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Journal of Research in Nursing
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research-article

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

Roma Maguire

Cancer Care Research Centre, Department of Nursing and Midwifery, University of Stirling, Stirling, UK

Julie Cowie

Department of Computing Science and Mathematics, University of Stirling, Stirling, UK

Clare Leadbetter

Department of Computing Science and Mathematics, University of Stirling, Stirling, UK

Kathryn McCall

Cancer Care Research Centre, Department of Nursing and Midwifery, University of Stirling, Stirling, UK

Kevin Swingler

Department of Computing Science and Mathematics, University of Stirling, Stirling, UK

Lisa McCann

Cancer Care Research Centre, Department of Nursing and Midwifery, University of Stirling, Stirling, UK

Nora Kearney

Cancer Care Research Centre, Department of Nursing and Midwifery, University of Stirling, Stirling, UK

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.

Key Words: breast cancer • chemotherapy • predictive risk modelling • symptoms

Journal of Research in Nursing, Vol. 14, No. 1, 27-40 (2009)
DOI: 10.1177/1744987108099235
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