Exploring the Language Used to Describe Older Patients at Multidisciplinary Cancer Conferences.

Older adults with cancer often present with distinct complexities that complicate their care, yet the language used to discuss their management at multidisciplinary cancer conferences (MCCs) remains poorly understood. A mixed methods study was conducted at a tertiary cancer centre in Toronto, Canada, where MCCs spanning five tumour sites were attended over six months. For presentations pertaining to a patient aged 75 or older, a standardized data collection form was used to record their demographic, cancer-related, and non-cancer-related information, as well as the presenter's specialty and training level. Descriptive statistics and thematic analysis were employed to explore MCC depictions of older patients (n = 75). Frailty status was explicitly mentioned in 20.0% of presentations, but discussions more frequently referenced comorbidity burden (50.7%), age (33.3%), and projected treatment tolerance (30.7%) as surrogate measures. None of the presentations included mentions of formal geriatric assessment (GA) or validated frailty tools; instead, presenters tended to feature select GA domains and subjective descriptions of appearance ("looks to be fit") or overall health ("relatively healthy"). In general, MCCs appeared to rely on age-focused language that may perpetuate ageism. Further work is needed to investigate how frailty and geriatric considerations can be objectively incorporated into discussions in geriatric oncology.

Defining an Abnormal Geriatric Assessment: Which Deficits Matter Most?

At present, there is no clear definition of what constitutes an abnormal geriatric assessment (GA) in geriatric oncology. Various threshold numbers of abnormal GA domains are often used, but how well these are associated with treatment plan modification (TPM) and whether specific GA domains are more important in this context remains uncertain. A retrospective review of the geriatric oncology clinic database at Princess Margaret Cancer Centre in Toronto, Canada, including new patients seen for treatment decision making from May 2015 to June 2022, was conducted. Logistic regression modelling was performed to determine the association between various predictor variables (including the GA domains and numerical thresholds) and TPM. The study cohort (n = 736) had a mean age of 80.7 years, 46.1% was female, and 78.3% had a VES-13 score indicating vulnerability (≥3). In the univariable analysis, the best-performing threshold number of abnormal domains based on area under the curve (AUC) was 4 (AUC 0.628). The best-performing multivariable model (AUC 0.704) included cognition, comorbidities, and falls risk. In comparison, the multivariable model with the sole addition of the threshold of 4 had an AUC of 0.689. Overall, an abnormal GA may be best defined as one with abnormalities in the domains of cognition, comorbidities, and falls risk. The optimal numerical threshold to predict TPM is 4.