Screening for Rheumatoid Arthritis-Associated Interstitial Lung Disease Using Low-Dose CT: An Emerging Approach — An Observational Prospective Case-Control Study

Screening for Rheumatoid Arthritis-Associated Interstitial Lung Disease Using Low-Dose CT: An Emerging Approach — An Observational Prospective Case-Control 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 Screening for Rheumatoid Arthritis-Associated Interstitial Lung Disease Using Low-Dose CT: An Emerging Approach — An Observational Prospective Case-Control Study Kinga Fritsch, Judit Majnik, Michal Tomčík, Janos Gyebnar, Tamas Munkacsi, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7204385/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Nov, 2025 Read the published version in Arthritis Research & Therapy → Version 1 posted 9 You are reading this latest preprint version Abstract Background Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is a major contributor to rheumatoid arthritis (RA) related morbidity and mortality. Early detection is challenging due to subclinical onset and limitations of conventional screening modalities. This study evaluated the diagnostic performance of low-dose photon-counting detector CT (LD PCD-CT) for RA-ILD and assessed its prevalence and risk factors in a Hungarian RA cohort. Methods In this prospective study (Feb 2022–June 2023), 492 consecutively enrolled RA patients without known ILD, underwent LD PCD-CT, digital chest radiography (DR) and pulmonary function testing (PFTs). Imaging was scored using a standardized LD severity scale. Clinical, demographic, and serological data were analyzed to identify ILD predictors. Statistical analyses included Kolmogorov–Smirnov, t-tests, Mann–Whitney U, chi-squared/Fisher’s exact tests, Pearson correlation, and ROC analysis. Logistic regression was used to identify independent risk factors. Results LD PCD-CT identified interstitial abnormalities in 35% of patients. By contrast, clinical assessment and PFTs detected abnormalities in only 44% and 22% of these cases, respectively. Among patients without CT-defined abnormalities, 42% had a positive clinical assessment and 23% had abnormal PFTs, indicating limited diagnostic specificity. The most frequent findings were interstitial reticular abnormalities (58%) and usual interstitial pneumonia (22%). Independent ILD predictors included age ≥ 50 years, male sex, ≥ 25 pack-year smoking history, rheumatoid factor (RF) positivity, and elevated lactate dehydrogenase (LDH) levels. LD PCD-CT had a mean effective radiation dose of 0.415 mSv, remaining within low-dose diagnostic thresholds. Conclusion LD PCD-CT demonstrated superior sensitivity and specificity for early RA-ILD detection compared to clinical assessment and PFTs, while maintaining low radiation exposure. Incorporating LD PCD-CT into risk-stratified screening protocols may facilitate earlier diagnosis and timely therapeutic interventions, ultimately improving patient outcomes. Clinical trial registration number: NCT05391100 rheumatoid arthritis interstitial lung disease screening low-dose computed tomography photon-counting detector computed tomography pulmonary function tests risk factors Figures Figure 1 Figure 2 Figure 3 Background Rheumatoid arthritis is a systemic autoimmune disease affecting 0.5-1% of the global population. [ 1 ] Among its various extra-articular manifestations, pulmonary involvement—particularly interstitial lung disease (ILD)—is one of the most serious. Alongside cardiovascular disease and infections, it contributes substantially to RA-related morbidity and mortality. [ 2 , 3 ] RA-associated ILD (RA-ILD) carries a three-fold increased risk of death compared to RA patients without ILD [ 4 , 5 ] and frequently complicates disease management, contributing to the subset of patients classified as having difficult-to-treat RA. [ 6 , 7 ] Clinically significant ILD occurs in approximately 11% of RA patients. [ 8 ] However, a substantially larger proportion may exhibit radiological features consistent with ILD despite being asymptomatic and having preserved lung function. This condition, referred to as subclinical ILD, is typically defined by the presence of mild interstitial abnormalities on high-resolution computed tomography (HRCT), normal PFTs, and the absence of respiratory symptoms. [ 9 ] The reported prevalence of subclinical ILD in RA varies widely, ranging from 5–67%, reflecting differences in study design, patient populations, imaging techniques, and the criteria used to define ILD. [ 10 ] Considering that ILD may occur at any point during the natural history of RA, and its clinical manifestations usually appear in advanced stages, early diagnosis is challenging and requires a multidisciplinary team approach. [ 11 , 12 ] This underscores an unmet need for timely, sensitive screening strategies that enable earlier diagnosis and intervention. Early detection offers a critical window of opportunity for timely adjustment of disease-modifying antirheumatic drug (DMARD) regimens and initiation of antifibrotic therapies—interventions shown to stabilize CT changes in progressive autoimmune ILDs and improve prognosis. [ 13 , 14 ] In this context, our primary objective was to assess the effectiveness of LD PCD-CT as a screening tool for RA-ILD. A secondary aim was to estimate the prevalence of ILD (including subclinical cases) among Hungarian RA patients and to develop a risk stratification model. Methods Ethics and study design This study was an observational prospective case-control study, in accordance with the ethical principles of the Declaration of Helsinki, the International Conference on Harmonization, and Good Clinical Practice with the permission of the Regional Research Ethics Committee (number: IV/2683-1/2022/EKU). It has been registered on the ClinicalTrials.gov (NCT05391100) web page. It was performed between February 2022. and June 2023. All patients provided written informed consent. Patient enrollment Consecutive RA patients aged > 40 years were recruited from the Rheumatology Outpatient Department of Semmelweis University (Polyclinic of the Hospitaller Brothers of St. John of God, Budapest, Hungary). All participants underwent regular follow-up at the institution, received standard RA medications, and had annual chest X-rays following their diagnosis. None of them had a previous indication for HRCT to rule out ILD. Inclusion required RA diagnosis per 2010 ACR/EULAR criteria. [ 15 ] To limit radiation exposure, reproductive-age patients were excluded. Further exclusion criteria included pregnancy, breastfeeding, prior ILD or lung cancer, and recent (within 2 months) lung infection. Serological data Demographic, clinical, and serological data, including RF, anti-citrullinated peptide antibodies (ACPA), and RA treatment history (synthetic/biological DMARDs and corticosteroids) were recorded at enrollment. RF, C-reactive protein (CRP), LDH, and cancer antigen 15 − 3 (CA 15 − 3) levels were measured using quantitative immunoturbidimetric assays on the Roche platform (F. Hoffmann–La Roche AG, Basel, Switzerland), with RF cutoff at 14 U/mL, CRP < 5 mg/L, and LDH reference ranges of 135–214 U/L for women and 135–225 U/L for men. Anti-cyclic citrullinated peptide (CCP) antibodies were measured using the Immunoscan CCPlus kit (SVAR Life Science, Malmö, Sweden) with a 25 U/mL cutoff. Erythrocyte sedimentation rate (ESR) was assessed using the Vacuette SRS100 system (Greiner Bio-One GmbH, Kremsmünster, Austria), with reference values of < 30 mm/h for women and < 20 mm/h for men. Imaging Patients underwent DR and consecutive chest LD PDC-CT scans on the same day at the Clinic for Medical Imaging, Semmelweis University. Anteroposterior and lateral digital radiographs were performed on a GE Discovery XR 656 HD system (GE Healthcare, Chicago, IL, USA). High-resolution (slice thickness: 0.4 mm) CT scans were carried out with a PCD-CT scanner (Naeotom Alpha Peak®, Siemens Healthineers, Erlangen, Germany). CT measurements were performed with a large field of view (FOV) [median (interquartile range): 35 (32–38) cm] and a 512 x 512 matrix. Additionally, 3 strength-level quantitative iterative reconstruction algorithms were utilized to enhance image quality. To exclude ground-glass opacity (GGO) from dependent atelectasis, prone inspiratory HRCT measurements were performed. Radiological findings were assessed by two radiology specialists according to the Fleischner Society White Paper statement on the diagnosis of idiopathic pulmonary fibrosis. [ 16 ] Then the images were reviewed by an interdisciplinary ILD board to reach an agreement with the pulmonologists. Evaluation of parenchymal abnormalities Interstitial abnormalities were classified into four categories: ground-glass opacity, reticulation, bronchiectasis, and honeycombing, and their extent was scored for each lung lobe using a Likert-type scale (0 = absent; 1 = 1–25%; 2 = 26–50%; 3 = 51–75%; 4 = 76–100%). [ 17 ] A total ILD CT score was obtained by summing all scores, the final value ranging from 0–80. The CT pattern of disease was recorded as usual interstitial pneumonia (UIP), non-specific interstitial pneumonia (NSIP), pleuro-parenchymal fibroelastosis (PPFE), respiratory bronchiolitis interstitial lung disease (RB-ILD), organizing pneumonia (OP), non-specified small extension parenchymal changes (NSSEPC) and other patterns. [ 18 ] Dose considerations Effective radiation dose (E) for DR and LD PCD-CT was assessed using dose area product (DAP) and area product (tDLP) values extracted via IMPAX (Agfa Corporate, Mortsel, Belgium) and syngo.via software (Siemens Healthineers, Heidelberg, Germany). Approximate E (mSv) was calculated using standard conversion factors: E = DAP × 0.16 mSv/mGy*cm for radiographs/tomosynthesis and E = tDLP × 0.014 mSv/mGy*cm for CT. [ 19 , 20 ] Pulmonary Function tests All patients were asked for pulmonary symptoms, underwent physical examination and detailed pulmonary function tests (PFTs) were performed by pulmonologists at the Department of Pulmonology, Semmelweis University as described previously. The percent of predicted forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1, FEV1/FVC), total lung capacity (TLC) the percent of predicted diffusing capacity of the lung for carbon monoxide (DLCO) according to the American Thoracic Society and European Respiratory Society (ARS/ETS) guidelines [ 21 ], and 6-minute walking tests (6MWT) were assessed. [ 22 ] PFT values were expressed as a percentage of predicted values. Statistics Statistical analysis was performed using SPSS software version 26.0 (IBM, Armonk, NY, USA). Data are expressed as the mean ± standard deviation (SD) or median (interquartile range (IQR)) for continuous variables and percentages for categorical variables.The distribution of continuous variables was evaluated by the Kolmogorov–Smirnov test. Continuous variables were analyzed using the t-test when normally distributed, and using the Mann–Whitney U test when non-normally distributed. Nominal variables were compared between groups using the chi-squared or Fisher’s exact test, as appropriate. Dose values were compared with paired t-tests. Correlations were determined by Pearson’s analysis. Sensitivity, specificity, and predictive values of chest X-ray and pulmonary function tests were calculated and compared with those of LD PCD-CT. Receiver operating characteristic (ROC) curve analysis was performed to identify smoking history cut-off value associated with the development of ILD. Univariable and multivariable binary logistic regression analyses were performed using the enter likelihood method and forward selection methods to evaluate the odds ratios associated with potential independent variables. P -values < 0.05 were considered statistically significant. Results Clinical data, enrollment A total of 544 consecutive RA patients were initially recruited for the study. Of these, 39 individuals were excluded during the study period due to either the development of acute lung infections or withdrawal of consent, resulting in 505 patients completing the baseline visit. Subsequently, four patients were excluded due to discrepancies in identification or laboratory data, and nine were excluded due to non-evaluable chest X-rays. Therefore, the final analysis included data from 492 RA patients. 