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

(2016) BRITISH JOURNAL OF CANCER. 114(6). p.623-630
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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.
Keywords
paediatric oncology, meta-analysis, supportive care, infectious complications, neutropenic sepsis, PEDIATRIC ONCOLOGY PATIENTS, ANTIBIOTIC MANAGEMENT, SYSTEMATIC REVIEWS, YOUNG-PEOPLE, CANCER, FEVER, RISK, PATIENT, BACTEREMIA, BIOMARKERS

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Citation

Please use this url to cite or link to this publication:

Chicago
Phillips, Robert S, Lillian Sung, Roland A Amman, Richard D Riley, Elio Castagnola, Gabrielle M Haeusler, Robert Klaassen, et al. 2016. “Predicting Microbiologically Defined Infection in Febrile Neutropenic Episodes in Children : Global Individual Participant Data Multivariable Meta-analysis.” British Journal of Cancer 114 (6): 623–630.
APA
Phillips, R. S., Sung, L., Amman, R. A., Riley, R. D., Castagnola, E., Haeusler, G. M., Klaassen, R., et al. (2016). Predicting microbiologically defined infection in febrile neutropenic episodes in children : global individual participant data multivariable meta-analysis. BRITISH JOURNAL OF CANCER, 114(6), 623–630.
Vancouver
1.
Phillips RS, Sung L, Amman RA, Riley RD, Castagnola E, Haeusler GM, et al. Predicting microbiologically defined infection in febrile neutropenic episodes in children : global individual participant data multivariable meta-analysis. BRITISH JOURNAL OF CANCER. 2016;114(6):623–30.
MLA
Phillips, Robert S, Lillian Sung, Roland A Amman, et al. “Predicting Microbiologically Defined Infection in Febrile Neutropenic Episodes in Children : Global Individual Participant Data Multivariable Meta-analysis.” BRITISH JOURNAL OF CANCER 114.6 (2016): 623–630. Print.
@article{8514644,
  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.},
  author       = {Phillips, Robert S and Sung, Lillian and Amman, Roland A and Riley, Richard D and Castagnola, Elio and Haeusler, Gabrielle M and Klaassen, Robert and Tissing, Wim JE and Lehrnbecher, Thomas and Chisholm, Julia and Hakim, Hana and Ranasinghe, Neil and Paesmans, Marianne and Hann, Ian M and Stewart, Lesley A and PICNICC Collaboration, on behalf of the and Bauters, Tieneke and Laureys, Genevieve},
  issn         = {0007-0920},
  journal      = {BRITISH JOURNAL OF CANCER},
  language     = {eng},
  number       = {6},
  pages        = {623--630},
  title        = {Predicting microbiologically defined infection in febrile neutropenic episodes in children : global individual participant data multivariable meta-analysis},
  url          = {http://dx.doi.org/10.1038/bjc.2016.28},
  volume       = {114},
  year         = {2016},
}

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