Submitted:
13 June 2024
Posted:
14 June 2024
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Abstract
Keywords:
1. Introduction
2. Materials and Methods
3. Results
3.1. Primary Outcome
3.2. Secondary Outcome
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Wijsenbeek, M.; Suzuki, A.; Maher, T.M. Interstitial Lung Diseases. Lancet 2022, 400, 769–786. [Google Scholar] [CrossRef] [PubMed]
- George, P.M.; Spagnolo, P.; Kreuter, M.; Altinisik, G.; Bonifazi, M.; Martinez, F.J.; Molyneaux, P.L.; Renzoni, E.A.; Richeldi, L.; Tomassetti, S.; et al. Progressive Fibrosing Interstitial Lung Disease: Clinical Uncertainties, Consensus Recommendations, and Research Priorities. Lancet Respir Med 2020, 8, 925–934. [Google Scholar] [CrossRef] [PubMed]
- Wijsenbeek, M.; Kreuter, M.; Olson, A.; Fischer, A.; Bendstrup, E.; Wells, C.D.; Denton, C.P.; Mounir, B.; Zouad-Lejour, L.; Quaresma, M.; et al. Progressive Fibrosing Interstitial Lung Diseases: Current Practice in Diagnosis and Management. Curr Med Res Opin 2019, 35, 2015–2024. [Google Scholar] [CrossRef]
- Yamano, Y.; Kataoka, K.; Takei, R.; Sasano, H.; Yokoyama, T.; Matsuda, T.; Kimura, T.; Mori, Y.; Furukawa, T.; Fukuoka, J.; et al. Interstitial Pneumonia with Autoimmune Features and Histologic Usual Interstitial Pneumonia Treated with Anti-Fibrotic versus Immunosuppressive Therapy. Respir Investig 2023, 61, 297–305. [Google Scholar] [CrossRef] [PubMed]
- Fischer, A.; du Bois, R. Interstitial Lung Disease in Connective Tissue Disorders. Lancet 2012, 380, 689–698. [Google Scholar] [CrossRef] [PubMed]
- Wijsenbeek, M.; Cottin, V. Spectrum of Fibrotic Lung Diseases. N Engl J Med 2020, 383, 958–968. [Google Scholar] [CrossRef] [PubMed]
- Raghu, G.; Remy-Jardin, M.; Richeldi, L.; Thomson, C.C.; Inoue, Y.; Johkoh, T.; Kreuter, M.; Lynch, D.A.; Maher, T.M.; Martinez, F.J.; et al. Idiopathic Pulmonary Fibrosis (an Update) and Progressive Pulmonary Fibrosis in Adults: An Official ATS/ERS/JRS/ALAT Clinical Practice Guideline. Am J Respir Crit Care Med 2022, 205, e18–e47. [Google Scholar] [CrossRef]
- Wells, A.U.; Brown, K.K.; Flaherty, K.R.; Kolb, M.; Thannickal, V.J. What’s in a Name? That Which We Call IPF, by Any Other Name Would Act the Same. Eur. Respir. J. 2018, 51, 1800692. [Google Scholar] [CrossRef]
- Ghazipura, M.; Mammen, M.J.; Herman, D.D.; Hon, S.M.; Bissell, B.D.; Macrea, M.; Kheir, F.; Khor, Y.H.; Knight, S.L.; Raghu, G.; et al. Nintedanib in Progressive Pulmonary Fibrosis: A Systematic Review and Meta-Analysis. Ann Am Thorac Soc 2022, 19, 1040–1049. [Google Scholar] [CrossRef] [PubMed]
- Rajan, S.K.; Cottin, V.; Dhar, R.; Danoff, S.; Flaherty, K.R.; Brown, K.K.; Mohan, A.; Renzoni, E.; Mohan, M.; Udwadia, Z.; et al. Progressive Pulmonary Fibrosis: An Expert Group Consensus Statement. Eur Respir J 2023, 61. [Google Scholar] [CrossRef]
