Neurosurgery Database for Brain Tumour Patients

• Published in: Neuro-oncology (Charlottesville, Va.) Va.), 2022-10, Vol.24 (Supplement_4), p.iv20-iv21
• Main Authors: De Luna, Paolo Jose, McKintosh, Edward, Scandura, Glenda, Hilling, Molly, Ghosh, Arnab
• Format: Article
• Language: English
• Subjects: BNOS 2022 Abstracts: Poster Presentations
• ISSN: 1522-8517; 1523-5866
• DOI: 10.1093/neuonc/noac200.093

Abstract AIMS We develop an electronic database system for brain tumour patients. This aims to provide accessible and comprehensive data in a unified database for all neuro-oncology patients at Barts Health NHS Trust. METHOD The database was created using Microsoft Access with data categorised using forms and tables. Initially, patients were added retrospectively using the neuropathology list and the neuro-oncology multi-disciplinary team patient lists. Once creation and validation was complete, patients were added prospectively. Queries on neuro-oncology patients can be launched to produce datasets which can be copied to Microsoft Excel spreadsheets for sorting, calculation, and graph creation. RESULTS At the time of writing, records of 1171 neuro-oncology patients who were accepted for MDT discussion at Barts Health NHS Trust are included in the database. It is used to routinely run data queries more efficiently and complete in a shorter time. Queries include post-operative complications, overall survival, histological diagnosis, and average surgery waiting time. These queries have already been used to publish audit-based research papers and will be used increasingly as part of routine clinical governance. CONCLUSION The database yields data about treatment of our brain tumour patients with increased completeness and significant reduction in time to return queries, compared to using standard data management. The database has been used to produce data, including tumour types, seizure incidence, and post-operative complications. We have been successful in producing published research, although low absolute complication numbers in the current dataset have not yet allowed comparison of complications rates at individual clinician and team level.