Cross-sectional evaluation of host biomarkers for guiding antibiotic use in bacterial and non-bacterial acute febrile illness in low- and middle-income tropical settings.
Fernandez-Carballo BL., Atzeni M., Escadafal C., Vettoretti M., Geis S., Agnandji ST., Siqueira AM., Malava JK., Banda L., Kabwende AL., Alabi A., Ondo JCB., Massinga-Loembe M., Essone PN., Moreira J., da Rocha Matos A., Caetano BC., Siqueira MM., Bispo de Filippis AM., Dos Santos Ribeiro da Silva ÉA., Lourenço MCS., Häring J., Günther A., Jakobi M., Schneiderhan-Marra N., Hoogland C., Brasil P., Pokharel S., Ongarello S., Harris V., Macé A., Lee SJ., Di Camillo B., Dittrich S.
OBJECTIVES: To evaluate the effectiveness of 18 different host biomarkers in differentiating bacterial from non-bacterial acute febrile illness (AFI) in resource-limited settings, specifically in Brazil, Malawi and Gabon. DESIGN: Multinational, cross-sectional study. SETTING: The study was carried out across multiple primary healthcare facilities, including urban and rural settings, with a total of three participating centres. Recruitment took place from October 2018 to July 2019 in Brazil, May to November 2019 in Gabon and April 2017 to April 2018 in Malawi. PARTICIPANTS: A total of 1915 participants, including children and adults aged 21-65 years with a fever of≤7 days, were recruited through convenience sampling from outpatient clinics in Brazil, Gabon and Malawi. Individuals with signs of severe illness were excluded. Written consent was obtained from all participants or their guardians. INTERVENTION: This is not applicable as the study primarily focused on biomarker evaluation without specific therapeutic interventions. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome measure was the ability of each host biomarker to differentiate between bacterial and non-bacterial AFI, as evaluated by area under the receiver operating characteristic (AUROC) curves. Secondary outcomes included the performance of individual biomarkers across the different study sites and in a multivariable setting. RESULTS: A Kruskal-Wallis test, adjusted by Benjamini-Hochberg, was performed for each biomarker to identify covariates with a significant difference in the distribution of biomarker values. The analysis revealed that country of origin (Brazil, Gabon, Malawi), age, sex and malaria status significantly impacted biomarker distribution (p≤0.001). The most widely known biomarkers, such as white blood cell (WBC) count and C-reactive protein (CRP), demonstrated the best performance in distinguishing between bacterial and non-bacterial infections, with AUROCs reaching up to 0.83 (0.77-0.88) for WBC count and 0.71 (0.59-0.82) for CRP. However, none of the evaluated novel host biomarkers exhibited high performance (AUROC<0.70 in most cases) and variations in biomarker performance were observed across the three settings. Multivariable analyses demonstrated that while the best combination of biomarkers achieved higher AUROCs, the increase was modest (1-13%), suggesting that the interaction of biomarkers contributed minimally to predictive accuracy. CONCLUSIONS: There is a continued need for innovation in the host-biomarker space as the available markers do not meet the needs of diverse populations around the globe. This highlights the importance of targeted evaluations in non-severe patients in multiple settings to understand the true potential for real-life use. The findings highlight that not one-marker fits all settings and novel innovations remain urgently needed. TRIAL REGISTRATION NUMBER: Clinical trial number: NCT03047642.