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Sharifi H, Bertini CD, Alkhunaizi M, Hernandez M, Musa Z, Borges C, Turk I, Bashoura L, Dickey BF, Cheng GS, Yanik G, Galban CJ, Guo HH, Godoy MCB, Reinhardt JM, Hoffman EA, Castro M, Rondon G, Alousi AM, Champlin RE, Shpall EJ, Lu Y, Peterson S, Datta K, Nicolls MR, Hsu J, Sheshadri A. CT strain metrics allow for earlier diagnosis of bronchiolitis obliterans syndrome after hematopoietic cell transplant. Blood Adv 2024; 8:5156-5165. [PMID: 39163616 DOI: 10.1182/bloodadvances.2024013748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 07/29/2024] [Accepted: 07/29/2024] [Indexed: 08/22/2024] Open
Abstract
ABSTRACT Bronchiolitis obliterans syndrome (BOS) after hematopoietic cell transplantation (HCT) is associated with substantial morbidity and mortality. Quantitative computed tomography (qCT) can help diagnose advanced BOS meeting National Institutes of Health (NIH) criteria (NIH-BOS) but has not been used to diagnose early, often asymptomatic BOS (early BOS), limiting the potential for early intervention and improved outcomes. Using pulmonary function tests (PFTs) to define NIH-BOS, early BOS, and mixed BOS (NIH-BOS with restrictive lung disease) in patients from 2 large cancer centers, we applied qCT to identify early BOS and distinguish between types of BOS. Patients with transient impairment or healthy lungs were included for comparison. PFTs were done at month 0, 6, and 12. Analysis was performed with association statistics, principal component analysis, conditional inference trees (CITs), and machine learning (ML) classifier models. Our cohort included 84 allogeneic HCT recipients, 66 with BOS (NIH-defined, early, or mixed) and 18 without BOS. All qCT metrics had moderate correlation with forced expiratory volume in 1 second, and each qCT metric differentiated BOS from those without BOS (non-BOS; P < .0001). CITs distinguished 94% of participants with BOS vs non-BOS, 85% of early BOS vs non-BOS, 92% of early BOS vs NIH-BOS. ML models diagnosed BOS with area under the curve (AUC) of 0.84 (95% confidence interval [CI], 0.74-0.94) and early BOS with AUC of 0.84 (95% CI, 0.69-0.97). qCT metrics can identify individuals with early BOS, paving the way for closer monitoring and earlier treatment in this vulnerable population.
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Affiliation(s)
- Husham Sharifi
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - Christopher D Bertini
- Division of Pulmonary and Critical Care Medicine, University of Texas Health Science Center, Houston, TX
| | - Mansour Alkhunaizi
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - Maria Hernandez
- Division of Hospital Medicine, Northwestern University, Chicago, IL
| | - Zayan Musa
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - Carlos Borges
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - Ihsan Turk
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - Lara Bashoura
- Department of Pulmonary Medicine, MD Anderson Cancer Center, Houston, TX
| | - Burton F Dickey
- Department of Pulmonary Medicine, MD Anderson Cancer Center, Houston, TX
| | - Guang-Shing Cheng
- Division of Pulmonary, Critical Care and Sleep Medicine, Fred Hutchinson Cancer Center, Seattle, WA
| | - Gregory Yanik
- Blood and Marrow Transplant Division, University of Michigan Health, Ann Arbor, MI
| | - Craig J Galban
- Department of Radiology, Blood and Marrow Transplant Division, University of Michigan Health, Ann Arbor, MI
| | - Huawei Henry Guo
- Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Myrna C B Godoy
- Department of Pulmonary Medicine, MD Anderson Cancer Center, Houston, TX
| | | | - Eric A Hoffman
- Department of Radiology, University of Iowa, Iowa City, IA
| | - Mario Castro
- Division of Pulmonary, Critical Care and Sleep Medicine, Kansas University Medical Center, Kansas City, KS
| | - Gabriela Rondon
- Department of Pulmonary Medicine, MD Anderson Cancer Center, Houston, TX
| | - Amin M Alousi
- Department of Pulmonary Medicine, MD Anderson Cancer Center, Houston, TX
| | - Richard E Champlin
- Department of Pulmonary Medicine, MD Anderson Cancer Center, Houston, TX
| | - Elizabeth J Shpall
- Department of Pulmonary Medicine, MD Anderson Cancer Center, Houston, TX
| | - Ying Lu
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA
| | | | - Keshav Datta
- Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Mark R Nicolls
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - Joe Hsu
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - Ajay Sheshadri
- Division of Pulmonary and Critical Care Medicine, University of Texas Health Science Center, Houston, TX
