ArticleBMC infectious diseases2022
Clinical sign and biomarker-based algorithm to identify bacterial pneumonia among outpatients with lower respiratory tract infection in Tanzania.
Article in BMC infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- ERS/ESCMID state-of-the-science statement on the role of rapid diagnostic tests for the diagnosis and management of community-acquired lower respiratory tract infections.The European respiratory journal · 2026Review
- A Narrative Review of Recent Antibiotic Prescribing Practices in Ambulatory Care in Tanzania: Findings and Implications.Medicina (Kaunas, Lithuania) · 2023Review
- Patient Stratification for Antibiotic Prescriptions Based on the Bound-Free Phase Detection Immunoassay of C-Reactive Protein in Serum Samples.Biosensors · 2023Article
- Clinical significance of serum S100 calcium-binding protein A12 concentrations in patients with community-acquired pneumonia.The Journal of international medical research · 2023Article
- Epidemiologic, clinical, and serum markers may improve discrimination between bacterial and viral etiologies of childhood pneumonia.Frontiers in medicine · 2023Article
Corrections and comments
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Authors and funding
13 authors at 6 institutions in 3 countries.
Funding
Abstract
backgroundInappropriate antibiotics use in lower respiratory tract infections (LRTI) is a major contributor to resistance. We aimed to design an algorithm based on clinical signs and host biomarkers to identify bacterial community-acquired pneumonia (CAP) among patients with LRTI.
methodsParticipants with LRTI were selected in a prospective cohort of febrile (≥ 38 °C) adults presenting to outpatient clinics in Dar es Salaam. Participants underwent chest X-ray, multiplex PCR for respiratory pathogens, and measurements of 13 biomarkers. We evaluated the predictive accuracy of clinical signs and biomarkers using logistic regression and classification and regression tree analysis.
resultsOf 110 patients with LRTI, 17 had bacterial CAP. Procalcitonin (PCT), interleukin-6 (IL-6) and soluble triggering receptor expressed by myeloid cells-1 (sTREM-1) showed an excellent predictive accuracy to identify bacterial CAP (AUROC 0.88, 95%CI 0.78-0.98; 0.84, 0.72-0.99; 0.83, 0.74-0.92, respectively). Combining respiratory rate with PCT or IL-6 significantly improved the model compared to respiratory rate alone (p = 0.006, p = 0.033, respectively). An algorithm with respiratory rate (≥ 32/min) and PCT (≥ 0.25 μg/L) had 94% sensitivity and 82% specificity.
conclusionsPCT, IL-6 and sTREM-1 had an excellent predictive accuracy in differentiating bacterial CAP from other LRTIs. An algorithm combining respiratory rate and PCT displayed even better performance in this sub-Sahara African setting.
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