Spectrum of Diffuse Parenchymal Lung Diseases in Bangladesh: Institutional-based Cross- sectional Study

Spectrum of Diffuse Parenchymal Lung Diseases in Bangladesh: Institutional-based Cross- sectional Study | 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 Spectrum of Diffuse Parenchymal Lung Diseases in Bangladesh: Institutional-based Cross- sectional Study Rajashish Chakrabortty, Samia Rahman, Mohammed Atiqur Rahman, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7244949/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background The definite patterns of Diffuse Parenchymal Lung Diseases (DPLD) are not known in Bangladesh. This is the first study in our country aimed at evaluating the frequency and clinical spectrum of various types of DPLDs. Methods This cross-sectional study was conducted at the Department of Respiratory Medicine, Bangladesh Medical University (BMU) in Bangladesh over one year. DPLD was diagnosed based on clinical, radiological, pulmonary function tests, and/or histopathological background. Patients with a diagnosis of chronic lung disease like tuberculosis, lung malignancy, asthma, chronic obstructive pulmonary disease (COPD), bronchiectasis, chronic heart disease like chronic heart failure, or valvular heart disease were excluded from this study. Results Seventy-seven participants were included in this study. Connective tissue disease-associated DPLD (CTD-DPLD) was the most common entity (n = 25, 32.4%), followed by non-specific interstitial pneumonia (NSIP) (n = 23, 29.9%), and idiopathic pulmonary fibrosis (IPF) (n = 16, 20.8%). The mean age was 53.16 ± 15.56 years, and females were predominant (58.4%). Age, sex, and occupation were significantly associated with DPLD (p < 0.05). The most common symptom was coughing (97.4%), followed by shortness of breath (96.1%). Clubbing (87.5%) and Gastroesophageal reflux disease (GERD) (62.5%) were most frequent in IPF, and extra-pulmonary manifestations in CTD-DPLD. Clubbing, arthralgia, Raynaud's, joint deformity, and GERD were significantly associated with DPLD. Bilateral (95%), subpleural (75.0%) involvement, and honeycombing (68.7%) were more frequent in IPF. On pulmonary function tests, most of them have restrictive lung disease. Conclusions DPLD encompasses a group of diseases with a wide range of causes that have various presentations and prognoses. We found CTD-DPLD, IPF, and NSIP were the most common causes. CTD-DPLD typically occurs in younger individuals with a higher prevalence of extrapulmonary manifestations. Connective tissue disease Diffuse parenchymal lung disease Idiopathic pulmonary fibrosis Spectrum Figures Figure 1 Figure 2 1 | INTRODUCTION The term “Diffuse Parenchymal Lung Diseases (DPLDs)” constitutes more than 200 heterogeneous groups of disorders that primarily affect the pulmonary interstitium or alveolar space. 1 Inflammation followed by fibrosis and loss of normal lung tissue architecture is the hallmark of the pathogenesis of these disorders. The resultant changes in the lung parenchyma due to fibrosis are usually progressive. Ultimately, these changes lead to impaired oxygen transfer and scarring within the lung internally and reduced functional capacity externally. 2 Although there are different causes of DPLD exist but they can produce identical patterns of inflammation and fibrosis, and for this reason, these groups of diseases may have similar clinical, radiological, physiologic, and/or histopathological features. 3 The exact incidence of DPLDs is unknown, and the prevalence of DPLDs varies from country to country. The data regarding the prevalence of DPLDs in the Indian subcontinent, including our country, is also limited. 4 – 8 Previously, DPLDs were termed as ILDs (Interstitial Lung Diseases). But this terminology created much confusion, as the term “Interstitial” is not specific. To clear this ambiguity, the American Thoracic Society (ATS) and European Respiratory Society (ERS) inaugurated the term diffuse parenchymal lung disease (DPLD). 5 The purpose of this change was to increase the gravity of lung parenchyma as the primary site of pathological alteration. The classification of DPLD is a complex one. DPLD can be broadly categorized as idiopathic interstitial pneumonitis (IIP), granulomatous lung disease, exposure-related, connective tissue disease-associated diffuse parenchymal lung disease (CTD-DPLD), and other various lung disease groups. 9 Earlier studies from South Asia mostly dealt with Connective Tissue disease-related ILDs. 10 , 11 But at the beginning of the twentieth century, the burden of Idiopathic Interstitial Pneumonia in the form of IPF increased with the worst possible outcomes according to some Indian studies. 12 , 13 Like other DPLDs, the prevalence of IPF varies worldwide. In Asia and Southern America, the incidence of IPF is lower (0.5 and 4.2 per 100000 people per year, respectively) than in Europe, and North America has a higher incidence (2.8 and 18 cases per 100,000 people per year, respectively). 14 , 15 No concrete data have been found regarding the incidence of IPF in South Africa. 16 DPLD, especially idiopathic pulmonary fibrosis, is a lung illness that is usually deadly and has no recognized cause. So we want to conduct a study that enlightens the facts regarding the span of various DPLDs in our country and helps to find the leading causes of DPLDs from our country’s perspective. RESEARCH QUESTION What is the spectrum of DPLDs at the Department of Respiratory Medicine of Bangladesh Medical University (BMU) in Bangladesh? 2 | METHODS 2.1 | Study design The Department of Respiratory Medicine at the BMU in Bangladesh carried out this institution-based cross-sectional observational study on DPLD cases over the course of a year. All patients provided written informed consent, and the institute's Ethics Committee approved the study (BSMMU/2021/12460). The inclusion criteria were: Patients with HRCT consistent with DPLD and clinical features/pulmonary function test suggestive of DPLD. 18 years of age or older, regardless of gender The exclusion criteria were: A registered physician's diagnosis of a chronic lung disease, such as tuberculosis, lung cancer, asthma, COPD, bronchiectasis, or a chronic heart disease, such as valvular heart disease or chronic heart failure. Patients are unwilling to take part in the study. 2.2 | Sample size calculation The formula is: N = Z 2 pq / d 2 Where, N = estimated sample size Z = 1.96 (in 95% CI) value of the standard normal distribution p = Prevalence of DPLD from previous study, 28% . 17 q = 1-p = 1-0.28 = 0.72 d = marginal error 10% (0.10) So, N= {(1.96) 2 x (0.28 x 0.72)} / (0.10) 2 = 77 Sample size: At least seventy-seven DPLD patients fulfilling the selection criteria 2.3 | Data collection tool Data was collected by using a pre-formatted questionnaire that comprises domains to obtain a socio-demographic profile, environmental and occupational information, Smoking-related information, drug history, respiratory symptoms, and necessary information related to clinical and physical parameters. All information was put together into a master data collection sheet (Questionnaire added to the supplementary file). 2.4 | Data collection procedure: A pre-formed questionnaire was used to gather data. The participants were fully briefed on the purpose of the study. Name, age, sex, and level of education were among the personal details that were documented. The following respiratory symptoms were recorded: wheezing, chest tightness, coughing, phlegm, shortness of breath, and extrapulmonary features (arthritis, rashes, Raynaud’s phenomenon, joint deformity, clubbing). Smoking and occupational history, exposure to drugs, and environment were documented. A chest HRCT was performed to assess for sub-pleural involvement, honeycombing/traction bronchiectasis, reticular shadows, ground-glass opacities, septal thickening, and nodular lesions. To identify pulmonary hypertension, color Doppler echocardiography was performed. Pulmonary hypertension will be defined as a tricuspid jet velocity of more than 3.4 m/s and an estimated pulmonary artery systolic pressure greater than 50 mmHg. 18 FVC, FEV1, and FEV1/FVC were assessed using spirometry to determine pulmonary function according to ATS guidelines. To confirm CTD-DPLD, specific tests were performed, including measuring the level of angiotensin-converting enzyme, calcium, anti-nuclear antibody, rheumatoid factor, anti-CCP (cyclic citrullinated peptide) antibody, extractable nuclear antigen profile, and anti-neutrophilic cytoplasmic and perinuclear anti-neutrophil cytoplasmic antibodies. Prior to a final diagnosis, all patients with ILD had a multidisciplinary approach that included radiologists, pulmonologists, rheumatologists, and/or pathologists. The American Thoracic Society, European Respiratory Society, Japanese Respiratory Society, and Latin American Thoracic Association's (ATS/ERS/JRS/ALAT) current criteria were followed in the diagnosis of IPF. 19 CTD-DPLD and Sarcoidosis were diagnosed based on their respective criteria. 2.5 | Data Processing and Analysis All data were carefully examined, tallied, and coded following data collection. The Statistical Package for the Social Sciences (SPSS) version 23 computer program was then used to perform statistical analysis on the data. The mean ± standard error of mean (SEM) was used to express descriptive frequencies. For categorical variables, the P value was determined using Fisher's exact test; for continuous variables, the student t-test was employed; a P < 0.05 was deemed significant. 