Dataset Information

Integrative omics to detect bacteremia in patients with febrile neutropenia.

ABSTRACT:

Background

Cancer 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.

Methods

We 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.

Results

For 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.

Conclusions

Classifiers 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.

SUBMITTER: Kelly RS

PROVIDER: S-EPMC5955575 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Integrative omics to detect bacteremia in patients with febrile neutropenia.

Kelly Rachel S RS Lasky-Su Jessica J Yeung Sai-Ching J SJ Stone Richard M RM Caterino Jeffrey M JM Hagan Sean C SC Lyman Gary H GH Baden Lindsey R LR Glotzbecker Brett E BE Coyne Christopher J CJ Baugh Christopher W CW Pallin Daniel J DJ

PloS one 20180516 5

<h4>Background</h4>Cancer 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.<h4>Methods</h4>We classified emergency department patients with FN and no apparent infection at pre ...[more]

PMID: 29768470

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Project description:The aim of this study is to investigate if neutrophil CD64 expression in febrile neutropenia patients could be used as an early indicator of bacteremia.All consecutive patients older than 18 years of age who had developed febrile neutropenia episodes due to hematological malignancies were included in the study. Those patients who had significant growth in their blood cultures constituted the case group, while those who had febrile neutropenia without any growth in their cultures and who did not have any documented infections formed the control group. Blood culture bottles were incubated in the Bact ALERT 3D system (bioMerieux, France), identification and susceptibility testing were performed using an automated broth microdilution method (VITEK 2, bioMerieux), and CD64 expression analysis was performed by the flow cytometry method. C-reactive protein (CRP) was measured by turbidimetric methods (Biosystems, Spain) and erythrocyte sedimentation rate (ESR) was measured by the Wintrobe method.In total, we prospectively evaluated 31 febrile episodes. The case group consisted of 17 patients while the control group included 14 patients. CD64 was found on neutrophils of the case group patients with a mean count of 8006 molecules/cell and of control group with a mean count of 2786 molecules/cell. CD64 levels of the case group were significantly higher than those of the control group (p=0.005). In the differentiation of the case group from the control group, a 2500 cut-off value for CD64 had significant [AUC=0.792 (0.619-0.965)] predictive value (p=0.001). In the prediction of patients with a 2500 cut-off value for CD64, sensitivity was 94.1%, positive predictive value was 76.2%, specificity was 64.3%, and negative predictive value was 90.0%. CRP levels and ESR values did not differ significantly between the groups (p=0.005).Neutrophil CD64 expression could be a good predictor as an immune parameter with high sensitivity and a negative predictive value for bacteremia in febrile neutropenic patients.

Project description:Febrile neutropenia (FN) is a common condition in children receiving chemotherapy. Our goal in this study was to develop a model for predicting blood stream infection (BSI) and transfer to intensive care (TIC) at time of presentation in pediatric cancer patients with FN. We conducted an observational cohort analysis of pediatric and adolescent cancer patients younger than 24 years admitted for fever and chemotherapy-induced neutropenia over a 7-year period. We excluded stem cell transplant recipients who developed FN after transplant and febrile non-neutropenic episodes. The primary outcome was onset of BSI, as determined by positive blood culture within 7 days of onset of FN. The secondary outcome was transfer to intensive care (TIC) within 14 days of FN onset. Predictor variables include demographics, clinical, and laboratory measures on initial presentation for FN. Data were divided into independent derivation (2009-2014) and prospective validation (2015-2016) cohorts. Prediction models were built for both outcomes using logistic regression and random forest and compared with Hakim model. Performance was assessed using area under the receiver operating characteristic curve (AUC) metrics. A total of 505 FN episodes (FNEs) were identified in 230 patients. BSI was diagnosed in 106 (21%) and TIC occurred in 56 (10.6%) episodes. The most common oncologic diagnosis with FN was acute lymphoblastic leukemia (ALL), and the highest rate of BSI was in patients with AML. Patients who had BSI had higher maximum temperature, higher rates of prior BSI and higher incidence of hypotension at time of presentation compared with patients who did not have BSI. FN patients who were transferred to the intensive care (TIC) had higher temperature and higher incidence of hypotension at presentation compared to FN patients who didn't have TIC. We compared 3 models: (1) random forest (2) logistic regression and (3) Hakim model. The areas under the curve for BSI prediction were (0.79, 0.65, and 0.64, P < 0.05) for models 1, 2, and 3, respectively. And for TIC prediction were (0.88, 0.76, and 0.65, P < 0.05) respectively. The random forest model demonstrated higher accuracy in predicting BSI and TIC and showed a negative predictive value (NPV) of 0.91 and 0.97 for BSI and TIC respectively at the best cutoff point as determined by Youden's Index. Likelihood ratios (LRs) (post-test probability) for RF model have potential utility of identifying low risk for BSI and TIC (0.24 and 0.12) and high-risk patients (3.5 and 6.8) respectively. Our prediction model has a very good diagnostic performance in clinical practices for both BSI and TIC in FN patients at the time of presentation. The model can be used to identify a group of individuals at low risk for BSI who may benefit from early discharge and reduced length of stay, also it can identify FN patients at high risk of complications who might benefit from more intensive therapies at presentation.

Dataset Information

Integrative omics to detect bacteremia in patients with febrile neutropenia.

Background

Methods

Results

Conclusions

Publications

Integrative omics to detect bacteremia in patients with febrile neutropenia.

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