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|Title||Multisite external validation of a risk prediction model for the diagnosis of blood stream infections in febrile pediatric oncology patients without severe neutropenia.|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||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 M-L, Friedman DL|
|Date Published||2017 May 23|
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. © 2017 American Cancer Society.