Development and External Validation of a Machine Learning Model to Predict Restriction from Spirometry.

ABSTRACT:

Background

Though European Respiratory Society and American Thoracic Society (ERS/ATS) guidelines for pulmonary function test (PFT) interpretation recommend the use of the forced vital capacity (FVC) lower limit of normal (LLN) to exclude restriction, recent data suggest that the negative predictive value (NPV) of the FVC LLN is lower than has been accepted, particularly among non-Hispanic Black patients. We sought to develop and externally validate a machine learning (ML) model to predict restriction from spirometry and determine whether its use may improve the accuracy and equity of PFT interpretation.

Methods

We included PFTs with both static and dynamic lung volume measurements for patients between 18 and 80 years of age who were tested at pulmonary diagnostic labs withi

SUBMITTER: Moffett AT 

PROVIDER: S-EPMC11722487 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature