Comparison of Pulmonary Computed Tomography Images and Quantitative Parameters in Patients with Different Degrees of Chronic Obstructive Pulmonary Disease

Comparison of Pulmonary Computed Tomography Images and Quantitative Parameters in Patients with Different Degrees of Chronic Obstructive Pulmonary Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparison of Pulmonary Computed Tomography Images and Quantitative Parameters in Patients with Different Degrees of Chronic Obstructive Pulmonary Disease Lina Wang, Zhigang Wang, Jie Go, Pei Wang, Li Zhang, Na Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3910388/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract to investigate the computed tomography (CT) pulmonary imaging characteristics and quantitative parameters of patients with various degrees of chronic obstructive pulmonary disease (COPD), 120 patients with stable COPD were classified into grade I of mild (n = 24), grade II of moderate (n = 58), grade III of severe (n = 30) and grade IV of very severe (n = 8) according to the 2011 Global Initiative for COPD (GOLD) strategy. The forced expiratory volume in one second (FEV 1 ), maximum forced vital capacity (FVC), FEV 1 /FVC, and FEV 1 /predicted value were measured. Routine blood tests were performed with an automatic hematology analyzer. The results showed a remarkabledifference in FVC between grade III and IV preoperatively and postoperatively ( P < 0.05). The FEV 1 values of grades II, III, and IV were drastically different postoperatively compared with those preoperatively ( P < 0.05). Pearson correlation analysis(PCA) showed that FEV 6 was positively correlated with FVC, R = 0.961 before treatment, R = 0.947 after treatment ( P < 0.05). No great differences were discoveredin white blood cell count (WBC), platelet count (PLT), C-reactive protein (CRP), or neutrophil count percentage (NEU)% among patients with the four grades ( P < 0.05). Quantitative CT can evaluate the severity of emphysema in COPD patients, and the pulmonary function of patients wasdrastically improved after treatment. chronic obstructive pulmonary disease alveolar inflammatory factor CT image features quantitative parameters lung function Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Chronic obstructive pulmonary disease (COPD) is characterized by persistent and progressive airflow limitation accompanied by significant systemic effects [ 1 ]. The airways and lungs have anacute inflammatory response to toxic gases, facilitating the airflow limitation. Clinical manifestations include cough, dyspnea, and peripheral escalation of sputum symptoms [ 2 , 3 ]. COPD caused by cough produces a large amount of mucus, leading to chest shortness of breath, asthma, and other symptoms. The elasticity of the alveoli and the mass of the organ decrease. Damage to the alveolar wall, inflammation, and thickening of the airway wall will produce more mucus attachment. Airway obstruction, acute exacerbation, or other complications may affect the severity and prognosis [ 4 , 5 ]. At present, there are more than 100 million COPD patients in China, and environmental pollution is increasingly aggravated. COPD has been the fourth leading cause of death in humans, with nearly 4.5 million patients dying of COPD in 2020 [ 6 ]. The pathogenesis of COPD includes both individual susceptibility factors and various environmental factors. The main external factors mainly include long-term smoking, dust inhalation, air pollutants, repeated respiratory infections, and some people with poor economic income (poor living conditions, malnutrition, crowded living environment) [ 7 ]. Among various pathogenic factors, the main internal causes are hereditary-related factors, neonatal infection, hypoxia caused by lung dysplasia, and airway hyperresponsiveness. In some developed countries, factors such as smoking have become a major cause of COPD. COPD has also become the third leading cause of death, seriously affecting normal human life and work [ 8 , 9 ]. Severe obstructive ventilatory dysfunction may occur in late COPD for the reduction of forced vital capacity (FVC) may approach or exceed the reduction of forced expiratory volume of 1 s (FEV 1 ) [ 10 ], which can reflect the severity of ventilatory dysfunction, not to identify the type of ventilatory dysfunction [ 11 , 12 ]. Nonspecific airway inflammation caused by the activation, invasion, and release of inflammatory cytokines by inflammatory cells are associated with COPD pathogenesis. At present, the pathological characteristics and pathological changes of the early stage of COPD caused by smoking and the mechanism of repeated acute exacerbation of the disease remain unclear [ 13 ]. The clinical guideline for the diagnosis of COPD is still a pulmonary function test, which lacks the corresponding recognized molecular diagnostic markers and cannot comprehensively reflect the disease and prognosis. The comprehensive pulmonary function, clinical manifestations, acute exacerbations, and comorbidities must be considered for COPD evaluation [ 14 – 16 ]. COPD not only invades the lungs but also poses a threat to whole-body health, with many comorbidities, such as cardiovascular diseases, osteoporosis, anxiety and depression, and lung cancer induced by chronic inflammation [ 17 , 18 ]. The correlation between clinical acquired immunodeficiency syndrome (AIDS) of COPD and other common diseases has attracted worldwide attention [ 19 ]. Guidelines on how to better treat COPD are also constantly updated, and it is very important to explore the pulmonary conditions of patients with various degrees of COPD [ 20 ]. COPD is mainly characterized by chronic airflow obstruction, showing progressive development, but its mechanism is still unclear. There is still a need for further research on COPD. In this study, stable COPD patients were selected and grouped according to various degrees of lung, and the different CT image characteristics of patients were observed to provide a theoretical basis for COPD therapy in clinical practice. 2. Materials and methodologies 2.1Clinical data and sample A total of 120 COPD patients admitted to XX Hospital from XX to XX were recruited, and stable COPD patients were recruited. According to the 2011 edition of the Global Initiative for COPD (GOLD) strategy, the patients were assigned into four groups. The COPD classification standard is shown in Table 1 , with a total of four levels, namely, grade I of mild (n = 24), grade II of moderate (n = 58), grade III of severe (n = 30), and IV of very severe (n = 8). Diagnostic criteria: (i) complete pulmonary function tests; (ii) chronic cough, sputum production, and dyspnea with COPD symptoms; (iii) after bronchodilator use, FEV 1 /FVC < 70% and the existence of irreversible airflow limitation was confirmed; (iv) complete chest X-ray examination to exclude lung cancer, pulmonary fibrosis, bronchial asthma, and other diseases caused by dyspnea; (v) patients with stable shortness of breath, sputum production, or cough, and other symptoms were stable or mild. Inclusion criteria: (i) patients who met the 2007 Chinese Medical Association guidelines for the diagnosis and treatment of COPD; (ii) for patients with multiple clinical diagnostic criteria for COPD, there was no aggravation of expectoration and asthma symptoms greater than 1 diaphragm; (iii) patients without other respiratory diseases; (iv) patients who received no other treatment except inhaled bronchodilators/glucocorticoids longer than one month before blood collection; (v) the study was approved by certain ethics committee. Exclusion criteria: (i) patients without symptoms of acute respiratory tract infection in the past 2 months; (ii) patients who were not voluntarily participating in the study; (iii) patients with heart, liver, kidney disease; (iv) mental patients; (v) patients with lung cancer, active pulmonary tuberculosis, and significant bronchiectasis. 