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

BackgroundThough 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. MethodsWe 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 within two health systems. We used PFTs from one health system to train logistic regression, random forest, and boosted tree models to predict restriction using demographic, anthropometric, and spirometric data. We used PFTs from the second health system to externally validate these models. The primary measure of model performance was the NPV. Racial equity was assessed by comparing the NPV among non-Hispanic Black and non-Hispanic White patients. FindingsA total of 42 462 PFTs were used for model development and 24 524 for external validation. The prevalence of restriction was 29.8% in the development dataset and 39.6% in the validation dataset. All three ML models outperformed the FVC LLN by a wide margin, both overall and among all demographic subgroups. The overall NPV of the random forest model (88.3%, 95% confidence interval [CI] 87.8% to 88.9%) was significantly greater than that of the FVC LLN (72.7%, 95% CI 72.1% to 73.3%). The NPV of the random forest model was greater than that of the FVC LLN among both non-Hispanic Black (74.6% [95% CI 72.5% to 76.6%] versus 49.5% [95% CI 47.8% to 51.2%]) and non-Hispanic White (90.9% [95% CI 90.3% to 91.5%] versus 79.6% [95% CI 78.9% to 80.3%]) patients. InterpretationML models to exclude restriction from spirometry improve the accuracy and equity of PFT interpretation but do not fully eliminate racial differences.