Moffett Alexander T, Balasubramanian Aparna, McCormack Meredith C, Aysola Jaya, Ungar Lyle H, Halpern Scott D, Weissman Gary E
Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania, Philadelphia, PA, USA.
medRxiv. 2025 Jan 14:2025.01.02.25319890. doi: 10.1101/2025.01.02.25319890.
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.
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 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.
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. 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.
ML models to exclude restriction from spirometry improve the accuracy and equity of PFT interpretation but do not fully eliminate racial differences.
尽管欧洲呼吸学会和美国胸科学会（ERS/ATS）关于肺功能测试（PFT）解读的指南推荐使用正常预计值下限（LLN）的用力肺活量（FVC）来排除限制，但最近的数据表明，FVC LLN的阴性预测值（NPV）低于以往公认的值，尤其是在非西班牙裔黑人患者中。我们试图开发并外部验证一种机器学习（ML）模型，以通过肺活量测定预测限制，并确定其使用是否可以提高PFT解读的准确性和公平性。
我们纳入了在两个医疗系统内的肺诊断实验室接受测试的18至80岁患者的同时进行静态和动态肺容积测量的PFT。我们使用来自一个医疗系统的PFT来训练逻辑回归、随机森林和增强树模型，以使用人口统计学、人体测量学和肺活量测定数据预测限制。我们使用来自第二个医疗系统的PFT对这些模型进行外部验证。模型性能的主要指标是NPV。通过比较非西班牙裔黑人和非西班牙裔白人患者的NPV来评估种族公平性。
共有42462份PFT用于模型开发，24524份用于外部验证。在开发数据集中限制的患病率为29.8%，在验证数据集中为39.6%。所有三种ML模型在总体上以及所有人口统计学亚组中均大幅优于FVC LLN。随机森林模型的总体NPV（88.3%，95%置信区间[CI]87.8%至88.9%）显著高于FVC LLN（72.7%，95%CI 72.1%至73.3%）。在非西班牙裔黑人（74.6%[95%CI 72.5%至76.6%]对49.5%[95%CI 47.8%至51.2%]）和非西班牙裔白人（90.9%[95%CI 90.3%至91.5%]对79.6%[95%CI 78.9%至80.3%]）患者中，随机森林模型的NPV均高于FVC LLN。
用于通过肺活量测定排除限制的ML模型提高了PFT解读的准确性和公平性，但并未完全消除种族差异。
