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Predicting microbiologically defined infection in febrile neutropenic episodes in children: global individual participant data multivariable meta-analysis

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Author(s)

  • Bob Phillips
  • Lillian Sung
  • Roland A Ammann
  • Richard Riley
  • Elio Castagnola
  • Gabrielle M Haeusler
  • Robert Klaassen
  • Wim J E Tissing
  • Thomas Lehrnbecher
  • Julia Chisholm
  • Hana Hakim
  • Neil Ranasinghe
  • Marianne Paesmans
  • Ian Hann
  • Lesley Stewart

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Publication details

JournalBritish journal of cancer
DateAccepted/In press - 16 Jan 2016
DateE-pub ahead of print - 8 Mar 2016
DatePublished (current) - 15 Mar 2016
Issue number6
Volume114
Number of pages8
Pages (from-to)623-630
Early online date8/03/16
Original languageEnglish

Abstract

BACKGROUND: Risk-stratified management of fever with neutropenia (FN), allows intensive management of high-risk cases and early discharge of low-risk cases. No single, internationally validated, prediction model of the risk of adverse outcomes exists for children and young people. An individual patient data (IPD) meta-analysis was undertaken to devise one.

METHODS: The 'Predicting Infectious Complications in Children with Cancer' (PICNICC) collaboration was formed by parent representatives, international clinical and methodological experts. Univariable and multivariable analyses, using random effects logistic regression, were undertaken to derive and internally validate a risk-prediction model for outcomes of episodes of FN based on clinical and laboratory data at presentation.

RESULTS: Data came from 22 different study groups from 15 countries, of 5127 episodes of FN in 3504 patients. There were 1070 episodes in 616 patients from seven studies available for multivariable analysis. Univariable analyses showed associations with microbiologically defined infection (MDI) in many items, including higher temperature, lower white cell counts and acute myeloid leukaemia, but not age. Patients with osteosarcoma/Ewings sarcoma and those with more severe mucositis were associated with a decreased risk of MDI. The predictive model included: malignancy type, temperature, clinically 'severely unwell', haemoglobin, white cell count and absolute monocyte count. It showed moderate discrimination (AUROC 0.723, 95% confidence interval 0.711-0.759) and good calibration (calibration slope 0.95). The model was robust to bootstrap and cross-validation sensitivity analyses.

CONCLUSIONS: This new prediction model for risk of MDI appears accurate. It requires prospective studies assessing implementation to assist clinicians and parents/patients in individualised decision making.

Bibliographical note

© 2016 Cancer Research UK. Acceptance date: 16/01/2016. Embargo period: 6 months. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details

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