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Article

The development of a side effect risk assessment tool (ASyMS?-SERAT) for use in patients with breast cancer undergoing adjuvant chemotherapy

Details

Citation

Maguire R, Cowie J, Leadbetter C, McCall K, Swingler K, McCann LA & Kearney N (2009) The development of a side effect risk assessment tool (ASyMS?-SERAT) for use in patients with breast cancer undergoing adjuvant chemotherapy. Journal of Research in Nursing, 14 (1), pp. 27-40. https://doi.org/10.1177/1744987108099235

Abstract
Patients with breast cancer receiving chemotherapy are at risk of developing toxicities which can be severe or life threatening. The aim of this study was to develop and test a side effect risk modeling tool (ASyMS?-SERAT) for use in patients with breast cancer undergoing adjuvant chemotherapy. The study was conducted in two phases. Phase 1 entailed the development of the ASyMS?-SERAT tool using a secondary data set and in collaboration with an expert group of clinicians and an advisory group of patients. In phase 2, the predictive accuracy of the tool was measured using a prospective data set of 24 patients with breast cancer undergoing adjuvant chemotherapy. A high level of accuracy was reported for four of the six symptoms measured (>70%) supporting the future development and application of ASyMS?-SERAT in the prediction of chemotherapy-related toxicity. For patients, such information can be used to target information on side effects that they are likely to experience thereby facilitating the provision of tailored information based on their individual needs. For clinicians, knowing the likelihood of potential side effects can assist them in identifying patients who are at greater risk of developing certain toxicities, facilitating more targeted and cost-effective interventions.

Keywords
breast cancer; chemotherapy; predictive risk modelling; symptoms; Cancer Research; Cancer prevention; Breast Cancer Treatment; Cancer Chemotherapy

Journal
Journal of Research in Nursing: Volume 14, Issue 1

StatusPublished
Publication date31/01/2009
URL
PublisherSage
ISSN1744-9871
eISSN1744-988X

People (1)

Professor Kevin Swingler

Professor Kevin Swingler

Professor, Computing Science