7% (n = 36) of them were found to have basal crackles, 18% (n = 87) had a dry cough and 29% (n = 139) had exertional dyspnoea. Detailed clinical and demographic characteristics are presented in tables 1 and 2. Table 1. Baseline demographic, serologic and medication characteristics of RA patients Overall RA patients (n = 492) LD PCD-CT total ILD score ≥ 3 (n = 171) LD PCD-CT total ILD score < 3 (n = 321) p-value Demographics Age (years) 62.79 ± 10.44 66.51 ± 9.47 60.81 ± 10.41 < 0.001 Age over 65 years, n (%) 236 (48%) 107 (63%) 129 (40%) < 0.001 Age over 50 years at RA onset, n (%) 249 (52%) 104 (63%) 145 (46%) < 0.001 Male sex, n (%) 88 (18%) 41 (24%) 47 (15%) 0.010 Smoking history Ever smoking, n (%) 249 (51%) 95 (57%) 154 (48%) 0.089 Current smoking, n (%) 71 (15%) 30 (18%) 41 (13%) 0.141 Smoking (pack-years) 10.07 ± 16.26 13.28 ± 18.96 8.42 ± 14.41 0.015 Pack-year ≥ 25, n (%) 76 (16%) 39 (23%) 37 (12%) 0.001 RA characteristics RA duration (years) 14.27 ± 10.23 15.53 ± 11.53 13.61 ± 9.43 0.250 DAS28-ESR 2.79 ± 1.29 2.85 ± 1.26 2.75 ± 1.30 0.381 BMI (kg/m 2 ) 26.66 ± 5.00 26.98 ± 5.04 26.49 ± 4.98 0.229 RA serologies RF (U/mL) 108.93 ± 195.21 146.37 ± 242.83 87.92 ± 159.19 0.013 RF positivity, n (%) (> 14U/mL) 335 (69%) 130 (77%) 205 (65%) 0.005 Anti-CCP (U/mL) 644.23 ± 1022.77 704.08 ± 1073.90 611.11 ± 993.66 0.086 Anti-CCP positivity, n (%) (> 25 U/mL) 311 (65%) 119 (71%) 192 (62%) 0.042 Anti-MCV positivity, n (%) (> 20 U/mL) 274 (61%) 104 (65%) 170 (58%) 0.171 RF + anti-CCP positivity, n (%) 282 (58%) 112 (67%) 170 (54%) 0.008 RA medication JAK inhibitors, n (%) 44 (9%) 15 (9%) 29 (9%) 0.944 Rituximab, n (%) 29 (6%) 9 (5%) 20 (6%) 0.680 Abatacept, n (%) 12 (2%) 6 (4%) 6 (2%) 0.356 Anti-IL6 antibodies, n (%) 69 (14%) 28 (17%) 41 (13%) 0.257 TNF inhibitors, n (%) 209 (43%) 70 (42%) 139 (44%) 0.665 Leflunomid, n (%) 137 (28%) 44 (26%) 93 (29%) 0.477 MTX, n (%) 466 (95%) 160 (95%) 306 (96%) 0.602 Steroid, n (%) 421 (86%) 142 (85%) 279 (88%) 0.322 Results are presented as mean ± standard deviation, median (interquartile range) and frequency (percentage). Significant differences (p<0.05) are highlighted in bold. RA: rheumatoid arthritis; RF: rheumatoid factor; anti-CCP: anti cyclic citrullinated peptide antibody; anti-MCV: anti citrullinated vimentin antibody; JAK: Janus kinases; IL-6: interleukin 6; TNF: tumor necrosis factor, MTX: methotrexate Table 2. Baseline clinical, radiological and laboratory parameters of RA patients Overall RA patients (n = 492) LD PCD-CT total ILD score ≥ 3 (n = 171) LD PCD-CT total ILD score < 3 (n = 321) p-value Respiratory signs, symptoms Basal crackles, n (%) 36 (7%) 21 (12%) 15 (5%) 0.002 Exertional dyspnoea, n (%) 139 (29%) 45 (27%) 94 (30%) 0.520 Dry cough, n (%) 87 (18%) 30 (18%) 57 (18%) 0.985 Pulmonary function tests and pulmonary parameters FEV1 (L) 2.49 ± 0.63 2.36 ± 0.50 2.56 ± 0.68 0.015 FEV1% predicted 95.32 ± 16.57 96.17 ± 16.71 94.84 ± 16.51 0.998 FEV 1 < 80% predicted 48 (14%) 14 (12%) 34 (16%) 0.286 FVC (L) 3.14 ± 0.77 3.00 ± 0.66 3.22 ± 0.81 0.026 FVC % predicted 94.99 ± 15.48 95.61 ± 16.63 94.65 ± 14.85 0.800 FVC < 80% predicted 56 (17%) 18 (15%) 38 (18%) 0.563 TLC (L) 5.92 ± 2.08 5.76 ± 1.01 6.02 ± 2.49 0.510 TLC % predicted 114.93 ± 20.09 115.44 ± 19.19 114.65 ± 20.60 0.607 DLCO (mmol/min/kPa) 8.83 ± 2.01 8.29 ± 1.90 9.14 ± 2.01 < 0.001 DLCO % predicted 122.16 ± 20.89 117.65 ± 20.09 124.65 ± 20.95 0.003 DLCO < 75% predicted 7 (2%) 4 (3%) 3 (1%) 0.251 Radiogical findings X-Ray Fibrosis, n (%) 14 (4%) 11 (6%) 3 (1%) 0.001 LD PCD-CT dUIP, n (%) 11 (2%) 10 (6%) 1 < 0.001 pUIP, n (%) 7 (1%) 7 (4%) 0 0.001 iUIP, n (%) 6 (1%) 6 (4%) 0 0.002 NSIP, n (%) 8 (2%) 8 (5%) 0 < 0.001 RB-ILD, n (%) 4 (1%) 4 (2%) 0 0.014 PPFE, n (%) 10 (2%) 4 (2%) 6 (2%) 0.744 NSSEPC, n (%) 64 (13%) 37 (22%) 27 (9%) 20 mm/h, n (%) 138 (31%) 55 (35%) 83 (29%) 0.191 CRP (mg/mL) 6.97 ± 11.86 7.35 ± 11.89 6.75 ± 11.86 0.042 CA 15 − 3 (U/mL) 21.33 ± 10.08 23.89 ± 12.54 20.01 ± 8.26 0.008 CA 15 − 3 > 25 U/mL, n (%) 128 (30%) 52 (35%) 76 (27%) 0.060 LDH (U/L) 208.28 ± 44.10 217.32 ± 47.17 203.19 ± 41.50 0.003 LDH > 214 U/L, n (%) 170 (39%) 75 (47%) 95 (34%) 0.005 Results are presented as mean ± standard deviation and frequency (percentage). Significant differences (p<0.05) are highlighted in bold. FEV1: forced expiratory volume in 1 second; FVC: forced vital capacity; TLC: total lung capacity; DLCO: diffusion capacity of the lung for carbon monoxide; dUIP: definite usual interstitial pneumonia; pUIP: probable usual interstitial pneumonia; iUIP: indeterminate usual interstitial pneumonia; NSIP: non-specific interstitial pneumonia; PPFE: pleuro-parenchymal fibroelastosis; RB-ILD: respiratory bronchiolitis interstitial lung disease; NSSEPC: non-specified small extension parenchymal changes; ESR: erythrocyte sedimentation rate; CRP: C-reactive protein; CA 15-3: cancer antigen 15-3; LDH: lactate dehydrogenase Imaging findings All patients underwent digital radiography and consecutive LD PCD-CT scans at baseline. The median (IQR) total ILD CT score was 2 (0; 3); a threshold score ≥ 3 was considered clinically significant. Fibrotic changes were identified in only 4% of patients on chest radiography (n = 14). In contrast, LD PCD-CT revealed interstitial alterations in 35% (n = 171), of whom only 6.5% showed fibrotic signs on X-ray. Regarding the CT patterns UIP has been found in 22%, PPFE in 9%, NSIP in 7% and RB-ILD in 4% of the patients. Small extension interstitial lung abnormalities were identified in 58% of the cases (Fig. 1). Radiation Exposure The effective radiation dose of DR was 0.171 ± 0.286 mSv, with a median dose of 0.067 (0.043–0.127) mSv. LD PCD-CT showed a significantly higher but still low radiation dose of 0.415 ± 0.316 mSv, with a median of 0.378 (0.322–0.455) mSv (p < 0.001). Pulmonary Function Parameters Pulmonary function tests were available for 363 patients. Mean spirometric parameters fell within the physiological ranges. The mean predicted FVC was 95.00 ± 15.48%, while the diffusing capacity for DLCO was 122.16 ± 20.89%. Among patients with ILD CT score ≥ 3, only 12% had FVC < 80%, and 3% had DLCO < 75%. A mild but significant negative correlation was observed between ILD CT score and DLCO (r = − 0.224, p < 0.001). The sensitivity and specificity of pulmonary function tests for ILD detection were 21% and 77%, respectively (Table 2, Fig. 2). Risk Profiles Clinical and laboratory characteristics of the RA patients were analyzed to identify potential associations with interstitial lung involvement. The median age at diagnosis in our cohort was 50 (40–60) years; accordingly, age ≥ 50 years at diagnosis was used as a clinical threshold for risk stratification. Receiver operating characteristic (ROC) curve analysis of smoking history identified an optimal cut-off value of 25 pack-years, which was subsequently adopted as the threshold to define high-risk smoking exposure. Univariate analysis using binary logistic regression (Enter method) revealed that older age, age ≥ 50 years at RA diagnosis, male sex at birth, smoking exposure ≥ 25 pack-years, RF and aCCP positivity, and elevated LDH levels were significantly associated with a total ILD CT score ≥ 3. Multivariate logistic regression using the backward (likelihood ratio) method identified five independent predictors of interstitial lung involvement: older age (OR 2.594 [95% CI 1.686, 3.989] p < 0.001), smoking exposure ≥ 25 pack-years (OR 1.885 [95% CI 1.070, 3.322] p < 0.028), male sex (OR 1.741 [95% CI 1.003, 3.023] P = 0.049), RF positivity (OR 1.952 [95% CI 1.214, 3.319] p = 0.006), and high LDH levels (OR 1.872 [95% CI 1.214, 2.885] p = 0.005) (Table 3). Table 3 Association between LD PCD-CT findings, clinical and serological features Univariate analysis Multivariate analysis ODDS 95% CI p ODDS 95% CI p Categorical variables Age over 65 years 2.488 1.699–3.645 < 0.001 2.594 1.686–3.989 < 0.001 Age over 50 years at RA onset 2.011 1.367–2.957 14 U/mL) 1.852 1.206–2.845 0.005 1.952 1.214–3.319 0.006 Anti-CCP positivity (> 25 U/mL) 1.518 1.014–2.272 0.043 RF + anti-CCP positivity 1.694 1.147–2.502 0.008 Anti-MCV positivity (> 20 U/mL) 1.322 0.886–1.972 0.171 JAK inhibitors 0.977 0.508–1.878 0.944 Rituximab 0.843 0.775–1.896 0.680 Abatacept 1.926 0.611–6.067 0.263 Anti-IL6 antibodies 1.351 0.802–2.277 0.258 TNF inhibitors 0.920 0.630–1.343 0.665 Leflunomid 0.858 0.564–1.307 0.477 MTX 0.784 0.314–1.958 0.603 Steroid 0.763 0.447–1.305 0.323 Elevated LDH level (> 214 U/L) 0.758 1.181–2.615 0.005 1.872 1.214–2.885 0.005 Elevated CRP level (> 5 mg/mL) 1.345 0.895–2.022 0.154 Elevated CA15-3 level (> 25 U/mL) 1.505 0.981–2.309 0,061 ESR > 20 mm/h 1.319 0.871–1.997 0.191 Basal crackles 2.886 1.446–5.761 0.003 Exertional dyspnoea 0.872 0.574–1.324 0.520 Dry cough 0.995 0.611–1.621 0.985 Continuous variables Age 1.058 1.037–1.079 < 0.001 Pack-years 1.018 1.006–1.029 0.003 DAS28-ESR 1.063 0.920–1.230 0.406 BMI 1.020 0.982–1.058 0.304 RA duration 1.018 1.000-1.037 0.050 RF 1.001 1.000-1.003 0.004 Anti-CCP 1.000 1.000–1.000 0.355 CRP 1.004 0.988–1.020 0.611 ESR 1.009 0.997–1.021 0.139 CA 15 − 3 1.039 1.018–1.061 < 0.001 LDH 1.007 1.003–1.012 0.002 Results from univariate analysis, binary logistic regression using the enter method, and multivariate logistic regression with the backward (likelihood ratio) method. Significant predictors (p<0.05) are highlighted in bold. The median age at diagnosis in our cohort was 50 years; thus, age ≥50 at diagnosis was considered a clinical risk threshold. Receiver operating characteristic (ROC) curve analysis for smoking history revealed an optimal cut-off at 25 pack-years, which was subsequently used as a threshold to define high-risk smoking exposure in further analysis. At univariate analysis, older age (>65 years), age ≥50 years at RA diagnosis, male sex at birth, smoking exposure ≥25 pack-years, rheumatoid factor (RF) and anti-cyclic citrullinated peptide (aCCP) positivity, elevated lactate dehydrogenase (LDH) levels were significantly associated with a total ILD CT score ≥3. Multivariate analysis identified five independent predictors of lung involvement: older age (>65 years), male sex, smoking exposure ≥25 pack-years, RF positivity, and elevated LDH levels. RF: rheumatoid factor; anti-CCP: anti cyclic citrullinated peptide antibody; anti-MCV: anti citrullinated vimentin antibody; JAK: Janus kinases; IL-6: interleukin 6; TNF: tumor necrosis factor, MTX: methotrexate; LDH: lactate dehydrogenase; CRP: C-reactive protein; CA 15-3: cancer antigen 15-3; ESR: erythrocyte sedimentation rate Considering a horizontal multi-level hierarchy of diagnostic modalities for detecting subclinical ILD, LD PCD-CT identified interstitial lung involvement-defined as a total CT ILD score ≥ 3-in 171 of 492 RA patients (35%). In contrast, clinical assessment and PFTs detected abnormalities in only 44% and 22% of these LD PVD-CT-positive cases, respectively. DR revealed fibrotic changes in just 6.5% of this subgroup. Notably, among patients without LD PCD-CT-defined ILD, 42% had positive clinical assessments and 23% demonstrated abnormal PFTs, underscoring the limited diagnostic specificity of these conventional approaches in the absence of imaging-confirmed disease. (Fig. 3) Discussion This prospective study provides compelling evidence that LD PCD-CT significantly enhances the detection of small extension ILD in patients with RA. In our cohort of 492 RA patients, LD PCD-CT identified interstitial alterations in 35% of individuals, a detection rate markedly higher than that achieved by