4.5 Article

Risk Prediction in Pediatric Cancer Patients With Fever and Neutropenia

Journal

PEDIATRIC INFECTIOUS DISEASE JOURNAL
Volume 29, Issue 1, Pages 53-59

Publisher

LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/INF.0b013e3181c3f6f0

Keywords

fever; neutropenia; children; cancer; risk; prediction

Funding

  1. National Institutes of Health [CA21765]
  2. American Lebanese Syrian Associated Charities (ALSAC)
  3. NATIONAL CANCER INSTITUTE [P30CA021765] Funding Source: NIH RePORTER

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Background: To identify predictors for 2 risk measures-proven invasive bacterial infection or culture-negative sepsis (IBD) and clinical complications (CC)-in pediatric cancer patients with fever and neutropenia (FN). Methods: Records of 390 patients with FN hospitalized over 2 years were reviewed. For the 332 who met inclusion criteria, one FN episode was randomly selected. Independent predictors at presentation were analyzed using multiple regression models. Optimal cut-off risk prediction scores were determined. These models were validated by bootstrap analysis. Results: Patients' median age was 6.0 years; 66% had an underlying diagnosis of leukemia. Independent predictors of IBD (n = 56) were absolute neutrophil count <100, temperature at presentation >= 39.0 degrees C, sick clinical appearance, and underlying diagnosis of acute myeloid leukemia. A total weighted score <24 reliably identified patients at low risk for IBD. Independent predictors of CC (n = 47) were relapse of malignancy, non-white race, sick clinical appearance, and underlying diagnosis of acute myeloid leukemia. A total weighted score <19 predicted patients at low risk for CC. Of those misclassified as low risk, 11 of 12 with IBD and 3 of 9 with CC had the outcome within 24 hours of presentation. Of the remaining patients classified as low-risk for IBD and CC, 99.5% and 97.1%, respectively, remained outcome-free after 24 hours of observation. Conclusions: This study identifies predictors of infection/complications in pediatric patients with FN, establishes clinical cut-off scores and highlights the importance of the initial clinical impression and 24 hours of observation. These prediction models warrant prospective validation.

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