3 | RESULT 3.1 | DPLD and sociodemographic pattern In our study, the majority of the patients were diagnosed as CTD-DPLD ( n = 25, 32.4%), followed by NSIP (n = 23, 29.9%) and IPF (n = 16, 20.8%). (Fig. 1 ). The mean age of the participants was 53.16 ± 15.56 years, and most of the patients were 40–60 years of age. Our study was female predominant (n = 45,58.4%) and non-smoker (71.4%). Housewife is the common occupation in our study (n = 40,51.9%). Diabetes mellitus (DM) followed by chronic kidney disease (CKD) were the most common co-morbid conditions, 23.3%, and 9.6% respectively. Pulmonary hypertension was prevalent in about 29.9% of participants (Table 1 ). Table 1 Socio-demographic profile of the DPLD patients (N = 77) Variables Frequency (n) Percentage (%) Age (years) ≤ 40 21 27.3 41–60 30 39 > 60 26 33.7 Mean ± SD 53.16 ± 15.56 Gender Male 32 41.6 Female 45 58.4 Occupation Housewife 40 51.9 Service 13 16.9 Business 9 11.7 Cultivator 5 6.5 Unemployed 4 5.2 Others 6 7.8 Smoking habit Yes 20 26 Ex-smoker 2 2.6 No 55 71.4 Co-morbidities DM 17 23.3 HTN 4 5.5 CLD 1 1.4 CKD 7 9.6 IHD 2 2.7 Pulmonary hypertension 23 29.9 3.2 | Association between DPLD and sociodemographical parameters The Mean age of CTD-DPLD and sarcoidosis was comparatively lower than IPF and NSIP (46.83 ± 16.68, and 47.60 ± 1.52 vs 59.19 ± 12.37 and 56.22 ± 16.64) (P = 0.023). CTD-DPLD and sarcoidosis were female predominant 75% and 66.75% and IPF was male predominant (75%) (p = 0.002) (Table 2 ). Patterns of DPLD were significantly associated with age, sex, and occupation (p < 0.05). Table 2 Association between DPLDs and Socio-demographic factors (N = 77) Variables CTD-DPLD IPF NSIP Sarcoidosis Post COVID HP p -value Age ≤ 40 13 (52) 2 (12.5) 5 (21.7) 0 (0.0) 0 (0.0) 1 (33.3) 0.023 41–60 7 (28) 4 (25.0) 9 (39.1) 6 (100.0) 3 (75) 1 (33.3) > 60 5 (20) 10 (62.5) 9 (39.1) 0 (0.0) 1 (25) 1 (33.3) Mean ± SD 46.83 ± 16.68 59.19 ± 12.37 56.22 ± 16.64 47.60 ± 1.52 59.33 ± 3.06 49.33 ± 21.01 Gender Male 4 (16) 12 (75.0) 11 (47.8) 2 (33.3) 3 (75) 0 (0.0) 0.002 Female 21 (84) 4 (25.0) 12 (52.2) 4 (66.7) 1 (25) 3 (100.0) Occupation Housewife 20 (80) 3 (18.8) 12 (52.2) 3 (50.0) 0 (0.0) 2 (66.7) 0.030 Service 2 (8) 5 (31.3) 3 (13.0) 2(33.3) 1 (25.0) 0 (0.0) Business 1 (4) 4 (25.0) 2 (8.7) 1 (16.7) 1 (25.0) 0 (0.0) Cultivator 1 (4) 3 (18.8) 0 (0.0) 0 (0.0) 1 (25.0) 0 (0.0) Worker 0 (0.0) 0 (0.0) 3 (13.0) 0 (0.0) 0 (0.0) 1 (33.3) Others 1 (4) 1 (6.3) 3 (13.0) 0 (0.0) 1 (25.0) 0 (0.0) Smoking Yes 2 (8) 6 (37.5) 9 (39.1) 1 (16.3) 2 (50) 0 (0.0) 0.053 Ex-smoker 0 (0.0) 2 (12.5) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) No 23 (92) 8 (50.0) 14 (60.9) 5 (83.3) 2 (50) 3 (100.0) 3.3 | Clinical parameters and their association with DPLD In this study, cough was the most common presenting symptom, followed by shortness of breath, 97.4% and 96.1% respectively. Fatigue (72.6%) and arthralgia (46.6%) were the predominant non-respiratory symptoms (Fig. 2 ). Clubbing and Gastroesophageal reflux disease were predominant among the IPF group (87.5% and 6.5%) (p < 0.001). Arthralgia, skin tightening, and joint deformity were prominent among the CTD-DPLD group (P < 0.001). In this study, phlegm, clubbing, GERD, joint deformity, Raynaud's, and skin tightening were significantly associated with DPLD (p < 0.005) (Table 3 ). Table 3 Association between DPLDs and clinical symptoms (N = 77) Variables CT-ILD IPF NSIP Sarcoidosis Post COVID HP p -value Cough 25 (100.0) 15 (93.8) 22 (95.7) 6 (100.0) 4 (100.0) 3 (100.0) 0.860 Phlegm 3 (13.0) 4 (25.0) 6 (26.1) 0 (0.0) 1 (33.3) 3 (100.0) 0.023 SOB 24 (96.0) 15 (93.8) 22 (95.7) 6 (100.0) 4 (100.0) 3 (100.0) 0.985 Fever 7 (30.4) 6 (37.5) 7 (30.4) 3 (60.0) 1 (33.3) 1 (33.3) 0.870 Weight loss 10 (43.5) 7 (43.8) 5 (21.7) 3 (60.0) 0 (0.0) 1 (33.3) 0.309 Fatigue 19 (82.6) 10 (62.5) 16 (69.6) 4 (80.0) 1 (33.3) 3 (100.0) 0.339 Clubbing 7 (30.4) 14 (87.5) 7 (30.4) 0 (0.0) 0 (0.0) 1 (33.3) < 0.001 Arthalgia 22 (95.7) 2 (12.5) 6 (26.1) 3 (60.0) 0 (0.0) 1 (33.3) < 0.001 Joint deformity 9 (39.1) 0 (0.0) 1 (4.3) 0 (0.0) 0 (0.0) 0 (0.0) 0.002 Raynauds 13 (56.5) 0 (0.0) 1 (4.3) 0 (0.0) 0 (0.0) 0 (0.0) < 0.001 Skin Tightening 8 (34.8) 0 (0.0) 1 (4.3) 0 (0.0) 0 (0.0) 0 (0.0) 0.007 GERD 1 (4.3) 10 (62.5) 1 (4.3) 0 (0.0) 0 (0.0) 0 (0.0) < 0.001 Others 7 (30.4) 4 (25.0) 2 (8.7) 4 (80.0) 0 (0.0) 0 (0.0) 0.014 3.4 | Radiological patterns and their association with DPLD High-resolution computed tomography (HRCT) scan showed that bilateral involvement was present in about 70 out of 77 participants (95.5%). Reticulonodular shadow (67.1%), followed by ground glass (52.1%), was the most frequent pattern. Honey combing pattern was prevalent in about 24.7% participants (Table 4 ). In this study, bilateral involvement was common in IPF, NSIP, and post-COVID patients. Sub-pleural (75.0%) involvement and honeycombing (68.7%) were more frequent in IPF (p-value < 0.004). Reticulonodular shadow was common in CTD-DPLD, IPF, and NSIP (p-value 0.002). Ground glass opacity was frequent in NSIP ( p = 0.001). Hilar lymphadenopathy was frequently seen in Sarcoidosis (p < 0.0001) (Table 5 ). Table 4 HRCT patterns of the DPLD patients (N = 77) HRCT patterns Frequency (n) Percentage (%) Bilateral 70 95.9 Sub pleural 26 35.6 Basal 19 26.0 Fibrosis 15 20.5 Septal thickening 24 32.9 Honeycomb infection 18 24.7 Reticulonodular shadows 49 67.1 Traction Bronchiectasis 11 15.1 Ground glass opacity 38 52.1 Hilar lymphadenopathy 6 8.2 Consolidation 28 38.4 Table 5 Association between DPLDs and HRCT patterns (N = 77) HRCT pattern CT-ILD IPF NSIP Sarcoidosis Post COVID HP p -value Bilateral 22 (95.7) 16 (100.0) 23 (100.0) 4 (66.6) 4 (100.0) 3 (100.0) 0.003 Sub pleural 8 (34.8) 12 (75.0) 3 (13.0) 1 (20.0) 1 (33.3) 1 (33.3) 0.006 Basal 8 (34.8) 9 (56.3) 6 (26.1) 0 (0.0) 0 (0.0) 1 (33.3) 0.318 Fibrosis 4 (17.4) 5 (31.3) 4 (17.4) 1 (20.0) 1 (33.3) 0 (0.0) 0.779 Septal thickening 8 (34.8) 7 (43.8) 6 (26.1) 1 (20.0) 0 (0.0) 2 (66.7) 0.444 Honeycombing 4 (17.4) 11 (68.7) 4 (17.4) 0 (0.0) 0 (0.0) 0 (0.0) 0.004 Reticulonodular shadow 16 (69.6) 13 (81.3) 17 (73.9) 1 (20.0) 1 (33.3) 1 (33.3) 0.002 Traction Bronchiectasis 3 (13.0) 4 (25.0) 3 (13.0) 0 (0.0) 0 (0.0) 1 (33.3) 0.661 Ground glass opacity 13 (56.5) 3 (18.8) 17 (73.9) 0 (0.0) 3 (100.0) 2 (66.7) 0.001 Hilar lymphadenopathy 2 (8.7) 0 (0.0) 0 (0.0) 5 (83.33) 0 (0.0) 0 (0.0) < 0.001 Consolidation 11 (47.8) 4 (25.0) 9 (39.1) 1 (20.0) 2 (66.7) 1 (33.3) 0.573 3.5 | Lung function parameters Lung function study showed that the mean FEV1/FVC ratio, FEV1, and FVC were 84.34 ± 12.93, 52.89 ± 16.09, and 51.06 ± 16.48, respectively (Table 6 ). The FEV1/FVC ratio was more than 80 in all groups except HP. The lowest mean FEV1 (48.31 ± 15.82) and FVC (47.46 ± 16.08) were found in the IPF group compared to other groups (Table 7 ). Table 6 Lung function parameters of the DPLD patients (N = 77) Lung function parameters Mean Min-Max FEV1/FVC 84.34 ± 12.93 45-113.5 FEV1 52.89 ± 16.09 20.0-87.1 FVC 51.06 ± 16.48 18.0–88.0 Table 7 Association between DPLDs and lung function parameters (N = 77) Lung function CT-ILD IPF NSIP Sarcoidosis Post COVID HP p -value FEV1/FVC 87.15 ± 10.27 83.57 ± 17.12 82.92 ± 12.72 87.14 ± 11.99 85.00 ± 11.79 73.00 ± 9.64 0.574 FEV1 53.10 ± 16.65 48.31 ± 15.82 52.50 ± 14.82 68.20 ± 8.90 51.70 ± 31.24 54.33 ± 8.62 0.323 FVC 49.91 ± 16.64 47.46 ± 16.08 51.26 ± 15.09 68.40 ± 14.01 48.43 ± 28.31 51.00 ± 14.00 0.264 4 | DISCUSSION Diffuse parenchymal lung diseases (DPLD) consist of a wide spectrum of disorders, which have various presentations and prognoses. DPLD is characterized by acute or chronic inflammation, which leads to mild to severe fibrosis. DPLD includes several disorders like IPF, Sarcoidosis, Connective tissues associated with DPLD (CTD-DPLD), Hypersensitivity Pneumonitis, LAM, NSIP, Post-COVID, etc. 20 Our study showed that CTD-DPLD was the most common entity (32.4%) among DPLD cases, followed by NSIP (29.9%), IPF (20.8%), sarcoidosis (7.8%), post-COVID (5.2%), and Hypersensitivity pneumonitis (3.9%). Our study findings were similar to the study done by Valappil et al. 21 In our study majority of patients were female, and CTD-DPLD was more common in females. However, contrary to Dhoonia et al., 22 and Kundu et al., 23 , sarcoidosis and IPF were the most common entities. In our study, the majority of the patients belonged to the 40–60 age group, and the mean age was 53.16 ± 15.56. It was higher than the mean age of Akl et al., 24 (36.7 ± 14.1), and lower than the study of Valappil et al., (55 ± 15.45), and Pandey et al., (55.15 ± 10.06). 21 , 25 The age was quite higher because our hospital is one of the reputed referral centers, so patients present late here when other hospitals have failed to evaluate or the patient is not responding to treatment. In our study, 45 (58.4%) patients were female and 32 (41.6%) patients were male. This indicates a higher incidence of DPLD in females compared to males among the study population, which contradicts the studies carried out by Pandey et al. 25 The higher female prevalence in this study was consistent with the studies done by Valappil et al. 21 This can be explained by majority of the patients are presented with CTD-DPLD. The most common symptoms of our study were cough (97.3%), shortness of breath (95.9%), and fatigue (72.6%). Other less frequent symptoms were weight loss, clubbing, arthralgia, and Raynaud’s phenomenon. Valappil et al. and Dhooria et al. studied that the most common symptoms were cough and shortness of breath. 21 , 22 Dyspnea is a gradually progressive and disabling symptom. Clubbing was more frequent in IPF. Clubbing is the sign of a chronic low oxygen level in the blood. Arthralgia, Raynaud’s phenomenon, and skin tightening are commonly found in CTD-DPLD. The mean age for IPF was 59.19 years, and the majority of the people belonged to the > 60 age group (62.5%) in our study. It was closer to a study done by Maheshwari et al., where the mean age was 61.5 years. 12 In the Trapani et al. study, the mean age was 64 years, which was higher than in our study. 26 IPF was predominant in males in our study, which was similar to the study done by Trapani et al., and Maheshwari et al. 12 , 26 IPF was more common in males. In IPF 10(62.5%), patients were non-smokers or ex-smokers, and 6 (37.5%) patients were smokers, but Maheshwari et al., most of the patients were smokers. 12 Valappil et al., 21 study shows smoking has a significant association with IPF, but we found no correlation between smoking and IPF, probably due to the small sample size. GERD was the most common symptom of IPF in our study, which consists of studies done by Valappil et al. 21 Genetic factors possibly play a role in IPF risk and progression. 17 In our study, CTD-DPLD was more common than other ILDs, and the majority of the patients were below 40 years (52%). The mean age for CTD-DPLD was 46.83 years. Kundu et al., study mean age for CTD-DPLD was 39.5, which was much lower than our study. 23 In the Agarwal et al. study, the mean age was 45.69, which was similar to our study. 27 CTD-DPLD included in our study are Rheumatoid arthritis, SLE, Systemic Sclerosis, and Mixed Connective Tissue Disease (MCTD). Connective tissue disorder was found to be predominant in women (84%) in our study, which is consistent with the study done by Valappil et al. 21 Most of the patients with CTD-DPLD were non-smokers, and the majority of them were female. 