Improving the Likelihood of Identifying Alpha-1 Antitrypsin Deficiency Among Patients With COPD: A Novel Predictive Model Using Real-World Data.

ABSTRACT:

Background

Despite guideline recommendations, most patients with chronic obstructive pulmonary disease (COPD) do not undergo alpha-1 antitrypsin deficiency (AATD) testing and approximately 90% of people with AATD in the United States remain undiagnosed. This study sought to develop a predictive model using real-world data to improve detection of AATD-positive patients in the general COPD population.

Methods

A predictive model using extreme gradient boosting was developed using the EVERSANA database, including longitudinal, patient-level medical claims, prescription claims, AATD-specific testing data, and electronic health records (EHR). The model was trained and then validated to predict AATD-positive status. Patients were coded as AATD positive based on the presence of any

SUBMITTER: Pfeffer DN 

PROVIDER: S-EPMC11925067 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature