Assessment and recalibration of seventeen lung cancer risk prediction models in approximately one million Chinese population utilising healthcare big data: a retrospective cohort analysis.

Background: A number of lung cancer prediction models have been developed worldwide. However, there have been limited validation studies conducted specifically on Chinese populations. The objective of this study is to evaluate the feasibility and performance of 17 global lung cancer risk prediction models when applied to Chinese healthcare big data. Methods: The study encompassed individuals whose information was recorded in the Yinzhou Regional Health Care Database (YRHCD) between January 1, 2010 and December 31, 2021. The 17 lung cancer risk prediction models, which comprised the Bach, the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial 2012 model (PLCO Findings: A total of 904,667 study participants were included in the analysis, comprising 66,730 ever smokers and 837,937 never smokers. Among the 17 models initially considered, only six (Bach, Pittsburgh Predictor, JPHC, Reduced HUNT, Constrained LCRAT, UCL-I) had complete information of predictive variables available in the YRHCD. Most models showed similar levels of discrimination, with C-indices ranging from 0.78 (95% CI 0.74-0.82) to 0.88 (0.87-0.89) and time-dependent AUCs ranging from 0.74 (95% CI 0.73-0.75) to 0.88 (0.87-0.89). The majority of models showed an overestimation of incidence risk among ever smokers, with EORs ranging from 1.10 (95% CI 1.02-1.19) to 4.37 (4.16-4.58), and an underestimation among never smokers with a few models showing exceptions - EORs ranging from 0.12 (95% CI 0.11-0.14) to 1.30 (1.26-1.35). After recalibration, all models showed improved accuracy of predicted probability. The five-year incidence rates observed in the model-selected population, ranging from 0.81% (95% CI 0.64%-0.96%) to 1.29% (1.08%-1.48%), were consistently higher than that observed in the criteria-selected population (0.75%, 95% CI 0.59%-0.90%). Following recalibration, the five-year incidence rates in the model-selected population improved, ranging from 0.81% (95% CI 0.64%-0.96%) to 1.60% (1.36%-1.82%). Interpretation: The majority of recalibrated models demonstrated comparable and favorable discrimination and calibration capability, and were capable of identifying individuals at an elevated risk of lung cancer with greater precision than the criteria. Models designed for the general population (such as LLPv3, LLPi, Korean Men, JPHC, and LCRS) are more appropriate for identifying high-risk groups compared to those exclusively for smokers. Funding: National Natural Science Foundation of China, General Project of Zhejiang Provincial Medical and Health Technology Plan for the Year 2024, Natural Science Foundation of Zhejiang Province.