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Could Neutrophil CD64 Expression Be Used as a Diagnostic Parameter of Bacteremia in Patients with Febrile Neutropenia?

ABSTRACT: 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.

SUBMITTER: Efe Iris N

PROVIDER: S-EPMC5440869 | biostudies-other | 2017 Jun

REPOSITORIES: biostudies-other

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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:BACKGROUND:Serum procalcitonin (PCT) and C-reactive protein (CRP) are biomarkers of infection. In patients with hematologic disorders with or without hematopoietic stem cell transplantation (HSCT), it is difficult to distinguish bloodstream infections from aseptic causes of febrile episodes. The objective of this study was to investigate diagnostic values of PCT and CRP in predicting systemic bacterial infection in patients with hematologic malignancies. METHODS:Clinical and laboratory data of 614 febrile episode cases from 511 patients were analyzed. Febrile episodes were classified into four groups: (1) culture-positive bacterial infection by Gram-positive cocci (GPC), (2) culture-positive bacterial infection by Gram-negative bacilli (GNB), (3) fungal infection, and (4) viral infection or a noninfectious etiology. RESULTS:Of 614 febrile cases, systemic bacterial infections were confirmed in 99 (16.1%) febrile episodes, including 38 (6.2%) GPC and 61 (9.9%) GNB infections. PCT levels were significantly higher in GNB infectious episodes than those in febrile episodes caused by fungal infection (0.58 ng/mL (95% CI: 0.26-1.61) vs. 0.22 ng/mL (0.16-0.38), P = 0.047). Bacterial infectious episodes showed higher PCT and CRP levels than non-bacterial events (PCT: 0.49 (0.26-0.93) ng/mL vs. 0.20 (0.18-0.22) ng/mL, P < 0.001; CRP: 76.6 (50.5-92.8) mg/L vs. 58.0 (51.1-66.5) mg/L, P = 0.036). For non-neutropenic febrile episodes, both PCT and CRP discriminated bacteremia from non-bacteremia. However, in neutropenic febrile episodes, PCT only distinguished bacteremia from non-bacteremia. In non-neutropenic episode, both PCT and CRP showed good diagnostic accuracy (AUC: 0.757 vs. 0.763). In febrile neutropenia, only PCT discriminated bacteremia from non-bacterial infection (AUC: 0.624) whereas CRP could not detect bacteremia (AUC: 0.500, 95% CI: 0.439-0.561, P > 0.05). CONCLUSIONS:In this single-center observational study, PCT was more valuable than CRP for discriminating between bacteremia and non-bacteremia independent of neutropenia or HSCT.

Project description:BackgroundDiagnosis of pneumonia is critical in managing patients with febrile neutropenia (FN), however, chest X-ray (CXR) has limited performance in the detection of pneumonia. We aimed to evaluate the performance of a deep learning-based computer-aided detection (CAD) system in pneumonia detection in the CXRs of consecutive FN patients and investigated whether CAD could improve radiologists' diagnostic performance when used as a second reader.MethodsCXRs of patients with FN (a body temperature ≥ 38.3 °C, or a sustained body temperature ≥ 38.0 °C for an hour; absolute neutrophil count < 500/mm3) obtained between January and December 2017 were consecutively included, from a single tertiary referral hospital. Reference standards for the diagnosis of pneumonia were defined by consensus of two thoracic radiologists after reviewing medical records and CXRs. A commercialized, deep learning-based CAD system was retrospectively applied to detect pulmonary infiltrates on CXRs. For comparing performance, five radiologists independently interpreted CXRs initially without the CAD results (radiologist-alone interpretation), followed by the interpretation with CAD. The sensitivities and specificities for detection of pneumonia were compared between radiologist-alone interpretation and interpretation with CAD. The standalone performance of the CAD was also evaluated, using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Moreover, sensitivity and specificity of standalone CAD were compared with those of radiologist-alone interpretation.ResultsAmong 525 CXRs from 413 patients (52.3% men; median age 59 years), pneumonia was diagnosed in 128 (24.4%) CXRs. In the interpretation with CAD, average sensitivity of radiologists was significantly improved (75.4% to 79.4%, P = 0.003) while their specificity remained similar (75.4% to 76.8%, P = 0.101), compared to radiologist-alone interpretation. The CAD exhibited AUC, sensitivity, and specificity of 0.895, 88.3%, and 68.3%, respectively. The standalone CAD exhibited higher sensitivity (86.6% vs. 75.2%, P < 0.001) and lower specificity (64.8% vs. 75.4%, P < 0.001) compared to radiologist-alone interpretation.ConclusionsIn patients with FN, the deep learning-based CAD system exhibited radiologist-level performance in detecting pneumonia on CXRs and enhanced radiologists' performance.

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.

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Could Neutrophil CD64 Expression Be Used as a Diagnostic Parameter of Bacteremia in Patients with Febrile Neutropenia?

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