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Combs MP, Belloli EA, Gargurevich N, Flaherty KR, Murray S, Galbán CJ, Lama VN. Results from randomized trial of pirfenidone in patients with chronic rejection (STOP-CLAD study). J Heart Lung Transplant 2024; 43:1468-1477. [PMID: 38796045 DOI: 10.1016/j.healun.2024.05.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 05/10/2024] [Accepted: 05/19/2024] [Indexed: 05/28/2024] Open
Abstract
BACKGROUND Chronic lung allograft dysfunction (CLAD) is the leading long-term cause of poor outcomes after transplant and manifests by fibrotic remodeling of small airways and/or pleuroparenchymal fibroelastosis. This study evaluated the effect of pirfenidone on quantitative radiographic and pulmonary function assessment in patients with CLAD. METHODS We performed a single-center, 6-month, randomized, placebo-controlled trial of pirfenidone in patients with CLAD. Randomization was stratified by CLAD phenotype. The primary outcome for this study was change in radiographic assessment of small airways disease, quantified as percentage of lung volume using parametric response mapping analysis of computed tomography scans (PRMfSAD); secondary outcomes included change in forced expiratory volume in 1 second (FEV1), change in forced vital capacity (FVC), and change in radiographic quantification of parenchymal disease (PRMPD). Linear mixed models were used to evaluate the treatment effect on outcome measures. RESULTS The goal enrollment of 60 patients was not met due to the coronavirus disease of 2019 pandemic, with 23 patients included in the analysis. There was no significant difference over the study period between the pirfenidone vs placebo groups with regards to the observed change in PRMfSAD (+4.2% vs -0.4%; p = 0.22), FEV1 (-3.5% vs -3.6%; p = 0.97), FVC (-1.9% vs -4.6%; p = 0.41), or PRMPD (-0.6% vs -2.5%; p = 0.30). The study treatment tolerance and adverse events were generally similar between the pirfenidone and placebo groups. CONCLUSIONS Pirfenidone had no apparent impact on radiographic evidence of allograft dysfunction or pulmonary function decline in a single-center randomized trial of CLAD patients that did not meet enrollment goals but had an acceptable tolerance and side-effect profile.
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Affiliation(s)
- Michael P Combs
- Department of Medicine, Division of Pulmonary & Critical Care, University of Michigan, Ann Arbor, Michigan
| | - Elizabeth A Belloli
- Department of Medicine, Division of Pulmonary & Critical Care, University of Michigan, Ann Arbor, Michigan
| | | | - Kevin R Flaherty
- Department of Medicine, Division of Pulmonary & Critical Care, University of Michigan, Ann Arbor, Michigan
| | - Susan Murray
- School of Public Health, University of Michigan, Ann Arbor, Michigan
| | - Craig J Galbán
- Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Vibha N Lama
- Department of Medicine, Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Emory University, Atlanta, Georgia.
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Pavlisko EN, Adam BA, Berry GJ, Calabrese F, Cortes-Santiago N, Glass CH, Goddard M, Greenland JR, Kreisel D, Levine DJ, Martinu T, Verleden SE, Weigt SS, Roux A. The 2022 Banff Meeting Lung Report. Am J Transplant 2024; 24:542-548. [PMID: 37931751 DOI: 10.1016/j.ajt.2023.10.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 10/11/2023] [Indexed: 11/08/2023]
Abstract
The Lung Session of the 2022 16th Banff Foundation for Allograft Pathology Conference-held in Banff, Alberta-focused on non-rejection lung allograft pathology and novel technologies for the detection of allograft injury. A multidisciplinary panel reviewed the state-of-the-art of current histopathologic entities, serologic studies, and molecular practices, as well as novel applications of digital pathology with artificial intelligence, gene expression analysis, and quantitative image analysis of chest computerized tomography. Current states of need as well as prospective integration of the aforementioned tools and technologies for complete assessment of allograft injury and its impact on lung transplant outcomes were discussed. Key conclusions from the discussion were: (1) recognition of limitations in current standard of care assessment of lung allograft dysfunction; (2) agreement on the need for a consensus regarding the standardized approach to the collection and assessment of pathologic data, inclusive of all lesions associated with graft outcome (eg, non-rejection pathology); and (3) optimism regarding promising novel diagnostic modalities, especially minimally invasive, which should be integrated into large, prospective multicenter studies to further evaluate their utility in clinical practice for directing personalized therapies to improve graft outcomes.
