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

• Published in: Cancer 2017-10, Vol.123 (19), p.3781-3790
• Main Authors: Esbenshade, Adam J., Zhao, Zhiguo, Aftandilian, Catherine, Saab, Raya, Wattier, Rachel L., Beauchemin, Melissa, Miller, Tamara P., Wilkes, Jennifer J., Kelly, Michael J., Fernbach, Alison, Jeng, Michael, Schwartz, Cindy L., Dvorak, Christopher C., Shyr, Yu, Moons, Karl G.M., Sulis, Maria‐Luisa, Friedman, Debra L.
• Format: Article
• Language: English
• Subjects: Bacteremia; Bacteremia - diagnosis; Bacteria; Bacterial infections; Blood; Calibration; Cancer; Child; Child, Preschool; Clinical medicine; Datasets; Datasets as Topic; Decision making; Diagnosis; febrile neutropenia; Febrile Neutropenia - microbiology; Fever; Fungi; Gram-Negative Bacterial Infections - diagnosis; Health risks; health services research; Humans; Immunocompromised Host; Immunosuppression; Infections; Invasiveness; Mathematical models; Medical diagnosis; Medical instruments; Models, Statistical; Neoplasms; Neutropenia; Neutrophils; Oncology; Patients; pediatric oncology; Pediatrics; Performance prediction; Predictive Value of Tests; Retrospective Studies; Risk; risk prediction; Sensitivity analysis; Staphylococcal Infections - diagnosis; Staphylococcus aureus; supportive care; Uncertainty
• ISSN: 0008-543X; 1097-0142
• DOI: 10.1002/cncr.30792

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 To the authors' knowledge, the optimal management of pediatric oncology patients with fever, a central venous catheter, and an absolute neutrophil count ≥500/μL is not well established, but a recently published risk prediction model was shown to predict bacterial and fungal infections with great discrimination. The current study applies this model to data sets from 5 external sites and demonstrates that the model has good discrimination and calibration, indicating it could be considered for adjuvant use in clinical practice.