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In 2022, the WHO conditionally recommended the use of treatment decision algorithms (TDAs) for treatment decision-making in children
Within the Decide-TB project (PACT ID: PACTR202407866544155, 23 July 2024), we aim to generate an individual-participant dataset (IPD) from prospective TB diagnostic accuracy cohorts (RaPaed-TB, UMOYA and two cohorts from TB-Speed). Using the IPD, we aim to: (1) assess the diagnostic accuracy of published TDAs using a set of consensus case definitions produced by the National Institute of Health as reference standard (confirmed and unconfirmed vs unlikely TB); (2) evaluate the added value of novel tools (including biomarkers and artificial intelligence-interpreted radiology) in the existing TDAs; (3) generate an artificial population, modelling the target population of children eligible for WHO-endorsed TDAs presenting at primary and secondary healthcare levels and assess the diagnostic accuracy of published TDAs and (4) identify clinical predictors of radiological disease severity in children from the study population of children with presumptive TB.
This study will externally validate the first data-driven WHO TDAs in a large, well-characterised and diverse paediatric IPD derived from four large paediatric cohorts of children investigated for TB. The study has received ethical clearance for sharing secondary deidentified data from the ethics committees of the parent studies (RaPaed-TB, UMOYA and TB Speed) and as the aims of this study were part of the parent studies’ protocols, a separate approval was not necessary. Study findings will be published in peer-reviewed journals and disseminated at local, regional and international scientific meetings and conferences. This database will serve as a catalyst for the assessment of the inclusion of novel tools and the generation of an artificial population to simulate the impact of novel diagnostic pathways for TB in children at lower levels of healthcare. TDAs have the potential to close the diagnostic gap in childhood TB. Further finetuning of the currently available algorithms will facilitate this and improve access to care.
Cognitive behavioural therapy (CBT) serves as a first-line treatment for internalising disorders (ID), encompassing depressive, anxiety or obsessive-compulsive disorders. Nonetheless, a substantial proportion of patients do not experience sufficient symptom relief. Recent advances in wearable technology and smartphone integration enable new, ecologically valid approaches to capture dynamic processes in real time. By combining ecological momentary assessment (EMA) with passive sensing of behavioural and physiological information, this project seeks to track daily fluctuations in symptom-associated constructs like affect, emotion regulation (ER) and physical activity. Our central goal is to determine whether dynamic, multimodal markers derived from EMA and passive sensing can predict treatment non-response and illuminate key factors that drive or hinder therapeutic change.
PREACT-digital is a subproject of the Research Unit FOR 5187 (PREACT), a large multicentre observational study in four outpatient clinics. PREACT channels state-of-the-art machine learning techniques identify predictors of non-response to CBT in ID. The study is currently running and will end in June 2026. Patients seeking CBT at one of four participating outpatient clinics are invited to join PREACT-digital. They can take part in (1) a short version with a 14-day EMA and passive sensing phase prior to therapy, or (2) a long version in which the short version’s assessments are extended throughout the therapy. It is estimated that 468 patients take part in PREACT-digital, of which 350 opt for the long version of the study. Participants are provided with a smartwatch and a customised study app. We collect passive data on heart rate, physical activity, sleep and location patterns. EMA assessments cover affect, ER strategies, context and therapeutic agency. Primary outcomes on (non)-response are assessed after 20 therapy sessions and therapy end. We employ predictive and exploratory analyses. Predictive analyses focus on classification of non-response using basic algorithms (ie, logistic regression and gradient boosting) for straightforward interpretability and advanced methods (LSTM, DSEM) to capture complex temporal and hierarchical patterns. Exploratory analyses investigate mechanistic links, examine the interplay of variables over time and analyse change trajectories. Study findings will inform more personalised and ecologically valid approaches to CBT for ID.
The study has received ethical approval from the Institutional Ethics Committee of the Department of Psychology at Humboldt Universität zu Berlin (Approval No. 2021–01) and the Ethics Committee of Charité-Universitätsmedizin Berlin (Approval No. EA1/186/22). Written informed consent will be obtained from all participants prior to enrolment. Results will be disseminated through peer-reviewed journals and presentations at national and international conferences.
DRKS00030915; OSF PREACT: http://osf.io/bcgax; OSF PREACT-digital:
Background and aims: Bacteria in wounds can lead to stagnation of wound healing as well as to local or even systemic wound infections up to potentially lethal sepsis. Consequently, the bacterial load should be reduced as part of wound treatment. Therefore, the efficacy of simple mechanical wound debridement should be investigated in terms of reducing bacterial colonisation. Patients and methods: Patients with acute or chronic wounds were assessed for bacterial colonisation with a fluorescence camera before and after mechanical wound debridement with sterile cotton pads. If bacterial colonisation persisted, a second, targeted wound debridement was performed. Results: A total of 151 patients, 68 (45.0%) men and 83 (55.0%) women were included in this study. The male mean age was 71.0 years and the female 65.1 years. By establishing a new analysis method for the image files, we could document that the bacterial colonised areas were distributed 21.9% on the wound surfaces, 60.5% on the wound edges (up to 0.5 cm) and 17.6% on the wound surroundings (up to 1.5 cm). One mechanical debridement achieved a significant reduction of bacterial colonised areas by an average of 29.6% in the wounds, 18.9% in the wound edges and 11.8% in the wound surroundings and was increased by performing it a second time. Conclusions: It has been shown that even a simple mechanical debridement with cotton pads can significantly reduce bacterial colonisation without relevant side effects. In particular, the wound edges were the areas that were often most contaminated with bacteria and should be included in the debridement with special attention. Since bacteria remain in wounds after mechanical debridement, it cannot replace antimicrobial therapy strategies, but offer a complementary strategy to improve wound care. Thus, it could be shown that simple mechanical debridement is effective in reducing bacterial load and should be integrated into a therapeutic approach to wounds whenever appropriate.
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