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Development and validation of a prediction model for diagnosing blood stream infections in febrile, non-neutropenic children with cancer.

ABSTRACT: BACKGROUND:Pediatric oncology patients are at increased risk for blood stream infections (BSI). Risk in the absence of severe neutropenia (absolute neutrophil count [ANC] ?500/µl) is not well defined. PROCEDURE:In a retrospective cohort of febrile (temperature ?38.0° for >1?hr or ?38.3°) pediatric oncology patients with ANC ?500/µl, a diagnostic prediction model for BSI was constructed using logistic regression modeling and the following candidate predictors: age, ANC, absolute monocyte count, body temperature, inpatient/outpatient presentation, sex, central venous catheter type, hypotension, chills, cancer diagnosis, stem cell transplant, upper respiratory symptoms, and exposure to cytarabine, anti-thymocyte globulin, or anti-GD2 antibody. The model was internally validated with bootstrapping methods. RESULTS:Among 932 febrile episodes in 463 patients, we identified 91 cases of BSI. Independently significant predictors for BSI were higher body temperature (Odds ratio [OR] 2.36 P?

SUBMITTER: Esbenshade AJ

PROVIDER: S-EPMC4402108 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Development and validation of a prediction model for diagnosing blood stream infections in febrile, non-neutropenic children with cancer.

Esbenshade Adam J AJ Pentima M Cecilia Di MC Zhao Zhiguo Z Shintani Ayumi A Esbenshade Jennifer C JC Simpson Monique E ME Montgomery Kathleen C KC Lindell Robert B RB Lee Haerin H Wallace Ato A Garcia Kelly L KL Moons Karel G M KG Friedman Debra L DL

Pediatric blood & cancer 20141018 2

<h4>Background</h4>Pediatric oncology patients are at increased risk for blood stream infections (BSI). Risk in the absence of severe neutropenia (absolute neutrophil count [ANC] ≥500/µl) is not well defined.<h4>Procedure</h4>In a retrospective cohort of febrile (temperature ≥38.0° for >1 hr or ≥38.3°) pediatric oncology patients with ANC ≥500/µl, a diagnostic prediction model for BSI was constructed using logistic regression modeling and the following candidate predictors: age, ANC, absolute mo ...[more]

PMID: 25327666

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Project description:BACKGROUND:Health-workers in developing countries rely on clinical algorithms, such as the Integrated Management of Childhood Illnesses (IMCI), for the management of patients, including diagnosis of serious bacterial infections (SBI). The diagnostic accuracy of IMCI in detecting children with SBI is unknown. Prediction rules and guidelines for SBI from well-resourced countries at outpatient level may help to improve current guidelines; however, their diagnostic performance has not been evaluated in resource-limited countries, where clinical conditions, access to care, and diagnostic capacity differ. The aim of this study was to estimate the diagnostic accuracy of existing prediction rules and clinical guidelines in identifying children with SBI in a cohort of febrile children attending outpatient health facilities in Tanzania. METHODS:Structured literature review to identify available prediction rules and guidelines aimed at detecting SBI and retrospective, external validation on a dataset containing 1005 febrile Tanzanian children with acute infections. The reference standard, SBI, was established based on rigorous clinical and microbiological criteria. RESULTS:Four prediction rules and five guidelines, including IMCI, could be validated. All examined rules and guidelines had insufficient diagnostic accuracy for ruling-in or ruling-out SBI with positive and negative likelihood ratios ranging from 1.04-1.87 to 0.47-0.92, respectively. IMCI had a sensitivity of 36.7% (95% CI 29.4-44.6%) at a specificity of 70.3% (67.1-73.4%). Rules that use a combination of clinical and laboratory testing had better performance compared to rules and guidelines using only clinical and or laboratory elements. CONCLUSIONS:Currently applied guidelines for managing children with febrile illness have insufficient diagnostic accuracy in detecting children with SBI. Revised clinical algorithms including simple point-of-care tests with improved accuracy for detecting SBI targeting in tropical resource-poor settings are needed. They should undergo careful external validation against clinical outcome before implementation, given the inherent limitations of gold standards for SBI.

