Dataset Information

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.

SUBMITTER: Valentine JC

PROVIDER: S-EPMC7480858 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

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

Valentine Jake C JC Worth Leon J LJ Verspoor Karin M KM Hall Lisa L Yeoh Daniel K DK Thursky Karin A KA Clark Julia E JE Haeusler Gabrielle M GM

PloS one 20200909 9

<h4>Background</h4>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.<h4>Objective</h4>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 refe ...[more]

PMID: 32903280

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Dataset Information

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

Background

Objective

Methods

Results

Conclusion

Publications

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

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