients with presumed TB disease in German and Indian sites. Dedicated POCUS protocols for the diagnosis of HIV-associated extra-pulmonary tuberculosis (EPTB) in high incidence settings have been proposed, but reliable accuracy data for both patients with and without HIV are thus far limited. Another arising ultrasound field with great potential is lung ultrasound (LUS) for the diagnosis and the assessment of disease extent for pulmonary TB. Our study ultrasound protocol will cover both abdominal and thoracic locations in both HIV(+) and HIV(-) patients. In addition, we will follow up patients with confirmed TB under anti-mycobacterial therapy to document the natural evolution of TB-associated sonographic findings and assess their value for monitoring of therapy success. With this study we want to fill this gap and to guide implementation efforts by better characterizing the sensitivity and specificity of sonographic findings for TB diagnosis against a comprehensive reference standard. POCUS has the potential to  increase TB case finding and avoid over-diagnosis. 


A study on the accuracy of point-of-care ultrasound for the diagnosis of thoracic and abdominal TB. We investigate the accuracy of both single tuberculosis associated findings as well as composite findings such as combinations of pathological changes. We are enrolling patients with presumed TB regardless of HIV-status and will perform subgroup analysis for patients with/without TB and patients with/without diabetes mellitus as possible risk factors. The aim is the description of possible combinations for a point-of-care protocol, which can be used as a non-sputum based triage test for TB. In addition, we evaluate patients longitudinally with ultrasound to determine the value for therapy monitoring for pulmonary and extrapulmonary findings.

Close the gap, increase Access, Provide adequate Therapy

To facilitate implementation of TB testing, TB-CAPT provides evidence for impactful implementation for TB and TB/HIV co-infection diagnostic strategies in multiple clinical trials in South Africa, Mozambique, and Tanzania. These trials aim to evaluate the most promising new technologies, inform policy change and implementation strategies on a regional, national, and global level as well as to consolidate and strengthen human capacity and in-country infrastructure for conducting clinical trials, implementation research and plans for new diagnostic methods. Three trials from part of TB-CAPT: the CORE trial on decentralized molecular diagnosis, the XDR trial on extended resistance testing and the HIV trial, on rapid, comprehensive diagnostics testing for most vulnerable and hospitalized people living with HIV. The project was initiated by Claudia while at FIND and is coordinated by FIND. The Heidelberg team contributes primarily on an economic and benefit incidence analysis in partnership with David Dowdy at John Hopkins University.


Machine learning-based tuberculosis screening tool

Tuberculosis (TB) is the leading infectious cause of death worldwide with delayed and missed diagnoses contributing to ongoing community transmission and mortality. Currently, none of the symptom screen and triage strategies meet the minimum recommended diagnostic accuracy targets recommended by the WHO. We will use machine learning methods to develop a novel individualized predictive model for active TB disease combining information from multiple sources, such as individual patient data and knowledge on local TB epidemiology. The resulting algorithm will be incorporated into a simple digital tool (mobile app) for the usage in limited-resource settings to rapidly and accurately stratify individuals by TB risk and recommend appropriate next steps (e.g., further diagnostic evaluations or TB preventative therapy).


Assessing the mid- and longterm effects of non-pharmaceutical interventions on disease burden

Mid- and longterm effects of non-pharmaceutical interventions (NPIs) used during the COVID-19 pandemic on respiratory infections like respiratory syncitial virus, influenza and pneumococcal disease remain poorly understood but are crucial in the assessment of overall disease burden after NPIs. We aim to develop an integrated model for simulation of transmission of several respiratory infections and collateral effects of NPIs on their disease burden in the mid- and long term. In a systematic review we will gather and synthesize available data on immunity markers, infection prevalence and disease burden of respiratory infections during and after NPIs and then analyze and evaluate the findings in a meta-analysis by means of generalized linear mixed models.


An agent-based simulation study of diagnostic interventions to address the diagnostic gap in hypertension

Cardiovascular diseases (CVD) are the leading cause of mortality globally with almost a third of all annual deaths worldwide. For CVDs hypertension is the leading modifiable risk factor, however, the necessity of at least one repeated measurement for a final diagnosis and the different accuracies of an operator-dependent blood pressure measurement constitute a barrier in access to treatment and management of the disease. We investigated the impact of scaling up diagnostics in reducing hypertension-induced morbidity and mortality in low-and-middle-income-countries by an agent-based simulation study. We clearly showed that increasing the coverage of screening for hypertension and reducing complexity of the screening is more important than reducing the diagnostic accuracy in lowering the global cardiovascular disease burden compared to the current standard of care. The manuscript is under review at PLOS Medicine.

Systematic review on ultrasound patterns of TB disease

A systematic review investigating the literature for sonographic findings associated with TB. The aim is to describe the spectrum of sonographic TB disease as well as identify possible targets for novel point-of-care ultrasound protocols.  

IPD meta-analysis to determine the diagnostic yield of TB tests

Sputum is the most widely used sample for TB diagnosis, but its collection presents challenges for many patients, particularly those living with HIV and children. In contrast, urine samples are readily available. In this project, we are conducting a large individual participant data meta-analysis to determine the two-day diagnostic yield (DY) of point-of-care urine lipoarabinomannan (LAM) tests in comparison to sputum molecular tests and sputum smear microscopy (SSM) for active TB detection.

Diagnostic yield, defined as the proportion of TB cases identified by a test on the first sample collected within 48h among those with a positive reference test, better reflects a test’s clinical usefulness by considering both its diagnostic accuracy and a person’s ability to provide the required sample. The analysis is conducted in two steps: first among studies with participants aged 15 years or older living with HIV, and secondly, separately in pediatric studies due to differing TB case definitions.

