1 row where funder_names contains "Healthcare Quality Improvement Partnership"
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|78||78||["Construction of the secondary care administrative records frailty (SCARF) index and validation on older women with operable invasive breast cancer in England and Wales: a cohort study"]||10.1136/bmjopen-2019-035395||http://dx.doi.org/10.1136/bmjopen-2019-035395||2020-05-06T10:36:50Z||["General Medicine"]||48||0||["2044-6055", "2044-6055"]||BMJ Open||<jats:sec><jats:title>Objectives</jats:title><jats:p>Studies that use national datasets to evaluate the management of older women with breast cancer are often constrained by a lack of information on patient fitness. This study constructed a frailty index for use with secondary care administrative records and evaluated its ability to improve models of treatment patterns and overall survival in women with breast cancer.</jats:p></jats:sec><jats:sec><jats:title>Design</jats:title><jats:p>Retrospective cohort study.</jats:p></jats:sec><jats:sec><jats:title>Participants</jats:title><jats:p>Women aged ≥50 years with oestrogen receptor (ER) positive early invasive breast cancer diagnosed between 2014 and 2017 in England.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>The secondary care administrative records frailty (SCARF) index was based on the cumulative deficit model of frailty, using International Statistical Classification of Diseases, Injuries and Causes of Death, 10th revision codes to define a set of deficits. The index was applied to administrative records that were linked to national cancer registry datasets. The ability of the SCARF index to improve the performance of regression models to explain observed variation in the rate of surgery and overall survival was evaluated using Harrell’s c-statistic and decision curve analysis. External validation was performed on a dataset of similar women diagnosed in Wales.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The SCARF index captured 32 deficits that cover functional impairment, geriatric syndromes, problems with nutrition, cognition and mood, and medical comorbidities. In the English dataset (n=67 925), the prevalence of frailty in women aged 50–69, 70–79 and ≥80 years was 15%, 28% and 47%, respectively. Adding a frailty measure to regression models containing age, tumour characteristics and comorbidity improved their ability to: (1) discriminate between whether a woman was likely to have surgery and (2) predict over…||11||["http://orcid.org/0000-0002-4761-8655"]||["Yasmin Jauhari", "Melissa Ruth Gannon", "David Dodwell", "Kieran Horgan", "Karen Clements", "Jibby Medina", "Carmen Tsang", "Thompson Robinson", "Sarah Shuk-Kay Tang", "Ruth Pettengell", "David A Cromwell"]||||["Healthcare Quality Improvement Partnership"]||[[""]]|
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