Open AccessDevelopment and validation of a prediction model of poor performance status and severe symptoms over time in cancer patients (PROVIEW+)
Author(s) -
Hsien Seow,
Peter Tanuseputro,
Lisa Barbera,
Craig C. Earle,
Dawn M. Guthrie,
Sarina R. Isenberg,
Rosalyn A. Juergens,
Jeffrey N. Myers,
Melissa Brouwers,
Semra Tibebu,
Rinku Sutradhar
Publication year - 2021
Publication title -
palliative medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.989
H-Index - 106
eISSN - 1477-030X
pISSN - 0269-2163
DOI - 10.1177/02692163211019302
Subject(s) - medicine , performance status , logistic regression , cancer , lung cancer , depression (economics) , population , breast cancer , palliative care , cohort , retrospective cohort study , physical therapy , nursing , environmental health , economics , macroeconomics
Background: Predictive cancer tools focus on survival; none predict severe symptoms.Aim: To develop and validate a model that predicts the risk for having low performance status and severe symptoms in cancer patients.Design: Retrospective, population-based, predictive studySetting/Participants: We linked administrative data from cancer patients from 2008 to 2015 in Ontario, Canada. Patients were randomly selected for model derivation (60%) and validation (40%). Using the derivation cohort, we developed a multivariable logistic regression model to predict the risk of an outcome at 6 months following diagnosis and recalculated after each of four annual survivor marks. Model performance was assessed using discrimination and calibration plots. Outcomes included low performance status (i.e. 10–30 on Palliative Performance Scale), severe pain, dyspnea, well-being, and depression (i.e. 7–10 on Edmonton Symptom Assessment System).Results: We identified 255,494 cancer patients (57% female; median age of 64; common cancers were breast (24%); and lung (13%)). At diagnosis, the predicted 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); the corresponding discrimination for each outcome model had high AUCs of 0.807, 0.713, 0.709, 0.790, and 0.723, respectively. Generally these covariates increased the outcome risk by >10% across all models: lung disease, dementia, diabetes; radiation treatment; hospital admission; pain; depression; transitional performance status; issues with appetite; or homecare.Conclusions: The model accurately predicted changing cancer risk for low performance status and severe symptoms over time.