Rapid Fire Best of the Best Oral 2025 Joint Meeting of the COSA ASM and IPOS Congress

Feasibility of extracting clinical quality indicators addressing supportive care management in a high-grade glioma cohort (126635)

Mirette Abraham 1 2 , Joe Chang 2 , Vanessa Estall 2 , Adam Cooper 3 4 , Annette Tognela 5 , Kate Wilkinson 3 , Wafa Trad 6 , Teresa Simpson 6 , Joanne Pearson 6 , Ganesh Shivapathasundram 7 , Ramesh Cuganesan 8 , Sugendran Pillay 8 , Ruwanthie Fernando 9 , Rosalind L Jeffree 10 , Eng-Siew Koh 1 2
  1. South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia
  2. Department of Radiation Oncology , Liverpool Cancer Therapy Centre , Liverpool , NSW, Australia
  3. Department of Medical Oncology, Liverpool Cancer Therapy Centre, Liverpool, NSW, Australia
  4. School of Medicine, Western Sydney University, Sydney, NSW, Australia
  5. Department of Medical Oncology, Macarthur Cancer Therapy Centre, Macarthur, NSW, Australia
  6. Liverpool Cancer Therapy Centre, Sydney, NSW, Australia
  7. Department of Neurosurgery, Liverpool Hospital, Liverpool, NSW, Australia
  8. Department of Radiology, Liverpool Hospital, Liverpool, NSW, Australia
  9. Department of Palliative Care, Liverpool Hospital, Liverpool , NSW, Australia
  10. Monash University, Melbourne, VIC, Australia

Purpose: In 2022, 58 Clinical Quality Indicators (CQIs) for glioma were developed for an Australian brain cancer registry using a Delphi process [1]. Structured supportive care referrals within a comprehensive care framework can improve quality of life for high-grade glioma (HGG) patients by addressing their complex, evolving needs [2]. This study aims to (1) describing CQIs   and supportive care referral metrics within a HGG cohort, and (2) determine the feasibility of extracting relevant CQI and referral data from health records.

Methods and Materials: Data sources included hospital/oncology EMRs, MDT minutes and correspondence. Thirty-nine of 58 CQIs related to HGG were selected, covering ECOG, diagnostics, MDT discussions, trial screening, and key referrals, namely allied health and specialist, including palliative care, were analysed.

Results: The study cohort diagnosed between 2016-2020 comprised 183 patients: WHO Grade IV (n=159) and Grade III (n=22) glioma, anaplastic astrocytoma (n=15) and oligodendroglioma (n=7). Trials screening captured 76 eligible patients with 50 participants recruited. Supportive care referrals comprised care coordinators (n=144), social workers (n=153), physiotherapists (n=126), rehabilitation specialists (n=25), occupational therapists (n=114), speech pathologists (n=65), psychologists (n=45), ophthalmologists (n=10), and palliative care teams (n=144). Most palliative care referrals (n=61) were initiated after confirmation of disease progression.

Automated data extraction was feasible for referral to the MDT, radiotherapy details, demographics, age, and date of diagnosis. However, manual data extraction was needed for all other CQIs including supportive care referrals to allied health, other specialist, including palliative care, referrals.

Conclusion: Aligning tailored data documentation with information systems will improve more efficient capture of HGG CQIs and especially supportive care metrics. Since supportive care CQIs are documented within data systems, integrating these measures into routine automated data systems will facilitate longitudinal outcome comparisons and benchmarking across neuro-oncology cohorts.