28 NSIP includes pathological features of inflammation and pulmonary fibrosis. Hino et al. 29 explained in their study that NSIP was classified into 2 subtypes: Fibrotic and cellular pattern. Cellular pattern NSIP mainly consists of inflammatory cell infiltration. The typical pathological features of fibrotic NSIP are characterized by homogeneous and diffuse fibrosis with interstitial deposition of collagen and chronic inflammatory cells. NSIP was susceptible to steroid therapy, had longer overall survival, and relatively preserved pulmonary function. In the Valappil et al. 21 study frequency of NSIP was 7.8% but in our study, NSIP was 31%, the mean age was 56.22 years, and most of them were female and non-smokers. In Kumar et al., study, the mean age was 49.83 years, and the majority of them were female, which was quite similar to our study. 8 The most frequent HRCT pattern was the reticulo-nodular pattern (67.1%); in almost all cases, there is bilateral (95.9%) involvement. The second common HRCT pattern in our study was the ground-glass opacification. The least frequently reported HRCT patterns in our cases were septal thickening (32.9%), fibrosis (24.7%), and hilar lymphadenopathy (8.2%). In AKI et al., the most frequent pattern was the nodular pattern, followed by ground glass opacification. 24 In IPF (idiopathic pulmonary fibrosis), the presence of HRCT features of UIP (usual interstitial pneumonia), which includes honeycombing and lower subpleural predominance. In our study, bilateral, subpleural honeycombing was predominant in IPF. Linear and reticular opacities are the most important HRCT findings in connective tissue disease. Reticular pattern occurs due to the thickening of the interlobular interstitium within the secondary pulmonary lobule. A honeycomb pattern may be seen in the end stage. Other pulmonary involvement includes nodular opacities, ground glass opacities, and airspace consolidation. In our study, the most frequent HRCT findings were reticulonodular shadow, ground glass opacity, and consolidation. The least common HRCT pattern was honeycombing and fibrosis. In Gharsalli et al., study most common HRCT pattern was ground glass opacity, reticular opacities, and airspace consolidation. 28 Ground glass opacity 17(73.9%) is frequent in NSIP. DPLD is characterized by restrictive lung function, which means a reduction in lung volume with a ratio of Forced expiratory volume in one second (FEV1) to forced vital capacity (FVC), normal or greater than normal. In our study, FEV1 and FVC were both reduced, and the FEV1/FVC ratio was increased. Verma et al., study mean FVC% of predicted was 58.92. 30 In our study mean FVC was 51.06, which was much less than Verma et al study. The FEV1/FVC ratio in our study was 84.34, which was consistent with Mohanty et al., study (83.54). 31 4.1 | Limitations of the study This is the first study from Bangladesh to evaluate the spectrum of DPLD from a prospective standpoint. The study was limited, though, in that it was not possible to do a surgical biopsy due to a lack of facilities, measure the lung's diffusing capacity, or perform the six-minute walk test. Treatment response to antifibrotic or other drugs was not evaluated in this study. In conclusion, CTD-DPLD patients exhibited NSIP patterns on HRCT, were younger, and had more extrapulmonary features compared to other DPLDs. Further prospective epidemiological investigations are needed to better understand the spectrum of DPLDs. Abbreviations ATS American Thoracic Society Anti-CCP Anti-cyclic citrullinated peptide BMI Body mass index BMU Bangladesh Medical University CTD-DPLD Connective tissue disease-associated diffuse parenchymal lung diseases COPD Chronic obstructive pulmonary disease CKD chronic kidney disease DM Diabetes mellitus DPLD Diffuse Parenchymal Lung Diseases ERS European Respiratory Society GERD Gastroesophageal reflux disease HRCT High-resolution computed tomography IIP Idiopathic interstitial pneumonitis ILDs Interstitial Lung Diseases IPF Idiopathic pulmonary fibrosis IRB Institutional Review Board MCTD Mixed Connective Tissue Disease NSIP Non-specific interstitial pneumonia SPSS Statistical Package for the Social Sciences UIP Usual interstitial pneumonia Declarations AUTHOR CONTRIBUTIONS Rajashish Chakrabortty - Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – original draft. Samia Rahman- Data curation, Formal analysis, Investigation, Methodology, Visualization. Mohammed A. Rahman – Project administration, Investigation, Methodology, Writing – review & editing. Goutam K. Acherjya- Data curation, Formal analysis, Investigation, Methodology, Writing – review & editing. Susanta K. Paul- Data curation, Investigation, Visualization, Methodology, Writing – review & editing. DATA AVAILABILITY STATEMENT The data supporting this study's findings are available on request from the corresponding author. ACKNOWLEDGEMENTS We sincerely appreciate all the participants in this study CONFLICT OF INTEREST STATEMENT The authors declare there is no conflict of interest TRANSPARENCY STATEMENT The lead author, Rajashish Chakrabortty, affirms that this manuscript is an honest, accurate, and transparent account of the study being reported, that no important aspects of the study have been omitted, and that any discrepancies from the study as planned (and if relevant, registered) have been explained. FUNDING DECLARATION: No Funding for this research. CLINICAL TRIAL NUMBER : Not applicable ETHICS DECLARATIONS ETHICAL APPROVAL and CONSENT to PARTICIPANT : This study was approved by the institutional review board of Bangladesh Medical University (No.BSMMU/2021/12460). Written informed consent was obtained from all participants. All procedures were conducted in accordance with the principles of the Declaration of Helsinki. References Maher TM. Diffuse parenchymal lung disease. Medicine. 2008;36(5):265–72. Migita K, Arai T, Jiuchi Y, Izumi Y, Iwanaga N, Kawahara C, Suematsu E, Miyamura T, Tsutani H, Kawabe Y, Matsumura R. Predictors of mortality in patients with interstitial lung disease treated with corticosteroids: results from a cohort study. Medicine. 2014;93(26):e175. Oldham JM, Noth I. Idiopathic pulmonary fibrosis: early detection and referral. Respir Med. 2014;108(6):819–29. King TE Jr, Tooze JA, Schwarz MI, Brown KR, Cherniack RM. Predicting survival in idiopathic pulmonary fibrosis: scoring system and survival model. Am J Respir Crit Care Med. 2001;164(7):1171–81. Demedts M, Wells AU, Anto JM, Costabel U, Hubbard R, Cullinan P, Slabbynck H, Rizzato G, Poletti V, Verbeken EK, Thomeer MJ. Interstitial lung diseases: an epidemiological overview. Eur Respir J. 2001;18(32 suppl):S2–16. Gribbin J, Hubbard RB, Le Jeune I, Smith CJ, West J, Tata LJ. Incidence and mortality of idiopathic pulmonary fibrosis and sarcoidosis in the UK. 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Prevalence of Diffuse Parenchymal Lung Disease (DPLD) and Associated Fibrosis in Northern Saudi Arabia. Int J Sci Res. 2015;4(5):1380–82. Galiè N, Hoeper MM, Humbert M, Torbicki A, Vachiery JL, Barbera JA, Beghetti M, Corris P, Gaine S, Gibbs JS, Gomez-Sanchez MA. Guidelines for the diagnosis and treatment of pulmonary hypertension: the Task Force for the Diagnosis and Treatment of Pulmonary Hypertension of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS), endorsed by the International Society of Heart and Lung Transplantation (ISHLT). Eur Heart J. 2009;30(20):2493–537. Raghu G, Collard HR, Egan JJ, Martinez FJ, Behr J, Brown KK, Colby TV, Cordier JF, Flaherty KR, Lasky JA, Lynch DA. An official ATS/ERS/JRS/ALAT statement: idiopathic pulmonary fibrosis: evidence-based guidelines for diagnosis and management. Am J Respir Crit Care Med. 2011;183(6):788–824. Yadav H, Srivastava R. Clinicoradiological and demographic pattern in diffuse parenchymal lung diseases: An observational study. Int J Med Res Rev. 2018;6(06):308–14. Valappil AT, Mehta AA, Kunoor A, Haridas N. Spectrum of diffuse parenchymal lung diseases: An experience from a tertiary care referral Centre from South India. Egypt J Chest Dis Tuberculosis. 2018;67(3):276–80. Dhooria S, Agarwal R, Sehgal IS, Prasad KT, Garg M, Bal A, Aggarwal AN, Behera D. Spectrum of interstitial lung diseases at a tertiary center in a developing country: A study of 803 subjects. PLoS ONE. 2018;13(2):e0191938. Kundu S, Mitra S, Ganguly J, Mukherjee S, Ray S, Mitra R. Spectrum of diffuse parenchymal lung diseases with special reference to idiopathic pulmonary fibrosis and connective tissue disease: An eastern India experience. Lung India. 2014;31(4):354–60. Akl YM, Elhabashy AH, Mostafa NM, Hasswa MK, Hussein SA. Spectrum of Diffuse Parenchymal Lung Diseases Other than Idiopathic Pulmonary Fibrosis Based on Clinical. Radiological Histopathological Correlation Archit;1:2. Pandey A, Deepak VR, Aditya V. To Evaluate the Role of Radiological and Histopathological Examination in the Diagnosis of Interstitial Lung Diseases. Int J Sci Res. 2019;8(12):67–8. Trapani D, Scalia R, Giordano E, Castellano G, Doi G, Gaeta A, Pellizzari G, Schianca AC, Katrini J, D’Ambrosio S, Santoro C. Interstitial lung disease in patients enrolled in early-phase clinical trials: the ILDE study. ESMO open. 2024;9(8):103658. Agarwal M, Gupta ML, Deokar K, Shadrach BJ, Bharti N, Sonigra M. Clinico-radiological profile of connective tissue disease-related interstitial lung diseases from a tertiary care centre of India: a cross-sectional study. Monaldi Arch Chest Dis. 2021;91(4). Gharsalli H, Attia M, Hantous-Zannad S, Sahnoun I, Maalej S, El Gharbi LD, Neji H, Miled-Mrad KB. Interstitial lung diseases associated with connective tissue pathologies: radiologic features. Open J Respiratory Dis. 2019;9(04):112. Hino T, Lee KS, Han J, Hata A, Ishigami K, Hatabu H. Spectrum of pulmonary fibrosis from interstitial lung abnormality to usual interstitial pneumonia: importance of identification and quantification of traction bronchiectasis in patient management. Korean J Radiol. 2020;22(5):811. Verma G, Kalmodia S, Prasanthi J, Amarnath R, MC S, Ravichanclran A, Mohanty L. Spirometry pattern on Diffuse Parenchymal Lung Diseases (DPLD) diagnosis. Indian J Basic Appl Med Res. 2020;9(3). Mohanty DL, Vachhani DR. Spirometry in Diffuse Parenchymal Lung Diseases. Int J Innovative Res Med Sci. 2018:1804–8. Pandhi N, Rana S, Malhotra B, Kajal NC, Gupta S, Neki NS, Kaur A, Randhawa D. Clinicoradiological Profile of Patients with Diffuse Parenchymal Lung Disease. 