Classification performance of administrative coding data for detection of invasive fungal infection in paediatric cancer patients
Jake C Valentine, Leon J Worth, Karin M Verspoor, Lisa Hall, Daniel K Yeoh, Karin A Thursky, Julia E Clark, Gabrielle M Haeusler
PLoS One | PUBLIC LIBRARY SCIENCE | Published : 2020
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 (<18-years) with haematologic or solid tumour ..View full abstract
Awarded by Australian Government Research Training Program Scholarship - University of Melbourne
Awarded by Gilead Sciences, Inc
J.C.V. was supported by an Australian Government Research Training Program Scholarship (grant number: 290465) awarded by the University of Melbourne (URL: https://www.education.gov.au/research-training-program) and a Cardinal Health Infection Control Scholarship awarded by the Australasian College for Infection Prevention and Control (URL: https://www.cardinalhealth.com/en.html). The TERIFIC study was supported by an Investigator Initiated Grant (grant number: IN-AU-131-1314) from Gilead Sciences, Inc. (https://www.gilead.com/). The funders had no role in study design, data collection and analysis, decision to publish, preparation or review of the manuscript.