Lung Cancer Risk Prediction Model Incorporating Lung Function : Development and Validation in the UK Biobank Prospective Cohort Study
Menée à partir de données portant sur 502 321 participants (âge : de 40 à 70 ans ; durée de suivi : 1 469 518 personnes-années), cette étude évalue la performance d'un modèle mathématique, incorporant notamment des variables de facteurs de risque (âge, sexe, statut tabagique, antécédents médicaux, ...) et un indicateur de la capacité respiratoire (volume expiratoire forcé durant la première seconde), pour prédire le risque de développer un cancer du poumon (738 cas)
Purpose : Several lung cancer risk prediction models have been developed, but none to date have assessed the predictive ability of lung function in a population-based cohort. We sought to develop and internally validate a model incorporating lung function using data from the UK Biobank prospective cohort study.
Methods : This analysis included 502,321 participants without a previous diagnosis of lung cancer, predominantly between 40 and 70 years of age. We used flexible parametric survival models to estimate the 2-year probability of lung cancer, accounting for the competing risk of death. Models included predictors previously shown to be associated with lung cancer risk, including sex, variables related to smoking history and nicotine addiction, medical history, family history of lung cancer, and lung function (forced expiratory volume in 1 second [FEV1]).
Results : During accumulated follow-up of 1,469,518 person-years, there were 738 lung cancer diagnoses. A model incorporating all predictors had excellent discrimination (concordance (c)-statistic [95% CI] = 0.85 [0.82 to 0.87]). Internal validation suggested that the model will discriminate well when applied to new data (optimism-corrected c-statistic = 0.84). The full model, including FEV1, also had modestly superior discriminatory power than one that was designed solely on the basis of questionnaire variables (c-statistic = 0.84 [0.82 to 0.86]; optimism-corrected c-statistic = 0.83; pFEV1 = 3.4 × 10−13). The full model had better discrimination than standard lung cancer screening eligibility criteria (c-statistic = 0.66 [0.64 to 0.69]).
Conclusion : A risk prediction model that includes lung function has strong predictive ability, which could improve eligibility criteria for lung cancer screening programs.
Journal of Clinical Oncology , article en libre accès, 2016