2019; 5(2). Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":62313,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency of DPLD\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7244949/v1/0bab34813d995a52204a06f5.png"},{"id":91932000,"identity":"884f49bc-832d-4834-96f9-267019ba78fb","added_by":"auto","created_at":"2025-09-23 02:32:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41059,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency of clinical features (n=77)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7244949/v1/bd4b656c6d2ee795570b7834.png"},{"id":91936405,"identity":"ec4bf077-b3a5-49a9-8fc9-35b0e4b3372c","added_by":"auto","created_at":"2025-09-23 02:48:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1277968,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7244949/v1/b9454da7-9708-4a9a-9f9a-5634a9a11092.pdf"},{"id":91934622,"identity":"c33d4a0f-dcbf-4918-b2c7-28dc046c124e","added_by":"auto","created_at":"2025-09-23 02:40:56","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":18593,"visible":true,"origin":"","legend":"","description":"","filename":"questinnaireDPLD.docx","url":"https://assets-eu.researchsquare.com/files/rs-7244949/v1/77c6439760131abd33ecf9ce.docx"},{"id":91932003,"identity":"8a1fe610-07ba-4f02-9524-e0e32f0d6bb7","added_by":"auto","created_at":"2025-09-23 02:32:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":173419,"visible":true,"origin":"","legend":"","description":"","filename":"questinnaireDPLDpdf.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7244949/v1/b3846fce328dbdd5da873f4f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spectrum of Diffuse Parenchymal Lung Diseases in Bangladesh: Institutional-based Cross- sectional Study","fulltext":[{"header":"1 | INTRODUCTION","content":"\u003cp\u003eThe term \u0026ldquo;Diffuse Parenchymal Lung Diseases (DPLDs)\u0026rdquo; constitutes more than 200 heterogeneous groups of disorders that primarily affect the pulmonary interstitium or alveolar space.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Inflammation followed by fibrosis and loss of normal lung tissue architecture is the hallmark of the pathogenesis of these disorders. The resultant changes in the lung parenchyma due to fibrosis are usually progressive. Ultimately, these changes lead to impaired oxygen transfer and scarring within the lung internally and reduced functional capacity externally.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Although there are different causes of DPLD exist but they can produce identical patterns of inflammation and fibrosis, and for this reason, these groups of diseases may have similar clinical, radiological, physiologic, and/or histopathological features.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e The exact incidence of DPLDs is unknown, and the prevalence of DPLDs varies from country to country. The data regarding the prevalence of DPLDs in the Indian subcontinent, including our country, is also limited.\u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003ePreviously, DPLDs were termed as ILDs (Interstitial Lung Diseases). But this terminology created much confusion, as the term \u0026ldquo;Interstitial\u0026rdquo; is not specific. To clear this ambiguity, the American Thoracic Society (ATS) and European Respiratory Society (ERS) inaugurated the term diffuse parenchymal lung disease (DPLD).\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e The purpose of this change was to increase the gravity of lung parenchyma as the primary site of pathological alteration.\u003c/p\u003e\u003cp\u003eThe classification of DPLD is a complex one. DPLD can be broadly categorized as idiopathic interstitial pneumonitis (IIP), granulomatous lung disease, exposure-related, connective tissue disease-associated diffuse parenchymal lung disease (CTD-DPLD), and other various lung disease groups.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eEarlier studies from South Asia mostly dealt with Connective Tissue disease-related ILDs. \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e But at the beginning of the twentieth century, the burden of Idiopathic Interstitial Pneumonia in the form of IPF increased with the worst possible outcomes according to some Indian studies.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eLike other DPLDs, the prevalence of IPF varies worldwide. In Asia and Southern America, the incidence of IPF is lower (0.5 and 4.2 per 100000 people per year, respectively) than in Europe, and North America has a higher incidence (2.8 and 18 cases per 100,000 people per year, respectively).\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e No concrete data have been found regarding the incidence of IPF in South Africa.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eDPLD, especially idiopathic pulmonary fibrosis, is a lung illness that is usually deadly and has no recognized cause. So we want to conduct a study that enlightens the facts regarding the span of various DPLDs in our country and helps to find the leading causes of DPLDs from our country\u0026rsquo;s perspective.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRESEARCH QUESTION\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhat is the spectrum of DPLDs at the Department of Respiratory Medicine of Bangladesh Medical University (BMU) in Bangladesh?\u003c/p\u003e"},{"header":"2 | METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 | Study design\u003c/h2\u003e\u003cp\u003eThe Department of Respiratory Medicine at the BMU in Bangladesh carried out this institution-based cross-sectional observational study on DPLD cases over the course of a year. All patients provided written informed consent, and the institute's Ethics Committee approved the study (BSMMU/2021/12460).\u003c/p\u003e\u003cp\u003eThe inclusion criteria were:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePatients with HRCT consistent with DPLD and clinical features/pulmonary function test suggestive of DPLD.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e18 years of age or older, regardless of gender\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThe exclusion criteria were:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eA registered physician's diagnosis of a chronic lung disease, such as tuberculosis, lung cancer, asthma, COPD, bronchiectasis, or a chronic heart disease, such as valvular heart disease or chronic heart failure.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePatients are unwilling to take part in the study.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 | Sample size calculation\u003c/h2\u003e\u003cp\u003eThe formula is: N\u0026thinsp;=\u0026thinsp;Z\u003csup\u003e2\u003c/sup\u003e pq / d\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eWhere,\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;estimated sample size\u003c/p\u003e\u003cp\u003eZ\u0026thinsp;=\u0026thinsp;1.96 (in 95% CI) value of the standard normal distribution\u003c/p\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;Prevalence of DPLD from previous study, 28% .\u003csup\u003e\u003cb\u003e17\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eq\u0026thinsp;=\u0026thinsp;1-p\u0026thinsp;=\u0026thinsp;1-0.28\u0026thinsp;=\u0026thinsp;0.72\u003c/p\u003e\u003cp\u003ed\u0026thinsp;=\u0026thinsp;marginal error 10% (0.10)\u003c/p\u003e\u003cp\u003eSo, N= {(1.96)\u003csup\u003e2\u003c/sup\u003e x (0.28 x 0.72)} / (0.10)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e=\u0026thinsp;77\u003c/p\u003e\u003cp\u003eSample size: At least seventy-seven DPLD patients fulfilling the selection criteria\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 | Data collection tool\u003c/h2\u003e\u003cp\u003eData was collected by using a pre-formatted questionnaire that comprises domains to obtain a socio-demographic profile, environmental and occupational information, Smoking-related information, drug history, respiratory symptoms, and necessary information related to clinical and physical parameters. All information was put together into a master data collection sheet (Questionnaire added to the supplementary file).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 | Data collection procedure:\u003c/h2\u003e\u003cp\u003eA pre-formed questionnaire was used to gather data. The participants were fully briefed on the purpose of the study. Name, age, sex, and level of education were among the personal details that were documented. The following respiratory symptoms were recorded: wheezing, chest tightness, coughing, phlegm, shortness of breath, and extrapulmonary features (arthritis, rashes, Raynaud\u0026rsquo;s phenomenon, joint deformity, clubbing). Smoking and occupational history, exposure to drugs, and environment were documented. A chest HRCT was performed to assess for sub-pleural involvement, honeycombing/traction bronchiectasis, reticular shadows, ground-glass opacities, septal thickening, and nodular lesions. To identify pulmonary hypertension, color Doppler echocardiography was performed. Pulmonary hypertension will be defined as a tricuspid jet velocity of more than 3.4 m/s and an estimated pulmonary artery systolic pressure greater than 50 mmHg.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e FVC, FEV1, and FEV1/FVC were assessed using spirometry to determine pulmonary function according to ATS guidelines. To confirm CTD-DPLD, specific tests were performed, including measuring the level of angiotensin-converting enzyme, calcium, anti-nuclear antibody, rheumatoid factor, anti-CCP (cyclic citrullinated peptide) antibody, extractable nuclear antigen profile, and anti-neutrophilic cytoplasmic and perinuclear anti-neutrophil cytoplasmic antibodies. Prior to a final diagnosis, all patients with ILD had a multidisciplinary approach that included radiologists, pulmonologists, rheumatologists, and/or pathologists. The American Thoracic Society, European Respiratory Society, Japanese Respiratory Society, and Latin American Thoracic Association's (ATS/ERS/JRS/ALAT) current criteria were followed in the diagnosis of IPF.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e CTD-DPLD and Sarcoidosis were diagnosed based on their respective criteria.