Subtyping preserved ratio impaired spirometry (PRISm) by using quantitative HRCT imaging characteristics

Respiratory Research

Research

Yuling Yang¹  Ming Li¹  Lin Qi¹  Shaojie Zhang¹  Xuemei Huang¹  Jinjuan Lu¹  Haiyan Ge² 

[1] Department of Radiology, Huadong Hospital Affiliated to Fudan University, 221 West Yanan Road, Jingan District, 200040, Shanghai, China;Department of Respiratory Medicine, Huadong Hospital Affiliated to Fudan University, Shanghai, China;

关键词: Chronic obstructive pulmonary disease;    Preserved ratio impaired spirometry;    Pulmonary function test;    Quantitative;    Computed tomography;   

DOI  :  10.1186/s12931-022-02113-7

 received in 2021-10-19, accepted in 2022-07-15,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundPreserved Ratio Impaired Spirometry (PRISm) is defined as FEV1/FVC ≥ 70% and FEV1 < 80%pred by pulmonary function test (PFT). It has highly prevalence and is associated with increased respiratory symptoms, systemic inflammation, and mortality. However, there are few radiological studies related to PRISm. The purpose of this study was to investigate the quantitative high-resolution computed tomography (HRCT) characteristics of PRISm and to evaluate the correlation between quantitative HRCT parameters and pulmonary function parameters, with the goal of establishing a nomogram model for predicting PRISm based on quantitative HRCT.MethodsA prospective and continuous study was performed in 488 respiratory outpatients from February 2020 to February 2021. All patients underwent both deep inspiratory and expiratory CT examinations, and received pulmonary function test (PFT) within 1 month. According to the exclusion criteria and Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification standard, 94 cases of normal pulmonary function, 51 cases of PRISm and 48 cases of mild to moderate chronic obstructive lung disease (COPD) were included in the study. The lung parenchyma, parametric response mapping (PRM), airway and vessel parameters were measured by automatic segmentation software (Aview). One-way analysis of variance (ANOVA) was used to compare the differences in clinical features, pulmonary function parameters and quantitative CT parameters. Spearman rank correlation analysis was used to evaluate the correlation between CT quantitative index and pulmonary function parameters. The predictors were obtained by binary logistics regression analysis respectively in normal and PRISm as well as PRISm and mild to moderate COPD, and the nomogram model was established.ResultsThere were significant differences in pulmonary function parameters among the three groups (P < 0.001). The differences in pulmonary parenchyma parameters such as emphysema index (EI), pixel indices-1 (PI-1) and PI-15 were mainly between mild to moderate COPD and the other two groups. The differences of airway parameters and pulmonary vascular parameters were mainly between normal and the other two groups, but were not found between PRISm and mild to moderate COPD. Especially there were significant differences in mean lung density (MLD) and the percent of normal in PRM (PRMNormal) among the three groups. Most of the pulmonary quantitative CT parameters had mild to moderate correlation with pulmonary function parameters. The predictors of the nomogram model using binary logistics regression analysis to distinguish normal from PRISm were smoking, MLD, the percent of functional small airways disease (fSAD) in PRM (PRMfSAD) and Lumen area. It had a good goodness of fit (χ2 = 0.31, P < 0.001) with the area under curve (AUC) value of 0.786. The predictor of distinguishing PRISm from mild to moderate COPD were PRMEmph (P < 0.001, AUC = 0.852).ConclusionsPRISm was significantly different from subjects with normal pulmonary function in small airway and vessel lesions, which was more inclined to mild to moderate COPD, but there was no increase in pulmonary parenchymal attenuation. The nomogram based on quantitative HRCT parameters has good predictive value and provide more objective evidence for the early screening of PRISm.

【 授权许可】

CC BY   
© The Author(s) 2022. corrected publication 2023

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