2.2Lung function tests Pulmonary function was measured by a JAEGER TYPE: APS-Pro pulmonary function instrument in Germany. The measurement time was arranged between 8 and 12 a.m., and the VC, FEV 1 /FVC%, and the predicted value of FEV 1 % were recorded. After the patient’s weight was measured with bare feet and precautions were indicated, the patient sat up straight with the head at the natural level, feet on the ground and legs flat. The patient was placed on a nose clip and demonstrated as necessary. A lung function number was performed for each patient. Computer software was employed to calculate different estimates for different individuals. The patient contained the mouth tube, breathed calmly and breathed in to the total lung volume as soon as possible, and exhaled as hard as possible. There was no interruption or relaxation in the breathing process. After five calm breaths, the patient should inhale to the total lung volume level and then breathe evenly to the residual gas level. With the help of computer cues, the patient exhaled forcefully to the sampling line without leaking air or opening his mouth. Pulmonary function tests were performed with inhaled salbutamol sulfate aerosol (Ventoline, GlaxoSmithKline Pharmaceuticals LTD., lot BB0537). 2.3 Quantitative CT examination After signing the informed consent form, patients eligible for quantitative CT examination were examined within 72 hours. Patients were scanned with a GE LightSpeed 64-slice spiral CT scanner. Subjects were recumbent and deeply aspirated to the total lung position. Thin-layer spiral scanning was used at the end of inspiration to scan from the base of the lung to the tip of the lung. During the examination, the doctor guided the patient’s movements and tested the patient’s indicators. If they were not qualified, the scan could be reperformed after a five-minute rest. The tube voltage was 120 kV, tube current was 240 mA, matrix was 512×512, and visual field was 32 cm. The rotation speed of the X-ray tube was 0.33 s per week, and the thickness and interval of the 64-slice spiral CT scanning were 0.625 mm. After standard reconstruction, the scanned images were transmitted to the AW4.3 processing station. Then, the analysis software was used to analyze emphysema, a pixel histogram was drawn for the region of interest, and the percentage of the lung density attenuation area in the total lung volume (LAA%) was obtained by computer software. The optimal threshold was used to analyze the difference in LAA% among the four ABCD groups, and the correlation between LAA% and pulmonary function and symptom scores was analyzed. CT examination and CT images were performed by two chief physicians with at least five years of experience. The bronchial cross-sectional area and tracheal lumen area of each segment were directly measured with an electronic ruler after magnifying the images on the display, and the average value was calculated three times. The ratio of bronchial area to total section (Ai/Ao) was calculated. 2.4 The modified Medical Research Council (mMRC) dyspnea scale questionnaire The MMRC dyspnea scale refers to a modified version of the UK Medical Research Council Dyspnea Questionnaire, which is used to assess dyspnea in patients with COPD. According to the symptoms of dyspnea, patients were divided into the following five levels. Level 0 is dyspnea during strenuous activity. Level 1 is when the patient has difficulty breathing while walking or climbing a gentle slope. Level 2 is when the patient is slower than his peers when walking on ground or needs to stop to rest due to difficulty breathing. Level 3 is when the patient has to stop breathing after walking 100 meters or a few minutes on the ground. Level 4 is that the patient is unable to get out of the house because of severe dyspnea or that he or she begins to have difficulty breathing while dressing or undressing. However, the score content is simple, reflecting only a single symptom of dyspnea. A more comprehensive COPD assessment test (CAT) questionnaire was also used to evaluate the degree of health damage in patients with COPD. 2.5 COPD pulmonary function classification criteria Pulmonary function classification criteria of COPD arepresented in Table 1 . Table 1 Pulmonary function classification standard of COPD Grade The classification standard I (mild) FEV 1 /FVC < 70%, FEV 1 ≥ 80% II(moderate) FEV 1 /FVC < 70%, 50%<FEV 1 < 80% III(severe) FEV 1 /FVC < 70%, 30%<FEV 1 < 50% IV (very severe) FEV 1 /FVC < 70%, FEV 1 < 30%/FEV 1 %<50% 2.6Measured indicators The general information of all the research subjects was collected in the form of a questionnaire, and the weight, age, address, and contact information of the review papers were recorded. Forced expiratory volume in one second (FEV 1 ), maximum forced vital capacity (FVC), FEV 1 /FVC, and FEV 1 /predicted value were measured using a German JAEGER TYPE: APS-Pro pulmonary function instrument. The bronchial cross-sectional area and tracheal lumen area of each segment were measured during CT examination, and the average value was calculated three times. The ratio of the opposite bronchial area to the total section was calculated. A laboratory automatic hematology analyzer was used to perform routine blood tests. 2.7 Statistical analysis SPSS 21.0 was employed.The measurement data were tested for normality and variance.Data with a normal distribution and homogeneity of variance were denoted as \(\overline{\text{x}}\) ± SD. The chi-square test and t test were adopted for comparison of count data that weredenoted as (n, %).Changes in each index were analyzed by repeated-measures ANOVA.The test level was set at 0.05, and P < 0.05 was deemed significant.Statistical analyses were performed with 95% confidence intervals using the ICC test for between-group consistency analysis of the parameters involved by the investigators. 3. Results 3.1 Contrast of general data and pulmonary function among groups Table 2 shows that age distribution and gender composition differed slightly among the four groups ( P > 0.05) (grade I mild (n = 24), grade II moderate (n = 58), grade III severe (n = 30), and grade IV very severe (n = 8)). Table 2 General data and pulmonary function Index I (n = 24) II (n = 58) III (n = 30) IV (n = 8) P Age (years old) 56.26 ± 8.61 57.64 ± 6.36 55.84 ± 6.03 55.84 ± 6.03 0.474 Gender (example/%) 0.801 Male 13 (54.17%) 34 (58.62%) 24 (80%) 7(87.5%) Female 11 (45.83%) 24 (41.38%) 6 (20%) 1(12.5%) Smoking index [M (Q 1 , Q 3 )] 265 (120,340) 460 a (317,647) 420 a (320,585) 28.612 < 0.001 MMRCscore [score, M (Q 1 , Q 3 )] 2.0 (1.0, 3.0) 2.0 (1.0, 3.0) 1.0 (1.0, 2.0) 1.0 (1.0, 2.0) < 0.001 FEV 1 %predicted value 80.69 ± 7.62 45.94 ± 7.62 54.21 ± 6.76 35.12 ± 5.17 < 0.001 FEV 1 /FVC (%) 83.44 ± 5.96 48.80 ± 15.35 51.56 ± 6.29 46.19 ± 3.18 < 0.001 3.2 Changes in pulmonary function indexes before and after treatment Figure 1 shows the comparison of pulmonary function-related indicators among the four groups. Figure 1 A shows the forced vital capacity (FVC), which was drastically different between grades III and IV preoperatively and postoperatively ( P < 0.05). Figure 1 B shows the forced expiratory volume of 1S (FEV 1 ), which was drastically different among grades II, III, and IV preoperatively and postoperatively ( P < 0.05). Figure 1 C shows the 6S forced expiratory volume (FEV 6 ). The results suggested substantial differences among grades I, II, III, and IV preoperatively and postoperatively ( P < 0.05). The pulmonary function-related indexes of the four groups are compared in Fig. 2 . Figure 2 A shows the ratio of FEV 1 /FVC (%) before and after treatment, indicating no considerable difference ( P > 0.05). Figure 2 B shows that the ratio of FEV 1 /FEV 6 (%) differed markedly among three groups preoperatively and postoperatively ( P < 0.05). Figure 2 C shows great difference between FEV 1 (%) preoperatively and postoperatively ( P < 0.05). 3.3 Correlation analysis of patients at all levels The correlation between FEV 6 and FVC before and after treatment in patients with variousgrades was analyzed. Pearson correlation analysis(PCA) showed that FEV 6 was positively correlated with FVC, R = 0.961 before treatment, R = 0.947 after treatment ( P < 0.05) (Fig. 3 ). The correlation between FEV 1 /FVC (%) and FEV 1 /FEV 6 (%) before and after treatment in patients with various grades was analyzed. PCArevealed that FEV 1 /FVC (%) was positively associated with FEV 1 /FEV 6 (%) (Fig. 4 ). 