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

BMC Infectious Diseases

Valérie D’Acremont¹  Blaise Genton¹  Estelle Tenisch²  Aline Mamin³  Laurent Kaiser³  Tarsis Mlaganile⁴  Josephine Samaka⁴  Noémie Boillat-Blanco⁵  Sarika K. L. Hogendoorn⁵  Loïc Lhopitallier⁵  Zainab Mbarack⁶  Kevin C. Kain⁷  Melissa Richard-Greenblatt⁷ 

[1] Center for Primary Care and Public Health, University of Lausanne;Department of Radiology, University Hospital and University of Lausanne;Division of Infectious Diseases and Center for Emerging Viral Diseases, University of Geneva Hospitals, and Faculty of Medicine;Ifakara Health Institute;Infectious Diseases Service, University Hospital and University of Lausanne;Mwananyamala Hospital;Tropical Disease Unit, Department of Medicine, Sandra Rotman Centre for Global Health, University Health Network-Toronto General Hospital, University of Toronto;

关键词: Bacterial community-acquired pneumonia;    Predicting algorithm;    Biomarkers;    PCT;   

DOI  :  10.1186/s12879-021-06994-9

来源: DOAJ

【 摘 要 】

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 °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.

【 授权许可】

Unknown