title: Development and validation of risk prediction algorithms to estimate future risk of common cancers in men and women: prospective cohort study.
abstract: OBJECTIVE
  To derive and validate a set of clinical risk prediction algorithm to estimate the 10-year risk of 11 common cancers.
  
  
  METHODS
  Prospective open cohort study using routinely collected data from 753 QResearch general practices in England. We used 565 practices to develop the scores and 188 for validation.
  
  
  METHODS
  4.96 million patients aged 25-84 years in the derivation cohort; 1.64 million in the validation cohort. Patients were free of the relevant cancer at baseline.
  
  
  METHODS
  Cox proportional hazards models in the derivation cohort to derive 10-year risk algorithms. Risk factors considered included age, ethnicity, deprivation, body mass index, smoking, alcohol, previous cancer diagnoses, family history of cancer, relevant comorbidities and medication. Measures of calibration and discrimination in the validation cohort.
  
  
  RESULTS
  Incident cases of blood, breast, bowel, gastro-oesophageal, lung, oral, ovarian, pancreas, prostate, renal tract and uterine cancers. Cancers were recorded on any one of four linked data sources (general practitioner (GP), mortality, hospital or cancer records).
  
  
  RESULTS
  We identified 228,241 incident cases during follow-up of the 11 types of cancer. Of these 25,444 were blood; 41,315 breast; 32,626 bowel, 12,808 gastro-oesophageal; 32,187 lung; 4811 oral; 6635 ovarian; 7119 pancreatic; 35,256 prostate; 23,091 renal tract; 6949 uterine cancers. The lung cancer algorithm had the best performance with an R(2) of 64.2%; D statistic of 2.74; receiver operating characteristic curve statistic of 0.91 in women. The sensitivity for the top 10% of women at highest risk of lung cancer was 67%. Performance of the algorithms in men was very similar to that for women.
  
  
  CONCLUSIONS
  We have developed and validated a prediction models to quantify absolute risk of 11 common cancers. They can be used to identify patients at high risk of cancers for prevention or further assessment. The algorithms could be integrated into clinical computer systems and used to identify high-risk patients.
  There is a simple web calculator to implement the Qcancer 10 year risk algorithm together with the open source software for download (available at http://qcancer.org/10yr/).
authors.affiliation: Division of Primary Care, University Park, Nottingham, UK.
authors.forename: Julia
authors.initials: J
authors.lastname: Hippisley-Cox
authors.affiliation: Division of Primary Care, University Park, Nottingham, UK.
authors.forename: Carol
authors.initials: C
authors.lastname: Coupland
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000428:Alcohol Drinking / Q000453:epidemiology; D000465:Algorithms; D014150:Antipsychotic Agents / Q000627:therapeutic use; D001714:Bipolar Disorder / Q000453:epidemiology; D015992:Body Mass Index; D015897:Comorbidity; D003276:Contraceptives, Oral / Q000627:therapeutic use; D003922:Diabetes Mellitus, Type 1 / Q000453:epidemiology; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology; D004739:England / Q000453:epidemiology; D005260:Female; D005544:Forecasting / Q000379:methods; D020249:Hormone Replacement Therapy; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D009369:Neoplasms / Q000453:epidemiology* / Q000208:ethnology / Q000235:genetics; D016609:Neoplasms, Second Primary / Q000453:epidemiology*; D011203:Poverty; D016016:Proportional Hazards Models; D011446:Prospective Studies; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods; D012559:Schizophrenia / Q000453:epidemiology; D012907:Smoking / Q000453:epidemiology
chemical_list: D014150:Antipsychotic Agents; D003276:Contraceptives, Oral
keywords: PRIMARY CARE; QResearch; cancer