Hogendoorn, S.K.L. (2020) Respiratory rate and procalcitonin-based algorithm helps diagnosing bacterial pneumonia among febrile patients with clinical lower respiratory tract infection in Tanzanian outpatient clinics. thesis, Medicine.
Full text available on request.Abstract
Due to the difficulties in identifying patients that could benefit from antibiotics among patients with lower respiratory tract infections (LRTI), a high rate of over-prescription occurs. This is resulting in antimicrobial resistance, as there is a strong association with the number of antibiotics prescribed. In low- and middle-income countries, the resources for managing LRTIs are often lacking, making it even more challenging to diagnose rightfully. This study aims to evaluate the predictive accuracy of clinical signs and host biomarkers for bacterial community-acquired pneumonia (CAP) among patients with LRTIs presenting to outpatient clinics in urban Tanzania. In adults presenting with acute fever (> 38°C) and clinical LRTI, molecular analyses for a broad range of respiratory pathogens was done in nasopharyngeal swabs, and plasma concentrations of markers of endothelial and immune activation pathways were determined. Via multivariate logistic regression and classification and regression tree analysis were respiratory rate and plasma procalcitonin (PCT) found the best combination for predicting bacterial CAP. An algorithm was developed which used respiratory rate combined with a PCT cut-off of 0.25 μg/l. The algorithm has a good positive likelihood ratio (5.1), and an excellent negative likelihood ratio (0.1). This study constitutes the basis for further research developing an algorithm that applies to all patients presenting with clinical LRTI. Such an algorithm will help to identify the correct patient population that could benefit from prescribing antibiotics, thus fighting overprescription in this patient’s category. Ultimately, it could contribute to a solution for antimicrobial resistance.
| Item Type: | Thesis (UNSPECIFIED) |
|---|---|
| Supervisor name: | dr. Huugen, D. and drs. Boillat-Blanco, N. |
| Faculty: | Medical Sciences |
| Date Deposited: | 18 Oct 2023 11:28 |
| Last Modified: | 18 Oct 2023 11:28 |
| URI: | https://umcg.studenttheses.ub.rug.nl/id/eprint/3709 |
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