conventional screening modalities as clinical assessment (44%), PFTs (15%) or DR (6.5%). Extra-articular manifestations occur in up to 50% of RA patients [ 23 ], with ILD among the most severe and life-limiting complications. Although clinically apparent RA-ILD affects 5–10% of patients, radiographic signs of subclinical disease have been reported in 20–60%. [ 24 , 25 ] Interstitial lung abnormalities (ILAs) are incidental CT findings involving at least 5% of lung parenchyma, detected in patients without prior clinical suspicion of ILD. [ 9 ] Given the elevated risk of progression to clinically significant ILD- ranging from 20% over 2 years(26) to 43% over 5 years [ 27 , 28 ]- the Fleischner Society recommends that ILAs in patients with RA and other autoimmune diseases be classified as preclinical ILD. [ 9 ] In our cohort, 35% of RA patients exhibited interstitial alterations, of which 58% were identified as NSSEPC on LD PCD-CT, aligning with previously reported prevalence ranges. These findings underscore the significant burden of asymptomatic parenchymal lung involvement in patients with RA. Emerging data, including the INBUILD trial [ 29 ], affirm that early intervention in progressive fibrosing ILDs can delay progression, reinforcing the importance of early detection. Despite this, no universally accepted RA-ILD screening strategy currently exists. While national initiatives are underway [ 30 – 32 ], formal RA-ILD–specific guidelines have yet to be issued by the European Alliance of Associations for Rheumatology (EULAR) or the American College of Rheumatology (ACR). However, ACR and the American College of Chest Physicians (CHEST) have published recommendations for the screening and monitoring of ILD in individuals with systemic autoimmune rheumatic diseases, including RA. They proposed an initial clinical assessment and PFTs, with HRCT reserved for high-risk individuals. [ 33 ] Consistent with previous studies [ 34 ], our findings suggest this approach may miss a substantial proportion of patients with early-stage disease. Notably, the majority of patients with LD PCD-CT–identified abnormalities were clinically silent. Only 7% presented with basal crackles, 18% reported dry cough, and 27% experienced exertional dyspnoea. Moreover, just 10.5% of them had abnormal PFTs, and just 6.5% demonstrated abnormalities on DR. While HRCT remains the gold standard for ILD diagnosis [ 35 , 36 ], its high radiation dose (5–15 mSv) limits its feasibility for routine screening. By contrast, LD PCD-CT in our study achieved a significantly lower effective dose (0.415 ± 0.316 mSv) while still providing superior image quality. Although it exceeds the dose of chest radiography (0.173 ± 0.286 mSv), LD PCD-CT remains within low-dose thresholds and is comparable to a two-view chest DR. [ 37 ] Importantly, LD PCD-CT detected hallmark features of ILD—ground-glass opacities, subpleural reticulations, bronchiectasis, and honeycombing—that are often undetectable on conventional radiographs. The screening protocol used in this study builds on our earlier work [ 38 ], which demonstrated that LD PCD-CT effectively identifies early interstitial changes at minimal radiation exposure. The most common interstitial finding was NSSEPC (referred to ILA previously). The most abundant patterns were UIP, PPFE and NSIP, mirroring previous cohort findings. [ 27 , 39 ] Even though the clinical implications of NSSEPC in RA are still being clarified, mounting evidence—including from our study—suggests that these early radiographic changes may predict future disease progression and warrant closer monitoring or preemptive therapy. [ 40 , 41 ] In addition to imaging, serological biomarkers may provide complementary information. Both LDH and CA 15 − 3 have been identified as markers that may help predict lung involvement in RA patients. LDH is an enzyme found in various body tissues, and elevated levels can signal tissue damage, including lung injury. In the context of RA-ILD, higher LDH levels have been strongly associated with the onset of pulmonary involvement. [ 42 ] CA15-3 and Krebs von den Lungen-6 (KL-6), encoded by the same mucin 1 (MUC1) gene, are members of the MUC1 family, which coats the surface of various epithelial cells, including those in the alveoli, breast, and gastrointestinal tract. [ 43 ] Previous studies have indicated that elevated CA15-3 levels were associated with reduced TLC, decreased DLCO, and more advanced pulmonary fibrosis, as indicated by HRCT findings. [ 43 , 44 ] Consistent with previous studies, risk factor analysis revealed that older age, male sex, high cumulative smoking exposure, RF positivity, and elevated LDH levels were independently associated with subclinical ILD. [ 45 – 48 ] These variables may serve as practical markers to prioritize patients for imaging-based screening. Notably, smoking exposure ≥ 25 pack-years and RF positivity have consistently emerged in prior studies as key predictors of RA-associated ILD, lending further validity to our findings. [ 35 , 42 ] One of the key strengths of our study is the application of PCD-CT technology, which provides improved spatial resolution and contrast-to-noise ratio at lower radiation doses compared to conventional CT. [ 49 , 50 ]. This advantage makes it particularly well-suited for repeated imaging in longitudinal monitoring and screening protocols. Here we provide for the first time that LD PCD-CT measurements could serve as a valuable screening tool in clinical practice. Despite these strengths, our study has limitations. First, it was conducted at a single center, potentially limiting the generalizability of the findings. Second, although radiographic abnormalities were well characterized, long-term clinical follow-up is needed to determine the progression and impact of these subclinical findings. Important to note, that in-and expiratory CT images are important to assess small airway involvement and air trapping, and no data on emphysema were collected. Future investigations should examine the natural history of small extension parenchymal changes detected by LD PCD-CT in RA and assess whether early therapeutic intervention can improve outcomes. Prospective, multicenter studies with standardized imaging protocols and longer follow-up periods are warranted. The development of AI-driven tools for automated detection and classification of ILD features may further enhance diagnostic precision. Additionally, cost-effectiveness analyses are necessary to evaluate the broader implementation of LD PCD-CT in routine RA-ILD screening. Conclusion Our findings emphasize the diagnostic limitations of relying solely on clinical symptoms and functional assessments (even in combinations with DR) in screening for RA-associated ILD. LD PCD-CT demonstrated markedly superior sensitivity in detecting interstitial lung abnormalities,, while maintaining low radiation exposure. These results support the integration of LD PCD-CT into risk-based screening strategies to enable earlier detection, intervention, and improved patient outcomes in RA-ILD. Abbreviations ACPA anti-citrullinated peptide antibodies ACR American College of Rheumatology ARS/ETS American Thoracic Society and European Respiratory Society guidelines DMARD disease-modifying antirheumatic drug CA 15-3 cancer antigen 15-3 CCP Anti-cyclic citrullinated peptide CHEST American College of Chest Physicians CRP C-reactive protein DAP dose area product DLCO percent of predicted diffusing capacity of the lung for carbon monoxide DR digital chest radiography E Effective radiation dose ESR erythrocyte sedimentation rate EULAR European Alliance of Associations for Rheumatology FEV1 forced expiratory volume in 1 s FVC forced vital capacity GGO ground-glass opacity HRCT high-resolution computed tomography ILAs Interstitial lung abnormalities ILD interstitial lung disease IQR interquartile range KL-6 Krebs von den Lungen-6 LDH lactate dehydrogenase LD PCD-CT low-dose photon-counting detector CT 6MWT 6-minute walking tests MUC1 mucin 1 NSIP non-specific interstitial pneumonia NSSEPC non-specified small extension parenchymal changes OP organizing pneumonia PFTs pulmonary function tests PPFE pleuro-parenchymal fibroelastosis RA-ILD rheumatoid arthritis-associated interstitial lung disease RA rheumatoid arthritis RB-ILD respiratory bronchiolitis interstitial lung disease RF rheumatoid factor ROC Receiver operating characteristic SD standard deviation tDLP area product TLC total lung capacity UIP usual interstitial pneumonia Declarations Ethics approval and consent to participate Ethical approval was obtained from the Regional Research Ethics Committee (reference number: IV/2683-1/2022/EKU). Written informed consent was obtained from all participants prior to inclusion in the study. Consent for publication Not applicable Availability of data and materials The data used in this study are available upon request from the corresponding author (GN) and subject to approval by the Regional Research Ethics Committee, in accordance with institutional data sharing policies. Competing interests Nikolett Marton received support from the ÚNKP-23-5 New National Excellence Program of the Ministry for Culture and Innovation, funded by the National Research, Development and Innovation Fund, as well as the Bolyai Research Scholarship. Michal Tomcik received institutional support from the Ministry of Health of the Czech Republic (grant no. 023728). The authors declare no other financial or non-financial competing interests. Funding No additional specific funding was received from any public, commercial, or not-for-profit funding bodies to support the work described in this manuscript. Authors’ contributions PD, DW, TC, KPL contributed to study conception and design; PD, DW, KD, DD, VF, HC, MW, NS contributed to data acquisition and analysis; PD, DW, TC, KPL contributed to interpretation of data; DW, TC, KPL had primary responsibility for final content; all authors contributed to critical revision and approved the final manuscript. Acknowledgements We would like to express our sincere gratitude to the participating patients and colleagues for their contributions to this work Dora Sarvari, Kinga Kohalmi, Marianna Bonacz, Eva Lanyi, Margit Szentesi, Tamas Gati, Kinga Futo, Nikolett Mong, Angela Fulop, Katalin Imre, Bernadette Rojkovich, Gyorgyi Meszaros, Timea Petri, Erzsebet Nagy, Bernadette Bereczkine Szabo, Judit Simon, Adam Domonkos Tarnoki, David Laszlo Tarnoki, Laszlo Szakacs, Leila Szeibel, Nora Kerkovits, Klaudia Borbely and Csenge Poka. Disclosure statement: The authors have declared no conflicts of interest. References Smolen JS, Aletaha D, McInnes IB. 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Progressive rheumatoid arthritis-interstitial lung disease (RA-ILD): a clinical concept supported by radiology and histopathology. Arthritis Rheumatol. 2021;73(9):1738–48. Almoguera B, Vazquez L, Mentch F, et al. Identification of four novel loci in asthma in European American and African American populations. Am J Respir Crit Care Med. 2017 Feb 15;195(4):456–63. Li L, Liu R, Zhang Y, et al. A retrospective study on the predictive implications of clinical characteristics and therapeutic management in patients with rheumatoid arthritis-associated interstitial lung disease. Clin Rheumatol. 2020 May;39(5):1457–70. Guo J, Huang H, Lin S, et al. Serum carbohydrate antigen 153 as a predictor of interstitial lung disease associated with rheumatoid arthritis is positively correlated with serum Krebs von den Lungen-6. BMC Pulm Med. 2025 Mar 7;25(1):102. Ricci A, Mariotta S, Bronzetti E, et al. Serum CA 15-3 is increased in pulmonary fibrosis. Sarcoidosis Vasc Diffuse Lung Dis. 2009 Jul;26(1):54–63. Zamora-Legoff JA, Krause ML, Crowson CS. Patterns of interstitial lung disease and mortality in rheumatoid arthritis. Rheumatology (Oxford). 