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Classification performance of administrative coding data for detection of invasive fungal infection in paediatric
Jake C Valentine1,2,3, Leon J Worth1,3,4, Karin M Verspoor1,5
1National Centre for Infections in Cancer, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
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.
Insights
Administrative coding data (ICD-10-AM) offers a simpler method for tracking invasive fungal infections (IFIs) in pediatric cancer patients. While moderately accurate overall, specific IFI types like invasive candidiasis are coded more reliably.
Area of Science:
- Medical Informatics
- Pediatric Oncology
- Infectious Diseases
Background:
- Accurate detection of invasive fungal infections (IFIs) in pediatric cancer patients is crucial for effective surveillance and treatment.
- Current IFI detection relies on complex case definitions, requiring specialized staff.
- Administrative coding data, such as ICD-10-AM, presents a potentially simplified approach to IFI surveillance.
Purpose of the Study:
- To evaluate the accuracy of ICD-10-AM codes for identifying IFIs in children with cancer.
- To determine the classification performance of administrative codes against a gold-standard dataset.
Main Methods:
- Retrieved ICD-10-AM codes for IFIs in pediatric patients (<18 years) with malignancies from the Victorian Admitted Episodes Dataset.
- Linked coding data to the r-TERIFIC gold-standard dataset for confirmed IFIs.
- Calculated sensitivity, positive predictive value (PPV), and F1 scores for ICD-10-AM code accuracy.
Main Results:
- Of 1,671 patients, 113 had confirmed IFIs, and 114 were identified via ICD-10-AM codes.
- Overall sensitivity, PPV, and F1 score for IFI detection using ICD-10-AM codes were 60%.
- Sensitivity was highest for proven IFI (77%) and invasive candidiasis (83%), with lower accuracy for other/unspecified IFIs.
Conclusions:
- ICD-10-AM codes show moderate accuracy for detecting IFIs in pediatric cancer patients.
- Specific diagnoses, particularly invasive candidiasis (B37.x), are more reliably captured by ICD-10-AM codes.
- Administrative data can aid IFI surveillance but requires careful interpretation due to variable accuracy.