- Distler, O.; Brown, K.K.; Distler, J.H.W.; Assassi, S.; Maher, T.M.; Cottin, V.; Varga, J.; Coeck, C.; Gahlemann, M.; Sauter, W.; et al. Design of a Randomised, Placebo-Controlled Clinical Trial of Nintedanib in Patients with Systemic Sclerosis-Associated Interstitial Lung Disease (SENSCISTM). Clin Exp Rheumatol 2017, 35 Suppl 106, 75–81. [Google Scholar]
- Ryerson, C.J.; Vittinghoff, E.; Ley, B.; Lee, J.S.; Mooney, J.J.; Jones, K.D.; Elicker, B.M.; Wolters, P.J.; Koth, L.L.; King, T.E.; et al. Predicting Survival across Chronic Interstitial Lung Disease: The ILD-GAP Model. Chest 2014, 145, 723–728. [Google Scholar] [CrossRef] [PubMed]
- Raghu, G.; Flaherty, K.R.; Lederer, D.J.; Lynch, D.A.; Colby, T.V.; Myers, J.L.; Groshong, S.D.; Larsen, B.T.; Chung, J.H.; Steele, M.P.; et al. Use of a Molecular Classifier to Identify Usual Interstitial Pneumonia in Conventional Transbronchial Lung Biopsy Samples: A Prospective Validation Study. Lancet Respir Med 2019, 7, 487–496. [Google Scholar] [CrossRef] [PubMed]
- Bowman, W.S.; Newton, C.A.; Linderholm, A.L.; Neely, M.L.; Pugashetti, J.V.; Kaul, B.; Vo, V.; Echt, G.A.; Leon, W.; Shah, R.J.; et al. Proteomic Biomarkers of Progressive Fibrosing Interstitial Lung Disease: A Multicentre Cohort Analysis. Lancet Respir Med 2022, 10, 593–602. [Google Scholar] [CrossRef] [PubMed]
- Lang, D.; Akbari, K.; Walcherberger, S.; Hergan, B.; Horner, A.; Hepp, M.; Kaiser, B.; Pieringer, H.; Lamprecht, B. Computed Tomography Findings as Determinants of Pulmonary Function Tests in Fibrotic Interstitial Lung Diseases-Network-Analyses and Multivariate Models. Chron Respir Dis 2020, 17, 1479973120967025. [Google Scholar] [CrossRef] [PubMed]
- Hoffmann-Vold, A.M.; Allanore, Y.; Alves, M.; Brunborg, C.; Airó, P.; Ananieva, L.P.; Czirják, L.; Guiducci, S.; Hachulla, E.; Li, M.; et al. Progressive Interstitial Lung Disease in Patients with Systemic Sclerosis-Associated Interstitial Lung Disease in the EUSTAR Database. Ann Rheum Dis 2021, 80, 219–227. [Google Scholar] [CrossRef] [PubMed]
- von Elm, E.; Altman, D.G.; Egger, M.; Pocock, S.J.; Gøtzsche, P.C.; Vandenbroucke, J.P. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies. J Clin Epidemiol 2008, 61, 344–349. [Google Scholar] [CrossRef] [PubMed]
- Gruden, J.F.; Naidich, D.P.; Machnicki, S.C.; Cohen, S.L.; Girvin, F.; Raoof, S. An Algorithmic Approach to the Interpretation of Diffuse Lung Disease on Chest CT Imaging: A Theory of Almost Everything. Chest 2020, 157, 612–635. [Google Scholar] [CrossRef] [PubMed]
- Shao, G.; Hawle, P.; Akbari, K.; Horner, A.; Hintenberger, R.; Kaiser, B.; Lamprecht, B.; Lang, D. Clinical, Imaging, and Blood Biomarkers to Assess 1-Year Progression Risk in Fibrotic Interstitial Lung Diseases-Development and Validation of the Honeycombing, Traction Bronchiectasis, and Monocyte (HTM)-Score. Front Med Lausanne 2022, 9, 1043720. [Google Scholar] [CrossRef]
- Ley, B.; Ryerson, C.J.; Vittinghoff, E.; Ryu, J.H.; Tomassetti, S.; Lee, J.S.; Poletti, V.; Buccioli, M.; Elicker, B.M.; Jones, K.D.; et al. A Multidimensional Index and Staging System for Idiopathic Pulmonary Fibrosis. Ann Intern Med 2012, 156, 684–691. [Google Scholar] [CrossRef]
