Bayesian modelling of combined FEV1 and FVC vital lung function trajectories: relationship with airflow obstruction and PRISm
Introduction: Airflow Obstruction and PRISm Through Bayesian Modelling of FEV1 and FVC Trajectories
Previous studies have shown that there is a range of FEV1 trajectories across the lifespan.
Yet, how trajectories defined by the combination of both FEV1 and FVC relate to airflow obstruction (AO; FEV1/FVC<0.7 and FEV1< 80% ref.) or Preserved Ratio Impaired Spirometry (PRISm; FEV1/FVC ≥ 0.7 and FEV1 < 80% ref.) is unknown.
Methods: Applying Bayesian Modelling to FEV1 and FVC Trajectories in the Framingham Offspring Cohort
We applied Bayesian non-parametric mixture modeling to data from the Framingham Offspring Cohort (FOC). Trajectory subgroups were identified by simultaneously modeling both pre-bronchodilator FEV1 and FVC (in liters) as a function of age and height.
We modeled all males >20 yr. (N=1,333). The prevalence of AO or PRISm and age of onset was compared across trajectories.
Results: Seven Distinct Trajectories Identified via Bayesian Modelling of FEV1 and FVC
This analysis identified seven FEV1 and FVC defined trajectories.
Their description (Table1) was based on both measurements, which were in accordance with each other.
Conclusion: Associations Between Airflow Obstruction, PRISm, and Bayesian Modelled FEV1 and FVC Trajectories
Bayesian non-parametric trajectory modeling identified seven combined FEV1 and FVC life-course trajectories differentially associated to AO or PRISm.
Authors
Núria Olvera Ocaña, Àlvar Agustí, James Ross, Rosa Faner
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