2017 Mar 1;56(3):344–50. Hyldgaard C, Hilberg O, Pedersen AB, et al. FRI0174 A population based cohort study of rheumatoid arthritis-associated interstitial lung disease: comorbidity and mortality. Ann Rheum Dis. 2017 Jun;76:546–7. Giles JT, Danoff SK, Sokolove J, et al. Association of fine specificity and repertoire expansion of anticitrullinated peptide antibodies with rheumatoid arthritis associated interstitial lung disease. Ann Rheum Dis. 2014 Aug;73(8):1487–94. Hu S, Ye J, Guo Q, Zou S, et al. Serum lactate dehydrogenase is associated with impaired lung function: NHANES 2011-2012. PLoS One. 2023 Feb 2;18(2):e0281203. Woeltjen MM, Niehoff JH, Michael AE, et al. Low-dose high-resolution photon-counting CT of the lung: Radiation dose and image quality in the clinical routine. Diagnostics (Basel). 2022 Jun 11;12(6):1441. Tárnoki ÁD, László Tárnoki D, Maurovich-Horvat P. Photon-counting CT in lung imaging. Expert Rev Respir Med. 2025 Feb;19(2):77–81. Additional Declarations No competing interests reported. Supplementary Files Onlinefloatimage1.png Graphical Abstract Cite Share Download PDF Status: Published Journal Publication published 06 Nov, 2025 Read the published version in Arthritis Research & Therapy → Version 1 posted Editorial decision: Revision requested 21 Sep, 2025 Reviews received at journal 19 Sep, 2025 Reviews received at journal 18 Sep, 2025 Reviewers agreed at journal 28 Aug, 2025 Reviewers agreed at journal 26 Aug, 2025 Reviewers invited by journal 26 Aug, 2025 Editor assigned by journal 01 Aug, 2025 Submission checks completed at journal 01 Aug, 2025 First submitted to journal 24 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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10:08:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7204385/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7204385/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13075-025-03674-w","type":"published","date":"2025-11-06T15:58:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90472946,"identity":"9f5df135-408a-4e23-b396-75d69c0bec05","added_by":"auto","created_at":"2025-09-03 06:37:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2282897,"visible":true,"origin":"","legend":"\u003cp\u003eLD PCD-CT patterns\u003c/p\u003e\n\u003cp\u003e(A) Distribution of the LD PCD-CT patterns\u003c/p\u003e\n\u003cp\u003e(B, C, D) Chest X-ray and LD PCD-CT images – 5% extent of UIP pattern\u003c/p\u003e\n\u003cp\u003e(E, F, G) Chest X-ray and LD PCD-CT images – 5% extent of NSIP pattern\u003c/p\u003e\n\u003cp\u003e(H) Distribution of ILD hallmark features: ground-glass opacities, subpleural reticulations, bronchiectasis, and honeycombing. Interstitial lung involvement was assessed using a total ILD CT scoring system, based on the sum of the interstitial abnormalities.\u003c/p\u003e\n\u003cp\u003eLD PCD-CT: low-dose photon-counting detector computed tomography; ILD: interstitial lung disease; UIP: usual interstitial pneumonia; NSIP: non-specific interstitial pneumonia; PPFE: pleuro-parenchymal fibroelastosis, RB-ILD: respiratory bronchiolitis interstitial lung disease; NSSEPC: non-specified small extension parenchymal changes\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7204385/v1/73641a1482646cbf5ae10b25.png"},{"id":90472940,"identity":"1b50f6cc-934f-4d70-939b-0d88d34a2830","added_by":"auto","created_at":"2025-09-03 06:37:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":514200,"visible":true,"origin":"","legend":"\u003cp\u003eLD PCD-CT, clinical asessment and PFTs stratification of RA patients\u003c/p\u003e\n\u003cp\u003e(A) One hundred seventy-one subjects (35%) were found to have interstitial lung involvement on LD PCD-CT, with a total ILD CT score ≥ 3, while only 12% (n=21) of them were found to have basal crackles, 18% (n=30) had a dry cough and 27% (n=45) had dyspnoea. Of the 171 patients screened, 15% (n = 18) had altered pulmonary function tests (e.g. FEV1 \u0026lt; 80% predicted, or FVC \u0026lt; 80% predicted, or DLCO \u0026lt; 75% predicted).\u003c/p\u003e\n\u003cp\u003e(B)Total ILD CT scores showed a mild, but significant negative correlation with DLCO, FVC and FEV1 values.\u003c/p\u003e\n\u003cp\u003e(C) Compared to LD PCD-CT, clinical assessment had a sensitivity of 29%, and specificity of 58%; PFTs had a sensitivity of 21% and specificity of 77%. The combination of clinical assessment and PFTs yielded an even lower sensitivity (12%) but a higher specificity (87%).\u003c/p\u003e\n\u003cp\u003eLD PCD-CT: low-dose photon-counting detector computed tomography; PFT: pulmonary function test; RA: rheumatoid arthritis; ILD: interstitial lung disease; FEV1: forced expiratory volume in 1 second; FVC: forced vital capacity; DLCO: diffusion capacity of the lung for carbon monoxide; PPV: positive predictive value; NPV: negative predictive value;\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7204385/v1/2a19c9056693b91171f3e45b.png"},{"id":90472944,"identity":"f18b777d-579c-4aaa-b43e-c662c29714fc","added_by":"auto","created_at":"2025-09-03 06:37:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":231026,"visible":true,"origin":"","legend":"\u003cp\u003eHorizontal multi-level hierarchy of different test methods to detect ILD\u003c/p\u003e\n\u003cp\u003eOne hundred seventy-one subjects (35%) were found to have interstitial lung involvement on LD-PCD-CT, with a total ILD CT score ≥ 3, while clinical assessment and PFTs detected abnormalities (e.g, FEV1 \u0026lt; 80% predicted, or FVC \u0026lt; 80% predicted, or DLCO \u0026lt; 75% predicted) in only 44% and 22% of these cases, respectively. Among patients without CT-defined abnormalities, 42% had a positive clinical assessment and 23% had abnormal PFTs, indicating limited diagnostic specificity.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7204385/v1/7aba729f275b221ac234e432.png"},{"id":95564462,"identity":"9ae875bb-7653-479a-9bcf-10d7e51601b8","added_by":"auto","created_at":"2025-11-10 16:09:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5210137,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7204385/v1/a0c238bc-5843-4036-b23b-e00a91aa53a3.pdf"},{"id":90472942,"identity":"22355ff9-8fe9-4555-8b38-6301c1b12904","added_by":"auto","created_at":"2025-09-03 06:37:49","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":68054,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical Abstract\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7204385/v1/368aecaa1c4097f9d19a6de1.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Screening for Rheumatoid Arthritis-Associated Interstitial Lung Disease Using Low-Dose CT: An Emerging Approach — An Observational Prospective Case-Control Study","fulltext":[{"header":"Background","content":"\u003cp\u003eRheumatoid arthritis is a systemic autoimmune disease affecting 0.5-1% of the global population. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] Among its various extra-articular manifestations, pulmonary involvement—particularly interstitial lung disease (ILD)—is one of the most serious. Alongside cardiovascular disease and infections, it contributes substantially to RA-related morbidity and mortality. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] RA-associated ILD (RA-ILD) carries a three-fold increased risk of death compared to RA patients without ILD [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and frequently complicates disease management, contributing to the subset of patients classified as having difficult-to-treat RA. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eClinically significant ILD occurs in approximately 11% of RA patients. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] However, a substantially larger proportion may exhibit radiological features consistent with ILD despite being asymptomatic and having preserved lung function. This condition, referred to as subclinical ILD, is typically defined by the presence of mild interstitial abnormalities on high-resolution computed tomography (HRCT), normal PFTs, and the absence of respiratory symptoms. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] The reported prevalence of subclinical ILD in RA varies widely, ranging from 5–67%, reflecting differences in study design, patient populations, imaging techniques, and the criteria used to define ILD. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eConsidering that ILD may occur at any point during the natural history of RA, and its clinical manifestations usually appear in advanced stages, early diagnosis is challenging and requires a multidisciplinary team approach. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] This underscores an unmet need for timely, sensitive screening strategies that enable earlier diagnosis and intervention. Early detection offers a critical window of opportunity for timely adjustment of disease-modifying antirheumatic drug (DMARD) regimens and initiation of antifibrotic therapies—interventions shown to stabilize CT changes in progressive autoimmune ILDs and improve prognosis. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eIn this context, our primary objective was to assess the effectiveness of LD PCD-CT as a screening tool for RA-ILD. A secondary aim was to estimate the prevalence of ILD (including subclinical cases) among Hungarian RA patients and to develop a risk stratification model.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eEthics and study design\u003c/b\u003e\u003c/p\u003e\u003cp\u003e This study was an observational prospective case-control study, in accordance with the ethical principles of the Declaration of Helsinki, the International Conference on Harmonization, and Good Clinical Practice with the permission of the Regional Research Ethics Committee (number: IV/2683-1/2022/EKU). It has been registered on the ClinicalTrials.gov (NCT05391100) web page. It was performed between February 2022. and June 2023. All patients provided written informed consent.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePatient enrollment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eConsecutive RA patients aged \u0026gt; 40 years were recruited from the Rheumatology Outpatient Department of Semmelweis University (Polyclinic of the Hospitaller Brothers of St. John of God, Budapest, Hungary). All participants underwent regular follow-up at the institution, received standard RA medications, and had annual chest X-rays following their diagnosis. None of them had a previous indication for HRCT to rule out ILD.\u003c/p\u003e\u003cp\u003eInclusion required RA diagnosis per 2010 ACR/EULAR criteria. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] To limit radiation exposure, reproductive-age patients were excluded. Further exclusion criteria included pregnancy, breastfeeding, prior ILD or lung cancer, and recent (within 2 months) lung infection.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSerological data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDemographic, clinical, and serological data, including RF, anti-citrullinated peptide antibodies (ACPA), and RA treatment history (synthetic/biological DMARDs and corticosteroids) were recorded at enrollment.\u003c/p\u003e\u003cp\u003eRF, C-reactive protein (CRP), LDH, and cancer antigen 15 − 3 (CA 15 − 3) levels were measured using quantitative immunoturbidimetric assays on the Roche platform (F. Hoffmann–La Roche AG, Basel, Switzerland), with RF cutoff at 14 U/mL, CRP \u0026lt; 5 mg/L, and LDH reference ranges of 135–214 U/L for women and 135–225 U/L for men. Anti-cyclic citrullinated peptide (CCP) antibodies were measured using the Immunoscan CCPlus kit (SVAR Life Science, Malmö, Sweden) with a 25 U/mL cutoff. Erythrocyte sedimentation rate (ESR) was assessed using the Vacuette SRS100 system (Greiner Bio-One GmbH, Kremsmünster, Austria), with reference values of \u0026lt; 30 mm/h for women and \u0026lt; 20 mm/h for men.