External validation of lung function predictions in real-world cohorts of COPD. (July 2022) - Zafari, Zafar; Lee, Tsung-Ying; Sadatsafavi, Mohsen
Abstract: Background: An individualized FEV1 prediction model has recently been developed based on the Lung Health Study (LHS). An ongoing evaluation of the utility of prediction models in representative settings outside the setup of clinical trials is needed. Objective: To evaluate the external validity of the LHS FEV1 prediction model in a representative sample of Canadian ever-smoker COPD patients. Methods: We used the sample of ever-smokers with spirometrically-confirmed COPD from the Canadian Cohort of Obstructive Lung Disease (CanCOLD), an ongoing prospective, longitudinal, multicenter population-based cohort study. We compared the mean predicted against the mean observed FEV1 at follow-up visits. We calculated the root mean square error (RMSE) and the actual coverage probability of the 95% individualized prediction intervals. Results: The final CanCOLD sample consisted of 360 ever-smoker COPD patients, contributing to 970 FEV1 observations over an average of 3 years of follow-up (standard deviation (SD): 0.6 years). The mean observed vs. predicted FEV1 for the first follow-up visit (1.67 years from baseline) was 2.28L vs. 2.28L, and for the second visit (3.13 years from baseline) was 2.19L vs. 2.18L. The RMSE of the predictions was 0.205L, and the actual coverage probability of the 95% prediction intervals was 93%. The model had a similar performance across multiple subgroups. Conclusions: The model showed robust performance in predicting FEV1 in the entire sample and in several subgroups of CanCOLD. Our results demonstrate that LHS predictions are valid for at least three years in the general COPD population. Highlights: The utility of prediction models needs to be continuously examined in external settings. The LHS FEV1 model showed robust performance in predicting FEV1 in the general COPD population. The LHS model was validated in subgroups who were not included in the original derivation sample. The LHS FEV1 model can support individualized care and shared decision-making.