Phillips, B, Morgan, JE, Haeusler, GM and Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735 (2020) Individual participant data validation of the PICNICC prediction model for febrile neutropenia. Archives of Disease in Childhood, 105 (5). pp. 439-445.

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Abstract

BACKGROUND: Risk-stratified approaches to managing cancer therapies and their consequent complications rely on accurate predictions to work effectively. The risk-stratified management of fever with neutropenia is one such very common area of management in paediatric practice. Such rules are frequently produced and promoted without adequate confirmation of their accuracy.

METHODS: An individual participant data meta-analytic validation of the 'Predicting Infectious ComplicatioNs In Children with Cancer' (PICNICC) prediction model for microbiologically documented infection in paediatric fever with neutropenia was undertaken. Pooled estimates were produced using random-effects meta-analysis of the area under the curve-receiver operating characteristic curve (AUC-ROC), calibration slope and ratios of expected versus observed cases (E/O).

RESULTS: The PICNICC model was poorly predictive of microbiologically documented infection (MDI) in these validation cohorts. The pooled AUC-ROC was 0.59, 95% CI 0.41 to 0.78, tau2=0, compared with derivation value of 0.72, 95% CI 0.71 to 0.76. There was poor discrimination (pooled slope estimate 0.03, 95% CI -0.19 to 0.26) and calibration in the large (pooled E/O ratio 1.48, 95% CI 0.87 to 2.1). Three different simple recalibration approaches failed to improve performance meaningfully.

CONCLUSION: This meta-analysis shows the PICNICC model should not be used at admission to predict MDI. Further work should focus on validating alternative prediction models. Validation across multiple cohorts from diverse locations is essential before widespread clinical adoption of such rules to avoid overtreating or undertreating children with fever with neutropenia.

Item Type: Article
Uncontrolled Keywords: Haematology, infectious diseases, oncology, statistics
Subjects: R Medicine > R Medicine (General)
Divisions: Faculty of Medicine and Health Sciences > Primary Care Health Sciences
Related URLs:
Depositing User: Symplectic
Date Deposited: 12 Nov 2019 10:59
Last Modified: 29 May 2020 10:13
URI: https://eprints.keele.ac.uk/id/eprint/7167

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