Development and internal validation of a predictive risk model for anxiety after completion of treatment for early stage breast cancer

Abstract Objective To develop a predictive risk model (PRM) for patient-reported anxiety after treatment completion for early stage breast cancer suitable for use in practice and underpinned by advances in data science and risk prediction. Methods Secondary analysis of a prospective survey of > 8...

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Main Authors: Jenny Harris (Author), Edward Purssell (Author), Victoria Cornelius (Author), Emma Ream (Author), Anne Jones (Author), Jo Armes (Author)
Format: Book
Published: SpringerOpen, 2020-12-01T00:00:00Z.
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001 doaj_aa35fe90e0fb49af8f3ba7410c2d54b1
042 |a dc 
100 1 0 |a Jenny Harris  |e author 
700 1 0 |a Edward Purssell  |e author 
700 1 0 |a Victoria Cornelius  |e author 
700 1 0 |a Emma Ream  |e author 
700 1 0 |a Anne Jones  |e author 
700 1 0 |a Jo Armes  |e author 
245 0 0 |a Development and internal validation of a predictive risk model for anxiety after completion of treatment for early stage breast cancer 
260 |b SpringerOpen,   |c 2020-12-01T00:00:00Z. 
500 |a 10.1186/s41687-020-00267-w 
500 |a 2509-8020 
520 |a Abstract Objective To develop a predictive risk model (PRM) for patient-reported anxiety after treatment completion for early stage breast cancer suitable for use in practice and underpinned by advances in data science and risk prediction. Methods Secondary analysis of a prospective survey of > 800 women at the end of treatment and again 6 months later using patient reported outcome (PRO) the hospital anxiety and depression scale-anxiety (HADS-A) and > 20 candidate predictors. Multiple imputation using chained equations (for missing data) and least absolute shrinkage and selection operator (LASSO) were used to select predictors. Final multivariable linear model performance was assessed (R2) and bootstrapped for internal validation. Results Five predictors of anxiety selected by LASSO were HADS-A (Beta 0.73; 95% CI 0.681, 0.785); HAD-depression (Beta 0.095; 95% CI 0.020, 0.182) and having caring responsibilities (Beta 0.488; 95% CI 0.084, 0.866) increased risk, whereas being older (Beta − 0.010; 95% CI -0.028, 0.004) and owning a home (Beta 0.432; 95% CI -0.954, 0.078) reduced the risk. The final model explained 60% of variance and bias was low (− 0.006 to 0.002). Conclusions Different modelling approaches are needed to predict rather than explain patient reported outcomes. We developed a parsimonious and pragmatic PRM. External validation is required prior to translation to digital tool and evaluation of clinical implementation. The routine use of PROs and data driven PRM in practice provides a new opportunity to target supportive care and specialist interventions for cancer patients. 
546 |a EN 
690 |a Anxiety 
690 |a Patient reported outcomes 
690 |a Breast cancer 
690 |a Predictive risk models 
690 |a Cancer survivors 
690 |a Supportive care 
690 |a Public aspects of medicine 
690 |a RA1-1270 
655 7 |a article  |2 local 
786 0 |n Journal of Patient-Reported Outcomes, Vol 4, Iss 1, Pp 1-9 (2020) 
787 0 |n https://doi.org/10.1186/s41687-020-00267-w 
787 0 |n https://doaj.org/toc/2509-8020 
856 4 1 |u https://doaj.org/article/aa35fe90e0fb49af8f3ba7410c2d54b1  |z Connect to this object online.