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImaging\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePatients underwent DR and consecutive chest LD PDC-CT scans on the same day at the Clinic for Medical Imaging, Semmelweis University. Anteroposterior and lateral digital radiographs were performed on a GE Discovery XR 656 HD system (GE Healthcare, Chicago, IL, USA). High-resolution (slice thickness: 0.4 mm) CT scans were carried out with a PCD-CT scanner (Naeotom Alpha Peak®, Siemens Healthineers, Erlangen, Germany). CT measurements were performed with a large field of view (FOV) [median (interquartile range): 35 (32–38) cm] and a 512 x 512 matrix. Additionally, 3 strength-level quantitative iterative reconstruction algorithms were utilized to enhance image quality. To exclude ground-glass opacity (GGO) from dependent atelectasis, prone inspiratory HRCT measurements were performed. Radiological findings were assessed by two radiology specialists according to the Fleischner Society White Paper statement on the diagnosis of idiopathic pulmonary fibrosis. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] Then the images were reviewed by an interdisciplinary ILD board to reach an agreement with the pulmonologists.\u003c/p\u003e\u003cp\u003eEvaluation of parenchymal abnormalities\u003c/p\u003e\u003cp\u003eInterstitial abnormalities were classified into four categories: ground-glass opacity, reticulation, bronchiectasis, and honeycombing, and their extent was scored for each lung lobe using a Likert-type scale (0 = absent; 1 = 1–25%; 2 = 26–50%; 3 = 51–75%; 4 = 76–100%). [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] A total ILD CT score was obtained by summing all scores, the final value ranging from 0–80. The CT pattern of disease was recorded as usual interstitial pneumonia (UIP), non-specific interstitial pneumonia (NSIP), pleuro-parenchymal fibroelastosis (PPFE), respiratory bronchiolitis interstitial lung disease (RB-ILD), organizing pneumonia (OP), non-specified small extension parenchymal changes (NSSEPC) and other patterns. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eDose considerations\u003c/p\u003e\u003cp\u003eEffective radiation dose (E) for DR and LD PCD-CT was assessed using dose area product (DAP) and area product (tDLP) values extracted via IMPAX (Agfa Corporate, Mortsel, Belgium) and syngo.via software (Siemens Healthineers, Heidelberg, Germany). Approximate E (mSv) was calculated using standard conversion factors: E = DAP × 0.16 mSv/mGy*cm for radiographs/tomosynthesis and E = tDLP × 0.014 mSv/mGy*cm for CT. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\u003cp\u003e\u003cb\u003ePulmonary Function tests\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll patients were asked for pulmonary symptoms, underwent physical examination and detailed pulmonary function tests (PFTs) were performed by pulmonologists at the Department of Pulmonology, Semmelweis University as described previously. The percent of predicted forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1, FEV1/FVC), total lung capacity (TLC) the percent of predicted diffusing capacity of the lung for carbon monoxide (DLCO) according to the American Thoracic Society and European Respiratory Society (ARS/ETS) guidelines [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and 6-minute walking tests (6MWT) were assessed. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] PFT values were expressed as a percentage of predicted values.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eStatistical analysis was performed using SPSS software version 26.0 (IBM, Armonk, NY, USA). Data are expressed as the mean ± standard deviation (SD) or median (interquartile range (IQR)) for continuous variables and percentages for categorical variables.The distribution of continuous variables was evaluated by the Kolmogorov–Smirnov test. Continuous variables were analyzed using the t-test when normally distributed, and using the Mann–Whitney U test when non-normally distributed. Nominal variables were compared between groups using the chi-squared or Fisher’s exact test, as appropriate. Dose values were compared with paired t-tests. Correlations were determined by Pearson’s analysis. Sensitivity, specificity, and predictive values of chest X-ray and pulmonary function tests were calculated and compared with those of LD PCD-CT. Receiver operating characteristic (ROC) curve analysis was performed to identify smoking history cut-off value associated with the development of ILD. Univariable and multivariable binary logistic regression analyses were performed using the enter likelihood method and forward selection methods to evaluate the odds ratios associated with potential independent variables. \u003cem\u003eP\u003c/em\u003e-values \u0026lt; 0.05 were considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eClinical data, enrollment\u003c/p\u003e\n\u003cp\u003eA total of 544 consecutive RA patients were initially recruited for the study. Of these, 39 individuals were excluded during the study period due to either the development of acute lung infections or withdrawal of consent, resulting in 505 patients completing the baseline visit. Subsequently, four patients were excluded due to discrepancies in identification or laboratory data, and nine were excluded due to non-evaluable chest X-rays. Therefore, the final analysis included data from 492 RA patients. 7% (n = 36) of them were found to have basal crackles, 18% (n = 87) had a dry cough and 29% (n = 139) had exertional dyspnoea. Detailed clinical and demographic characteristics are presented in tables 1 and 2.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eTable\u0026nbsp;1. Baseline demographic, serologic and medication characteristics of RA patients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall RA patients (n = 492)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLD PCD-CT\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003etotal ILD score\u003c/strong\u003e ≥ \u003cstrong\u003e3 (n = 171)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLD PCD-CT\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003etotal ILD score \u0026lt; 3 (n = 321)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.79 ± 10.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.51 ± 9.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.81 ± 10.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge over 65 years, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e236 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107 (63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge over 50 years at RA onset, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249 (52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104 (63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145 (46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale sex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEver smoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95 (57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent smoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking (pack-years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.07 ± 16.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.28 ± 18.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.42 ± 14.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePack-year ≥ 25, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eRA characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRA duration (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.27 ± 10.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.53 ± 11.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.61 ± 9.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDAS28-ESR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.79 ± 1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.85 ± 1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.75 ± 1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.66 ± 5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.98 ± 5.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.49 ± 4.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eRA serologies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF (U/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108.93 ± 195.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146.37 ± 242.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.92 ± 159.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF positivity, n (%) (\u0026gt; 14U/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e335 (69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130 (77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205 (65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-CCP (U/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e644.23 ± 1022.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e704.08 ± 1073.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e611.11 ± 993.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-CCP positivity, n (%) (\u0026gt; 25 U/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e311 (65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119 (71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192 (62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-MCV positivity, n (%) (\u0026gt; 20 U/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e274 (61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104 (65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170 (58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF + anti-CCP positivity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e282 (58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170 (54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eRA medication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJAK inhibitors, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRituximab, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbatacept, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-IL6 antibodies, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNF inhibitors, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e209 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70 (42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139 (44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeflunomid, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93 (29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMTX, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e466 (95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160 (95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e306 (96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSteroid, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e421 (86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142 (85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e279 (88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eResults are presented as mean ± standard deviation, median (interquartile range) and frequency (percentage). Significant differences (p\u0026lt;0.05) are highlighted in bold.\u003c/p\u003e\n\u003cp\u003eRA: rheumatoid arthritis; RF: rheumatoid factor; anti-CCP: anti cyclic citrullinated peptide antibody; anti-MCV: anti citrullinated vimentin antibody; JAK: Janus kinases; IL-6: interleukin 6; TNF: tumor necrosis factor, MTX: methotrexate\u0026nbsp;\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eTable\u0026nbsp;2. Baseline clinical, radiological and laboratory parameters of RA patients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall RA patients (n = 492)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLD PCD-CT\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003etotal ILD score\u003c/strong\u003e ≥ \u003cstrong\u003e3 (n = 171)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLD PCD-CT\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003etotal ILD score \u0026lt; 3 (n = 321)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eRespiratory signs, symptoms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasal crackles, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExertional dyspnoea, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139 (29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.520\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDry cough, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003ePulmonary function tests and pulmonary parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFEV1 (L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.49 ± 0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.36 ± 0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.56 ± 0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFEV1% predicted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.32 ± 16.