Multisite external validation of a risk prediction model for the diagnosis of blood stream infections in febrile pediatric oncology patients without severe neutropenia

Adam J. Esbenshade, Zhiguo Zhao, Catherine Aftandilian, Raya Saab, Rachel L. Wattier, Melissa Beauchemin, Tamara P. Miller, Jennifer J. Wilkes, Michael J. Kelly, Alison Fernbach, Michael Jeng, Cindy L. Schwartz, Christopher C. Dvorak, Yu Shyr, Karl G.M. Moons, Maria Luisa Sulis, Debra L. Friedman

研究成果: Article同行評審

11 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)3781-3790
頁數10
期刊Cancer
123
發行號19
DOIs
出版狀態Published - 2017 十月 1

All Science Journal Classification (ASJC) codes

  • 腫瘤科
  • 癌症研究

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