RT Journal Article SR Electronic T1 Symptoms and risk factors to identify men with suspected cancer in primary care: derivation and validation of an algorithm JF British Journal of General Practice JO Br J Gen Pract FD British Journal of General Practice SP e1 OP e10 DO 10.3399/bjgp13X660724 VO 63 IS 606 A1 Julia Hippisley-Cox A1 Carol Coupland YR 2013 UL http://bjgp.org/content/63/606/e1.abstract AB Background Early diagnosis of cancer could improve survival so better tools are needed.Aim To derive an algorithm to estimate absolute risks of different types of cancer in men incorporating multiple symptoms and risk factors.Design and setting Cohort study using data from 452 UK QResearch® general practices for development and 224 for validation.Method Included patients were males aged 25–89 years. The primary outcome was incident diagnosis of cancer over the next 2 years (lung, colorectal, gastro-oesophageal, pancreatic, renal, blood, prostate, testicular, other cancer). Factors examined were: ‘red flag’ symptoms such as weight loss, abdominal distension, abdominal pain, indigestion, dysphagia, abnormal bleeding, lumps; general symptoms such as tiredness, constipation; and risk factors including age, family history, smoking, alcohol intake, deprivation score and medical conditions. Multinomial logistic regression was used to develop a risk equation to predict cancer type. Performance was tested on a separate validation cohort.Results There were 22 521 cancers from 1 263 071 males in the derivation cohort. The final model included risk factors (age, BMI, chronic pancreatitis, COPD, diabetes, family history, alcohol, smoking, deprivation); 22 symptoms, anaemia and venous thrombo-embolism. The model was well calibrated with good discrimination. The receiver operator curve statistics values were: lung (0.92), colorectal (0.92), gastro-oesophageal (0.93), pancreas (0.89), renal (0.94), prostate (0.90) blood (0.83, testis (0.82); other cancers (0.86). The 10% of males with the highest risks contained 59% of all cancers diagnosed over 2 years.Conclusion The algorithm has good discrimination and could be used to identify those at highest risk of cancer to facilitate more timely referral and investigation.