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.17 ± 16.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.84 ± 16.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFEV 1 \u0026lt; 80% predicted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFVC (L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.14 ± 0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00 ± 0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.22 ± 0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFVC % predicted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.99 ± 15.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.61 ± 16.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.65 ± 14.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFVC \u0026lt; 80% predicted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTLC (L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.92 ± 2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.76 ± 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.02 ± 2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTLC % predicted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114.93 ± 20.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.44 ± 19.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114.65 ± 20.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDLCO (mmol/min/kPa)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.83 ± 2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.29 ± 1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.14 ± 2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDLCO % predicted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122.16 ± 20.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117.65 ± 20.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124.65 ± 20.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDLCO \u0026lt; 75% predicted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiogical findings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eX-Ray\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFibrosis, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eLD PCD-CT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edUIP, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epUIP, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eiUIP, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNSIP, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRB-ILD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePPFE, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNSSEPC, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt; \u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eESR (mm/h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.25 ± 16.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.78 ± 16.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.40 ± 16.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eESR \u0026gt; 20 mm/h, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83 (29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCRP (mg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.97 ± 11.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.35 ± 11.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.75 ± 11.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCA 15 − 3 (U/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.33 ± 10.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.89 ± 12.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.01 ± 8.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCA 15 − 3 \u0026gt; 25 U/mL, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDH (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e208.28 ± 44.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e217.32 ± 47.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203.19 ± 41.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDH \u0026gt; 214 U/L, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75 (47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eResults are presented as mean ± standard deviation and frequency (percentage). Significant differences (p\u0026lt;0.05) are highlighted in bold.\u003c/p\u003e\n\u003cp\u003eFEV1: forced expiratory volume in 1 second; FVC: forced vital capacity; TLC: total lung capacity; DLCO: diffusion capacity of the lung for carbon monoxide; dUIP: definite usual interstitial pneumonia; pUIP: probable usual interstitial pneumonia; iUIP: indeterminate usual interstitial pneumonia; NSIP: non-specific interstitial pneumonia; PPFE: pleuro-parenchymal fibroelastosis; RB-ILD: respiratory bronchiolitis interstitial lung disease; NSSEPC: non-specified small extension parenchymal changes; ESR: erythrocyte sedimentation rate; CRP: C-reactive protein; CA 15-3: cancer antigen 15-3; LDH: lactate dehydrogenase\u003c/p\u003e\n\u003cp\u003eImaging findings\u003c/p\u003e\n\u003cp\u003eAll patients underwent digital radiography and consecutive LD PCD-CT scans at baseline. The median (IQR) total ILD CT score was 2 (0; 3); a threshold score ≥ 3 was considered clinically significant. Fibrotic changes were identified in only 4% of patients on chest radiography (n = 14). In contrast, LD PCD-CT revealed interstitial alterations in 35% (n = 171), of whom only 6.5% showed fibrotic signs on X-ray.\u003c/p\u003e\n\u003cp\u003eRegarding the CT patterns UIP has been found in 22%, PPFE in 9%, NSIP in 7% and RB-ILD in 4% of the patients. Small extension interstitial lung abnormalities were identified in 58% of the cases (Fig. 1).\u003c/p\u003e\n\u003cp\u003eRadiation Exposure\u003c/p\u003e\n\u003cp\u003eThe effective radiation dose of DR was 0.171 ± 0.286 mSv, with a median dose of 0.067 (0.043–0.127) mSv. LD PCD-CT showed a significantly higher but still low radiation dose of 0.415 ± 0.316 mSv, with a median of 0.378 (0.322–0.455) mSv (p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003ePulmonary Function Parameters\u003c/p\u003e\n\u003cp\u003ePulmonary function tests were available for 363 patients. Mean spirometric parameters fell within the physiological ranges. The mean predicted FVC was 95.00 ± 15.48%, while the diffusing capacity for DLCO was 122.16 ± 20.89%. Among patients with ILD CT score ≥ 3, only 12% had FVC \u0026lt; 80%, and 3% had DLCO \u0026lt; 75%. A mild but significant negative correlation was observed between ILD CT score and DLCO (r = − 0.224, p \u0026lt; 0.001). The sensitivity and specificity of pulmonary function tests for ILD detection were 21% and 77%, respectively (Table 2, Fig. 2).\u003c/p\u003e\n\u003cp\u003eRisk Profiles\u003c/p\u003e\n\u003cp\u003eClinical and laboratory characteristics of the RA patients were analyzed to identify potential associations with interstitial lung involvement. The median age at diagnosis in our cohort was 50 (40–60) years; accordingly, age ≥ 50 years at diagnosis was used as a clinical threshold for risk stratification. Receiver operating characteristic (ROC) curve analysis of smoking history identified an optimal cut-off value of 25 pack-years, which was subsequently adopted as the threshold to define high-risk smoking exposure.\u003c/p\u003e\n\u003cp\u003eUnivariate analysis using binary logistic regression (Enter method) revealed that older age, age ≥ 50 years at RA diagnosis, male sex at birth, smoking exposure ≥ 25 pack-years, RF and aCCP positivity, and elevated LDH levels were significantly associated with a total ILD CT score ≥ 3.\u003c/p\u003e\n\u003cp\u003eMultivariate logistic regression using the backward (likelihood ratio) method identified five independent predictors of interstitial lung involvement: older age (OR 2.594 [95% CI 1.686, 3.989] p \u0026lt; 0.001), smoking exposure ≥ 25 pack-years (OR 1.885 [95% CI 1.070, 3.322] p \u0026lt; 0.028), male sex (OR 1.741 [95% CI 1.003, 3.023] P = 0.049), RF positivity (OR 1.952 [95% CI 1.214, 3.319] p = 0.006), and high LDH levels (OR 1.872 [95% CI 1.214, 2.885] p = 0.005) (Table 3).\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAssociation between LD PCD-CT findings, clinical and serological features\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eODDS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eODDS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eCategorical variables\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge over 65 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.699–3.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.686–3.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge over 50 years at RA onset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.367–2.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.151–2.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.003–3.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEver smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.951–2.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePack-years ≥ 25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.393–3.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.070–3.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF positivity (\u0026gt; 14 U/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.206–2.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.214–3.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-CCP positivity \u0026nbsp;(\u0026gt; 25 U/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.014–2.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.043\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF + anti-CCP positivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.147–2.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-MCV positivity (\u0026gt; 20 U/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.886–1.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJAK inhibitors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.508–1.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRituximab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.775–1.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbatacept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.611–6.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-IL6 antibodies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.802–2.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNF inhibitors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.630–1.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeflunomid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.564–1.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMTX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.314–1.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSteroid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.447–1.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElevated LDH level (\u0026gt; 214 U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.181–2.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.214–2.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElevated CRP level (\u0026gt; 5 mg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.895–2.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElevated CA15-3 level (\u0026gt; 25 U/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.981–2.