title: Identifying suspected breast cancer: development and validation of a clinical prediction rule.
abstract: BACKGROUND
  An evidence-based approach is needed to identify women with breast symptoms who are most likely to have breast cancer so that timely and appropriate referral can take place.
  
  
  OBJECTIVE
  To report the development and validation of a clinical prediction rule for the diagnosis of breast cancer.
  
  
  METHODS
  Cohort study with two prospective groups of women: those presenting to a symptomatic breast clinic (derivation cohort) and a separate cohort presenting to 11 general practices (validation cohort) in Tayside, Scotland.
  
  
  METHODS
  Regression analysis was used to derive a clinical prediction rule from presenting symptoms, personal and family history, and clinical findings. Validation 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
  In the derivation cohort of 802 patients, 59 (7%) were diagnosed with breast cancer. Independent clinical predictors for breast cancer were: increasing age by year (adjusted odds ratio [AOR] 1.10, 95% confidence interval [CI] = 1.07 to 1.13); presence of a discrete lump (AOR 15.20, 95% CI = 4.88 to 47.34); breast thickening (AOR 7.64, 95% CI = 2.23 to 26.11); lymphadenopathy (AOR 3.63, 95% CI = 1.33 to 9.92); and lump ≥ 2 cm (AOR 5.41, 95% CI = 2.36 to 12.38). All eight patients with skin tethering had breast cancer. The regression model had good predictive power, identifying all five breast cancers in the validation cohort of 97 patients in the top two deciles of risk.
  
  
  CONCLUSIONS
  The clinical prediction rule discriminates between patients at high risk of breast cancer from those at low risk, and can be implemented as an evidence-based recommendation to enhance appropriate referral from general practice to a symptomatic breast clinic. Ongoing validation in further populations is required.
authors.affiliation: University of Dundee, Dundee, UK.
authors.forename: Colin
authors.initials: C
authors.lastname: McCowan
authors.forename: Peter T
authors.initials: PT
authors.lastname: Donnan
authors.forename: John
authors.initials: J
authors.lastname: Dewar
authors.forename: Alastair
authors.initials: A
authors.lastname: Thompson
authors.forename: Tom
authors.initials: T
authors.lastname: Fahey
mesh_terms: D000328:Adult; D017677:Age Distribution; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000453:epidemiology; D015331:Cohort Studies; D005260:Female; D058006:General Practice; D006801:Humans; D008207:Lymphatic Metastasis; D008875:Middle Aged; D012017:Referral and Consultation / Q000706:statistics & numerical data*; D012044:Regression Analysis; D012606:Scotland / Q000453:epidemiology