Development and validation of a prediction model of poor performance status and severe symptoms over time in cancer patients (PROVIEW+).
ADL
Cancer
depression
dyspnea
logistic model
pain
palliative care
prognosis
Journal
Palliative medicine
ISSN: 1477-030X
Titre abrégé: Palliat Med
Pays: England
ID NLM: 8704926
Informations de publication
Date de publication:
Oct 2021
Oct 2021
Historique:
pubmed:
16
6
2021
medline:
15
1
2022
entrez:
15
6
2021
Statut:
ppublish
Résumé
Predictive cancer tools focus on survival; none predict severe symptoms. To develop and validate a model that predicts the risk for having low performance status and severe symptoms in cancer patients. Retrospective, population-based, predictive study. 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). 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. The model accurately predicted changing cancer risk for low performance status and severe symptoms over time.
Sections du résumé
BACKGROUND
BACKGROUND
Predictive cancer tools focus on survival; none predict severe symptoms.
AIM
OBJECTIVE
To develop and validate a model that predicts the risk for having low performance status and severe symptoms in cancer patients.
DESIGN
METHODS
Retrospective, population-based, predictive study.
SETTING/PARTICIPANTS
METHODS
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
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
CONCLUSIONS
The model accurately predicted changing cancer risk for low performance status and severe symptoms over time.
Identifiants
pubmed: 34128429
doi: 10.1177/02692163211019302
pmc: PMC8532207
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
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