3.4 Comparison of routine blood parameters of patients at all levels The comparison results of white blood cell count (WBC), platelet count (PLT), C-reactive protein (CRP), and neutrophil percentage (NEU%) in patients with various grades are shown in Table 3 . WBC, PLT, CRP, and NEU% differed slightly among patients with various grades ( P > 0.05). Table 3 Inflammatory cells in patients with variousgrades WBC (10 9 /L) PLT CRP (mg/mL) NEU% I 6.32 ± 2.03 113.21 4.58 (8.56) 5.37 ± 2.61 II 6.81 ± 2.71 128.32 4.78 (11.02) 6.87 ± 2.01 III 7.31 ± 2.43 136.48 7.62 (12.81) 6.92 ± 2.31 IV 7.71 ± 2.17 171.43 4.19 (5.83) 6.39 ± 2.84 P 0.763 0.831 0.127 0.728 3.5 Comparison of LAA% of each grade with different CT values Under the CT values of -1,024, -960, -950, -940, -930, -910 HU, LAA% in various groups were drastically different between groups IV and III ( P < 0.05), and the corresponding chi-square values were 153.6, 184.6, 176.3, 184.7, 190.3, and 186.3, respectively (Fig. 5 ). 3.6 Comparison of MMRC% of each grade with different CT values The group IV had drastically superiorCT value to the other three groups under − 1,024 ~ -910 (Fig. 6 ). 3.7 Comparison of LAA% of each grade with different CT values The CT values of LAA% under − 1,024 ~ -910 showed great differences between groups II and I ( P < 0.05), III and II ( P < 0.05), and IV and III ( P < 0.05) (Fig. 7 ). 4. Results COPD requires long-term treatment and brings serious physical and mental harm and economic burden to patients [ 21 ], whose progression is related to many inflammatory factors, including TNF-α, L-1β, IL-6, NF-kB, and interleukin [ 22 , 23 ]. In this study, the WBC, PLT, CRP, and NEU% of patients with various grades were graded. WBC, PLT, CRP, and NEU% of different inflammatory cells differed slightly among patients with the four grades ( P > 0.05). COPD is an inhibitory disease, and emphysema can lead to severe COPD. GOLD used the degree of airway obstruction in COPD patients to evaluate the severity of the disease. The clinical diagnosis of emphysema mainly depends on lung function. The common examination method is to use CT for examination, and the diagnosis is based on the determination of lung density. The standard of lung density is not uniform. Below − 950 HU, there are also − 960 HU as a low-density area for the diagnosis of emphysema. At this stage, the severity of emphysema classification is also very unclear. The severity of emphysema affects the severity of COPD. Many patients have emphysema. The destruction of lung tissue by emphysema is also a decisive factor in COPD. In the study of emphysema in patients with stage I and II COPD, 50% of patients had varying emphysema. In many cases, there is more severe emphysema in the upper lung field and near the hilum. COPD subtypes have been identified by visual or quantitative assessment using CT images. Park et al. (2020) [ 24 ] used a combination of visual and quantitative CT imaging features to reflect various underlying pathological phases in heterogeneous COPD syndromes and provided an effective method for reclassifying COPD types. In this study, LAA% differed drastically between groups II and I ( P < 0.05), III and II ( P < 0.05), and IV and III ( P < 0.05) under the CT values of -1,024 ~ -910 HU. Inflammatory factors directly participate in normal airway mucosal epithelium injury, induce neutrophil infiltration, and promote the secretion of C-reactive protein [ 25 ]. The level of inflammatory factors in normal people is drastically lower than that in COPD patients, which is also associated to the quality of life of patients [ 26 ]. Many scholars’ reports on cardiovascular changes in COPD patients have shown that many patients’ manifestations mainly include right ventricular dysfunction, arrhythmia, and coronary heart disease. The increased pulmonary artery pressure, pulmonary hyperinflation, abnormal vascular endothelium, and systemic inflammatory reaction in COPD patients all affect theincreased right ventricular load. COPD is deemed as a highly heterogeneous disease consisting of a distinct pathophysiology. There is an urgent need for the accurate classification of COPD subtypes through imaging biomarkers to achieve individualized treatment and improve patient prognosis. It is very important to explore the CT image features of patients with various degrees of COPD. This study analyzed the correlation between FEV 6 and FVC before and after treatment in patients with different grades. PCAindicated that FEV 6 was correlated with FVC positively, R = 0.961 before treatment, and R = 0.947 after treatment ( P < 0.05). The correlation between FEV 1 /FVC (%) and FEV 1 /FEV 6 (%) before and after treatment in patients with variousgrades was analyzed. PCA showed that FEV 1 /FVC (%) was positively correlated with FEV 1 /FEV 6 (%). Mokari-Yamchi et al. (2019) [ 27 ] analyzed the CT pulmonary vascular parameters and the severity in patients and found marked differences in COPD course, FEV 1 , FVC, and FEV 1 /FVC among patients having various severities (all P < 0.05). COPD severity was associated with its duration, CSA, and PA/A ( P < 0.05). 5. Conclusion In this study, the changes in lung CT images and the correlation of lung function in patients with stable COPD of different grades were analyzed. The changes in airflow limitation severity, small airway function, and diffusion function in patients were related to the characteristics of lung CT images. Quantitative CT can be used to evaluate the severity of emphysema in COPD patients. Furthermore, the pulmonary function of patients was drastically enhanced after treatment, which ultimately affects the development of COPD, providing a reference for the diagnosis and therapy of COPD. Nevertheless, the number of cases needs to be further increased to provide more reliable evidence for relevant studies. Declarations Ethics Approval and Consent to Participate: No participation of humans takes place in this implementation process Human and Animal Rights: No violation of Human and Animal Rights is involved. Funding : No funding is involved in this work. Data availability statement: Data sharing not applicable to this article as no datasets were generated or analyzed during the current study Conflict of Interest: Conflict of Interest is not applicable in this work. Authorship contributions: All authors are contributed equally to this work Acknowledgement : There is no acknowledgement involved in this work References Vogelmeier C, Román-Rodríguez M, Singh D. Goals of COPD treatment: Focus on symptoms and exacerbations. Respir Med 2020; 166: 105938. Asensio V, Tomás A, Iglesias A. Eosinophilic COPD Patients Display a Distinctive Serum miRNA Profile From Asthma and Non-eosinophilic COPD. Arch Bronconeumol (Engl Ed) 2020; 56(4): 234–241. Waatevik M, Frisk B, Francisco G. CT-defined emphysema in COPD patients and risk for change in desaturation status in 6-min walk test. Respir Med 2021; 187: 106542. Vogelmeier C, Criner G, Martinez F. 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Mokari-Yamchi A, Jabbari M, Sharifi A. Low FEV1 Is Associated With Increased Risk Of Cachexia In COPD Patients. Int J Chron Obstruct Pulmon Dis 2019; 14: 2433–2440. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3910388","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270157299,"identity":"4a6c4d12-f1ab-4374-bc9b-1c1ffa059662","order_by":0,"name":"Lina Wang","email":"","orcid":"","institution":"Weapons Industrial Hygiene Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Lina","middleName":"","lastName":"Wang","suffix":""},{"id":270157300,"identity":"a8950208-a995-4193-bbbf-adbbbbf22343","order_by":1,"name":"Zhigang Wang","email":"","orcid":"","institution":"Weapons Industrial Hygiene Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Zhigang","middleName":"","lastName":"Wang","suffix":""},{"id":270157301,"identity":"b5bc63b0-f4b2-4ff7-aff4-59a54aef9581","order_by":2,"name":"Jie Go","email":"","orcid":"","institution":"Weapons Industrial Hygiene Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Go","suffix":""},{"id":270157302,"identity":"2ebe15df-6da2-4cac-9339-74656d1c9bc3","order_by":3,"name":"Pei Wang","email":"","orcid":"","institution":"Weapons Industrial Hygiene Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Pei","middleName":"","lastName":"Wang","suffix":""},{"id":270157303,"identity":"201d0209-bc69-476a-a9b0-4d35d49f0844","order_by":4,"name":"Li Zhang","email":"","orcid":"","institution":"Weapons Industrial Hygiene Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhang","suffix":""},{"id":270157304,"identity":"b8ef7611-6dd6-48c6-b48b-22e5f5dfbfb6","order_by":5,"name":"Na Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYFAC5oYDDBX/5djYG4AcAwtitDACtZxhNubjOQDSIkGcFgbGNubEeRIJIB4RWszZDzYe5jnDltgm+fzqhh8FEgz87d0JeLVY9iQ2HOap4DFuk84pu9kDdJjEmbMb8GoxOADSckZCFqgl7QYPUIuBRC4BLecfNhzmbTNgbJM8k3bzD1FabiSCtCQotkmwH7tNnC03HjYcnHPmgDEbTw7bbRkDCR7CfjmffPjDm4oDcvLtx5/dfPPHRo6/vRe/FiTAYwAmiVUOAuwPSFE9CkbBKBgFIwgAAN4dTDLhwh8zAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Xi’an Medical University","correspondingAuthor":true,"prefix":"","firstName":"Na","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-01-30 11:19:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3910388/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3910388/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50513250,"identity":"0b9f1785-dd60-46fd-811e-93d40aa16ec4","added_by":"auto","created_at":"2024-02-01 16:26:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99272,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in pulmonary function indexes.\u003c/p\u003e\n\u003cp\u003e(*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs. after treatment.)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3910388/v1/7b6e63276b1b2e6cce1d74fa.png"},{"id":50512064,"identity":"f53020b8-f395-4bc2-b324-825cf609f0af","added_by":"auto","created_at":"2024-02-01 16:18:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49927,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in FEV\u003csub\u003e1\u003c/sub\u003e/FVC, FEV\u003csub\u003e1\u003c/sub\u003e/FEV\u003csub\u003e6\u003c/sub\u003e, and FEV\u003csub\u003e1\u003c/sub\u003e in patients with various grades.\u003c/p\u003e\n\u003cp\u003e(*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs. after treatment.)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3910388/v1/b45a5df8f0942f8870609947.png"},{"id":50514438,"identity":"544f212b-eef3-4a4b-a83d-2d659a7ffbb8","added_by":"auto","created_at":"2024-02-01 16:34:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26069,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of FEV\u003csub\u003e6\u003c/sub\u003e and FVC in patients with variousgrades.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3910388/v1/02d1283159fdaffc70b60359.png"},{"id":50512061,"identity":"a0f11967-4342-4789-9d41-67ecaea03588","added_by":"auto","created_at":"2024-02-01 16:18:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":23385,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between FEV\u003csub\u003e1\u003c/sub\u003e/FVC (%) and FEV\u003csub\u003e1\u003c/sub\u003e/FEV\u003csub\u003e6\u003c/sub\u003e (%) in patients with various grades.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3910388/v1/d2fbffbdc386c511b8cde272.png"},{"id":50513252,"identity":"1e63aa8c-5dec-4a7e-931f-e71b56e8a915","added_by":"auto","created_at":"2024-02-01 16:26:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":20455,"visible":true,"origin":"","legend":"\u003cp\u003eLAA% comparison results of different CT values for each grade.\u003c/p\u003e\n\u003cp\u003e(*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs. group III.)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3910388/v1/6503b3eda305a544b0d957b9.png"},{"id":50512066,"identity":"27c1bd1d-9f37-4093-92ff-4f561ed81d02","added_by":"auto","created_at":"2024-02-01 16:18:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":15879,"visible":true,"origin":"","legend":"\u003cp\u003eMMRC% comparison results of different CT values for each grade.\u003c/p\u003e\n\u003cp\u003e(*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs. group III.)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3910388/v1/7f8de90a0abe1454f9c920af.png"},{"id":50512060,"identity":"109b8ccb-4804-4748-bf30-cd60dc875fc5","added_by":"auto","created_at":"2024-02-01 16:18:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":19853,"visible":true,"origin":"","legend":"\u003cp\u003eLAA% comparison results of different CT values for each grade.\u003c/p\u003e\n\u003cp\u003e(*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 vs. group I; #\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05vs. group II;\u0026amp;\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05vs. group III.)\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3910388/v1/fe9ff68a47752596f96caa3e.png"},{"id":50614553,"identity":"3be68b3a-851b-440f-92d6-f362899d55e9","added_by":"auto","created_at":"2024-02-04 04:54:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":686542,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3910388/v1/ec5dc90a-9eb7-422e-8320-155f8c15296d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of Pulmonary Computed Tomography Images and Quantitative Parameters in Patients with Different Degrees of Chronic Obstructive Pulmonary Disease","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChronic obstructive pulmonary disease (COPD) is characterized by persistent and progressive airflow limitation accompanied by significant systemic effects [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The airways and lungs have anacute inflammatory response to toxic gases, facilitating the airflow limitation. Clinical manifestations include cough, dyspnea, and peripheral escalation of sputum symptoms [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. COPD caused by cough produces a large amount of mucus, leading to chest shortness of breath, asthma, and other symptoms. The elasticity of the alveoli and the mass of the organ decrease. Damage to the alveolar wall, inflammation, and thickening of the airway wall will produce more mucus attachment. Airway obstruction, acute exacerbation, or other complications may affect the severity and prognosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. At present, there are more than 100\u0026nbsp;million COPD patients in China, and environmental pollution is increasingly aggravated. COPD has been the fourth leading cause of death in humans, with nearly 4.5\u0026nbsp;million patients dying of COPD in 2020 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The pathogenesis of COPD includes both individual susceptibility factors and various environmental factors. The main external factors mainly include long-term smoking, dust inhalation, air pollutants, repeated respiratory infections, and some people with poor economic income (poor living conditions, malnutrition, crowded living environment) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Among various pathogenic factors, the main internal causes are hereditary-related factors, neonatal infection, hypoxia caused by lung dysplasia, and airway hyperresponsiveness. In some developed countries, factors such as smoking have become a major cause of COPD. COPD has also become the third leading cause of death, seriously affecting normal human life and work [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSevere obstructive ventilatory dysfunction may occur in late COPD for the reduction of forced vital capacity (FVC) may approach or exceed the reduction of forced expiratory volume of 1 s (FEV\u003csub\u003e1\u003c/sub\u003e) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], which can reflect the severity of ventilatory dysfunction, not to identify the type of ventilatory dysfunction [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Nonspecific airway inflammation caused by the activation, invasion, and release of inflammatory cytokines by inflammatory cells are associated with COPD pathogenesis. At present, the pathological characteristics and pathological changes of the early stage of COPD caused by smoking and the mechanism of repeated acute exacerbation of the disease remain unclear [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The clinical guideline for the diagnosis of COPD is still a pulmonary function test, which lacks the corresponding recognized molecular diagnostic markers and cannot comprehensively reflect the disease and prognosis. The comprehensive pulmonary function, clinical manifestations, acute exacerbations, and comorbidities must be considered for COPD evaluation [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. COPD not only invades the lungs but also poses a threat to whole-body health, with many comorbidities, such as cardiovascular diseases, osteoporosis, anxiety and depression, and lung cancer induced by chronic inflammation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The correlation between clinical acquired immunodeficiency syndrome (AIDS) of COPD and other common diseases has attracted worldwide attention [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Guidelines on how to better treat COPD are also constantly updated, and it is very important to explore the pulmonary conditions of patients with various degrees of COPD [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCOPD is mainly characterized by chronic airflow obstruction, showing progressive development, but its mechanism is still unclear. There is still a need for further research on COPD. In this study, stable COPD patients were selected and grouped according to various degrees of lung, and the different CT image characteristics of patients were observed to provide a theoretical basis for COPD therapy in clinical practice.