Abstract: Preserved ratio impaired spirometry (PRISm) is a pulmonary function pattern characterized by a forced expiratory volume in one second (FEV1) to forced vital capacity ratio greater than 0.70, with an FEV1 that is below 80% of the predicted value, even after the use of bronchodilators. PRISm is considered a form of “Pre-Chronic Obstructive Pulmonary Disease (Pre-COPD)” within the broader scope of COPD. Clinically, it presents with respiratory symptoms and is more commonly observed in individuals with high body mass index, females, and those who are current smokers. Additionally, it is frequently associated with metabolic disorders and cardiovascular diseases. Regarding prognosis, PRISm shows considerable variation, ranging from improvement in lung function to the development of COPD. In this article, we review the epidemiology, comorbidities, and clinical outcomes of PRISm, with a particular emphasis on the crucial role of imaging assessments, especially computed tomography scans and magnetic resonance imaging (MRI) technology, in diagnosing, evaluating, and predicting the prognosis of PRISm. Comprehensive imaging provides a quantitative evaluation of lung volume, density, airways, and vasculature, while MRI technology can directly quantify ventilation function and pulmonary blood flow. We also emphasize the future potential of X-ray technology in this field. Moreover, the article discusses the application of artificial intelligence, including its role in predicting PRISm subtypes and modeling ventilation function.
Keywords: Preserved ratio impaired spirometry (PRISm); chronic obstructive pulmonary disease (COPD); computed tomography imaging (CT imaging); magnetic resonance imaging (MRI)
A decade ago, the Global Initiative for Chronic Obstructive Lung Disease (GOLD) introduced a new category called “Preserved Ratio Impaired Spirometry (PRISm),” a subtype of Pre-COPD. According to the GOLD 2025 report, Pre-COPD refers to individuals who show respiratory symptoms, lung abnormalities, or physiological issues but do not have airflow obstruction, as confirmed by a specific lung function test (FEV1/FVC ratio above 0.70). PRISm is identified when the FEV1/FVC ratio remains ≥0.7 after bronchodilation, but FEV1 is below 80% of the predicted value. This condition is often seen in people at risk for COPD, such as smokers, those born prematurely, or individuals with alpha-1-antitrypsin deficiency.
PRISm can sometimes resemble restrictive lung diseases, where both FEV1 and FVC are reduced, but it differs from conditions like COPD, where lung volume measurements are normal or increased. Studies show that PRISm can be detected with or without bronchodilator testing, and many patients may not show symptoms, making diagnosis challenging without detailed lung function tests or imaging.
The authors of this paper highlight that PRISm patients already show symptoms or structural abnormalities, indicating early airflow limitation. Early detection through lung function tests and chest CT scans is crucial for better management. Imaging, particularly quantitative CT, plays a key role in identifying disease subtypes, tracking progression, and understanding underlying mechanisms. It also helps predict long-term outcomes and guide treatment. Future definitions of PRISm may include imaging features alongside lung function criteria. More research is needed to improve early diagnosis, prognosis, and the use of advanced imaging and AI in managing PRISm.
Cite this article as: Jin Q, Zhang Z, Zhou T, Zhou X, Jiang X, Xia Y, Guan Y, Liu S, Fan L. Preserved ratio impaired spirometry: clinical, imaging and artificial intelligence perspective. J Thorac Dis 2025;17(1):450-460. doi: 10.21037/jtd-24-1582
Journal
Journal of Thoracic Disease
Method of Research
Literature review
Subject of Research
People
Article Title
Preserved ratio impaired spirometry: clinical, imaging and artificial intelligence perspective
Article Publication Date
22-Jan-2025
COI Statement
All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1582/coif). S.L. reports funding from National Key R&D Program of China (No. 2022YFC2010000). L.F. reports funding from National Natural Science Foundation of China (Nos. 82430065 and 82171926), National Key R&D Program of China (Nos. 2022YFC2010002 and 2022YFC2010005), and the Medical Imaging Database Construction Program of National Health Commission (No. YXFSC2022JJSJ002). The other authors have no conflicts of interest to declare.