Identifying patients with suspected lung cancer in primary care: derivation and validation of an algorithm

Background Lung cancer has one of the lowest survival outcomes of any cancer because more then two-thirds of patients are diagnosed when curative treatment is not possible. The challenge is to help earlier diagnosis of lung cancer and hence improve prognosis. Aim To derive and validate an algorithm incorporating information on symptoms, to estimate the absolute risk of having lung cancer Design and setting Cohort study of 375 UK QResearch (R) general practices for development, and 189 for validation. Method Selected patients were aged 30-84 years and free of lung cancer at baseline and haemoptysis, loss of appetite, or weight loss in previous 12 months. Primary outcome was incident diagnosis of lung cancer recorded in the next 2 years. Risk factors examined were: haemoptysis, appetite loss, weight loss, cough, dyspnoea, tiredness, hoarseness, smoking, body mass index, deprivation score, family history of lung cancer, other cancers, asthma, chronic obstructive airways disease, pneumonia, asbestos exposure, and anaemia. Cox proportional hazards models with age as the underlying time variable were used to develop separate risk equations in males and females. Measures of calibration and discrimination assessed performance in the validation cohort. Results There were 3785 incident cases of lung cancer arising from 4,289,282 person-years in the derivation cohort. Independent predictors were haemoptysis, appetite loss, weight loss, cough, body mass index, deprivation score, smoking status, chronic obstructive airways disease, anaemia, and prior cancer (females only). On validation, the algorithms explained 72% of the variation. The receiver operating characteristic (ROC) statistics were 0.92 for both females and males. The D statistic was 3.25 for females and 3.29 for males. The 10% of patients with the highest predicted risks included 77% of all lung cancers diagnosed over the subsequent 2 years. Conclusion The algorithm has good discrimination and calibration and could potentially be used to identify those at highest risk of lung cancer, to facilitate early referral and investigation.