Study protocol for a national cohort of adults focused on respiratory health: the American Lung Association Lung Health Cohort (ALA-LHC) Study.

INTRODUCTION: The current framework for investigating respiratory diseases is based on defining lung health as the absence of lung disease. In order to develop a comprehensive approach to prevent the development of lung disease, there is a need to evaluate the full spectrum of lung health spanning from ideal to impaired lung health. The American Lung Association (ALA) Lung Health Cohort is a new, population-based, cohort study focused primarily on characterising lung health in members of the millennial generation without diagnosed severe respiratory disease. Participants will be enrolled for the baseline study visit starting in 2021, and funding will be sought to support future study exams as part of a longitudinal cohort study. This study will be crucial for developing a novel paradigm of lung health throughout the adult life course. METHODS AND ANALYSIS: This study will leverage the existing infrastructure of the ALA Airways Clinical Research Centers network to enrol 4000 participants between ages 25 and 35 years old at 39 sites across the USA between April 2021 and December 2024. Study procedures will include physical assessment, spirometry, chest CT scan, accelerometry and collection of nasal epithelial lining fluid, nasal epithelial cells, blood and urine. Participants will complete questionnaires about their sociodemographic characteristics, home address histories and exposures, work history and exposure, medical histories, lung health and health behaviours and activity. ETHICS AND DISSEMINATION: The study was approved by the Johns Hopkins Medicine Institutional Review Board. Findings will be disseminated to the scientific community through peer-reviewed journals and at professional conferences. The lay public will receive scientific findings directly through the ALA infrastructure including the official public website. Deidentified datasets will be deposited to BioLINCC, and deidentified biospecimens may be made available to qualified investigators along with a limited-use datasets.

Machine Learning and Prediction of All-Cause Mortality in COPD.

BACKGROUND: COPD is a leading cause of mortality. RESEARCH QUESTION: We hypothesized that applying machine learning to clinical and quantitative CT imaging features would improve mortality prediction in COPD. STUDY DESIGN AND METHODS: We selected 30 clinical, spirometric, and imaging features as inputs for a random survival forest. We used top features in a Cox regression to create a machine learning mortality prediction (MLMP) in COPD model and also assessed the performance of other statistical and machine learning models. We trained the models in subjects with moderate to severe COPD from a subset of subjects in Genetic Epidemiology of COPD (COPDGene) and tested prediction performance in the remainder of individuals with moderate to severe COPD in COPDGene and Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE). We compared our model with the BMI, airflow obstruction, dyspnea, exercise capacity (BODE) index; BODE modifications; and the age, dyspnea, and airflow obstruction index. RESULTS: We included 2,632 participants from COPDGene and 1,268 participants from ECLIPSE. The top predictors of mortality were 6-min walk distance, FEV1 % predicted, and age. The top imaging predictor was pulmonary artery-to-aorta ratio. The MLMP-COPD model resulted in a C index ≥ 0.7 in both COPDGene and ECLIPSE (6.4- and 7.2-year median follow-ups, respectively), significantly better than all tested mortality indexes (P < .05). The MLMP-COPD model had fewer predictors but similar performance to that of other models. The group with the highest BODE scores (7-10) had 64% mortality, whereas the highest mortality group defined by the MLMP-COPD model had 77% mortality (P = .012). INTERPRETATION: An MLMP-COPD model outperformed four existing models for predicting all-cause mortality across two COPD cohorts. Performance of machine learning was similar to that of traditional statistical methods. The model is available online at: https://cdnm.shinyapps.io/cgmortalityapp/.