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. Using a machine learning (ML) model—rather than the FVC LLN—to exclude restriction may improve the accuracy and equity of PFT interpretation. We sought to develop and externally validate a ML model to predict restriction from spirometry and to assess the potential impact of this model on PFT interpretation.

Methods We included PFTs with both static and dynamic lung volume measurements for patients between 18 and 80 years of age 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. Model equity was assessed by comparing the NPV among non-Hispanic Black and non-Hispanic White patients.

Findings A 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. Performance was similar across the three ML models with the best performance seen with the random forest model. 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.

Interpretation Using a ML model to exclude restriction from spirometry improves both the accuracy and equity of PFT interpretation.

ATM, AB, JA, LHU, SDH, and GEW have no financial disclosures to report relevant to this manuscript. MCM has received royalties from UpToDate, and consulting income from GlaxoSmithKline, Boehringer Ingelheim, Aridis, MCG Diagnostics and NDD Medical Technologies.

ATM reports funding from NHLBI F32 HL167456. GEW reports funding from NHBLI R03 HL171424 and NIGMS R35 GM155262.

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