دورية أكاديمية
Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models
| العنوان: | Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models |
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| المؤلفون: | Liao, W, Coupland, CAC, Burchardt, J, Baldwin, DR, Gleeson, FV, Hippisley-Cox, J |
| المساهمون: | initiative, DART |
| بيانات النشر: | Elsevier |
| سنة النشر: | 2023 |
| المجموعة: | Oxford University Research Archive (ORA) |
| الوصف: | Background Lung cancer is the second most common cancer in incidence and the leading cause of cancer deaths worldwide. Meanwhile, lung cancer screening with low-dose CT can reduce mortality. The UK National Screening Committee recommended targeted lung cancer screening on Sept 29, 2022, and asked for more modelling work to be done to help refine the recommendation. This study aims to develop and validate a risk prediction model—the CanPredict (lung) model—for lung cancer screening in the UK and compare the model performance against seven other risk prediction models. Methods For this retrospective, population-based, cohort study, we used linked electronic health records from two English primary care databases: QResearch (Jan 1, 2005–March 31, 2020) and Clinical Practice Research Datalink (CPRD) Gold (Jan 1, 2004–Jan 1, 2015). The primary study outcome was an incident diagnosis of lung cancer. We used a Cox proportional-hazards model in the derivation cohort (12·99 million individuals aged 25–84 years from the QResearch database) to develop the CanPredict (lung) model in men and women. We used discrimination measures (Harrell's C statistic, D statistic, and the explained variation in time to diagnosis of lung cancer [R2D]) and calibration plots to evaluate model performance by sex and ethnicity, using data from QResearch (4·14 million people for internal validation) and CPRD (2·54 million for external validation). Seven models for predicting lung cancer risk (Liverpool Lung Project [LLP]v2, LLPv3, Lung Cancer Risk Assessment Tool [LCRAT], Prostate, Lung, Colorectal, and Ovarian [PLCO]M2012, PLCOM2014, Pittsburgh, and Bach) were selected to compare their model performance with the CanPredict (lung) model using two approaches: (1) in ever-smokers aged 55–74 years (the population recommended for lung cancer screening in the UK), and (2) in the populations for each model determined by that model's eligibility criteria. Findings There were 73 380 incident lung cancer cases in the QResearch derivation cohort, 22 838 ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| العلاقة: | https://ora.ox.ac.uk/objects/uuid:df4b5c4b-0958-4acf-82d6-4c9b9c584971Test; https://doi.org/10.1016/S2213-2600Test(23)00050-4 |
| DOI: | 10.1016/S2213-2600(23)00050-4 |
| الإتاحة: | https://doi.org/10.1016/S2213-2600Test(23)00050-4 https://ora.ox.ac.uk/objects/uuid:df4b5c4b-0958-4acf-82d6-4c9b9c584971Test |
| حقوق: | info:eu-repo/semantics/openAccess ; CC Attribution (CC BY) |
| رقم الانضمام: | edsbas.3E7FF881 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1016/S2213-2600(23)00050-4 |
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