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Affiliation(s)
- Elizabeth N Pavlisko
- Department of Pathology, Duke University Medical Center, Durham, North Carolina, USA.
| | - Benjamin A Adam
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Canada
| | - Gerald J Berry
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Fiorella Calabrese
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova Medical School, Padova, Italy
| | - Nahir Cortes-Santiago
- Department of Pathology and Immunology, Texas Children's Hospital, Houston, Texas, USA
| | - Carolyn H Glass
- Department of Pathology, Duke University Medical Center, Durham, North Carolina, USA
| | - Martin Goddard
- Pathology Department, Royal Papworth Hospital, NHS Trust, Papworth Everard, Cambridge, UK
| | - John R Greenland
- Department of Medicine, University of California, San Francisco, USA; Veterans Affairs Health Care System, San Francisco, California, USA
| | - Daniel Kreisel
- Department of Surgery, Department of Pathology and Immunology, Washington University, St. Louis, Missouri, USA
| | - Deborah J Levine
- Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford University, California, USA
| | - Tereza Martinu
- Division of Respirology, Department of Medicine, University Health Network and University of Toronto, Toronto, Ontario, Canada; Toronto Lung Transplant Program, University Health Network, Toronto, Ontario, Canada
| | - Stijn E Verleden
- Lung Transplant Unit, Department of Chronic Diseases and Metabolism, Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium; Department of ASTARC, University of Antwerp, Wilrijk, Belgium
| | - S Sam Weigt
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Antoine Roux
- Department of Respiratory Medicine, Foch Hospital, Suresnes, France
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Huang XQ, Pan J, Fang YY, Wang X, Shen M, Yuan Y, Guo SL. Interaction of smoking and aging on emphysema and small airways disease in asymptomatic healthy men by CT-based parametric response mapping analysis. Clin Radiol 2024; 79:e156-e163. [PMID: 37867079 DOI: 10.1016/j.crad.2023.09.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 09/06/2023] [Accepted: 09/26/2023] [Indexed: 10/24/2023]
Abstract
AIM To explore whether small airway disease and emphysema were affected by the interaction between smoking and aging on chest computed tomography (CT) images of asymptomatic healthy men analysed using a quantitative imaging tool parametric response mapping (PRM). MATERIALS AND METHODS In this retrospective study, 95 asymptomatic healthy men underwent biphasic chest CT. The PRM classifies lung as a percentage of normal (PRMNormal%), functional small airway disease (PRMfSAD%), and emphysema (PRMEmph%). The patients were divided into groups based on their age and smoking status. Multiple linear regression analysis was applied to explore the factors influencing lung injury. Simple effects analysis was performed to explore the interaction between different age groups and smoking status. RESULTS The interaction between aging and smoking significantly affected PRMfSAD% and PRMEmph% (p<0.001). The age range 60-69 and smoking were associated with increased PRMfSAD% and PRMEmph% (p<0.05). Futher stratification into different age subgroups showed that smoking was associated with increased PRMfSAD% and PRMEmph% in the 50-59 year age group. Besides, smoking in the 50-59 and 60-69 years group was associated with decreased PRMNormal%, while smoking in the 60-69 years group did not significantly influence the prevalence of PRMfSAD% and PRMEmph% (p>0.05). CONCLUSIONS PRM reveals the interplay between smoking and aging in the development of lung injury in asymptomatic healthy men. Aging and smoking are important factors of emphysema and small airway disease in the 50-69 years group. In the 60-69 years group, aging poses a greater risk of lung injury compared to smoking.
