McMaster University, Hamilton, ON, Canada
Hsien Seow , Rinku Sutradhar , Lisa Catherine Barbera , Peter Tanuseputro , Dawn Guthrie , Sarina Isenberg , Rosalyn A. Juergens , Jeffrey A. Myers , Melissa C. Brouwers , Semra Tibebu , Craig Earle
Background: There are numerous predictive cancer tools that focus on survival. However, no tools predict risk of low performance status or severe symptoms, which are important for patient decision-making and early integration of palliative care. The aim of this study was to develop and validate a model for all cancer types that predicts the risk for having low performance status and severe symptoms. Methods: A retrospective, population-based, predictive study using linked administrative data from cancer patients from 2008-2015 in Ontario, Canada. Patients were randomly selected for model derivation (60%) and validation (40%). The derivation cohort was used to develop a multivariable logistic regression model to predict the risk of having the reported outcomes in the subsequent 6 months. Model performance was assessed using discrimination and calibration plots. The main outcome was low performance status using the Palliative Performance Scale. Secondary outcomes included severe pain, dyspnea, well-being, and depression using the Edmonton Symptom Assessment System. Outcomes were recalculated after each of 4 annual survivor marks. Results: We identified 255,494 cancer patients (57% female; median age of 64; common cancers were breast (24%) and lung (13%)). At diagnosis, the risk of having low performance status, severe pain, well-being, dyspnea, and depression in 6-months is 1%, 3%, 6%, 13% and 4%, respectively for the reference case (i.e. male, lung cancer, stage I, no symptoms). Generally these covariates increased the outcome risk by > 10% across all models: obstructive lung disease, dementia, diabetes; radiation treatment; hospital admission; high pain; depression; Palliative Performance Scale score of 60-10; issues with appetite; or homecare. Model discrimination was high across all models. Conclusions: The model accurately predicted changing cancer risk for low performance status and severe symptoms over time. Providing accurate predictions of future performance status and symptom severity can support decision-making and earlier initiation of palliative care, even alongside disease modifying therapies.
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