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 1st April 2004 to 31st 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 F1 scores of the ICD-10-AM codes were calculated. RESULTS: Of 1,671 evaluable patients, 113 (6.76%) had confirmed IFI diagnoses according to gold-standard 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 F1 score of 60%, respectively. Sensitivity was highest for proven IFI (77% [95% CI: 58-90]; F1 = 47%) and invasive candidiasis (83% [95% CI: 61-95]; F1 = 76%) and lowest for other/unspecified IFI (20% [95% CI: 5.05-72%]; F1 = 5.00%). The most frequent misclassification was coding of invasive aspergillosis as invasive candidiasis. CONCLUSION: ICD-10-AM codes demonstrate moderate sensitivity and PPV to detect IFI in children with cancer. However, specific subsets of proven IFI and invasive candidiasis (codes B37.x) are more accurately coded.