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eESR \u0026gt; 20 mm/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.871–1.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasal crackles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.446–5.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExertional dyspnoea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.574–1.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDry cough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.611–1.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eContinuous variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.037–1.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePack-years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.006–1.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDAS28-ESR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.920–1.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.982–1.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRA duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000-1.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.050\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000-1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-CCP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000–1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.988–1.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eESR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.997–1.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCA 15 − 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.018–1.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.003–1.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eResults from univariate analysis, binary logistic regression using the enter method, and multivariate logistic regression with the backward (likelihood ratio) method. Significant predictors (p\u0026lt;0.05) are highlighted in bold.\u003c/p\u003e\n\u003cp\u003eThe median age at diagnosis in our cohort was 50 years; thus, age ≥50 at diagnosis was considered a clinical risk threshold. Receiver operating characteristic (ROC) curve analysis for smoking history revealed an optimal cut-off at 25 pack-years, which was subsequently used as a threshold to define high-risk smoking exposure in further analysis.\u003c/p\u003e\n\u003cp\u003eAt univariate analysis, older age (\u0026gt;65 years), age ≥50 years at RA diagnosis, male sex at birth, smoking exposure ≥25 pack-years, rheumatoid factor (RF) and anti-cyclic citrullinated peptide (aCCP) positivity, elevated lactate dehydrogenase (LDH) levels were significantly associated with a total ILD CT score ≥3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMultivariate analysis identified five independent predictors of lung involvement: older age (\u0026gt;65 years), male sex, smoking exposure ≥25 pack-years, RF positivity, and elevated LDH levels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRF: rheumatoid factor; anti-CCP: anti cyclic citrullinated peptide antibody; anti-MCV: anti citrullinated vimentin antibody; JAK: Janus kinases; IL-6: interleukin 6; TNF: tumor necrosis factor, MTX: methotrexate; LDH: lactate dehydrogenase; CRP: C-reactive protein; CA 15-3: cancer antigen 15-3; ESR: erythrocyte sedimentation rate\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Considering a horizontal multi-level hierarchy of diagnostic modalities for detecting subclinical ILD, LD PCD-CT identified interstitial lung involvement-defined as a total CT ILD score ≥ 3-in 171 of 492 RA patients (35%). In contrast, clinical assessment and PFTs detected abnormalities in only 44% and 22% of these LD PVD-CT-positive cases, respectively. DR revealed fibrotic changes in just 6.5% of this subgroup. Notably, among patients without LD PCD-CT-defined ILD, 42% had positive clinical assessments and 23% demonstrated abnormal PFTs, underscoring the limited diagnostic specificity of these conventional approaches in the absence of imaging-confirmed disease. (Fig. 3)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis prospective study provides compelling evidence that LD PCD-CT significantly enhances the detection of small extension ILD in patients with RA. In our cohort of 492 RA patients, LD PCD-CT identified interstitial alterations in 35% of individuals, a detection rate markedly higher than that achieved by conventional screening modalities as clinical assessment (44%), PFTs (15%) or DR (6.5%).\u003c/p\u003e\u003cp\u003eExtra-articular manifestations occur in up to 50% of RA patients [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], with ILD among the most severe and life-limiting complications. Although clinically apparent RA-ILD affects 5\u0026ndash;10% of patients, radiographic signs of subclinical disease have been reported in 20\u0026ndash;60%. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] Interstitial lung abnormalities (ILAs) are incidental CT findings involving at least 5% of lung parenchyma, detected in patients without prior clinical suspicion of ILD. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Given the elevated risk of progression to clinically significant ILD- ranging from 20% over 2 years(26) to 43% over 5 years [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]- the Fleischner Society recommends that ILAs in patients with RA and other autoimmune diseases be classified as preclinical ILD. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] In our cohort, 35% of RA patients exhibited interstitial alterations, of which 58% were identified as NSSEPC on LD PCD-CT, aligning with previously reported prevalence ranges. These findings underscore the significant burden of asymptomatic parenchymal lung involvement in patients with RA.\u003c/p\u003e\u003cp\u003eEmerging data, including the INBUILD trial [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], affirm that early intervention in progressive fibrosing ILDs can delay progression, reinforcing the importance of early detection. Despite this, no universally accepted RA-ILD screening strategy currently exists. While national initiatives are underway [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], formal RA-ILD\u0026ndash;specific guidelines have yet to be issued by the European Alliance of Associations for Rheumatology (EULAR) or the American College of Rheumatology (ACR). However, ACR and the American College of Chest Physicians (CHEST) have published recommendations for the screening and monitoring of ILD in individuals with systemic autoimmune rheumatic diseases, including RA. They proposed an initial clinical assessment and PFTs, with HRCT reserved for high-risk individuals. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] Consistent with previous studies [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], our findings suggest this approach may miss a substantial proportion of patients with early-stage disease. Notably, the majority of patients with LD PCD-CT\u0026ndash;identified abnormalities were clinically silent. Only 7% presented with basal crackles, 18% reported dry cough, and 27% experienced exertional dyspnoea. Moreover, just 10.5% of them had abnormal PFTs, and just 6.5% demonstrated abnormalities on DR.\u003c/p\u003e\u003cp\u003eWhile HRCT remains the gold standard for ILD diagnosis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], its high radiation dose (5\u0026ndash;15 mSv) limits its feasibility for routine screening. By contrast, LD PCD-CT in our study achieved a significantly lower effective dose (0.415\u0026thinsp;\u0026plusmn;\u0026thinsp;0.316 mSv) while still providing superior image quality. Although it exceeds the dose of chest radiography (0.173\u0026thinsp;\u0026plusmn;\u0026thinsp;0.286 mSv), LD PCD-CT remains within low-dose thresholds and is comparable to a two-view chest DR. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] Importantly, LD PCD-CT detected hallmark features of ILD\u0026mdash;ground-glass opacities, subpleural reticulations, bronchiectasis, and honeycombing\u0026mdash;that are often undetectable on conventional radiographs. The screening protocol used in this study builds on our earlier work [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], which demonstrated that LD PCD-CT effectively identifies early interstitial changes at minimal radiation exposure. The most common interstitial finding was NSSEPC (referred to ILA previously). The most abundant patterns were UIP, PPFE and NSIP, mirroring previous cohort findings. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] Even though the clinical implications of NSSEPC in RA are still being clarified, mounting evidence\u0026mdash;including from our study\u0026mdash;suggests that these early radiographic changes may predict future disease progression and warrant closer monitoring or preemptive therapy. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eIn addition to imaging, serological biomarkers may provide complementary information. Both LDH and CA 15\u0026thinsp;\u0026minus;\u0026thinsp;3 have been identified as markers that may help predict lung involvement in RA patients. LDH is an enzyme found in various body tissues, and elevated levels can signal tissue damage, including lung injury. In the context of RA-ILD, higher LDH levels have been strongly associated with the onset of pulmonary involvement. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] CA15-3 and Krebs von den Lungen-6 (KL-6), encoded by the same mucin 1 (MUC1) gene, are members of the MUC1 family, which coats the surface of various epithelial cells, including those in the alveoli, breast, and gastrointestinal tract. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] Previous studies have indicated that elevated CA15-3 levels were associated with reduced TLC, decreased DLCO, and more advanced pulmonary fibrosis, as indicated by HRCT findings. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eConsistent with previous studies, risk factor analysis revealed that older age, male sex, high cumulative smoking exposure, RF positivity, and elevated LDH levels were independently associated with subclinical ILD. [\u003cspan additionalcitationids=\"CR46 CR47\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] These variables may serve as practical markers to prioritize patients for imaging-based screening. Notably, smoking exposure\u0026thinsp;\u0026ge;\u0026thinsp;25 pack-years and RF positivity have consistently emerged in prior studies as key predictors of RA-associated ILD, lending further validity to our findings. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eOne of the key strengths of our study is the application of PCD-CT technology, which provides improved spatial resolution and contrast-to-noise ratio at lower radiation doses compared to conventional CT. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This advantage makes it particularly well-suited for repeated imaging in longitudinal monitoring and screening protocols. Here we provide for the first time that LD PCD-CT measurements could serve as a valuable screening tool in clinical practice.\u003c/p\u003e\u003cp\u003eDespite these strengths, our study has limitations. First, it was conducted at a single center, potentially limiting the generalizability of the findings. Second, although radiographic abnormalities were well characterized, long-term clinical follow-up is needed to determine the progression and impact of these subclinical findings. Important to note, that in-and expiratory CT images are important to assess small airway involvement and air trapping, and no data on emphysema were collected. Future investigations should examine the natural history of small extension parenchymal changes detected by LD PCD-CT in RA and assess whether early therapeutic intervention can improve outcomes. Prospective, multicenter studies with standardized imaging protocols and longer follow-up periods are warranted. The development of AI-driven tools for automated detection and classification of ILD features may further enhance diagnostic precision. Additionally, cost-effectiveness analyses are necessary to evaluate the broader implementation of LD PCD-CT in routine RA-ILD screening.