Scientific article
Open access

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

Published inBMC infectious diseases, vol. 22, no. 1, 39
Publication date2022-01-06
First online date2022-01-06

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 °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 μg/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.

  • Bacterial community-acquired pneumonia
  • Biomarkers
  • PCT
  • Predicting algorithm
  • Algorithms
  • Biomarkers
  • C-Reactive Protein / analysis
  • Humans
  • Outpatients
  • Pneumonia, Bacterial / diagnosis
  • Prospective Studies
  • Respiratory Tract Infections / diagnosis
  • Tanzania
  • Bill and Melinda Gates Foundation - [OPP-1022128]
  • Canadian Institutes of Health Research,Canada - [FDN-148439]
  • Fondation Leenaards - [postdoctoral fellowship]
  • Canada Research Chairs -
Citation (ISO format)
HOGENDOORN, Sarika K L et al. Clinical sign and biomarker-based algorithm to identify bacterial pneumonia among outpatients with lower respiratory tract infection in Tanzania. In: BMC infectious diseases, 2022, vol. 22, n° 1, p. 39. doi: 10.1186/s12879-021-06994-9
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Article (Published version)
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ISSN of the journal1471-2334

Technical informations

Creation10/07/2022 8:52:00 AM
First validation10/07/2022 8:52:00 AM
Update time03/16/2023 10:24:10 AM
Status update03/16/2023 10:24:08 AM
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