Predicting the severity of radiation skin reactions in women with breast cancer.
OBJECTIVE
To develop a method of predicting the severity of acute radiation skin reactions.


METHODS
Prospective, descriptive correlational with repeated measures.


METHODS
Outpatient radiation oncology department in a metropolitan public teaching hospital in Western Australia.


METHODS
126 adult women aged 30-78 receiving postlumpectomy radiation therapy for breast cancer.


METHODS
Data about potential predictive factors were collected during the first week of radiation. Weekly observations of the skin reaction using the Radiation Therapy Oncology Group scoring system were recorded throughout treatment with an inter-rater reliability of 0.85.


RESULTS
Chi square analysis and t-tests revealed many factors related to severe skin reactions on a univariate level. Logistic regression analysis calculated the relative risk and probability of developing a severe skin reaction. Predictive factors included weight, breast size, lymphocele aspiration, smoking, age, skin cancer, tumor stage, and radiation dose.


CONCLUSIONS
In addition to radiation dose, some factors that impair wound healing also contribute to the cause of patient-to-patient variation, thus making it possible to predict who is likely to develop a severe skin reaction.


CONCLUSIONS
The findings will assist in individualizing the assessment, education, and management of women undergoing radiation therapy for breast cancer. Further research could lead to the development of a method of risk assessment for skin reactions for use in radiation therapy nursing.
Porock
D
D
School of Nursing, University of Hull, Willerby, England.
Kristjanson
L
L
Nikoletti
S
S
Cameron
F
F
Pedler
P
P
D000328:Adult; D000368:Aged; D000704:Analysis of Variance; D001943:Breast Neoplasms / Q000532:radiotherapy*; D005260:Female; D005544:Forecasting; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D011446:Prospective Studies; D011855:Radiodermatitis / Q000473:pathology*; D012306:Risk; D012720:Severity of Illness Index