edoc

Clinical sign and biomarker-based algorithm to identify bacterial pneumonia among outpatients with lower respiratory tract infection in Tanzania

Hogendoorn, S. K. L. and Lhopitallier, L. and Richard-Greenblatt, M. and Tenisch, E. and Mbarack, Z. and Samaka, J. and Mlaganile, T. and Mamin, A. and Genton, B. and Kaiser, L. and D'Acremont, V. and Kain, K. C. and Boillat-Blanco, N.. (2022) Clinical sign and biomarker-based algorithm to identify bacterial pneumonia among outpatients with lower respiratory tract infection in Tanzania. BMC Infect Dis, 22. p. 39.

[img] PDF - Published Version
Available under License CC0 (Public Domain Dedication).

2571Kb

Official URL: https://edoc.unibas.ch/90543/

Downloads: Statistics Overview

Abstract

BACKGROUND: Inappropriate 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. METHODS: Participants with LRTI were selected in a prospective cohort of febrile (>/= 38 degrees 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. RESULTS: Of 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 mug/L) had 94% sensitivity and 82% specificity. CONCLUSIONS: PCT, 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.
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH)
09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Chronic Disease Epidemiology > Exposome Science (Probst-Hensch)
03 Faculty of Medicine > Departement Public Health > Sozial- und Präventivmedizin > Exposome Science (Probst-Hensch)
09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Medicine (MED) > Clinical Research (Reither)
UniBasel Contributors:Genton, Blaise and D'Acremont, Valérie
Item Type:Article, refereed
Article Subtype:Research Article
ISSN:1471-2334 (Electronic)1471-2334 (Linking)
Note:Publication type according to Uni Basel Research Database: Journal article
Language:English
Related URLs:
Identification Number:
edoc DOI:
Last Modified:27 Dec 2022 10:11
Deposited On:27 Dec 2022 10:11

Repository Staff Only: item control page