posted on 2015-11-30, 14:58authored byRose GalvinRose Galvin, Doireann Joyce, Eithne Downey, Fiona Boland, Tom Fahey, Arnold K Hill
Background: The number of primary care referrals of women with breast symptoms to symptomatic breast units
(SBUs) has increased exponentially in the past decade in Ireland. The aim of this study is to develop and validate a
clinical prediction rule (CPR) to identify women with breast cancer so that a more evidence based approach to
referral from primary care to these SBUs can be developed.
Methods: We analysed routine data from a prospective cohort of consecutive women reviewed at a SBU with
breast symptoms. The dataset was split into a derivation and validation cohort. Regression analysis was used to
derive a CPR from the patient’s history and clinical findings. Validation of the CPR consisted of estimating the
number of breast cancers predicted to occur compared with the actual number of observed breast cancers across
deciles of risk.
Results: A total of 6,590 patients were included in the derivation study and 4.9% were diagnosed with breast cancer.
Independent clinical predictors for breast cancer were: increasing age by year (adjusted odds ratio 1.08, 95% CI 1.07-1.09);
presence of a lump (5.63, 95% CI 4.2-7.56); nipple change (2.77, 95% CI 1.68-4.58) and nipple discharge (2.09, 95% CI 1.
1-3.97). Validation of the rule (n = 911) demonstrated that the probability of breast cancer was higher with an increasing
number of these independent variables. The Hosmer-Lemeshow goodness of fit showed no overall significant difference
between the expected and the observed numbers of breast cancer (χ2
HL: 6.74, p-value: 0.56).
Conclusions: This study derived and validated a CPR for breast cancer in women attending an Irish national SBU. We
found that increasing age, presence of a lump, nipple discharge and nipple change are all associated with increased risk
of breast cancer. Further validation of the rule is necessary as well as an assessment of its impact on referral practice.
Funding
Development of a structure identification methodology for nonlinear dynamic systems