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

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 1 st April 2004 to 31 st December 2013. Methods ICD-10-AM codes denoting IFI in paediatric patients (<18-years) with haematologic or solid tumour malignancies were extracted from the Victorian Admitted Episodes Dataset and linked to the r-TERIFIC dataset. Sensitivity, positive predictive value (PPV) and the F 1 scores of the ICD-10-AM codes were calculated. Results Of 1,671 evaluable patients, 113 (6.76%) had confirmed IFI diagnoses according to goldstandard criteria, while 114 (6.82%) cases were identified using the codes. Of the clinical IFI cases, 68 were in receipt of �1 ICD-10-AM code(s) for IFI, corresponding to an overall sensitivity, PPV and F 1 score of 60%, respectively. Sensitivity was highest for proven IFI (77% PLOS ONE

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