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 | Data Processing and Analysis\u003c/h2\u003e\u003cp\u003eAll data were carefully examined, tallied, and coded following data collection. The Statistical Package for the Social Sciences (SPSS) version 23 computer program was then used to perform statistical analysis on the data. The mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of mean (SEM) was used to express descriptive frequencies. For categorical variables, the P value was determined using Fisher's exact test; for continuous variables, the student t-test was employed; a P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was deemed significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 | RESULT","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 | DPLD and sociodemographic pattern\u003c/h2\u003e\u003cp\u003eIn our study, the majority of the patients were diagnosed as CTD-DPLD ( n\u0026thinsp;=\u0026thinsp;25, 32.4%), followed by NSIP (n\u0026thinsp;=\u0026thinsp;23, 29.9%) and IPF (n\u0026thinsp;=\u0026thinsp;16, 20.8%). (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean age of the participants was 53.16\u0026thinsp;\u0026plusmn;\u0026thinsp;15.56 years, and most of the patients were 40\u0026ndash;60 years of age. Our study was female predominant (n\u0026thinsp;=\u0026thinsp;45,58.4%) and non-smoker (71.4%). Housewife is the common occupation in our study (n\u0026thinsp;=\u0026thinsp;40,51.9%). Diabetes mellitus (DM) followed by chronic kidney disease (CKD) were the most common co-morbid conditions, 23.3%, and 9.6% respectively. Pulmonary hypertension was prevalent in about 29.9% of participants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\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\u003eSocio-demographic profile of the DPLD patients (N\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e41\u0026ndash;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.16\u0026thinsp;\u0026plusmn;\u0026thinsp;15.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\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\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.6\u003c/p\u003e\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\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousewife\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eService\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBusiness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCultivator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking habit\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEx-smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCo-morbidities\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHTN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCKD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIHD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePulmonary hypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.9\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=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 | Association between DPLD and sociodemographical parameters\u003c/h2\u003e\u003cp\u003eThe Mean age of CTD-DPLD and sarcoidosis was comparatively lower than IPF and NSIP (46.83\u0026thinsp;\u0026plusmn;\u0026thinsp;16.68, and 47.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52 vs 59.19\u0026thinsp;\u0026plusmn;\u0026thinsp;12.37 and 56.22\u0026thinsp;\u0026plusmn;\u0026thinsp;16.64) (P\u0026thinsp;=\u0026thinsp;0.023). CTD-DPLD and sarcoidosis were female predominant 75% and 66.75% and IPF was male predominant (75%) (p\u0026thinsp;=\u0026thinsp;0.002) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Patterns of DPLD were significantly associated with age, sex, and occupation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003eAssociation between DPLDs and Socio-demographic factors (N\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCTD-DPLD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIPF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNSIP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSarcoidosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePost COVID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e41\u0026ndash;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (39.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (62.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (39.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46.83\u0026thinsp;\u0026plusmn;\u0026thinsp;16.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.19\u0026thinsp;\u0026plusmn;\u0026thinsp;12.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56.22\u0026thinsp;\u0026plusmn;\u0026thinsp;16.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e59.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e49.33\u0026thinsp;\u0026plusmn;\u0026thinsp;21.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\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\u003e4 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (75.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (47.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\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\u003e21 (84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (52.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousewife\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (52.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2 (66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eService\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (31.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2(33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBusiness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCultivator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (39.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (16.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2 (50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEx-smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (60.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (83.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2 (50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3 (100.0)\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=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 | Clinical parameters and their association with DPLD\u003c/h2\u003e\u003cp\u003e In this study, cough was the most common presenting symptom, followed by shortness of breath, 97.4% and 96.1% respectively. Fatigue (72.6%) and arthralgia (46.6%) were the predominant non-respiratory symptoms (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Clubbing and Gastroesophageal reflux disease were predominant among the IPF group (87.5% and 6.5%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Arthralgia, skin tightening, and joint deformity were prominent among the CTD-DPLD group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In this study, phlegm, clubbing, GERD, joint deformity, Raynaud's, and skin tightening were significantly associated with DPLD (p\u0026thinsp;\u0026lt;\u0026thinsp;0.005) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\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\u003eAssociation between DPLDs and clinical symptoms (N\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCT-ILD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIPF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNSIP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSarcoidosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePost COVID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCough\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15 (93.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22 (95.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.860\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhlegm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3 (13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6 (26.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24 (96.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15 (93.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22 (95.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.985\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7 (30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3 (60.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.870\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10 (43.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7 (43.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5 (21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3 (60.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.309\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFatigue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19 (82.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10 (62.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16 (69.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4 (80.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.339\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClubbing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7 (30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14 (87.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eArthalgia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22 (95.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6 (26.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3 (60.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eJoint deformity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9 (39.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRaynauds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13 (56.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eSkin Tightening\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8 (34.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGERD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10 (62.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7 (30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4 (80.