- Dhooria, S.; Agarwal, R.; Sehgal, I.S.; Prasad, K.T.; Garg, M.; Bal, A.; Aggarwal, A.N.; Behera, D. Spectrum of Interstitial Lung Diseases at a Tertiary Center in a Developing Country: A Study of 803 Subjects. PLoS One 2018, 13, e0191938. [Google Scholar] [CrossRef]
- Joung, K.I.; Park, H.; Park, S.; Shin, J.Y.; Kim, Y.H. Nationwide Epidemiologic Study for Fibrosing Interstitial Lung Disease (F-ILD) in South Korea: A Population-Based Study. BMC Pulm Med 2023, 23, 98. [Google Scholar] [CrossRef]
- Hopkins, R.B.; Burke, N.; Fell, C.; Dion, G.; Kolb, M. Epidemiology and Survival of Idiopathic Pulmonary Fibrosis from National Data in Canada. Eur Respir J 2016, 48, 187–195. [Google Scholar] [CrossRef]
- Fernández Pérez, E.R.; Daniels, C.E.; Schroeder, D.R.; St Sauver, J.; Hartman, T.E.; Bartholmai, B.J.; Yi, E.S.; Ryu, J.H. Incidence, Prevalence, and Clinical Course of Idiopathic Pulmonary Fibrosis: A Population-Based Study. Chest 2010, 137, 129–137. [Google Scholar] [CrossRef]
- Navaratnam, V.; Fleming, K.M.; West, J.; Smith, C.J.; Jenkins, R.G.; Fogarty, A.; Hubbard, R.B. The Rising Incidence of Idiopathic Pulmonary Fibrosis in the U.K. Thorax 2011, 66, 462–467. [Google Scholar] [CrossRef]
- Duchemann, B.; Annesi-Maesano, I.; Jacobe de Naurois, C.; Sanyal, S.; Brillet, P.Y.; Brauner, M.; Kambouchner, M.; Huynh, S.; Naccache, J.M.; Borie, R.; et al. Prevalence and Incidence of Interstitial Lung Diseases in a Multi-Ethnic County of Greater Paris. Eur Respir J 2017, 50. [Google Scholar] [CrossRef] [PubMed]
- He, S.H.; He, Y.J.; Guo, K.J.; Liang, X.; Li, S.S.; Li, T.F. Risk Factors for Progression of Interstitial Lung Disease in Sjögren’s Syndrome: A Single-Centered, Retrospective Study. Clin Rheumatol 2022, 41, 1153–1161. [Google Scholar] [CrossRef] [PubMed]
- Cao, M.; Sheng, J.; Qiu, X.; Wang, D.; Wang, D.; Wang, Y.; Xiao, Y.; Cai, H. Acute Exacerbations of Fibrosing Interstitial Lung Disease Associated with Connective Tissue Diseases: A Population-Based Study. BMC Pulm Med 2019, 19, 215. [Google Scholar] [CrossRef]
- Fu, H.; Zheng, Z.; Zhang, Z.; Yang, Y.; Cui, J.; Wang, Z.; Xue, J.; Chi, S.; Cao, M.; Chen, J. Prediction of Progressive Pulmonary Fibrosis in Patients with Anti-Synthetase Syndrome-Associated Interstitial Lung Disease. Clin Rheumatol 2023, 42, 1917–1929. [Google Scholar] [CrossRef] [PubMed]
- Scott, M.K.D.; Quinn, K.; Li, Q.; Carroll, R.; Warsinske, H.; Vallania, F.; Chen, S.; Carns, M.A.; Aren, K.; Sun, J.; et al. Increased Monocyte Count as a Cellular Biomarker for Poor Outcomes in Fibrotic Diseases: A Retrospective, Multicentre Cohort Study. Lancet Respir Med 2019, 7, 497–508. [Google Scholar] [CrossRef]
- Kreuter, M.; Lee, J.S.; Tzouvelekis, A.; Oldham, J.M.; Molyneaux, P.L.; Weycker, D.; Atwood, M.; Kirchgaessler, K.U.; Maher, T.M. Monocyte Count as a Prognostic Biomarker in Patients with Idiopathic Pulmonary Fibrosis. Am J Respir Crit Care Med 2021, 204, 74–81. [Google Scholar] [CrossRef] [PubMed]