\u003c/p\u003e"},{"header":"2. Materials and methodologies","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1Clinical data and sample\u003c/h2\u003e \u003cp\u003eA total of 120 COPD patients admitted to XX Hospital from XX to XX were recruited, and stable COPD patients were recruited. According to the 2011 edition of the Global Initiative for COPD (GOLD) strategy, the patients were assigned into four groups. The COPD classification standard is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, with a total of four levels, namely, grade I of mild (n\u0026thinsp;=\u0026thinsp;24), grade II of moderate (n\u0026thinsp;=\u0026thinsp;58), grade III of severe (n\u0026thinsp;=\u0026thinsp;30), and IV of very severe (n\u0026thinsp;=\u0026thinsp;8).\u003c/p\u003e \u003cp\u003eDiagnostic criteria: (i) complete pulmonary function tests; (ii) chronic cough, sputum production, and dyspnea with COPD symptoms; (iii) after bronchodilator use, FEV\u003csub\u003e1\u003c/sub\u003e/FVC\u0026thinsp;\u0026lt;\u0026thinsp;70% and the existence of irreversible airflow limitation was confirmed; (iv) complete chest X-ray examination to exclude lung cancer, pulmonary fibrosis, bronchial asthma, and other diseases caused by dyspnea; (v) patients with stable shortness of breath, sputum production, or cough, and other symptoms were stable or mild.\u003c/p\u003e \u003cp\u003e Inclusion criteria: (i) patients who met the 2007 Chinese Medical Association guidelines for the diagnosis and treatment of COPD; (ii) for patients with multiple clinical diagnostic criteria for COPD, there was no aggravation of expectoration and asthma symptoms greater than 1 diaphragm; (iii) patients without other respiratory diseases; (iv) patients who received no other treatment except inhaled bronchodilators/glucocorticoids longer than one month before blood collection; (v) the study was approved by certain ethics committee.\u003c/p\u003e \u003cp\u003eExclusion criteria: (i) patients without symptoms of acute respiratory tract infection in the past 2 months; (ii) patients who were not voluntarily participating in the study; (iii) patients with heart, liver, kidney disease; (iv) mental patients; (v) patients with lung cancer, active pulmonary tuberculosis, and significant bronchiectasis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2Lung function tests\u003c/h2\u003e \u003cp\u003ePulmonary function was measured by a JAEGER TYPE: APS-Pro pulmonary function instrument in Germany. The measurement time was arranged between 8 and 12 a.m., and the VC, FEV\u003csub\u003e1\u003c/sub\u003e/FVC%, and the predicted value of FEV\u003csub\u003e1\u003c/sub\u003e% were recorded.\u003c/p\u003e \u003cp\u003eAfter the patient\u0026rsquo;s weight was measured with bare feet and precautions were indicated, the patient sat up straight with the head at the natural level, feet on the ground and legs flat. The patient was placed on a nose clip and demonstrated as necessary. A lung function number was performed for each patient. Computer software was employed to calculate different estimates for different individuals. The patient contained the mouth tube, breathed calmly and breathed in to the total lung volume as soon as possible, and exhaled as hard as possible. There was no interruption or relaxation in the breathing process. After five calm breaths, the patient should inhale to the total lung volume level and then breathe evenly to the residual gas level. With the help of computer cues, the patient exhaled forcefully to the sampling line without leaking air or opening his mouth. Pulmonary function tests were performed with inhaled salbutamol sulfate aerosol (Ventoline, GlaxoSmithKline Pharmaceuticals LTD., lot BB0537).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Quantitative CT examination\u003c/h2\u003e \u003cp\u003e After signing the informed consent form, patients eligible for quantitative CT examination were examined within 72 hours. Patients were scanned with a GE LightSpeed 64-slice spiral CT scanner. Subjects were recumbent and deeply aspirated to the total lung position. Thin-layer spiral scanning was used at the end of inspiration to scan from the base of the lung to the tip of the lung. During the examination, the doctor guided the patient\u0026rsquo;s movements and tested the patient\u0026rsquo;s indicators. If they were not qualified, the scan could be reperformed after a five-minute rest. The tube voltage was 120 kV, tube current was 240 mA, matrix was 512\u0026times;512, and visual field was 32 cm. The rotation speed of the X-ray tube was 0.33 s per week, and the thickness and interval of the 64-slice spiral CT scanning were 0.625 mm. After standard reconstruction, the scanned images were transmitted to the AW4.3 processing station. Then, the analysis software was used to analyze emphysema, a pixel histogram was drawn for the region of interest, and the percentage of the lung density attenuation area in the total lung volume (LAA%) was obtained by computer software. The optimal threshold was used to analyze the difference in LAA% among the four ABCD groups, and the correlation between LAA% and pulmonary function and symptom scores was analyzed.\u003c/p\u003e \u003cp\u003eCT examination and CT images were performed by two chief physicians with at least five years of experience. The bronchial cross-sectional area and tracheal lumen area of each segment were directly measured with an electronic ruler after magnifying the images on the display, and the average value was calculated three times. The ratio of bronchial area to total section (Ai/Ao) was calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 The modified Medical Research Council (mMRC) dyspnea scale questionnaire\u003c/h2\u003e \u003cp\u003eThe MMRC dyspnea scale refers to a modified version of the UK Medical Research Council Dyspnea Questionnaire, which is used to assess dyspnea in patients with COPD. According to the symptoms of dyspnea, patients were divided into the following five levels. Level 0 is dyspnea during strenuous activity. Level 1 is when the patient has difficulty breathing while walking or climbing a gentle slope. Level 2 is when the patient is slower than his peers when walking on ground or needs to stop to rest due to difficulty breathing. Level 3 is when the patient has to stop breathing after walking 100 meters or a few minutes on the ground. Level 4 is that the patient is unable to get out of the house because of severe dyspnea or that he or she begins to have difficulty breathing while dressing or undressing. However, the score content is simple, reflecting only a single symptom of dyspnea. A more comprehensive COPD assessment test (CAT) questionnaire was also used to evaluate the degree of health damage in patients with COPD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 COPD pulmonary function classification criteria\u003c/h2\u003e \u003cp\u003ePulmonary function classification criteria of COPD arepresented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePulmonary function classification standard of