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Affiliation(s)
- X Q Huang
- The First Clinical Medical College, Lanzhou University, Lanzhou, 730000, China; Department of Radiology, Lanzhou University First Hospital, Lanzhou, 730000, China
| | - J Pan
- Department of Geriatrics, Yan'an People's Hospital, Yan'an, 716000, China
| | - Y Y Fang
- Department of Imaging, Medical College of Yan'an University, Yan'an, 716000, China
| | - X Wang
- Department of Imaging, Medical College of Yan'an University, Yan'an, 716000, China
| | - M Shen
- Department of Radiology, Yan'an University Affiliated Hospital, Yan'an, 716000, China
| | - Y Yuan
- Department of Radiology, Yan'an University Affiliated Hospital, Yan'an, 716000, China
| | - S L Guo
- Department of Radiology, Lanzhou University First Hospital, Lanzhou, 730000, China.
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Chen B, Liu Z, Lu J, Li Z, Kuang K, Yang J, Wang Z, Sun Y, Du B, Qi L, Li M. Deep learning parametric response mapping from inspiratory chest CT scans: a new approach for small airway disease screening. Respir Res 2023; 24:299. [PMID: 38017476 PMCID: PMC10683250 DOI: 10.1186/s12931-023-02611-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 11/16/2023] [Indexed: 11/30/2023] Open
Abstract
OBJECTIVES Parametric response mapping (PRM) enables the evaluation of small airway disease (SAD) at the voxel level, but requires both inspiratory and expiratory chest CT scans. We hypothesize that deep learning PRM from inspiratory chest CT scans can effectively evaluate SAD in individuals with normal spirometry. METHODS We included 537 participants with normal spirometry, a history of smoking or secondhand smoke exposure, and divided them into training, tuning, and test sets. A cascaded generative adversarial network generated expiratory CT from inspiratory CT, followed by a UNet-like network predicting PRM using real inspiratory CT and generated expiratory CT. The performance of the prediction is evaluated using SSIM, RMSE and dice coefficients. Pearson correlation evaluated the correlation between predicted and ground truth PRM. ROC curves evaluated predicted PRMfSAD (the volume percentage of functional small airway disease, fSAD) performance in stratifying SAD. RESULTS Our method can generate expiratory CT of good quality (SSIM 0.86, RMSE 80.13 HU). The predicted PRM dice coefficients for normal lung, emphysema, and fSAD regions are 0.85, 0.63, and 0.51, respectively. The volume percentages of emphysema and fSAD showed good correlation between predicted and ground truth PRM (|r| were 0.97 and 0.64, respectively, p < 0.05). Predicted PRMfSAD showed good SAD stratification performance with ground truth PRMfSAD at thresholds of 15%, 20% and 25% (AUCs were 0.84, 0.78, and 0.84, respectively, p < 0.001). CONCLUSION Our deep learning method generates high-quality PRM using inspiratory chest CT and effectively stratifies SAD in individuals with normal spirometry.
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Affiliation(s)
- Bin Chen
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, 221, Yanan West Road, Jingan Temple Street, Jingan District, Shanghai, China
- Zhang Guozhen Small Pulmonary Nodules Diagnosis and Treatment Center, Shanghai, China
| | - Ziyi Liu
- School of Computer Science, Wuhan University, LuoJiaShan, WuChang District, Wuhan, Hubei, China
- Artificial Intelligence Institute of Wuhan University, Wuhan, Hubei, China
- Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan, Hubei, China
| | - Jinjuan Lu
- Department of Radiology, Shanghai Geriatric Medical Center, Shanghai, China
| | - Zhihao Li
- School of Computer Science, Wuhan University, LuoJiaShan, WuChang District, Wuhan, Hubei, China
- Artificial Intelligence Institute of Wuhan University, Wuhan, Hubei, China
- Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan, Hubei, China
| | - Kaiming Kuang
- Dianei Technology, Shanghai, China
- University of California San Diego, La Jolla, USA
| | - Jiancheng Yang
- Dianei Technology, Shanghai, China
- Computer Vision Laboratory, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
| | - Zengmao Wang
- School of Computer Science, Wuhan University, LuoJiaShan, WuChang District, Wuhan, Hubei, China
- Artificial Intelligence Institute of Wuhan University, Wuhan, Hubei, China
- Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan, Hubei, China
| | - Yingli Sun
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, 221, Yanan West Road, Jingan Temple Street, Jingan District, Shanghai, China
- Zhang Guozhen Small Pulmonary Nodules Diagnosis and Treatment Center, Shanghai, China
| | - Bo Du
- School of Computer Science, Wuhan University, LuoJiaShan, WuChang District, Wuhan, Hubei, China.