- Wynn, T.A.; Vannella, K.M. Macrophages in Tissue Repair, Regeneration, and Fibrosis. Immunity 2016, 44, 450–462. [Google Scholar] [CrossRef] [PubMed]
- Zhou, Y.; Peng, H.; Sun, H.; Peng, X.; Tang, C.; Gan, Y.; Chen, X.; Mathur, A.; Hu, B.; Slade, M.D.; et al. Chitinase 3-like 1 Suppresses Injury and Promotes Fibroproliferative Responses in Mammalian Lung Fibrosis. Sci Transl Med 2014, 6, 240ra76. [Google Scholar] [CrossRef] [PubMed]
- Misharin, A.V.; Morales-Nebreda, L.; Reyfman, P.A.; Cuda, C.M.; Walter, J.M.; McQuattie-Pimentel, A.C.; Chen, C.I.; Anekalla, K.R.; Joshi, N.; Williams, K.J.N.; et al. Monocyte-Derived Alveolar Macrophages Drive Lung Fibrosis and Persist in the Lung over the Life Span. J Exp Med 2017, 214, 2387–2404. [Google Scholar] [CrossRef]
- Ehrchen, J.M.; Roth, J.; Barczyk-Kahlert, K. More Than Suppression: Glucocorticoid Action on Monocytes and Macrophages. Front Immunol 2019, 10, 2028. [Google Scholar] [CrossRef]
- Elmér, E.; Nived, P.; Pettersson, Å.; Skattum, L.; Hellmark, T.; Kapetanovic, M.C.; Johansson Å, C.M. Methotrexate Treatment Suppresses Monocytes in Nonresponders to Pneumococcal Conjugate Vaccine in Rheumatoid Arthritis Patients. J Immunol Res 2022, 2022, 7561661. [Google Scholar] [CrossRef]


| All patients (n=142) | |||||
| Variable | All patients (n=142) |
Progression at 1 year (n=73) |
Stable at 1 year (n=25) |
Improvement at 1 year (n=44) |
p-value |
| Baseline characteristics | |||||
| Mean age (SE) | 67.0 (1.1) | 70.4 (1.0) | 64.8 (2.5) | 62.4 (2.4) | 0.023 |
| Age ≥70 years (%) | 47.2 | 54.8 | 36.0 | 40.9 | 0.162 |
| Female sex (%) | 36.6 | 32.9 | 28.0 | 47.7 | 0.167 |
| Treatment characteristics (%) | |||||
| Antiinflammatory | 52.1 | 41.1 | 52.0 | 70.5 | 0.066 |
| Antifibrotic | 12.0 | 15.1 | 12.0 | 6.8 | |
| Antiinflammatory and antifibrotic | 7.0 | 11.0 | 8.0 | 0.0 | |
| No ILD-specific therapy | 28.9 | 32.0 | 28.0 | 22.7 | |
| Pulmonary functions tests; mean (SE) | |||||
| FVC (% pred.) | 81.3 (1.5) | 84.3 (2.1) | 84.6 (3.9) | 74.5 (2.6) | 0.017 |
| FEV1 (% pred.) | 82.8 (1.6) | 86.6 (2.1) | 84.4 (4.1) | 75.9 (2.5) | 0.018 |
| DLCO (% pred.) | 55.2 (1.5) | 57.1 (2.0) | 58.2 (3.4) | 50.3 (2.6) | 0.104 |
| Peripheral blood Biomarkers; mean (SE) | |||||
| Absolute leukocyte count (G/L) | 8.8 (0.3) | 8.6 (0.4) | 8.8 (1.0) | 9.0 (0.4) | 0.442 |
| Absolute monocyte count (G/L) | 0.6 (0.1) | 0.6 (0.1) | 0.5 (0.1) | 0.6 (0.1) | 0.046 |
| Absolute eosinophil count (G/L) | 0.2 (0.1) | 0.2 (0.1) | 0.1 (0.0) | 0.2 (0.1) | 0.397 |
| C-reactive protein (mg/dL) | 1.2 (0.2) | 0.9 (0.2) | 1.1 (0.5) | 1.8 (0.4) | 0.285 |
| Lactate Dehydrogenase (U/L) | 248.3 | 241.4 (9.8) | 224.8 (12.1) | 272.5 (15.7) | 0.095 |
| Bronchoalveolar lavage; mean (SE) n = 81 | |||||
| BAL - macrophage fraction | 57.7 (3.1) | 61.2 (4.2) | 57.5 (8.3) | 52.9 (5.3) | 0.484 |
