title: Ovarian cancer prediction: development of a scoring system for primary care.
abstract: OBJECTIVE
  Recent studies have identified specific symptoms of ovarian cancer at all stages, raising the hope of reducing diagnostic delays. We aimed to devise a scoring system for symptoms of ovarian cancer in primary care.
  
  
  METHODS
  Secondary analysis of data from a case-control study.
  
  
  METHODS
  Thirty-nine general practices in Exeter, mid-Devon and east Devon.
  
  
  METHODS
  Two hundred and twelve women with ovarian cancer and 1060 age-, sex- and practice-matched controls.
  
  
  METHODS
  Conditional logistic regression was used to produce an additive scoring system and its receiver operator characteristic (ROC) curve. Several different cut-offs were then tested using a simple costs model.
  
  
  METHODS
  The ROC curve value.
  
  
  RESULTS
  Each woman was assigned a score based on her symptoms in the year before diagnosis: we added a score for women aged ≥ 50 years, reflecting their increased incidence of ovarian cancer. The area under the ROC curve was 0.883 (95% confidence interval 0.853-0.912). The chosen cut-off had a sensitivity of 72.6% and a specificity of 91.3%.
  
  
  CONCLUSIONS
  This scoring system could potentially direct general practitioners to appropriate investigations for ovarian cancer on the basis of symptoms and save a substantial number of unnecessary ultrasound scans being requested.
authors.affiliation: University Hospital Bristol, Bristol Royal Infirmary, Bristol, UK. grewal.karen86@gmail.com
authors.forename: K
authors.initials: K
authors.lastname: Grewal
authors.forename: W
authors.initials: W
authors.lastname: Hamilton
authors.forename: D
authors.initials: D
authors.lastname: Sharp
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D015994:Incidence; D016015:Logistic Models; D008875:Middle Aged; D017063:Outcome Assessment, Health Care; D010051:Ovarian Neoplasms / Q000175:diagnosis*; D011320:Primary Health Care; D012372:ROC Curve; D012107:Research Design; D012680:Sensitivity and Specificity