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings emphasize the diagnostic limitations of relying solely on clinical symptoms and functional assessments (even in combinations with DR) in screening for RA-associated ILD. LD PCD-CT demonstrated markedly superior sensitivity in detecting interstitial lung abnormalities,, while maintaining low radiation exposure. These results support the integration of LD PCD-CT into risk-based screening strategies to enable earlier detection, intervention, and improved patient outcomes in RA-ILD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACPA anti-citrullinated peptide antibodies \u003c/p\u003e\n\u003cp\u003eACR American College of Rheumatology\u003c/p\u003e\n\u003cp\u003eARS/ETS American Thoracic Society and European Respiratory Society guidelines \u003c/p\u003e\n\u003cp\u003eDMARD disease-modifying antirheumatic drug \u003c/p\u003e\n\u003cp\u003eCA 15-3 cancer antigen 15-3 \u003c/p\u003e\n\u003cp\u003eCCP Anti-cyclic citrullinated peptide \u003c/p\u003e\n\u003cp\u003eCHEST American College of Chest Physicians \u003c/p\u003e\n\u003cp\u003eCRP C-reactive protein \u003c/p\u003e\n\u003cp\u003eDAP dose area product \u003c/p\u003e\n\u003cp\u003eDLCO percent of predicted diffusing capacity of the lung for carbon monoxide \u003c/p\u003e\n\u003cp\u003eDR digital chest radiography \u003c/p\u003e\n\u003cp\u003eE Effective radiation dose \u003c/p\u003e\n\u003cp\u003eESR erythrocyte sedimentation rate \u003c/p\u003e\n\u003cp\u003eEULAR European Alliance of Associations for Rheumatology \u003c/p\u003e\n\u003cp\u003eFEV1 forced expiratory volume in 1 s \u003c/p\u003e\n\u003cp\u003eFVC forced vital capacity \u003c/p\u003e\n\u003cp\u003eGGO ground-glass opacity \u003c/p\u003e\n\u003cp\u003eHRCT high-resolution computed tomography \u003c/p\u003e\n\u003cp\u003eILAs Interstitial lung abnormalities \u003c/p\u003e\n\u003cp\u003eILD interstitial lung disease \u003c/p\u003e\n\u003cp\u003eIQR interquartile range \u003c/p\u003e\n\u003cp\u003eKL-6 Krebs von den Lungen-6 \u003c/p\u003e\n\u003cp\u003eLDH lactate dehydrogenase\u003c/p\u003e\n\u003cp\u003eLD PCD-CT low-dose photon-counting detector CT \u003c/p\u003e\n\u003cp\u003e6MWT 6-minute walking tests \u003c/p\u003e\n\u003cp\u003eMUC1 mucin 1\u003c/p\u003e\n\u003cp\u003eNSIP non-specific interstitial pneumonia \u003c/p\u003e\n\u003cp\u003eNSSEPC non-specified small extension parenchymal changes \u003c/p\u003e\n\u003cp\u003eOP organizing pneumonia \u003c/p\u003e\n\u003cp\u003ePFTs pulmonary function tests\u003c/p\u003e\n\u003cp\u003ePPFE pleuro-parenchymal fibroelastosis \u003c/p\u003e\n\u003cp\u003eRA-ILD rheumatoid arthritis-associated interstitial lung disease \u003c/p\u003e\n\u003cp\u003eRA rheumatoid arthritis \u003c/p\u003e\n\u003cp\u003eRB-ILD respiratory bronchiolitis interstitial lung disease \u003c/p\u003e\n\u003cp\u003eRF rheumatoid factor \u003c/p\u003e\n\u003cp\u003eROC Receiver operating characteristic \u003c/p\u003e\n\u003cp\u003eSD standard deviation \u003c/p\u003e\n\u003cp\u003etDLP area product \u003c/p\u003e\n\u003cp\u003eTLC total lung capacity \u003c/p\u003e\n\u003cp\u003eUIP usual interstitial pneumonia \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Regional Research Ethics Committee (reference number: IV/2683-1/2022/EKU). Written informed consent was obtained from all participants prior to inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are available upon request from the corresponding author (GN) and subject to approval by the Regional Research Ethics Committee, in accordance with institutional data sharing policies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNikolett Marton received support from the \u0026Uacute;NKP-23-5 New National Excellence Program of the Ministry for Culture and Innovation, funded by the National Research, Development and Innovation Fund, as well as the Bolyai Research Scholarship. Michal Tomcik received institutional support from the Ministry of Health of the Czech Republic (grant no. 023728). The authors declare no other financial or non-financial competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNo additional specific funding was received from any public, commercial, or not-for-profit funding bodies to support the work described in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePD, DW, TC, KPL contributed to study conception and design; PD, DW, KD, DD, VF, HC, MW, NS contributed to data acquisition and analysis; PD, DW, TC, KPL contributed to interpretation of data; DW, TC, KPL had primary responsibility for final content; all authors contributed to critical revision and approved the final manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to the participating patients and colleagues for their contributions to this work Dora Sarvari, Kinga Kohalmi, Marianna Bonacz, Eva Lanyi, Margit Szentesi, Tamas Gati, Kinga Futo, Nikolett Mong, Angela Fulop, Katalin Imre, Bernadette Rojkovich, Gyorgyi Meszaros, Timea Petri, Erzsebet Nagy, Bernadette Bereczkine Szabo, Judit Simon, Adam Domonkos Tarnoki, David Laszlo Tarnoki, Laszlo Szakacs, Leila Szeibel, Nora Kerkovits, Klaudia Borbely and Csenge Poka. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDisclosure statement: The authors have declared no conflicts of interest. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSmolen JS, Aletaha D, McInnes IB. 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Association of fine specificity and repertoire expansion of anticitrullinated peptide antibodies with rheumatoid arthritis associated interstitial lung disease. Ann Rheum Dis. 2014 Aug;73(8):1487\u0026ndash;94.\u003c/li\u003e\n\u003cli\u003eHu S, Ye J, Guo Q, Zou S, et al. Serum lactate dehydrogenase is associated with impaired lung function: NHANES 2011-2012. PLoS One. 2023 Feb 2;18(2):e0281203.\u003c/li\u003e\n\u003cli\u003eWoeltjen MM, Niehoff JH, Michael AE, et al. Low-dose high-resolution photon-counting CT of the lung: Radiation dose and image quality in the clinical routine. Diagnostics (Basel). 2022 Jun 11;12(6):1441.\u003c/li\u003e\n\u003cli\u003eT\u0026aacute;rnoki \u0026Aacute;D, L\u0026aacute;szl\u0026oacute; T\u0026aacute;rnoki D, Maurovich-Horvat P. Photon-counting CT in lung imaging. Expert Rev Respir Med. 2025 Feb;19(2):77\u0026ndash;81.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"arthritis-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arrt","sideBox":"Learn more about [Arthritis Research \u0026 Therapy](http://arthritis-research.biomedcentral.com/)","snPcode":"13075","submissionUrl":"https://submission.nature.com/new-submission/13075/3","title":"Arthritis Research \u0026 Therapy","twitterHandle":"@ArthritisRes","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"rheumatoid arthritis, interstitial lung disease, screening, low-dose computed tomography, photon-counting detector computed tomography, pulmonary function tests, risk factors","lastPublishedDoi":"10.21203/rs.3.rs-7204385/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7204385/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eRheumatoid arthritis-associated interstitial lung disease (RA-ILD) is a major contributor to rheumatoid arthritis (RA) related morbidity and mortality. Early detection is challenging due to subclinical onset and limitations of conventional screening modalities. This study evaluated the diagnostic performance of low-dose photon-counting detector CT (LD PCD-CT) for RA-ILD and assessed its prevalence and risk factors in a Hungarian RA cohort.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn this prospective study (Feb 2022\u0026ndash;June 2023), 492 consecutively enrolled RA patients without known ILD, underwent LD PCD-CT, digital chest radiography (DR) and pulmonary function testing (PFTs). Imaging was scored using a standardized LD severity scale. Clinical, demographic, and serological data were analyzed to identify ILD predictors. Statistical analyses included Kolmogorov\u0026ndash;Smirnov, t-tests, Mann\u0026ndash;Whitney U, chi-squared/Fisher\u0026rsquo;s exact tests, Pearson correlation, and ROC analysis. Logistic regression was used to identify independent risk factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eLD PCD-CT identified interstitial abnormalities in 35% of patients. By contrast, clinical assessment and PFTs detected abnormalities in only 44% and 22% of these cases, respectively. Among patients without CT-defined abnormalities, 42% had a positive clinical assessment and 23% had abnormal PFTs, indicating limited diagnostic specificity. The most frequent findings were interstitial reticular abnormalities (58%) and usual interstitial pneumonia (22%). Independent ILD predictors included age\u0026thinsp;\u0026ge;\u0026thinsp;50 years, male sex, \u0026ge;\u0026thinsp;25 pack-year smoking history, rheumatoid factor (RF) positivity, and elevated lactate dehydrogenase (LDH) levels. LD PCD-CT had a mean effective radiation dose of 0.415 mSv, remaining within low-dose diagnostic thresholds.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eLD PCD-CT demonstrated superior sensitivity and specificity for early RA-ILD detection compared to clinical assessment and PFTs, while maintaining low radiation exposure. Incorporating LD PCD-CT into risk-stratified screening protocols may facilitate earlier diagnosis and timely therapeutic interventions, ultimately improving patient outcomes.\u003c/p\u003e\u003ch2\u003eClinical trial registration number:\u003c/h2\u003e\u003cp\u003eNCT05391100\u003c/p\u003e","manuscriptTitle":"Screening for Rheumatoid Arthritis-Associated Interstitial Lung Disease Using Low-Dose CT: An Emerging Approach — An Observational Prospective Case-Control Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 06:37:44","doi":"10.21203/rs.3.rs-7204385/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-21T09:47:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-19T18:31:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-18T21:00:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291621265897481258853503256420965680789","date":"2025-08-28T09:38:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143263088822041059613504604871930505868","date":"2025-08-26T12:25:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-26T09:36:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-01T13:10:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-01T07:57:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Arthritis Research \u0026 Therapy","date":"2025-07-24T09:57:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"arthritis-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arrt","sideBox":"Learn more about [Arthritis Research \u0026 Therapy](http://arthritis-research.biomedcentral.com/)","snPcode":"13075","submissionUrl":"https://submission.nature.com/new-submission/13075/3","title":"Arthritis Research \u0026 Therapy","twitterHandle":"@ArthritisRes","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"621b4f8c-03a0-427e-b0af-a70155f30dcc","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T16:08:03+00:00","versionOfRecord":{"articleIdentity":"rs-7204385","link":"https://doi.org/10.1186/s13075-025-03674-w","journal":{"identity":"arthritis-research-and-therapy","isVorOnly":false,"title":"Arthritis Research \u0026 Therapy"},"publishedOn":"2025-11-06 15:58:15","publishedOnDateReadable":"November 6th, 2025"},"versionCreatedAt":"2025-09-03 06:37:44","video":"","vorDoi":"10.1186/s13075-025-03674-w","vorDoiUrl":"https://doi.org/10.1186/s13075-025-03674-w","workflowStages":[]},"version":"v1","identity":"rs-7204385","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7204385","identity":"rs-7204385","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}