Project description:ObjectiveTo derive, cross validate, and externally validate a clinical prediction model that assesses the risks of different serious bacterial infections in children with fever at the emergency department.DesignProspective observational diagnostic study.SettingThree paediatric emergency care units: two in the Netherlands and one in the United Kingdom.ParticipantsChildren with fever, aged 1 month to 15 years, at three paediatric emergency care units: Rotterdam (n=1750) and the Hague (n=967), the Netherlands, and Coventry (n=487), United Kingdom. A prediction model was constructed using multivariable polytomous logistic regression analysis and included the predefined predictor variables age, duration of fever, tachycardia, temperature, tachypnoea, ill appearance, chest wall retractions, prolonged capillary refill time (>3 seconds), oxygen saturation <94%, and C reactive protein.Main outcome measuresPneumonia, other serious bacterial infections (SBIs, including septicaemia/meningitis, urinary tract infections, and others), and no SBIs.ResultsOxygen saturation <94% and presence of tachypnoea were important predictors of pneumonia. A raised C reactive protein level predicted the presence of both pneumonia and other SBIs, whereas chest wall retractions and oxygen saturation <94% were useful to rule out the presence of other SBIs. Discriminative ability (C statistic) to predict pneumonia was 0.81 (95% confidence interval 0.73 to 0.88); for other SBIs this was even better: 0.86 (0.79 to 0.92). Risk thresholds of 10% or more were useful to identify children with serious bacterial infections; risk thresholds less than 2.5% were useful to rule out the presence of serious bacterial infections. External validation showed good discrimination for the prediction of pneumonia (0.81, 0.69 to 0.93); discriminative ability for the prediction of other SBIs was lower (0.69, 0.53 to 0.86).ConclusionA validated prediction model, including clinical signs, symptoms, and C reactive protein level, was useful for estimating the likelihood of pneumonia and other SBIs in children with fever, such as septicaemia/meningitis and urinary tract infections.

Project description:ObjectiveThis study aims to assess the performance of biomarkers used for the prediction of bacterial, viral, and fungal infection in immunocompromised children upon presentation with fever.MethodsWe performed a literature search using PubMed and MEDLINE and In-Process & Other Non-indexed Citations databases. Cohort and case-control studies assessing biomarkers for the prediction of bacterial, viral, or fungal infection in immunocompromised children vs. conventional microbiological investigations were eligible. Studies including adult patients were eligible if pediatric data were separately assessable. Data on definitions used for infections, fever, and neutropenia and predictive values were collected. Risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool.ResultsFifty-two studies involving 13,939 febrile episodes in 7,059 children were included. In total, 92.2% were in cancer patients (n = 48), and 15.7% also included hematopoietic stem cell transplantation patients (n = 8). Forty-three biomarkers were investigated, of which 6 (CRP, PCT, IL-8, IL-6, IL-10, and TNFα) were significantly associated with bacterial infection at admission, studied in multiple studies, and provided predictive data. Literature on the prediction of viral and fungal infection was too limited. Eight studies compared C-reactive protein (CRP) and procalcitonin (PCT), with PCT demonstrating superiority in 5. IL-6, IL-8, and IL-10 were compared with CRP in six, four, and one study, respectively, with mixed results on diagnostic superiority. No clear superior biomarker comparing PCT vs. IL-6, IL-8, or IL-10 was identified.DiscussionThere is great heterogeneity in the biomarkers studied and cutoff values and definitions used, thus complicating the analysis. Literature for immunocompromised children with non-malignant disease and for non-bacterial infection is sparse. Literature on novel diagnostics was not available. We illustrated the challenges of diagnosing fever adequately in this study population and the need for improved biomarkers and clinical decision-making tools.

Dataset Information

Development and validation of a prediction model for diagnosing blood stream infections in febrile, non-neutropenic children with cancer.

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Development and validation of a prediction model for diagnosing blood stream infections in febrile, non-neutropenic children with cancer.

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