title: Identifying patients with suspected gastro-oesophageal cancer in primary care: derivation and validation of an algorithm.
abstract: BACKGROUND
  Gastro-oesphageal is one of the most common cancers worldwide. Evidence suggested that increased awareness of symptoms and earlier diagnosis could help improve treatment options and improve survival.
  
  
  OBJECTIVE
  To derive and validate an algorithm to estimate the absolute risk of having gastro-oesophageal cancer in patients in primary care with and without symptoms.
  
  
  METHODS
  Cohort study of 375 UK QResearch® general practices for development, and 189 for validation.
  
  
  METHODS
  Included patients were aged 30-84 years, free at baseline of a diagnosis of gastro-oesophageal cancer, and without dysphagia, haematemesis, abdominal pain, appetite loss, or weight loss recorded in previous 12 months. The primary outcome was incident diagnosis of gastro-oesophageal cancer recorded in the next 2 years. Risk factors examined were age, body mass index, alcohol status, smoking status, deprivation, family history of gastrointestinal cancer, dysphagia, previous diagnosis of cancer apart from gastro-oesophageal cancer, haematemesis, abdominal pain, appetite loss, weight loss, tiredness, and anaemia. Cox proportional hazards models were used to develop risk equations. Measures of calibration and discrimination assessed performance in the validation cohort.
  
  
  RESULTS
  There were 2527 incident cases of gastro-oesophageal cancer from 4.1 million person-years in the derivation cohort. Independent predictors were age, smoking, dysphagia, haematemesis, abdominal pain, appetite loss, weight loss, and anaemia. On validation, the algorithms explained 71% of the variation in females and 73% in males. The receiver operating curve statistics were 0.89 (females) and 0.92 (males). The D statistic was 3.2 (females) and 3.3 (males). The 10% of patients with the highest predicted risks included 77% of all gastro-oesophageal cancers diagnosed over the next 2 years.
  
  
  CONCLUSIONS
  The algorithm has good performance and could potentially be used to help identify those at highest risk of gastro-oesophageal cancer, to facilitate early referral and investigation.
authors: Division of Primary Care, University of Nottingham, UK. julia.hippisley-cox@nottingham.ac.uk
authors: Julia
authors: J
authors: Hippisley-Cox
authors: Carol
authors: C
authors: Coupland
mesh_terms: D015746:Abdominal Pain / Q000209:etiology; D000328:Adult; D000368:Aged; D000465:Algorithms; D001066:Appetite; D003680:Deglutition Disorders / Q000209:etiology; D055088:Early Detection of Cancer; D004739:England / Q000453:epidemiology; D004938:Esophageal Neoplasms / Q000175:diagnosis* / Q000453:epidemiology; D005260:Female; D058006:General Practice / Q000706:statistics & numerical data; D006396:Hematemesis / Q000209:etiology; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D011379:Prognosis; D016016:Proportional Hazards Models; D011897:Random Allocation; D018570:Risk Assessment; D012307:Risk Factors; D013274:Stomach Neoplasms / Q000175:diagnosis* / Q000453:epidemiology; D014852:Wales / Q000453:epidemiology; D015431:Weight Loss / Q000502:physiology