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Classification performance of administrative coding data for detection of invasive fungal infection in paediatric cancer patients

Authors :
Jake C. Valentine
Leon J. Worth
Karin M. Verspoor
Lisa Hall
Daniel K. Yeoh
Karin A. Thursky
Julia E. Clark
Gabrielle M. Haeusler
Ales Vicha
Source :
PLoS ONE, Vol 15, Iss 9 (2020)
Publication Year :
2020
Publisher :
Public Library of Science (PLoS), 2020.

Abstract

Background Invasive fungal infection (IFI) detection requires application of complex case definitions by trained staff. Administrative coding data (ICD-10-AM) may provide a simplified method for IFI surveillance, but accuracy of case ascertainment in children with cancer is unknown. Objective To determine the classification performance of ICD-10-AM codes for detecting IFI using a gold-standard dataset (r-TERIFIC) of confirmed IFIs in paediatric cancer patients at a quaternary referral centre (Royal Children’s Hospital) in Victoria, Australia from 1st April 2004 to 31st December 2013. Methods ICD-10-AM codes denoting IFI in paediatric patients (

Subjects

Subjects :
Medicine
Science

Details

Language :
English
ISSN :
19326203
Volume :
15
Issue :
9
Database :
Directory of Open Access Journals
Journal :
PLoS ONE
Publication Type :
Academic Journal
Accession number :
edsdoj.68cb2dd1cf3445baf22324e299ba4ab
Document Type :
article