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.014\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\u003e3.4 | Radiological patterns and their association with DPLD\u003c/h2\u003e\u003cp\u003eHigh-resolution computed tomography (HRCT) scan showed that bilateral involvement was present in about 70 out of 77 participants (95.5%). Reticulonodular shadow (67.1%), followed by ground glass (52.1%), was the most frequent pattern. Honey combing pattern was prevalent in about 24.7% participants (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In this study, bilateral involvement was common in IPF, NSIP, and post-COVID patients. Sub-pleural (75.0%) involvement and honeycombing (68.7%) were more frequent in IPF (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.004). Reticulonodular shadow was common in CTD-DPLD, IPF, and NSIP (p-value 0.002). Ground glass opacity was frequent in NSIP (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Hilar lymphadenopathy was frequently seen in Sarcoidosis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHRCT patterns of the DPLD patients (N\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHRCT patterns\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBilateral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSub pleural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFibrosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeptal thickening\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHoneycomb infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReticulonodular shadows\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraction Bronchiectasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGround glass opacity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHilar lymphadenopathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConsolidation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between DPLDs and HRCT patterns (N\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHRCT pattern\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCT-ILD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIPF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNSIP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSarcoidosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePost COVID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBilateral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22 (95.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4 (66.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSub pleural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8 (34.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12 (75.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3 (13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8 (34.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9 (56.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6 (26.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.318\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFibrosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4 (17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5 (31.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4 (17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.779\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeptal thickening\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8 (34.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7 (43.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6 (26.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2 (66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHoneycombing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4 (17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11 (68.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4 (17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReticulonodular shadow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16 (69.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13 (81.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17 (73.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraction Bronchiectasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3 (13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3 (13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.661\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGround glass opacity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13 (56.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17 (73.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2 (66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHilar lymphadenopathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5 (83.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\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\u003eConsolidation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11 (47.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9 (39.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2 (66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.573\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=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.5 | Lung function parameters\u003c/h2\u003e\u003cp\u003eLung function study showed that the mean FEV1/FVC ratio, FEV1, and FVC were 84.34\u0026thinsp;\u0026plusmn;\u0026thinsp;12.93, 52.89\u0026thinsp;\u0026plusmn;\u0026thinsp;16.09, and 51.06\u0026thinsp;\u0026plusmn;\u0026thinsp;16.48, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The FEV1/FVC ratio was more than 80 in all groups except HP. The lowest mean FEV1 (48.31\u0026thinsp;\u0026plusmn;\u0026thinsp;15.82) and FVC (47.46\u0026thinsp;\u0026plusmn;\u0026thinsp;16.08) were found in the IPF group compared to other groups (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLung function parameters of the DPLD patients (N\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLung function parameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMin-Max\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1/FVC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e84.34\u0026thinsp;\u0026plusmn;\u0026thinsp;12.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45-113.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e52.89\u0026thinsp;\u0026plusmn;\u0026thinsp;16.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.0-87.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFVC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e51.06\u0026thinsp;\u0026plusmn;\u0026thinsp;16.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.0\u0026ndash;88.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between DPLDs and lung function parameters (N\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLung function\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCT-ILD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIPF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNSIP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSarcoidosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePost COVID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1/FVC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e87.15\u0026thinsp;\u0026plusmn;\u0026thinsp;10.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e83.57\u0026thinsp;\u0026plusmn;\u0026thinsp;17.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e82.92\u0026thinsp;\u0026plusmn;\u0026thinsp;12.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e87.14\u0026thinsp;\u0026plusmn;\u0026thinsp;11.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e85.00\u0026thinsp;\u0026plusmn;\u0026thinsp;11.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e73.00\u0026thinsp;\u0026plusmn;\u0026thinsp;9.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.574\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e53.10\u0026thinsp;\u0026plusmn;\u0026thinsp;16.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e48.31\u0026thinsp;\u0026plusmn;\u0026thinsp;15.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e52.50\u0026thinsp;\u0026plusmn;\u0026thinsp;14.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e68.20\u0026thinsp;\u0026plusmn;\u0026thinsp;8.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e51.70\u0026thinsp;\u0026plusmn;\u0026thinsp;31.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e54.33\u0026thinsp;\u0026plusmn;\u0026thinsp;8.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.323\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFVC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e49.91\u0026thinsp;\u0026plusmn;\u0026thinsp;16.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e47.46\u0026thinsp;\u0026plusmn;\u0026thinsp;16.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e51.26\u0026thinsp;\u0026plusmn;\u0026thinsp;15.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e68.40\u0026thinsp;\u0026plusmn;\u0026thinsp;14.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e48.43\u0026thinsp;\u0026plusmn;\u0026thinsp;28.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e51.00\u0026thinsp;\u0026plusmn;\u0026thinsp;14.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.264\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"},{"header":"4 | DISCUSSION","content":"\u003cp\u003eDiffuse parenchymal lung diseases (DPLD) consist of a wide spectrum of disorders, which have various presentations and prognoses. DPLD is characterized by acute or chronic inflammation, which leads to mild to severe fibrosis. DPLD includes several disorders like IPF, Sarcoidosis, Connective tissues associated with DPLD (CTD-DPLD), Hypersensitivity Pneumonitis, LAM, NSIP, Post-COVID, etc.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eOur study showed that CTD-DPLD was the most common entity (32.4%) among DPLD cases, followed by NSIP (29.9%), IPF (20.8%), sarcoidosis (7.8%), post-COVID (5.2%), and Hypersensitivity pneumonitis (3.9%). Our study findings were similar to the study done by Valappil et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e In our study majority of patients were female, and CTD-DPLD was more common in females. However, contrary to Dhoonia et al.,\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e and Kundu et al.,\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003c/sup\u003e sarcoidosis and IPF were the most common entities. In our study, the majority of the patients belonged to the 40\u0026ndash;60 age group, and the mean age was 53.16\u0026thinsp;\u0026plusmn;\u0026thinsp;15.56. It was higher than the mean age of Akl et al.,\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e (36.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1), and lower than the study of Valappil et al., (55\u0026thinsp;\u0026plusmn;\u0026thinsp;15.45), and Pandey et al., (55.15\u0026thinsp;\u0026plusmn;\u0026thinsp;10.06). \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e The age was quite higher because our hospital is one of the reputed referral centers, so patients present late here when other hospitals have failed to evaluate or the patient is not responding to treatment.