The Value of Blood-Based Measures of Liver Function and Urate in Lung Cancer Risk Prediction: A Cohort Study and Health Economic Analysis

Background : Several studies have reported associations between low-cost blood-based measurements and lung cancer but their role in risk prediction is unclear. We examined the value of expanding lung cancer risk models for targeting low-dose computed tomography (LDCT) to include blood measurements of liver function and urate. Methods : We analysed a cohort of 388,199 UK Biobank participants with 1,873 events and calculated the c-index and fraction of new information (FNI) for models expanded to include combinations of blood measurements, lung function (forced expiratory volume in 1 second FEV 1 ), alcohol status and waist circumference. We calculated the hypothetical cost per lung cancer case detected by LDCT for different scenarios using a threshold of ≥ 1.51% risk at 6 years. Results : The c-index was 0.805 (95%CI:0.794-0.816) for the model containing conventional predictors. Expanding to include blood measurements increased the c-index to 0.815 (95%CI: 0.804-0.826;p<0.0001;FNI:0.06). Expanding to include FEV 1 , alcohol status, and waist circumference increased the c-index to 0.811 (95%CI:0.800-0.822;p<0.0001;FNI:0.04). The c-index for the fully expanded model containing all variables was 0.819 (95%CI:0.808-0.830; p<0.0001;FNI:0.09). Model expansion had a greater impact on the c-index and FNI for people with a history of smoking cigarettes relative to the full cohort. Compared with the conventional risk model, the expanded models reduced the number of participants meeting the criteria for LDCT screening by 15-21%, and lung cancer cases detected by 7-8%. The additional cost per lung cancer case detected relative to the conventional model was £1,018 for the addition of blood tests and £9,775 for the fully expanded model.