ArticlePalliative medicine2021
Development and validation of a prediction model of poor performance status and severe symptoms over time in cancer patients (PROVIEW+).
Article in Palliative medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 15 citations in OpenAlex.
- Development and validation of hybrid machine learning approach for predicting survival in patients with cervical cancer: a SEER-based population study.Frontiers in oncology · 2025Article
- Evaluation of functional capacity and risk of depression in older patients with cancer as part of the comprehensive geriatric assessment.Frontiers in aging neuroscience · 2025Article
- Using real-world electronic health record data to predict the development of 12 cancer-related symptoms in the context of multimorbidity.JAMIA open · 2024Article
- Article
- Development and validation of risk of CPS decline (RCD): a new prediction tool for worsening cognitive performance among home care clients in Canada.BMC geriatrics · 2023Article
- Benchmarking machine learning approaches to predict radiation-induced toxicities in lung cancer patients.Clinical and translational radiation oncology · 2023Article
- Artificial neural networks for simultaneously predicting the risk of multiple co-occurring symptoms among patients with cancer.Cancer medicine · 2021Article
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Authors and funding
11 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredictive cancer tools focus on survival; none predict severe symptoms.
aimTo develop and validate a model that predicts the risk for having low performance status and severe symptoms in cancer patients.
designRetrospective, population-based, predictive study. SETTING/
participantsWe 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).
resultsWe 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.
conclusionsThe model accurately predicted changing cancer risk for low performance status and severe symptoms over time.
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