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Classification performance of administrative coding data for detection of invasive fungal infection in paediatric cancer patients
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 d...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7480858/ https://www.ncbi.nlm.nih.gov/pubmed/32903280 http://dx.doi.org/10.1371/journal.pone.0238889 |
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author | Valentine, Jake C. Worth, Leon J. Verspoor, Karin M. Hall, Lisa Yeoh, Daniel K. Thursky, Karin A. Clark, Julia E. Haeusler, Gabrielle M. |
author_facet | Valentine, Jake C. Worth, Leon J. Verspoor, Karin M. Hall, Lisa Yeoh, Daniel K. Thursky, Karin A. Clark, Julia E. Haeusler, Gabrielle M. |
author_sort | Valentine, Jake C. |
collection | PubMed |
description | 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 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 F(1) score of 60%, respectively. Sensitivity was highest for proven IFI (77% [95% CI: 58–90]; F(1) = 47%) and invasive candidiasis (83% [95% CI: 61–95]; F(1) = 76%) and lowest for other/unspecified IFI (20% [95% CI: 5.05–72%]; F(1) = 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. |
format | Online Article Text |
id | pubmed-7480858 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-74808582020-09-18 Classification performance of administrative coding data for detection of invasive fungal infection in paediatric cancer patients Valentine, Jake C. Worth, Leon J. Verspoor, Karin M. Hall, Lisa Yeoh, Daniel K. Thursky, Karin A. Clark, Julia E. Haeusler, Gabrielle M. PLoS One Research Article 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 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 F(1) score of 60%, respectively. Sensitivity was highest for proven IFI (77% [95% CI: 58–90]; F(1) = 47%) and invasive candidiasis (83% [95% CI: 61–95]; F(1) = 76%) and lowest for other/unspecified IFI (20% [95% CI: 5.05–72%]; F(1) = 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. Public Library of Science 2020-09-09 /pmc/articles/PMC7480858/ /pubmed/32903280 http://dx.doi.org/10.1371/journal.pone.0238889 Text en © 2020 Valentine et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Valentine, Jake C. Worth, Leon J. Verspoor, Karin M. Hall, Lisa Yeoh, Daniel K. Thursky, Karin A. Clark, Julia E. Haeusler, Gabrielle M. Classification performance of administrative coding data for detection of invasive fungal infection in paediatric cancer patients |
title | Classification performance of administrative coding data for detection of invasive fungal infection in paediatric cancer patients |
title_full | Classification performance of administrative coding data for detection of invasive fungal infection in paediatric cancer patients |
title_fullStr | Classification performance of administrative coding data for detection of invasive fungal infection in paediatric cancer patients |
title_full_unstemmed | Classification performance of administrative coding data for detection of invasive fungal infection in paediatric cancer patients |
title_short | Classification performance of administrative coding data for detection of invasive fungal infection in paediatric cancer patients |
title_sort | classification performance of administrative coding data for detection of invasive fungal infection in paediatric cancer patients |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7480858/ https://www.ncbi.nlm.nih.gov/pubmed/32903280 http://dx.doi.org/10.1371/journal.pone.0238889 |
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