The initial MA on adults has been published in The Lancet Global Health in 2022 proposing new urine LAM guidelines for TB testing. The findings strongly support combined testing strategies using both AlereLAM and Xpert for TB testing in adults living with HIV. The yield of sputum-based Xpert testing is significantly undermined by people who struggle to produce sputum (82% provided sputum within 48h), whereas nearly all participants were able to provide urine (96%). Especially in inpatient settings, AlereLAM should be prioritized and used alongside sputum-based tests (combined DY of 72%), as this approach significantly outperformed testing with Xpert (DY of 48%) or Alere LAM (50%) alone.

Currently, our team is working on the analysis of pediatric studies, which presents specific challenges due to complex case definitions and the lack of a golden standard for TB testing in children. 


Supporting, Mobilizing, and Accelerating Research for Tuberculosis Elimination is a five-year initiative made possible by the United States Agency for International Development (USAID), with the assistance of the American people, that aims to transform TB prevention and care. In 2021, an estimated 10.6 million people fell sick with TB worldwide, and 1.6 million died, making it second only to COVID-19 as leading infectious disease killer. SMART4TB will design and implement research studies with local partners to identify effective person-centered methods for finding, treating, and preventing TB; strengthen local capacity to conduct high-quality research; and engage communities to build demand for new interventions, drive policy change, and improve implementation of new and existing interventions to reach the End TB targets. 

Economic & Equity Analysis of Centralized vs Decentralized TB Diagnosis

While TB testing has improved greatly in the past decades, there is still a need to improve equitable, accessible, cost-effective, and affordable diagnostic testing approaches for TB.  This project investigates the impact of decentralized versus centralized diagnostic approaches on economic and health outcomes and equitable access from a health system and patient perspective.  We looked at data from 4200 patients in two high burden countries (Mozambique and Tanzania) in an effort to perform a cost-effectiveness and a benefit incidence analysis, map needs, and carry out qualitative research. 

Decentralized TB diagnostic algorithms will become increasingly feasible with the introduction of innovative diagnostic tests, such as the TrueNat test, which solves some of the bottlenecks by bringing the diagnostic system closer to patients in more peripheral clinics.

Parallel to this, the WHO recently recommended centralized assays for TB diagnosis that allows for multi-disease diagnosis (e.g., HIV, hepatitis) and high-throughput testing which could generate economies of scale. According to the WHO, such platforms could enhance system efficiency, provide cost savings, expand patient access, and ultimately improve quality of care.

These diagnostic advancements raise new questions about whether to pursue a centralized or decentralized strategy. To inform this decision making, this project provides the evidence required beyond clinical/diagnostic effectiveness, looking into relevant policy issues such as costs in relation to benefits, the potential for financial risk protection, equity in access to diagnostics and resulting care, and affordability of the technology at national level.

A qualitative evaluation of preferences of healthcare providers, patients, and decision makers for the implementation of decentralized TB testing is also ongoing as part of the CORE trial.


Tongue Swab Tuberculosis Diagnostic Yield Study

This highly pragmatic cross-sectional study evaluates diagnostic yield of tongue-swab based vs. sputum based molecular TB testing. Tongue-swab based TB testing is anticipated to enable penetration of molecular testing for TB to lower-level health facilities in high burden countries where affected patients most often first seek care. While preliminary results currently suggest a sensitivity deficit of swab-based testing vs. sputum-based testing, a potential increased diagnostic yield, would favour swab-based testing. By reflecting outcomes that can be expected in routine care this study can provide important evidence to potentially support WHO policy and country-level uptake of swab-based molecular tests.




A Randomized Open label Phase-II Clinical Trial with or without Infusion of Plasma from Subjects after Convalescence of SARS-CoV-2 Infection in High-Risk Patients with Confirmed Severe SARS-CoV-2 Disease. This is a study investigating the potential value of plasma in subgroups in which plasma has not been previously investigated. This includes patients with severe COVID-19 who have cancer, immunosuppression, laboratory risk factors of advanced age. We also investigate the impact on antibody neutralization before and after plasma donation to identify possible mechanisms or markers of protection. This work is aimed to identify therapeutic options for risk patients in the evolving pandemic landscape, where plasma is an adaptable therapy as opposed to e.g. monoclonal antibody therapy. 


Assessing acceptability and feasibility of urine-based LAM testing

In this qualitative study addressing needs and feasibility, we assessed the possible implementation of using urine-based LAM testing.  The study consisted of 42 semi-structured interviews with patients, health care providers, lab technicians, and decision makers in Malawi and Zambia from September 2020 to March 2021.  The assessment was conducted using the health equity implementation framework, which guided the presentation of the results.  Some possible barriers included the limited availability of sanitary facilities, change in agency, and the need of CD4 counts in HIV patients as a prerequisite.  The decentralization of diagnostics was considered possible with FujiLAM due to low infrastructure requirements. The easy testing and sample method as well as the test requiring no electricity or maintenance makes it a promising alternative in low- and middle-income countries.


TB hostmarker analysis – using machine learning

Within TB testing, there is still a need for a TB point of care triage test with 90% sensitivity and 70% specificity or better.  This design centered analysis will address the question: Can we develop such a triage test with at most 3 host-biomarkers? In order to answer this, we will develop a method using machine learning expertise, efficient python programming, parallelization of programs, and computer cluster usage with the hopes of improving TB testing