A Mortality Risk Prediction Tool for Older Adults with Lymph Node-Positive Colon Cancer — ASN Events

A Mortality Risk Prediction Tool for Older Adults with Lymph Node-Positive Colon Cancer (#97)

Mikaela Jorgensen 1 , Jane Young 1 , Timothy Dobbins 1 , Michael Solomon 2
  1. Cancer Epidemiology and Services Research (CESR), Sydney School of Public Health, University of Sydney, Sydney, Australia
  2. Discipline of Surgery, University of Sydney, Sydney, Australia

Background: Adjuvant chemotherapy for node-positive colon cancer can reduce the risk of recurrence and increase life expectancy, even for those over 80 years. However, surgeons and oncologists are less likely to recommend chemotherapy for people over 65 on the basis of age alone. Physicians may rely on chronological age as a proxy for other age-related factors when making treatment recommendations. The aim of this study was to develop a risk model and tool to assist treatment decision-making by clarifying which patients may not benefit from adjuvant chemotherapy.

Methods: All lymph node-positive patients aged ≥65 years who received surgery for colon cancer in NSW in 2007/2008 were identified using a linked routinely collected population-based dataset (n=1,483). Multilevel logistic regression modelling was used to determine predictors of 1-year mortality.

Results: Patient age was a significant predictor of mortality in simple regression (p<0.001), but was much less significant after accounting for other factors such as comorbidities, perioperative factors (e.g. prolonged length of stay), frailty markers (e.g. malnutrition), and other health markers (e.g. history of emergency hospital admissions). An online risk prediction tool was developed from the model which estimates 1-year mortality based on the input of patient risk factors. The tool will be prospectively tested to determine its usefulness and efficacy.

Conclusions: The emphasis on a range of factors that predict mortality may help to reduce variation in guideline-recommended treatment that occurs on the basis of patient age alone. The model and tool also assist the identification of vulnerable older adults who may require more comprehensive assessment and management in order to cope with treatment and to improve outcomes.