Development and validation of a coding framework to identify severe acute toxicity from systemic anti-cancer therapy using hospital administrative data. (April 2022)

• Record Type: Journal Article
• Authors: Boyle, Jemma M.; Cowling, Thomas E.; Kuryba, Angela; Fearnhead, Nicola S.; van der Meulen, Jan; Braun, Michael S.; Walker, Kate; Aggarwal, Ajay
• Is Part Of: Cancer epidemiology. Volume 77(2022)
• Publication Date: 2022-04
• Subjects: SACT Systemic anti-cancer therapy -- RCT Randomised controlled trial -- NBOCA National Bowel Cancer Audit -- HES Hospital Episode Statistics -- NHS National Health Service -- CTCAE Common Terminology Criteria for Adverse Events; Chemotherapy -- Colorectal cancer -- Toxicity -- Hospital administrative data; Cancer -- Epidemiology -- Periodicals; Cancer -- Prevention -- Periodicals; Cancer -- Diagnosis -- Periodicals; Carcinogenesis -- Periodicals; 616.994005
• DOI: 10.1016/j.canep.2022.102096
• Languages: English
• ISSNs: 1877-7821

Abstract

Abstract: Background: The capture of toxicities from systemic anti-cancer therapy (SACT) in real-world data will complement results from clinical trials. The aim of this study was to develop and validate a comprehensive coding framework to identify severe acute toxicity in hospital administrative data. Methods: A coding framework was developed to identify diagnostic codes representing severe acute toxicity in hospital administrative data. The coding framework was validated on a sample of 23, 265 colon cancer patients treated in the English National Health Service between 1 June 2014 and 31 December 2017. This involved comparing individual toxicities according to the receipt of SACT and according to different SACT regimens, as well as assessing the associations of predictive factors and outcomes with toxicity. Results: The severe acute toxicities captured by the developed coding framework were shown to vary across clinical groups with an overall rate of 26.4% in the adjuvant cohort, 53.4% in the metastatic cohort, and 12.5% in the comparison group receiving no chemotherapy. Results were in line with regimen-specific findings from clinical trials. For example, patients receiving additional bevacizumab had higher rates of bleeding (12.5% vs. 2.7%), gastrointestinal perforation (5.6% vs. 2.9%) and fistulation (1.4% vs. 0.5%), and allergic drug reactions (1.4% vs. 0.5%). Severe acute toxicity was associated with pre-existing renal ( p = 0.001) and cardiac disease ( p = 0.038), and urgency of surgery ( p = 0.004). Severe toxicity also predicted lower rates of completion of chemotherapy ( p = <0.001) and an increased likelihood of altered administration route ( p = <0.001). Conclusion: These results demonstrate that the developed coding framework captures severe acute toxicities from hospital administrative data of colon cancer patients. A similar approach can be used for patients with other cancer types, receiving different regimens. Toxicity captured in administrative data can be used to compare treatment outcomes, inform clinical decision making, and provide opportunities for benchmarking and provider performance monitoring. Highlights: Development of a diagnostic coding framework to identify severe acute chemotherapy toxicity. Hospital administrative data used alongside a dedicated chemotherapy dataset. Coding framework validated successfully in a large national sample of colon cancer patients. Framework transferable to different systemic anti-cancer therapy agents and cancer types. Vital for informing clinical practice, benchmarking, and quality improvement processes.