Defining an Abnormal Geriatric Assessment: Which Deficits Matter Most?

Simple Summary The geriatric assessment is used to identify frailty in older cancer patients and guide treatment decision making, but there is no consensus on the definition of what constitutes a globally abnormal assessment. The aim of our study was to propose a definition by determining whether a particular number or constellation of geriatric assessment domains more frequently leads to oncologic treatment plan modification. We found that an abnormal GA may be best defined as one with >= 4/8 abnormalities or abnormalities in the domains of cognition, comorbidities, and falls risk. Our results highlight which domains oncologists should regularly assess in older cancer patients when making treatment decisions. They will also aid cross-study comparisons in geriatric oncology and provide an empirically based frailty definition to be used in study inclusion/exclusion criteria. Finally, our findings will encourage future studies which aim to improve current screening tools for geriatric oncology and/or a simplified version of the geriatric assessment.Abstract 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.