An Individualized Prediction Model for Long-term Lung Function Trajectory and Risk of COPD in the General Population.

Pubmed ID: 31542453

Journal: Chest

Publication Date: March 1, 2020

Affiliation: Respiratory Evaluation Sciences Program, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada; Division of Respiratory Medicine, Department of Medicine, The UBC Centre for Heart Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, BC, Canada; Institute for Heart and Lung Health, Division of Respiratory Medicine, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada; Centre for Clinical Epidemiology and Evaluation, University of British Columbia, Vancouver, BC, Canada.

Link: https://www.sciencedirect.com/science/article/abs/pii/S0012369219338681

MeSH Terms: Humans, Male, Adult, Female, Cohort Studies, Aging, Algorithms, Age Factors, Middle Aged, Longitudinal Studies, Risk Assessment, Sex Factors, Forced Expiratory Volume, Lung, Vital Capacity, Alcohol Drinking, Body Height, Hematocrit, Triglycerides, Pulmonary Disease, Chronic Obstructive, Spirometry, Electrocardiography, Leukocyte Count, Serum Albumin, Dyspnea, Bronchodilator Agents, Machine Learning, Cigarette Smoking, Alkaline Phosphatase, Cough, Serum Globulins

Grants: 142238

Authors: Chen W, Sin DD, Sadatsafavi M, FitzGerald JM, Safari A, Adibi A

Cite As: Chen W, Sin DD, FitzGerald JM, Safari A, Adibi A, Sadatsafavi M. An Individualized Prediction Model for Long-term Lung Function Trajectory and Risk of COPD in the General Population. Chest 2020 Mar;157(3):547-557. Epub 2019 Sep 19.

Studies:

Abstract

BACKGROUND: Prediction of future lung function will enable the identification of individuals at high risk of developing COPD, but the trajectory of lung function decline varies greatly among individuals. This study involved the development and validation of an individualized prediction model of lung function trajectory and risk of airflow limitation in the general population. METHODS: Data were obtained from the Framingham Offspring Cohort, which included 4,167 participants ≥ 20 years of age and who had ≥ 2 valid spirometry assessments. The primary outcome was prebronchodilator FEV<sub>1</sub>; the secondary outcome was the risk of airflow limitation (defined as FEV<sub>1</sub>/FVC less than the lower limit of normal). Mixed effects regression models were developed for individualized prediction, and a machine learning algorithm was used to determine essential predictors. The model was validated in two large, independent multicenter cohorts (N = 2,075 and 12,913, respectively). RESULTS: With 20 common predictors, the model explained 79% of the variation in FEV<sub>1</sub> decline in the derivation cohort. In two validation datasets, the model had low error in predicting FEV<sub>1</sub> decline (root mean square error range, 0.18-0.22 L) and high discriminative power in predicting risk of airflow limitation (C-statistic range, 0.86-0.87). This model was implemented in a freely accessible website-based application, which allows prediction based on flexible sets of predictors (http://resp.core.ubc.ca/ipress/FraminghamFEV1). CONCLUSIONS: The individualized predictor is an accurate tool to predict long-term lung function trajectories and risk of airflow limitation in the general population. This model enables identifying individuals at higher risk of COPD, who can then be targeted for preventive therapies.