Julia Hippisley-Cox1, Carol Coupland. 1. Division of Primary Care, Tower Building, University Park, Nottingham NG2 7RD, UK. julia.hippisley-cox@nottingham.ac.uk
Abstract
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.
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.
Authors: Avi Rosenfeld; David G Graham; Sarah Jevons; Jose Ariza; Daryl Hagan; Ash Wilson; Samuel J Lovat; Sarmed S Sami; Omer F Ahmad; Marco Novelli; Manuel Rodriguez Justo; Alison Winstanley; Eliyahu M Heifetz; Mordehy Ben-Zecharia; Uria Noiman; Rebecca C Fitzgerald; Peter Sasieni; Laurence B Lovat Journal: Lancet Digit Health Date: 2019-12-05
Authors: Benjamin A Goldstein; Ann Marie Navar; Michael J Pencina; John P A Ioannidis Journal: J Am Med Inform Assoc Date: 2016-05-17 Impact factor: 4.497