Faculty Collaboration Database

Multisite external validation of a risk prediction model for the diagnosis of blood stream infections in febrile pediatric oncology patients without severe neutropenia. Cancer 2017 Oct 01;123(19):3781-3790

Date

05/26/2017

Pubmed ID

28542918

Pubmed Central ID

PMC5610619

DOI

10.1002/cncr.30792

Scopus ID

2-s2.0-85029832642 (requires institutional sign-in at Scopus site)   22 Citations

Abstract

BACKGROUND: Pediatric oncology patients are at an increased risk of invasive bacterial infection due to immunosuppression. The risk of such infection in the absence of severe neutropenia (absolute neutrophil count ≥ 500/μL) is not well established and a validated prediction model for blood stream infection (BSI) risk offers clinical usefulness.

METHODS: A 6-site retrospective external validation was conducted using a previously published risk prediction model for BSI in febrile pediatric oncology patients without severe neutropenia: the Esbenshade/Vanderbilt (EsVan) model. A reduced model (EsVan2) excluding 2 less clinically reliable variables also was created using the initial EsVan model derivative cohort, and was validated using all 5 external validation cohorts. One data set was used only in sensitivity analyses due to missing some variables.

RESULTS: From the 5 primary data sets, there were a total of 1197 febrile episodes and 76 episodes of bacteremia. The overall C statistic for predicting bacteremia was 0.695, with a calibration slope of 0.50 for the original model and a calibration slope of 1.0 when recalibration was applied to the model. The model performed better in predicting high-risk bacteremia (gram-negative or Staphylococcus aureus infection) versus BSI alone, with a C statistic of 0.801 and a calibration slope of 0.65. The EsVan2 model outperformed the EsVan model across data sets with a C statistic of 0.733 for predicting BSI and a C statistic of 0.841 for high-risk BSI.

CONCLUSIONS: The results of this external validation demonstrated that the EsVan and EsVan2 models are able to predict BSI across multiple performance sites and, once validated and implemented prospectively, could assist in decision making in clinical practice. Cancer 2017;123:3781-3790. © 2017 American Cancer Society.

Author List

Esbenshade AJ, Zhao Z, Aftandilian C, Saab R, Wattier RL, Beauchemin M, Miller TP, Wilkes JJ, Kelly MJ, Fernbach A, Jeng M, Schwartz CL, Dvorak CC, Shyr Y, Moons KGM, Sulis ML, Friedman DL

Author

Cindy L. Schwartz MD, MPH Professor in the Pediatrics department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Bacteremia
Child
Child, Preschool
Datasets as Topic
Febrile Neutropenia
Gram-Negative Bacterial Infections
Humans
Immunocompromised Host
Models, Statistical
Neoplasms
Predictive Value of Tests
Retrospective Studies
Risk
Staphylococcal Infections
Staphylococcus aureus
Uncertainty