Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

*In Chest*

**BACKGROUND** :

**METHODS** : _{1}); the secondary outcome was the risk of airflow limitation (defined as FEV_{1}/forced vital capacity < Lower Limit of Normal). We developed mixed-effects regression models for individualized prediction and employed a machine-learning algorithm to determine essential predictors. The model was validated in two large, independent multi-center cohorts (N=2,075 and 12,913, respectively).

**RESULTS** : _{1} decline in the derivation cohort. In two validation datasets, the model had low error in predicting FEV_{1} decline (root mean square error range: 0.18-0.22L) and high discriminative power in predicting risk of airflow limitation (C statistic range: 0.86-0.87). We implemented this model in a freely accessible Web-based application, which allows prediction based on flexible sets of predictors (http://resp.core.ubc.ca/ipress/FraminghamFEV1).

**CONCLUSIONS** :

*Chen Wenjia, Sin Don D, FitzGerald J Mark, Safari Abdollah, Adibi Amin, Sadatsafavi Mohsen*

*2019-Sep-19*

**COPD, FEV(1), FEV(1)/FVC, airflow limitation, lung function, predictive modelling**

## Weekly Summary

Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.