- Artificial Intelligence Institute of Wuhan University, Wuhan, Hubei, China.
- Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan, Hubei, China.
| | - Lin Qi
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, 221, Yanan West Road, Jingan Temple Street, Jingan District, Shanghai, China.
- Zhang Guozhen Small Pulmonary Nodules Diagnosis and Treatment Center, Shanghai, China.
| | - Ming Li
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, 221, Yanan West Road, Jingan Temple Street, Jingan District, Shanghai, China.
- Zhang Guozhen Small Pulmonary Nodules Diagnosis and Treatment Center, Shanghai, China.
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Li DJ, Abele J, Sunner P, Varughese RA, Hirji AS, Weinkauf JG, Nagendran J, Weatherald JC, Lien DC, Halloran KM. Relative Lung Perfusion on Ventilation-Perfusion Scans After Double Lung Transplant. Transplantation 2023; 107:2262-2270. [PMID: 37291709 DOI: 10.1097/tp.0000000000004683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
BACKGROUND Pulmonary blood flow can be assessed on ventilation-perfusion (VQ) scan with relative lung perfusion, with a 55% to 45% (or 10%) right-to-left differential considered normal. We hypothesized that wide perfusion differential on routine VQ studies at 3 mo posttransplant would be associated with an increased risk of death or retransplantation, chronic lung allograft (CLAD), and baseline lung allograft dysfunction. METHODS We conducted a retrospective cohort study on all patients who underwent double-lung transplant in our program between 2005 and 2016, identifying patients with a wide perfusion differential of >10% on a 3-mo VQ scan. We used Kaplan-Meier estimates and proportional hazards models to assess the association between perfusion differential and time to death or retransplant and time to CLAD onset. We used correlation and linear regression to assess the relationship with lung function at time of scan and with baseline lung allograft dysfunction. RESULTS Of 340 patients who met inclusion criteria, 169 (49%) had a relative perfusion differential of ≥ 10% on a 3-mo VQ scan. Patients with increased perfusion differential had increased risk of death or retransplantation ( P = 0.011) and CLAD onset ( P = 0.012) after adjustment for other radiographic/endoscopic abnormalities. Increased perfusion differential was associated with lower lung function at time of scan. CONCLUSIONS Wide lung perfusion differential was common after lung transplant in our cohort and associated with increased risk of death, poor lung function, and CLAD onset. The nature of this abnormality and its use as a predictor of future risk warrant further investigation.
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Affiliation(s)
- David J Li
- Department of Medicine, University of Alberta, Edmonton, Canada
| | - Jonathan Abele
- Department of Diagnostic Imaging and Radiology, University of Alberta, Edmonton, Canada
| | - Parveen Sunner
- Department of Diagnostic Imaging and Radiology, University of Alberta, Edmonton, Canada
| | | | - Alim S Hirji
- Department of Medicine, University of Alberta, Edmonton, Canada
| | | | - Jayan Nagendran
- Department of Surgery, University of Alberta, Edmonton, Canada
| | | | - Dale C Lien
- Department of Medicine, University of Alberta, Edmonton, Canada
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Fain SB, Schiebler ML. Using Functional Lung MRI to Predict Chronic Lung Allograft Dysfunction. Radiology 2023; 307:e230636. [PMID: 37070992 PMCID: PMC10323287 DOI: 10.1148/radiol.230636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 03/19/2023] [Accepted: 03/21/2023] [Indexed: 04/19/2023]
Affiliation(s)
- Sean B. Fain
- From the Department of Radiology, Carver College of Medicine,
University of Iowa, 200 Hawkins Dr, Iowa City, IA 52242 (S.B.F.); and Department
of Radiology, School of Medicine and Public Health, University of
Wisconsin–Madison, Madison, Wis (M.L.S.)
| | - Mark L. Schiebler
- From the Department of Radiology, Carver College of Medicine,
University of Iowa, 200 Hawkins Dr, Iowa City, IA 52242 (S.B.F.); and Department
of Radiology, School of Medicine and Public Health, University of
Wisconsin–Madison, Madison, Wis (M.L.S.)