COPD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe classification standard\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI (mild)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e/FVC\u0026thinsp;\u0026lt;\u0026thinsp;70%, FEV\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026ge;\u0026thinsp;80%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII(moderate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e/FVC\u0026thinsp;\u0026lt;\u0026thinsp;70%, 50%\u0026lt;FEV\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;80%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII(severe)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e/FVC\u0026thinsp;\u0026lt;\u0026thinsp;70%, 30%\u0026lt;FEV\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV (very severe)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e/FVC\u0026thinsp;\u0026lt;\u0026thinsp;70%, FEV\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;30%/FEV\u003csub\u003e1\u003c/sub\u003e%\u0026lt;50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6Measured indicators\u003c/h2\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe general information of all the research subjects was collected in the form of a questionnaire, and the weight, age, address, and contact information of the review papers were recorded.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eForced expiratory volume in one second (FEV\u003csub\u003e1\u003c/sub\u003e), maximum forced vital capacity (FVC), FEV\u003csub\u003e1\u003c/sub\u003e/FVC, and FEV\u003csub\u003e1\u003c/sub\u003e/predicted value were measured using a German JAEGER TYPE: APS-Pro pulmonary function instrument.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe bronchial cross-sectional area and tracheal lumen area of each segment were measured during CT examination, and the average value was calculated three times. The ratio of the opposite bronchial area to the total section was calculated.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eA laboratory automatic hematology analyzer was used to perform routine blood tests.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e \u003cp\u003eSPSS 21.0 was employed.The measurement data were tested for normality and variance.Data with a normal distribution and homogeneity of variance were denoted as\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\overline{\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e \u0026plusmn; SD. The chi-square test and \u003cem\u003et\u003c/em\u003e test were adopted for comparison of count data that weredenoted as (n, %).Changes in each index were analyzed by repeated-measures ANOVA.The test level was set at 0.05, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was deemed significant.Statistical analyses were performed with 95% confidence intervals using the ICC test for between-group consistency analysis of the parameters involved by the investigators.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Contrast of general data and pulmonary function among groups\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that age distribution and gender composition differed slightly among the four groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (grade I mild (n\u0026thinsp;=\u0026thinsp;24), grade II moderate (n\u0026thinsp;=\u0026thinsp;58), grade III severe (n\u0026thinsp;=\u0026thinsp;30), and grade IV very severe (n\u0026thinsp;=\u0026thinsp;8)).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral data and pulmonary function\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eII (n\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIII (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIV (n\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years old)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.26\u0026thinsp;\u0026plusmn;\u0026thinsp;8.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.64\u0026thinsp;\u0026plusmn;\u0026thinsp;6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.84\u0026thinsp;\u0026plusmn;\u0026thinsp;6.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.84\u0026thinsp;\u0026plusmn;\u0026thinsp;6.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (example/%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (54.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (58.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(87.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (45.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (41.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking index [M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e265 (120,340)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e460\u003csup\u003ea\u003c/sup\u003e (317,647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e420\u003csup\u003ea\u003c/sup\u003e (320,585)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMRCscore [score, M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0 (1.0, 3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0 (1.0, 3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (1.0, 2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0 (1.0, 2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e%predicted value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.69\u0026thinsp;\u0026plusmn;\u0026thinsp;7.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.94\u0026thinsp;\u0026plusmn;\u0026thinsp;7.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.21\u0026thinsp;\u0026plusmn;\u0026thinsp;6.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.12\u0026thinsp;\u0026plusmn;\u0026thinsp;5.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e/FVC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.44\u0026thinsp;\u0026plusmn;\u0026thinsp;5.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.80\u0026thinsp;\u0026plusmn;\u0026thinsp;15.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.56\u0026thinsp;\u0026plusmn;\u0026thinsp;6.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Changes in pulmonary function indexes before and after treatment\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the comparison of pulmonary function-related indicators among the four groups. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA shows the forced vital capacity (FVC), which was drastically different between grades III and IV preoperatively and postoperatively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB shows the forced expiratory volume of 1S (FEV\u003csub\u003e1\u003c/sub\u003e), which was drastically different among grades II, III, and IV preoperatively and postoperatively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC shows the 6S forced expiratory volume (FEV\u003csub\u003e6\u003c/sub\u003e). The results suggested substantial differences among grades I, II, III, and IV preoperatively and postoperatively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cp\u003eThe pulmonary function-related indexes of the four groups are compared in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA shows the ratio of FEV\u003csub\u003e1\u003c/sub\u003e/FVC (%) before and after treatment, indicating no considerable difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB shows that the ratio of FEV\u003csub\u003e1\u003c/sub\u003e/FEV\u003csub\u003e6\u003c/sub\u003e (%) differed markedly among three groups preoperatively and postoperatively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC shows great difference between FEV\u003csub\u003e1\u003c/sub\u003e (%) preoperatively and postoperatively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Correlation analysis of patients at all levels\u003c/h2\u003e \u003cp\u003eThe correlation between FEV\u003csub\u003e6\u003c/sub\u003e and FVC before and after treatment in patients with variousgrades was analyzed. Pearson correlation analysis(PCA) showed that FEV\u003csub\u003e6\u003c/sub\u003e was positively correlated with FVC, \u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.961 before treatment, \u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.947 after treatment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe correlation between FEV\u003csub\u003e1\u003c/sub\u003e/FVC (%) and FEV\u003csub\u003e1\u003c/sub\u003e/FEV\u003csub\u003e6\u003c/sub\u003e (%) before and after treatment in patients with various grades was analyzed. PCArevealed that FEV\u003csub\u003e1\u003c/sub\u003e/FVC (%) was positively associated with FEV\u003csub\u003e1\u003c/sub\u003e/FEV\u003csub\u003e6\u003c/sub\u003e (%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Comparison of routine blood parameters of patients at all levels\u003c/h2\u003e \u003cp\u003eThe comparison results of white blood cell count (WBC), platelet count (PLT), C-reactive protein (CRP), and neutrophil percentage (NEU%) in patients with various grades are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. WBC, PLT, CRP, and NEU% differed slightly among patients with various grades (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInflammatory cells in patients with variousgrades\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWBC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePLT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCRP (mg/mL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNEU%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.32\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4.58 (8.