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Published on: April 20, 2021
Predicting microbiologically defined infection in febrile neutropenic episodes in children: global individual
Robert S Phillips1,2, Lillian Sung3,4, Roland A Ammann5
1Centre for Reviews and Dissemination, University of York, York, UK.
Insights
A new prediction model accurately identifies children with cancer at risk of serious infections during fever with neutropenia (FN). This tool aids clinicians in managing high-risk cases and discharging low-risk patients safely.
Area of Science:
- Pediatric Oncology
- Infectious Diseases
- Clinical Epidemiology
Background:
- Risk-stratified management of fever with neutropenia (FN) is crucial for optimizing care in pediatric cancer patients.
- Current prediction models for adverse outcomes in pediatric FN lack international validation.
- An individual patient data (IPD) meta-analysis was conducted to develop a novel risk-prediction model.
Purpose of the Study:
- To develop and internally validate a risk-prediction model for adverse outcomes in pediatric cancer patients experiencing fever with neutropenia.
- To improve the individualized decision-making process for managing FN episodes in children and young people.
Main Methods:
- An individual patient data (IPD) meta-analysis was performed by the Predicting Infectious Complications in Children with Cancer (PICNICC) collaboration.
- Random effects logistic regression was used for univariable and multivariable analyses to derive and validate the prediction model.
- Clinical and laboratory data at presentation were utilized to predict outcomes of FN episodes.
Main Results:
- The analysis included 5127 episodes of FN from 3504 patients across 15 countries.
- The final multivariable model incorporated malignancy type, temperature, clinical status, hemoglobin, white cell count, and absolute monocyte count.
- The model demonstrated moderate discrimination (AUROC 0.723) and good calibration, with robust validation through sensitivity analyses.
Conclusions:
- A newly developed prediction model shows accuracy in assessing the risk of microbiologically defined infection (MDI) in pediatric FN.
- Prospective studies are needed to evaluate the implementation and clinical utility of this model.
- The model aims to support clinicians and families in making informed decisions for individualized patient care.
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.
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