| BAL – lymphocyte fraction | 17.9 (2.2) | 14.7 (2.4) | 9.3 (2.8) | 26.5 (5.0) | 0.038 |
| BAL – Neutrophile fraction | 15.1 (2.2) | 17.5 (3.5) | 18.9 (7.3) | 10.1 (1.9) | 0.592 |
| BAL – Eosinophile fraction | 4.1 (0.9) | 3.8 (1.4) | 5.1 (1.8) | 4.1 (1.4) | 0.631 |
| Computed tomography finding scores; median, range | |||||
| Reticular abnormalities | 6 (0 – 6) | 6 (0 – 6) | 6 (0 – 6) | 6 (1 – 6) | 0.807 |
| Honeycombing | 0 (0 – 6) | 0 (0 – 6) | 0 (0 – 6) | 0 (0 – 3) | 0.063 |
| Ground Glass Opacities | 0 (0 – 6) | 0 (0 – 6) | 0 (0 – 6) | 2 (0 – 6) | 0.047 |
| Emphysema | 0 (0 – 6) | 0 (0 – 6) | 0 (0 – 6) | 0 (0 – 6) | 0.534 |
| Traction bronchiectasis | 2 (0 – 6) | 2 (0 – 6) | 2 (0 – 6) | 2 (0 – 6) | 0.019 |

| Patients with antiinflammatory treatment (n=84) | |||||
|---|---|---|---|---|---|
| Variable | All patients (n=84) |
Progression at 1 year (n=38) |
Stable at 1 year (n=15) |
Improvement at 1 year( n=31) |
p-value |
| Baseline characteristics | |||||
| Mean age (SE) | 66.2 (1.5) | 70.2 (1.4) | 66.8 (2.9) | 61.1 (3.1) | 0.122 |
| Age ≥70 years (%) | 40 (47.6) | 22 (57.9) | 6 (40.0) | 12 (38.7) | 0.229 |
| Female sex (%) | 35 (41.7) | 17 (44.7) | 4 (26.7) | 14 (45.2 | 0.429 |
| Pulmonary functions tests; mean (SE) | |||||
| FVC (% pred.) | 80.3 (2.1) | 82.9 (3.0) | 89.4 (4.8) | 72.7 (3.0) | 0.008 |
| FEV1 (% pred.) | 81.9 (2.0) | 84.9 (2.9) | 89.3 (4.4) | 74.8 (3.0) | 0.018 |
| DLCO (% pred.) | 52.9 (1.8) | 53.9 (2.1) | 58.9 (5.4) | 48.7 (3.1) | 0.183 |
| Peripheral blood Biomarkers; mean (SE) | |||||
| Absolute leukocyte count (G/L) | 8.9 (0.3) | 8.7 (0.6) | 8.5 (0.9) | 9.2 (0.5) | 0.379 |
| Absolute monocyte count (G/L) | 0.6 (0.1) | 0.6 (0.1) | 0.6 (0.1) | 0.5 (0.1) | 0.638 |
| Absolute eosinophil count (G/L) | 0.2 (0.1) | 0.2 (0.1) | 0.1 (0.0) | 0.2 (0.1) | 0.662 |
| C-reactive protein (mg/dL) | 1.2 (0.2) | 0.7 (0.2) | 0.7 (0.2) | 2.1 (0.6) | 0.308 |
| Lactate Dehydrogenase (U/L) | 259.7 (11.1) | 253.1 (16.2) | 218.9 (12.7) | 288.6 (21.0) | 0.078 |
| Bronchoalveolar lavage; mean (SE) n = 81 | |||||
| BAL – Macrophage fraction | 54.9 (3.5) | 58.7 (4.9) | 59.7 (9.9) | 49.0 (5.7) | 0.429 |
| BAL – Lymphocyte fraction | 22.1 (2.9) | 19.3 (3.3) | 11.9 (3.7) | 28.9 (5.7) | 0.215 |
| BAL – Neutrophile fraction | 12.8 (1.9) | 13.0 (3.1) | 17.4 (6.6) | 10.8 (2.2) | 0.551 |
| BAL – Eosinophile fraction | 4.7 (1.2) | 4.6 (2.1) | 5.6 (2.4) | 4.5 (1.6) | 0.647 |
| Computed tomography finding scores; median, range | |||||
| rReticular abnormalities | 6 (0 – 6) | 6 (0 – 6) | 6 (2 – 6) | 6 (1 – 6) | 0.740 |
| Honeycombing | 0 (0 – 6) | 0 (0 – 6) | 0 (0 – 2) | 0 (0 – 2) | 0.179 |
| Ground glass opacities | 1.5 (0 – 6) | 0.5 (0 – 6) | 0 (0 – 6) | 2 (0 – 6) | 0.118 |
| Emphysema | 0 (0 – 6) | 0 (0 – 6) | 0 (0 – 6) | 0 (0 – 4) | 0.151 |
| Traction bronchiectasis | 2 (0 – 6) | 2 (0 – 6) | 2 (0 – 6) | 2 (0 – 6) | 0.043 |
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