Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Kapasi AJ

PROVIDER: S-EPMC4972355 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

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

Kapasi Anokhi J AJ Dittrich Sabine S González Iveth J IJ Rodwell Timothy C TC

PloS one 20160803 8

<h4>Background</h4>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.<h4>Methods</h4>Online databases of publishe ...[more]

PMID: 27486746

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Project description:Rickettsial diseases (RDs) are major under-diagnosed causes of arthropod borne acute febrile illness (AFI) presenting with a range of symptoms from mild self-limiting fever to fatal sepsis. The spotted fever group (SFG) and typhus group (TG) are major RDs, which are commonly caused by Rickettsia conorii and Rickettsia typhi, respectively. The limited availability and role of serological tests in the acute phase of illness warrants rapid reliable molecular methods for diagnosis and epidemiological studies. Two hundred patients with AFI in whom the routine fever diagnostics were negative, were enrolled over a period of two months (April 2019 to May 2019). DNA was extracted and in-house nested PCR using primers specific for both SPG and TG pathogens was used. The positive amplified products were sequenced for species identification and phylogenetic analysis was performed using MEGA 7.0.14 software (iGEM, Temple University, Philadelphia, PA 19122, USA). The demographic details of the RD cases were documented. The prevalence of RD among AFI cases was 7% (14/200); SFG and TG were identified as the cause in 4% and 3% of AFI cases, respectively. The median age of the RD cases was 22 years (range 2-65). The median duration of fever was 3 days (range 1-12). The RD cases presented with respiratory symptoms or signs (44.44%), jaundice (22.22%), abdominal pain (22.22%), diarrhea (22.22), vesicular rash (11.11%), vomiting (11.11%), loss of appetite (11.11%), headache (11.11%), leukocytosis (88.88% with mean count 22,750/mm3), and thrombocytopenia (33.33%). The cases were treated empirically with piperacillin-tazobactam (66.66%), clindamycin (44.44%), cefotaxime (33.33%), meropenem (33.33%), metronidazole (33.33%), doxycycline (22.22%), azithromycin (22.22%), ceftriaxone (11.11%), and amoxicillin-clavulanic acid (11.11%). The mortality among the RD cases was 11.11%. The present pilot study shows that RD is not an uncommon cause of AFI in north India. The febrile episodes are usually transient, not severe and associated with heterogenous clinical presentation without documented history of tick exposure in the hospitalized patients. The transient, non-severe, febrile illness could be due to transient rickettsemia resulting from empirical antimicrobial therapy as the rickettsial organisms are expected to be more susceptible to higher doses of ?-lactam antibiotics. The study emphasizes the molecular method as a useful tool to identify rickettsial etiology in AFI.

Project description:ImportanceThe current lack of reliable rapid tests for distinguishing between bacterial and viral infections has contributed to antibiotic misuse.ObjectiveThis study aimed to develop a novel biomarker assay that integrates FAM89A and IFI44L measurements to assist in differentiating between bacterial and viral infections.MethodsThis prospective study recruited children with febrile illness from two hospitals between July 1, 2018, and June 30, 2019. A panel of three experienced pediatricians performed reference standard diagnoses of all patients (i.e., bacterial or viral infection) using available clinical and laboratory data, including a 28-day follow-up assessment. Assay operators were blinded to the reference standard diagnoses. The expression levels of FAM89A and IFI44L were determined by quantitative real-time polymerase chain reaction assessment.ResultsOf 133 potentially eligible patients with suspected bacterial or viral infection, 35 were excluded after the application of exclusion criteria. The resulting cohort included 98 patients: 59 with viral diagnoses and 39 with bacterial diagnoses. The areas under the curve (AUCs) of diagnoses using FAM89A and IFI44L were 0.694 [95% confidence interval (CI): 0.583-0.804] and 0.751 (95% CI: 0.651-0.851), respectively. The disease risk score (DRS) [log2(FAM89A expression) - log2(IFI44L expression)] signature achieved an improved area under the receiver operating characteristic curve (AUC, 0.825; 95% CI: 0.735-0.915), compared with the AUC generated from individual host RNA. A combination of the DRS and the C-reactive protein (CRP) level achieved an AUC of 0.896 (95% CI: 0.825-0.966). Optimal cutoffs for the DRS and CRP level were -3.18 and 19.80 mg/L, respectively.InterpretationThe DRS was significantly more accurate than the CRP level in distinguishing between bacterial and viral infections; the combination of these two parameters exhibited greater sensitivity and specificity. This study provides information that could be useful for the clinical application of FAM89A and IFI44L in terms of distinguishing between viral and bacterial infections.

Dataset Information

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

Background

Methods

Findings

Discussion

Publications

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

Similar Datasets

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