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.81\u0026thinsp;\u0026plusmn;\u0026thinsp;2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4.78 (11.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.87\u0026thinsp;\u0026plusmn;\u0026thinsp;2.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.31\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e7.62 (12.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.92\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4.19 (5.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.39\u0026thinsp;\u0026plusmn;\u0026thinsp;2.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Comparison of LAA% of each grade with different CT values\u003c/h2\u003e \u003cp\u003eUnder the CT values of -1,024, -960, -950, -940, -930, -910 HU, LAA% in various groups were drastically different between groups IV and III (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the corresponding chi-square values were 153.6, 184.6, 176.3, 184.7, 190.3, and 186.3, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Comparison of MMRC% of each grade with different CT values\u003c/h2\u003e \u003cp\u003eThe group IV had drastically superiorCT value to the other three groups under \u0026minus;\u0026thinsp;1,024 ~ -910 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Comparison of LAA% of each grade with different CT values\u003c/h2\u003e \u003cp\u003eThe CT values of LAA% under \u0026minus;\u0026thinsp;1,024 ~ -910 showed great differences between groups II and I (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), III and II (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and IV and III (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eCOPD requires long-term treatment and brings serious physical and mental harm and economic burden to patients [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], whose progression is related to many inflammatory factors, including TNF-α, L-1β, IL-6, NF-kB, and interleukin [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this study, the WBC, PLT, CRP, and NEU% of patients with various grades were graded. WBC, PLT, CRP, and NEU% of different inflammatory cells differed slightly among patients with the four grades (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). COPD is an inhibitory disease, and emphysema can lead to severe COPD. GOLD used the degree of airway obstruction in COPD patients to evaluate the severity of the disease. The clinical diagnosis of emphysema mainly depends on lung function. The common examination method is to use CT for examination, and the diagnosis is based on the determination of lung density. The standard of lung density is not uniform. Below \u0026minus;\u0026thinsp;950 HU, there are also \u0026minus;\u0026thinsp;960 HU as a low-density area for the diagnosis of emphysema. At this stage, the severity of emphysema classification is also very unclear. The severity of emphysema affects the severity of COPD. Many patients have emphysema. The destruction of lung tissue by emphysema is also a decisive factor in COPD. In the study of emphysema in patients with stage I and II COPD, 50% of patients had varying emphysema. In many cases, there is more severe emphysema in the upper lung field and near the hilum. COPD subtypes have been identified by visual or quantitative assessment using CT images. Park et al. (2020) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] used a combination of visual and quantitative CT imaging features to reflect various underlying pathological phases in heterogeneous COPD syndromes and provided an effective method for reclassifying COPD types. In this study, LAA% differed drastically between groups II and I (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), III and II (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and IV and III (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) under the CT values of -1,024 ~ -910 HU.\u003c/p\u003e \u003cp\u003eInflammatory factors directly participate in normal airway mucosal epithelium injury, induce neutrophil infiltration, and promote the secretion of C-reactive protein [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The level of inflammatory factors in normal people is drastically lower than that in COPD patients, which is also associated to the quality of life of patients [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Many scholars\u0026rsquo; reports on cardiovascular changes in COPD patients have shown that many patients\u0026rsquo; manifestations mainly include right ventricular dysfunction, arrhythmia, and coronary heart disease. The increased pulmonary artery pressure, pulmonary hyperinflation, abnormal vascular endothelium, and systemic inflammatory reaction in COPD patients all affect theincreased right ventricular load. COPD is deemed as a highly heterogeneous disease consisting of a distinct pathophysiology. There is an urgent need for the accurate classification of COPD subtypes through imaging biomarkers to achieve individualized treatment and improve patient prognosis. It is very important to explore the CT image features of patients with various degrees of COPD. This study analyzed the correlation between FEV\u003csub\u003e6\u003c/sub\u003e and FVC before and after treatment in patients with different grades. PCAindicated that FEV\u003csub\u003e6\u003c/sub\u003e was correlated with FVC positively, \u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.961 before treatment, and \u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.947 after treatment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The correlation between FEV\u003csub\u003e1\u003c/sub\u003e/FVC (%) and FEV\u003csub\u003e1\u003c/sub\u003e/FEV\u003csub\u003e6\u003c/sub\u003e (%) before and after treatment in patients with variousgrades was analyzed. PCA showed that FEV\u003csub\u003e1\u003c/sub\u003e/FVC (%) was positively correlated with FEV\u003csub\u003e1\u003c/sub\u003e/FEV\u003csub\u003e6\u003c/sub\u003e (%). Mokari-Yamchi et al. (2019) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] analyzed the CT pulmonary vascular parameters and the severity in patients and found marked differences in COPD course, FEV\u003csub\u003e1\u003c/sub\u003e, FVC, and FEV\u003csub\u003e1\u003c/sub\u003e/FVC among patients having various severities (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). COPD severity was associated with its duration, CSA, and PA/A (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study, the changes in lung CT images and the correlation of lung function in patients with stable COPD of different grades were analyzed. The changes in airflow limitation severity, small airway function, and diffusion function in patients were related to the characteristics of lung CT images. Quantitative CT can be used to evaluate the severity of emphysema in COPD patients. Furthermore, the pulmonary function of patients was drastically enhanced after treatment, which ultimately affects the development of COPD, providing a reference for the diagnosis and therapy of COPD. Nevertheless, the number of cases needs to be further increased to provide more reliable evidence for relevant studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo participation of humans takes place in this implementation process\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman and Animal Rights:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo violation of Human and Animal Rights is involved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo funding is involved in this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData sharing not applicable to this article as no datasets were generated or analyzed during the current study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConflict of Interest is not applicable in this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors are contributed equally to this work\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere is no acknowledgement involved in this work\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVogelmeier C, Rom\u0026aacute;n-Rodr\u0026iacute;guez M, Singh D. 