Individual participant data validation of the PICNICC prediction model for febrile neutropenia.
haematology
infectious diseases
oncology
statistics
Journal
Archives of disease in childhood
ISSN: 1468-2044
Titre abrégé: Arch Dis Child
Pays: England
ID NLM: 0372434
Informations de publication
Date de publication:
05 2020
05 2020
Historique:
received:
26
03
2019
revised:
20
09
2019
accepted:
18
10
2019
pubmed:
7
11
2019
medline:
31
7
2020
entrez:
7
11
2019
Statut:
ppublish
Résumé
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. 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). 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, tau 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.
Sections du résumé
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, tau
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.
Identifiants
pubmed: 31690548
pii: archdischild-2019-317308
doi: 10.1136/archdischild-2019-317308
pmc: PMC7212933
doi:
Types de publication
Journal Article
Meta-Analysis
Research Support, Non-U.S. Gov't
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
439-445Subventions
Organisme : Medical Research Council
ID : G0800472
Pays : United Kingdom
Organisme : Department of Health
ID : PDF-2014-07-072
Pays : United Kingdom
Informations de copyright
© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.
Déclaration de conflit d'intérêts
Competing interests: None declared.
Références
Arch Dis Child. 2013 Jul;98(7):567-8
pubmed: 23661573
J Clin Oncol. 2011 Mar 1;29(7):e182-4; author reply e185
pubmed: 21245423
Eur Phys J C Part Fields. 2016;76:154
pubmed: 27471432
Br J Cancer. 2017 Jul 11;117(2):171-178
pubmed: 28609435
J Clin Oncol. 2004 Oct 1;22(19):3922-9
pubmed: 15459214
Pediatr Blood Cancer. 2007 Oct 15;49(5):678-81
pubmed: 17066460
PLoS One. 2012;7(5):e38300
pubmed: 22693615
Ann Intern Med. 2015 Jan 6;162(1):W1-73
pubmed: 25560730
J Clin Epidemiol. 2008 Nov;61(11):1085-94
pubmed: 19208371
Stat Med. 2000 Feb 29;19(4):453-73
pubmed: 10694730
Health Technol Assess. 2007 Dec;11(51):iii-iv, 1-164
pubmed: 18021578
Child Care Health Dev. 2009 Jul;35(4):489-95
pubmed: 19638023
J Clin Epidemiol. 2016 Jan;69:40-50
pubmed: 26142114
Pediatr Blood Cancer. 2015 Mar;62(3):483-9
pubmed: 25446628
BJOG. 2017 Feb;124(3):423-432
pubmed: 27362778
Syst Rev. 2012 Feb 09;1:8
pubmed: 22588015
Pediatr Blood Cancer. 2008 Dec;51(6):778-83
pubmed: 18726920
Lancet Oncol. 2009 Jun;10(6):589-97
pubmed: 19482247
J Clin Oncol. 2010 Apr 20;28(12):2008-14
pubmed: 20231680
Pediatr Blood Cancer. 2015 Feb;62(2):262-268
pubmed: 25327666
J Clin Oncol. 2000 Aug;18(16):3038-51
pubmed: 10944139
Br J Cancer. 2016 Jun 14;114(12):e17
pubmed: 27228292
Stat Med. 2016 Jan 30;35(2):214-26
pubmed: 26553135
Pediatr Crit Care Med. 2005 Jan;6(1):2-8
pubmed: 15636651
BMJ. 2017 Jan 5;356:i6460
pubmed: 28057641
Pediatr Blood Cancer. 2011 Aug;57(2):283-8
pubmed: 21671364