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. 2016 Aug 3;11(8):e0160278.
doi: 10.1371/journal.pone.0160278. eCollection 2016.

Host Biomarkers for Distinguishing Bacterial From Non-Bacterial Causes of Acute Febrile Illness: A Comprehensive Review

Free PMC article

Host Biomarkers for Distinguishing Bacterial From Non-Bacterial Causes of Acute Febrile Illness: A Comprehensive Review

Anokhi J Kapasi et al. PLoS One. .
Free PMC article


Background: In resource limited settings acute febrile illnesses are often treated empirically due to a lack of reliable, rapid point-of-care diagnostics. This contributes to the indiscriminate use of antimicrobial drugs and poor treatment outcomes. The aim of this comprehensive review was to summarize the diagnostic performance of host biomarkers capable of differentiating bacterial from non-bacterial infections to guide the use of antibiotics.

Methods: Online databases of published literature were searched from January 2010 through April 2015. English language studies that evaluated the performance of one or more host biomarker in differentiating bacterial from non-bacterial infection in patients were included. Key information extracted included author information, study methods, population, pathogens, clinical information, and biomarker performance data. Study quality was assessed using a combination of validated criteria from the QUADAS and Lijmer checklists. Biomarkers were categorized as hematologic factors, inflammatory molecules, cytokines, cell surface or metabolic markers, other host biomarkers, host transcripts, clinical biometrics, and combinations of markers.

Findings: Of the 193 citations identified, 59 studies that evaluated over 112 host biomarkers were selected. Most studies involved patient populations from high-income countries, while 19% involved populations from low- and middle-income countries. The most frequently evaluated host biomarkers were C-reactive protein (61%), white blood cell count (44%) and procalcitonin (34%). Study quality scores ranged from 23.1% to 92.3%. There were 9 high performance host biomarkers or combinations, with sensitivity and specificity of ≥85% or either sensitivity or specificity was reported to be 100%. Five host biomarkers were considered weak markers as they lacked statistically significant performance in discriminating between bacterial and non-bacterial infections.

Discussion: This manuscript provides a summary of host biomarkers to differentiate bacterial from non-bacterial infections in patients with acute febrile illness. Findings provide a basis for prioritizing efforts for further research, assay development and eventual commercialization of rapid point-of-care tests to guide use of antimicrobials. This review also highlights gaps in current knowledge that should be addressed to further improve management of febrile patients.

Conflict of interest statement

Competing Interests: The contents of this manuscript have not been published previously, but some of the key findings were presented at a meeting in Geneva convened by WHO, ReAct, MSF Access Campaign and FIND. While none of the authors or organization members has any relationship(s) that could be perceived as constituting a conflict of interest, it is important for the sake of complete transparency to note the Dr. Kapasi is also employed by The Degge Group, Ltd. and Dr. Rodwell is also employed by University of California, San Diego.


Fig 1
Fig 1. PRISMA flow chart of study selection.
(a) Includes Cochrane Database of Systematic Review, Science Daily, and free-text searches online. (b) Includes instances of: detection of a single type of pathogen, measures prognosis/severity, does not differentiate bacterial infection, prevalence studies, and detection of co-infections or cross-reactivity. (c) Study evaluation was limited solely to tick-born infections. (d) Assessed for additional relevant references.
Fig 2
Fig 2. Number of publications that reported on specific biomarkers (2010–2105).
The counts in this figure represent the number of publications evaluating a specific host biomarker, regardless of specimen. Biomarker combinations are not represented in this graph. The multi-gene classifier studies screened >1000 host transcripts each, with a final data set of ranging from 10–52 host gene transcripts; however, for the purposes of this graph, a single count was entered for each multi-gene classifier study, regardless of the number of transcripts profiled.
Fig 3
Fig 3. Risk of Bias for 26 Quality Measures: Systematic Review (2010-April 2015).
* Criteria that are specified by both QUADAS tool and Lijmer et al. (1999).

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Grant support

All the manuscript authors were affiliated with and funded by FIND for the implementation of the study, analysis of results and publishing of results. The first author, Dr. Anokhi Kapasi was (and still is) also employed by The Degge Group, Ltd., a private, for-profit company, but Dr. Kapasi was working independently of Degge for this study and publication. The Degge Group did not provide any funding support for this work, and was/is not affiliated with the work or writing of this manuscript in any way. The author affiliations have been updated accordingly in the manuscript, indicating Dr. Kapasi is affiliated with FIND not Degge for the purposes of this work and manuscript. Similarly, Dr. Rodwell, is also employed by University of California, San Diego, but is involved with this work through his affiliation with FIND only. This work was funded by UK Aid, UK Government, and the Australian Government.