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Effect of Theophylline as Adjunct to Inhaled Corticosteroids on Exacerbations in Patients With COPD: A Randomized Clinical Trial. JAMA 2018; 320(15): 1548\u0026ndash;1559.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu M, Ye M, Wang J. Construction of Potential miRNA-mRNA Regulatory Network in COPD Plasma by Bioinformatics Analysis. Int J Chron Obstruct Pulmon Dis 2020; 15: 2135\u0026ndash;2145.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchie A, Wedzicha J. Definition, Causes, Pathogenesis, and Consequences of Chronic Obstructive Pulmonary Disease Exacerbations. Clin Chest Med 2020; 41(3): 421\u0026ndash;438.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerry P, Dhand R. Inhalation Therapy for Stable COPD: 20 Years of GOLD Reports. Adv Ther 2020; 37(5): 1812\u0026ndash;1828.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMouronte-Roib\u0026aacute;s C, Leiro-Fern\u0026aacute;ndez V, Fern\u0026aacute;ndez-Villar A. COPD, emphysema and the onset of lung cancer. A systematic review. Cancer Lett 2016; 382(2): 240\u0026ndash;244.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrandsma C, Vries M, Costa R. Lung ageing and COPD: is there a role for ageing in abnormal tissue repair? Eur Respir Rev 2017; 26(146): 170073.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRogliani P, Ora J, Puxeddu E. Airflow obstruction: is it asthma or is it COPD? Int J Chron Obstruct Pulmon Dis 2016; 11: 3007\u0026ndash;3013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoffel M, Bracke K, Heijink I. miR-223: A Key Regulator in the Innate Immune Response in Asthma and COPD. Front Med (Lausanne) 2020; 7: 196.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe S, Chen D, Hu M. Bronchial epithelial cell extracellular vesicles ameliorate epithelial-mesenchymal transition in COPD pathogenesis by alleviating M2 macrophage polarization. Nanomedicine 2019; 18: 259\u0026ndash;271.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang I, Brown J, George J. COPD-X Australian and New Zealand guidelines for the diagnosis and management of chronic obstructive pulmonary disease: 2017 update. Med J Aust 2017; 207(10): 436\u0026ndash;442.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirai K, Shirai T, Shimoshikiryo T. Circulating microRNA-15b-5p as a biomarker for asthma-COPD overlap. Allergy 2021; 76(3): 766\u0026ndash;774.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLacedonia D, Pia P, Foschino-Barbaro M. Expression profiling of miRNA-145 and miRNA-338 in serum and sputum of patients with COPD, asthma, and asthma-COPD overlap syndrome phenotype. Int J Chron Obstruct Pulmon Dis 2017; 12: 1811\u0026ndash;1817.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKubincov\u0026aacute; A, Tak\u0026aacute;č P, Kendrov\u0026aacute; L. The Effect of Pulmonary Rehabilitation in Mountain Environment on Exercise Capacity and Quality of Life in Patients with Chronic Obstructive Pulmonary Disease (COPD) and Chronic Bronchitis. Med Sci Monit 2018; 24: 6375\u0026ndash;6386.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonnell D, Milne K, James M. Dyspnea in COPD: New Mechanistic Insights and Management Implications. Adv Ther 2020; 37(1): 41\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Xu Z, Kong L. miRNA-486-5p Promotes COPD Progression by Targeting HAT1 to Regulate the TLR4-Triggered Inflammatory Response of Alveolar Macrophages. Int J Chron Obstruct Pulmon Dis 2020; 15: 2991\u0026ndash;3001.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark J, Brian D, Crapo J. Subtyping COPD by Using Visual and Quantitative CT Imaging Features. Chest 2020; 157(1): 47\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones B, Donovan C, Liu G. Animal models of COPD: What do they tell us? Respirology 2017; 22(1): 21\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang T, Chen C, Chen Z. The CT pulmonary vascular parameters and disease severity in COPD patients on acute exacerbation: a correlation analysis. BMC Pulm Med 2021; 21(1): 34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMokari-Yamchi A, Jabbari M, Sharifi A. Low FEV1 Is Associated With Increased Risk Of Cachexia In COPD Patients. Int J Chron Obstruct Pulmon Dis 2019; 14: 2433\u0026ndash;2440.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"chronic obstructive pulmonary disease, alveolar inflammatory factor, CT image features, quantitative parameters, lung function","lastPublishedDoi":"10.21203/rs.3.rs-3910388/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3910388/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eto investigate the computed tomography (CT) pulmonary imaging characteristics and quantitative parameters of patients with various degrees of chronic obstructive pulmonary disease (COPD), 120 patients with stable COPD were classified into grade I of mild (n\u0026thinsp;=\u0026thinsp;24), grade II of moderate (n\u0026thinsp;=\u0026thinsp;58), grade III of severe (n\u0026thinsp;=\u0026thinsp;30) and grade IV of very severe (n\u0026thinsp;=\u0026thinsp;8) according to the 2011 Global Initiative for COPD (GOLD) strategy. The forced expiratory volume in one second (FEV\u003csub\u003e1\u003c/sub\u003e), maximum forced vital capacity (FVC), FEV\u003csub\u003e1\u003c/sub\u003e/FVC, and FEV\u003csub\u003e1\u003c/sub\u003e/predicted value were measured. Routine blood tests were performed with an automatic hematology analyzer. The results showed a remarkabledifference in FVC between grade III and IV preoperatively and postoperatively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The FEV\u003csub\u003e1\u003c/sub\u003e values of grades II, III, and IV were drastically different postoperatively compared with those preoperatively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Pearson correlation analysis(PCA) showed that FEV\u003csub\u003e6\u003c/sub\u003e was positively correlated with FVC, \u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.961 before treatment, \u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.947 after treatment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No great differences were discoveredin white blood cell count (WBC), platelet count (PLT), C-reactive protein (CRP), or neutrophil count percentage (NEU)% among patients with the four grades (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Quantitative CT can evaluate the severity of emphysema in COPD patients, and the pulmonary function of patients wasdrastically improved after treatment.\u003c/p\u003e","manuscriptTitle":"Comparison of Pulmonary Computed Tomography Images and Quantitative Parameters in Patients with Different Degrees of Chronic Obstructive Pulmonary Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-01 16:18:39","doi":"10.21203/rs.3.rs-3910388/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"89e142b5-b4fc-46a4-b77e-2d526d028088","owner":[],"postedDate":"February 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-04T04:46:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-01 16:18:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3910388","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3910388","identity":"rs-3910388","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}