Abstract
BackgroundCancer chemotherapy-associated febrile neutropenia (FN) is a common condition that is deadly when bacteremia is present. Detection of bacteremia depends on culture, which takes days, and no accurate predictive tools applicable to the initial evaluation are available. We utilized metabolomics and transcriptomics to develop multivariable predictors of bacteremia among FN patients.MethodsWe classified emergency department patients with FN and no apparent infection at presentation as bacteremic (cases) or not (controls), according to blood culture results. We assessed relative metabolite abundance in plasma, and relative expression of 2,560 immunology and cancer-related genes in whole blood. We used logistic regression to identify multivariable predictors of bacteremia, and report test characteristics of the derived predictors.ResultsFor metabolomics, 14 bacteremic cases and 25 non-bacteremic controls were available for analysis; for transcriptomics we had 7 and 22 respectively. A 5-predictor metabolomic model had an area under the receiver operating characteristic curve of 0.991 (95%CI: 0.972,1.000), 100% sensitivity, and 96% specificity for identifying bacteremia. Pregnenolone steroids were more abundant in cases and carnitine metabolites were more abundant in controls. A 3-predictor gene expression model had corresponding results of 0.961 (95%CI: 0.896,1.000), 100%, and 86%. Genes involved in innate immunity were differentially expressed.ConclusionsClassifiers derived from metabolomic and gene expression data hold promise as objective and accurate predictors of bacteremia among FN patients without apparent infection at presentation, and can provide insights into the underlying biology. Our findings should be considered illustrative, but may lay the groundwork for future biomarker development.
Highlights
14 bacteremic cases and 25 non-bacteremic controls were available for analysis; for transcriptomics we had 7 and 22 respectively
Presumptive broadspectrum antibiotic treatment is recommended for all febrile neutropenia (FN) patients within 1 hour of onset of symptoms, and the vast majority are admitted to the hospital.[3,4,5,6]
We have demonstrated methods for discovery of a multi-omics-based predictor for detection of bacteremia among FN patients without apparent infection
Summary
Detection of bacteremia depends on culture, which rules out bacteremia only after several days; too late to inform initial decisions regarding hospitalization and therapy. Guidelines recommend that initial treatment decisions be based on clinical evaluation including the Multinational Association for Supportive Care in Cancer (MASCC) score.[4, 10] this score was not designed to detect bacteremia, and is insufficiently accurate even for its intended use, prediction of safe discharge, with a negative predictive value for complications of only 83%.[11] many clinicians do not rely on it, admitting all FN patients by default.[5, 6] A newer score, the Clinical Index of Stable Febrile Neutropenia, is inadequate, with a 9.1% rate of bacteremia in the low-risk group.[12] PCR for bacterial DNA, and measurement of host markers such as procalcitonin, lack sufficient sensitivity.[13,14,15] Objective tests are needed to detect bacteremia during the initial evaluation, so that patient-specific management strategies can be employed. We utilized metabolomics and transcriptomics to develop multivariable predictors of bacteremia among FN patients
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