\u003c/p\u003e\u003cp\u003eIn our study, 45 (58.4%) patients were female and 32 (41.6%) patients were male. This indicates a higher incidence of DPLD in females compared to males among the study population, which contradicts the studies carried out by Pandey et al.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e The higher female prevalence in this study was consistent with the studies done by Valappil et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e This can be explained by majority of the patients are presented with CTD-DPLD.\u003c/p\u003e\u003cp\u003eThe most common symptoms of our study were cough (97.3%), shortness of breath (95.9%), and fatigue (72.6%). Other less frequent symptoms were weight loss, clubbing, arthralgia, and Raynaud\u0026rsquo;s phenomenon. Valappil et al. and Dhooria et al. studied that the most common symptoms were cough and shortness of breath. \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Dyspnea is a gradually progressive and disabling symptom. Clubbing was more frequent in IPF. Clubbing is the sign of a chronic low oxygen level in the blood. Arthralgia, Raynaud\u0026rsquo;s phenomenon, and skin tightening are commonly found in CTD-DPLD.\u003c/p\u003e\u003cp\u003eThe mean age for IPF was 59.19 years, and the majority of the people belonged to the \u0026gt;\u0026thinsp;60 age group (62.5%) in our study. It was closer to a study done by Maheshwari et al., where the mean age was 61.5 years.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e In the Trapani et al. study, the mean age was 64 years, which was higher than in our study.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e IPF was predominant in males in our study, which was similar to the study done by Trapani et al., and Maheshwari et al.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e IPF was more common in males. In IPF 10(62.5%), patients were non-smokers or ex-smokers, and 6 (37.5%) patients were smokers, but Maheshwari et al., most of the patients were smokers.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Valappil et al.,\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e study shows smoking has a significant association with IPF, but we found no correlation between smoking and IPF, probably due to the small sample size. GERD was the most common symptom of IPF in our study, which consists of studies done by Valappil et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Genetic factors possibly play a role in IPF risk and progression.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIn our study, CTD-DPLD was more common than other ILDs, and the majority of the patients were below 40 years (52%). The mean age for CTD-DPLD was 46.83 years. Kundu et al., study mean age for CTD-DPLD was 39.5, which was much lower than our study.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e In the Agarwal et al. study, the mean age was 45.69, which was similar to our study.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e CTD-DPLD included in our study are Rheumatoid arthritis, SLE, Systemic Sclerosis, and Mixed Connective Tissue Disease (MCTD). Connective tissue disorder was found to be predominant in women (84%) in our study, which is consistent with the study done by Valappil et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Most of the patients with CTD-DPLD were non-smokers, and the majority of them were female.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eNSIP includes pathological features of inflammation and pulmonary fibrosis. Hino et al.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e explained in their study that NSIP was classified into 2 subtypes: Fibrotic and cellular pattern. Cellular pattern NSIP mainly consists of inflammatory cell infiltration. The typical pathological features of fibrotic NSIP are characterized by homogeneous and diffuse fibrosis with interstitial deposition of collagen and chronic inflammatory cells. NSIP was susceptible to steroid therapy, had longer overall survival, and relatively preserved pulmonary function. In the Valappil et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e study frequency of NSIP was 7.8% but in our study, NSIP was 31%, the mean age was 56.22 years, and most of them were female and non-smokers. In Kumar et al., study, the mean age was 49.83 years, and the majority of them were female, which was quite similar to our study.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe most frequent HRCT pattern was the reticulo-nodular pattern (67.1%); in almost all cases, there is bilateral (95.9%) involvement. The second common HRCT pattern in our study was the ground-glass opacification. The least frequently reported HRCT patterns in our cases were septal thickening (32.9%), fibrosis (24.7%), and hilar lymphadenopathy (8.2%). In AKI et al., the most frequent pattern was the nodular pattern, followed by ground glass opacification.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e In IPF (idiopathic pulmonary fibrosis), the presence of HRCT features of UIP (usual interstitial pneumonia), which includes honeycombing and lower subpleural predominance. In our study, bilateral, subpleural honeycombing was predominant in IPF.\u003c/p\u003e\u003cp\u003eLinear and reticular opacities are the most important HRCT findings in connective tissue disease. Reticular pattern occurs due to the thickening of the interlobular interstitium within the secondary pulmonary lobule. A honeycomb pattern may be seen in the end stage. Other pulmonary involvement includes nodular opacities, ground glass opacities, and airspace consolidation. In our study, the most frequent HRCT findings were reticulonodular shadow, ground glass opacity, and consolidation. The least common HRCT pattern was honeycombing and fibrosis. In Gharsalli et al., study most common HRCT pattern was ground glass opacity, reticular opacities, and airspace consolidation.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Ground glass opacity 17(73.9%) is frequent in NSIP.\u003c/p\u003e\u003cp\u003eDPLD is characterized by restrictive lung function, which means a reduction in lung volume with a ratio of Forced expiratory volume in one second (FEV1) to forced vital capacity (FVC), normal or greater than normal. In our study, FEV1 and FVC were both reduced, and the FEV1/FVC ratio was increased. Verma et al., study mean FVC% of predicted was 58.92.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e In our study mean FVC was 51.06, which was much less than Verma et al study. The FEV1/FVC ratio in our study was 84.34, which was consistent with Mohanty et al., study (83.54).\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.1 | Limitations of the study\u003c/h2\u003e\u003cp\u003eThis is the first study from Bangladesh to evaluate the spectrum of DPLD from a prospective standpoint. The study was limited, though, in that it was not possible to do a surgical biopsy due to a lack of facilities, measure the lung's diffusing capacity, or perform the six-minute walk test. Treatment response to antifibrotic or other drugs was not evaluated in this study.\u003c/p\u003e\u003cp\u003eIn conclusion, CTD-DPLD patients exhibited NSIP patterns on HRCT, were younger, and had more extrapulmonary features compared to other DPLDs. Further prospective epidemiological investigations are needed to better understand the spectrum of DPLDs.\u003c/p\u003e\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eATS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAmerican Thoracic Society\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAnti-CCP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAnti-cyclic citrullinated peptide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMU\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBangladesh Medical University\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCTD-DPLD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConnective tissue disease-associated diffuse parenchymal lung diseases\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCKD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003echronic kidney disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDiabetes mellitus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDPLD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDiffuse Parenchymal Lung Diseases\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eERS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEuropean Respiratory Society\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGERD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGastroesophageal reflux disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHRCT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHigh-resolution computed tomography\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIIP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIdiopathic interstitial pneumonitis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eILDs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterstitial Lung Diseases\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIPF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIdiopathic pulmonary fibrosis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIRB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInstitutional Review Board\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMCTD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMixed Connective Tissue Disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNSIP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNon-specific interstitial pneumonia\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSPSS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUIP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUsual interstitial pneumonia\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRajashish Chakrabortty - Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – original draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSamia Rahman- Data curation, Formal analysis, Investigation, Methodology, Visualization.