One of the major challenges for health care workers in low-resource tropical fields is to avoid missing severe (and treatable) infections, but not at the price of indiscriminate use of antimicrobials, in the alarming context of increasing resistance. No perfect biomarker exists so far to accurately distinguish bacterial infections from other conditions, but two generic biomarkers (C-reactive protein, CRP and procalcitonin PCT) have recently shown their clinical utility in reducing antibiotic misuse. Many clinical algorithms have been elaborated to support decision in the field, but their adoption by medical doctors is rather poor because of conceptual weaknesses, lack of evidence and absence of evaluation. We have developed an innovative “panoramic” teaching model prioritizing the not-to-miss conditions, that has a theoretical superiority on conventional algorithms. Thanks to a unique dataset from a large multicenter diagnostic study (NIDIAG) investigating the neurological and febrile syndromes in tropical fields, we could feed our model with real-life data and design a proof-of-concept evidence-based diagnostic aid tool integrating clinical predictors and a relevant set of rapid diagnostic tests (RDTs). This study aims to determine the added value of CRP and/or PCT for diagnosing bacterial diseases, and to integrate them, if and when relevant, in our tool to improve its general accuracy. Once optimized, we plan its external validation and clinical evaluation in large prospective field studies.
|Effective start/end date||15/10/17 → 13/01/19|
- Institute of Tropical Medicine Antwerp: €47,438.10
- Flemish Government - Department of Economy, Science & Innovation: €47,438.10