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: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.

Project description:BackgroundThe 2011 4th European Conference on Infections in Leukemia (ECIL4) guidelines recommend antibiotics de-escalation/discontinuation in selected febrile neutropenia (FN) patients. We aimed to assess the impact of an antimicrobial stewardship (AMS) program based on these guidelines on antibiotics use and clinical outcomes in high-risk FN patients.MethodsWe conducted an observational study in the hematology department of Cochin University Hospital in Paris, France. An ECIL4-based antibiotics de-escalation and discontinuation strategy was implemented jointly by the hematologists and the AMS team. The pre-intervention (January-October 2018) and post-intervention (January-October 2019) periods were compared. We retrospectively collected clinical and microbiological data. We compiled antibiotics consumptions via hospital pharmacy data and standardized them by calculating defined daily doses per 1000 patient-days. We analyzed the two-monthly antibiotic consumption using an interrupted time series method and built a composite endpoint for clinical outcomes based on transfer to the intensive care unit (ICU) and/or hospital death.ResultsOverall, 273 hospital stays (164 patients) in the pre-intervention and 217 (148 patients) in the post-intervention periods were analyzed. Patients were mainly hospitalized for intensive chemotherapy for acute leukemia or autologous stem-cell transplant for myeloma. Patients were slightly younger in the pre-intervention compared to the post-intervention period (median age 60.4 vs 65.2 years, p = 0.049), but otherwise comparable. After implementation of the AMS program, glycopeptide and carbapenem use decreased by 85% (p = 0.03) and 72% (p = 0.04), respectively. After adjustment on confounders, the risk of transfer to the ICU/death decreased significantly after implementation of the AMS program (post-intervention period: odds-ratio = 0.29, 95% Confidence Interval: 0.15-0.53, p < 0.001).ConclusionImplementation of a multidisciplinary AMS program for high-risk neutropenic patients was associated with lower carbapenem and glycopeptide use and improved clinical outcomes.

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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