\u003c/p\u003e\n\u003cp\u003eMohammed A. Rahman\u0026nbsp;– Project administration, Investigation, Methodology, Writing – review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eGoutam K. Acherjya- Data curation, Formal analysis, Investigation, Methodology, Writing – review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eSusanta K. Paul- Data curation, Investigation, Visualization, Methodology, Writing – review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting this study's findings are available on request from the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely appreciate all the participants in this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare there is no conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTRANSPARENCY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe lead author, Rajashish Chakrabortty, affirms that this manuscript is an honest, accurate, and transparent account of the study being reported, that no important aspects of the study have been omitted, and that any discrepancies from the study as planned (and if relevant, registered) have been explained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING DECLARATION:\u003c/strong\u003e No Funding for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCLINICAL TRIAL NUMBER\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eETHICS DECLARATIONS\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICAL APPROVAL and CONSENT to PARTICIPANT\u003c/strong\u003e: This study was approved by the institutional review board of Bangladesh Medical University (No.BSMMU/2021/12460). Written informed consent was obtained from all participants. All procedures were conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMaher TM. Diffuse parenchymal lung disease. Medicine. 2008;36(5):265\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMigita K, Arai T, Jiuchi Y, Izumi Y, Iwanaga N, Kawahara C, Suematsu E, Miyamura T, Tsutani H, Kawabe Y, Matsumura R. Predictors of mortality in patients with interstitial lung disease treated with corticosteroids: results from a cohort study. Medicine. 2014;93(26):e175.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOldham JM, Noth I. Idiopathic pulmonary fibrosis: early detection and referral. Respir Med. 2014;108(6):819\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKing TE Jr, Tooze JA, Schwarz MI, Brown KR, Cherniack RM. 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Guidelines for the diagnosis and treatment of pulmonary hypertension: the Task Force for the Diagnosis and Treatment of Pulmonary Hypertension of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS), endorsed by the International Society of Heart and Lung Transplantation (ISHLT). Eur Heart J. 2009;30(20):2493\u0026ndash;537.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRaghu G, Collard HR, Egan JJ, Martinez FJ, Behr J, Brown KK, Colby TV, Cordier JF, Flaherty KR, Lasky JA, Lynch DA. An official ATS/ERS/JRS/ALAT statement: idiopathic pulmonary fibrosis: evidence-based guidelines for diagnosis and management. Am J Respir Crit Care Med. 2011;183(6):788\u0026ndash;824.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYadav H, Srivastava R. Clinicoradiological and demographic pattern in diffuse parenchymal lung diseases: An observational study. Int J Med Res Rev. 2018;6(06):308\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eValappil AT, Mehta AA, Kunoor A, Haridas N. Spectrum of diffuse parenchymal lung diseases: An experience from a tertiary care referral Centre from South India. Egypt J Chest Dis Tuberculosis. 2018;67(3):276\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDhooria S, Agarwal R, Sehgal IS, Prasad KT, Garg M, Bal A, Aggarwal AN, Behera D. Spectrum of interstitial lung diseases at a tertiary center in a developing country: A study of 803 subjects. PLoS ONE. 2018;13(2):e0191938.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKundu S, Mitra S, Ganguly J, Mukherjee S, Ray S, Mitra R. Spectrum of diffuse parenchymal lung diseases with special reference to idiopathic pulmonary fibrosis and connective tissue disease: An eastern India experience. Lung India. 2014;31(4):354\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAkl YM, Elhabashy AH, Mostafa NM, Hasswa MK, Hussein SA. Spectrum of Diffuse Parenchymal Lung Diseases Other than Idiopathic Pulmonary Fibrosis Based on Clinical. Radiological Histopathological Correlation Archit;1:2.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePandey A, Deepak VR, Aditya V. To Evaluate the Role of Radiological and Histopathological Examination in the Diagnosis of Interstitial Lung Diseases. Int J Sci Res. 2019;8(12):67\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTrapani D, Scalia R, Giordano E, Castellano G, Doi G, Gaeta A, Pellizzari G, Schianca AC, Katrini J, D\u0026rsquo;Ambrosio S, Santoro C. Interstitial lung disease in patients enrolled in early-phase clinical trials: the ILDE study. ESMO open. 2024;9(8):103658.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAgarwal M, Gupta ML, Deokar K, Shadrach BJ, Bharti N, Sonigra M. Clinico-radiological profile of connective tissue disease-related interstitial lung diseases from a tertiary care centre of India: a cross-sectional study. Monaldi Arch Chest Dis. 2021;91(4).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGharsalli H, Attia M, Hantous-Zannad S, Sahnoun I, Maalej S, El Gharbi LD, Neji H, Miled-Mrad KB. Interstitial lung diseases associated with connective tissue pathologies: radiologic features. Open J Respiratory Dis. 2019;9(04):112.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHino T, Lee KS, Han J, Hata A, Ishigami K, Hatabu H. Spectrum of pulmonary fibrosis from interstitial lung abnormality to usual interstitial pneumonia: importance of identification and quantification of traction bronchiectasis in patient management. Korean J Radiol. 2020;22(5):811.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVerma G, Kalmodia S, Prasanthi J, Amarnath R, MC S, Ravichanclran A, Mohanty L. Spirometry pattern on Diffuse Parenchymal Lung Diseases (DPLD) diagnosis. Indian J Basic Appl Med Res. 2020;9(3).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMohanty DL, Vachhani DR. Spirometry in Diffuse Parenchymal Lung Diseases. Int J Innovative Res Med Sci. 2018:1804\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePandhi N, Rana S, Malhotra B, Kajal NC, Gupta S, Neki NS, Kaur A, Randhawa D. Clinicoradiological Profile of Patients with Diffuse Parenchymal Lung Disease. 2019; 5(2).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Connective tissue disease, Diffuse parenchymal lung disease, Idiopathic pulmonary fibrosis, Spectrum","lastPublishedDoi":"10.21203/rs.3.rs-7244949/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7244949/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe definite patterns of Diffuse Parenchymal Lung Diseases (DPLD) are not known in Bangladesh. This is the first study in our country aimed at evaluating the frequency and clinical spectrum of various types of DPLDs.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis cross-sectional study was conducted at the Department of Respiratory Medicine, Bangladesh Medical University (BMU) in Bangladesh over one year. DPLD was diagnosed based on clinical, radiological, pulmonary function tests, and/or histopathological background. Patients with a diagnosis of chronic lung disease like tuberculosis, lung malignancy, asthma, chronic obstructive pulmonary disease (COPD), bronchiectasis, chronic heart disease like chronic heart failure, or valvular heart disease were excluded from this study.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSeventy-seven participants were included in this study. Connective tissue disease-associated DPLD (CTD-DPLD) was the most common entity (n\u0026thinsp;=\u0026thinsp;25, 32.4%), followed by non-specific interstitial pneumonia (NSIP) (n\u0026thinsp;=\u0026thinsp;23, 29.9%), and idiopathic pulmonary fibrosis (IPF) (n\u0026thinsp;=\u0026thinsp;16, 20.8%). The mean age was 53.16\u0026thinsp;\u0026plusmn;\u0026thinsp;15.56 years, and females were predominant (58.4%). Age, sex, and occupation were significantly associated with DPLD (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The most common symptom was coughing (97.4%), followed by shortness of breath (96.1%). Clubbing (87.5%) and Gastroesophageal reflux disease (GERD) (62.5%) were most frequent in IPF, and extra-pulmonary manifestations in CTD-DPLD. Clubbing, arthralgia, Raynaud's, joint deformity, and GERD were significantly associated with DPLD. Bilateral (95%), subpleural (75.0%) involvement, and honeycombing (68.7%) were more frequent in IPF. On pulmonary function tests, most of them have restrictive lung disease.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eDPLD encompasses a group of diseases with a wide range of causes that have various presentations and prognoses. We found CTD-DPLD, IPF, and NSIP were the most common causes. CTD-DPLD typically occurs in younger individuals with a higher prevalence of extrapulmonary manifestations.\u003c/p\u003e","manuscriptTitle":"Spectrum of Diffuse Parenchymal Lung Diseases in Bangladesh: Institutional-based Cross- sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 02:32:51","doi":"10.21203/rs.3.rs-7244949/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-10-25T23:59:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-29T08:56:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"56144145371202346474786801099986500181","date":"2025-09-23T13:42:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255956555373033588060265413357942782775","date":"2025-09-22T03:41:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129121105962276978580157661434782201027","date":"2025-09-20T10:29:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223547777704639189123430330483035482869","date":"2025-09-12T14:45:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-12T13:16:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-10T04:24:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-22T11:37:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-20T14:52:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2025-08-20T14:42:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c51b6b95-e827-4f54-a47f-c6e8f204b734","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-23T02:32:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 02:32:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7244949","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7244949","identity":"rs-7244949","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}