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Halitim P, Tissot A. [Chronic lung allograft dysfunction in 2022, past and updates]. Rev Mal Respir 2023; 40:324-334. [PMID: 36858879 DOI: 10.1016/j.rmr.2023.01.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 01/24/2023] [Indexed: 03/03/2023]
Abstract
INTRODUCTION While short-term results of lung transplantation have improved considerably, long-term survival remains below that achieved for other solid organ transplants. CURRENT KNOWLEDGE The main cause of late mortality is chronic lung allograft dysfunction (CLAD), which affects nearly half of the recipients 5 years after transplantation. Immunological and non-immune risk factors have been identified. These factors activate the innate and adaptive immune system, leading to lesional and altered wound-healing processes, which result in fibrosis affecting the small airways or interstitial tissue. Several phenotypes of CLAD have been identified based on respiratory function and imaging pattern. Aside from retransplantation, which is possible for only small number of patients, no treatment can reverse the CLAD process. PERSPECTIVES Current therapeutic research is focused on anti-fibrotic treatments and photopheresis. Basic research has identified numerous biomarkers that could prove to be relevant as therapeutic targets. CONCLUSION While the pathophysiological mechanisms of CLAD are better understood than before, a major therapeutic challenge remains.
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Affiliation(s)
- P Halitim
- Service de pneumologie et soins intensifs, Hôpital européen Georges-Pompidou, Assistance publique-Hôpitaux de Paris, 75015 Paris, France; Service de pneumologie, CHU de Nantes, l'Institut du thorax, Nantes Université, Inserm, Center for Research in Transplantation and Translational Immunology, UMR 1064, 44093 Nantes cedex, France
| | - A Tissot
- Service de pneumologie, CHU de Nantes, l'Institut du thorax, Nantes Université, Inserm, Center for Research in Transplantation and Translational Immunology, UMR 1064, 44093 Nantes cedex, France.
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Biomarkers for Chronic Lung Allograft Dysfunction: Ready for Prime Time? Transplantation 2023; 107:341-350. [PMID: 35980878 PMCID: PMC9875844 DOI: 10.1097/tp.0000000000004270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Chronic lung allograft dysfunction (CLAD) remains a major hurdle impairing lung transplant outcome. Parallel to the better clinical identification and characterization of CLAD and CLAD phenotypes, there is an increasing urge to find adequate biomarkers that could assist in the earlier detection and differential diagnosis of CLAD phenotypes, as well as disease prognostication. The current status and state-of-the-art of biomarker research in CLAD will be discussed with a particular focus on radiological biomarkers or biomarkers found in peripheral tissue, bronchoalveolar lavage' and circulating blood' in which significant progress has been made over the last years. Ultimately, although a growing number of biomarkers are currently being embedded in the follow-up of lung transplant patients, it is clear that one size does not fit all. The future of biomarker research probably lies in the rigorous combination of clinical information with findings in tissue, bronchoalveolar lavage' or blood. Only by doing so, the ultimate goal of biomarker research can be achieved, which is the earlier identification of CLAD before its clinical manifestation. This is desperately needed to improve the prognosis of patients with CLAD after lung transplantation.
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Markers of Bronchiolitis Obliterans Syndrome after Lung Transplant: Between Old Knowledge and Future Perspective. Biomedicines 2022; 10:biomedicines10123277. [PMID: 36552035 PMCID: PMC9775233 DOI: 10.3390/biomedicines10123277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/02/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022] Open
Abstract
Bronchiolitis obliterans syndrome (BOS) is the most common form of CLAD and is characterized by airflow limitation and an obstructive spirometric pattern without high-resolution computed tomography (HRCT) evidence of parenchymal opacities. Computed tomography and microCT analysis show abundant small airway obstruction, starting from the fifth generation of airway branching and affecting up to 40-70% of airways. The pathogenesis of BOS remains unclear. It is a multifactorial syndrome that leads to pathological tissue changes and clinical manifestations. Because BOS is associated with the worst long-term survival in LTx patients, many studies are focused on the early identification of BOS. Markers may be useful for diagnosis and for understanding the molecular and immunological mechanisms involved in the onset of BOS. Diagnostic and predictive markers of BOS have also been investigated in various biological materials, such as blood, BAL, lung tissue and extracellular vesicles. The aim of this review was to evaluate the scientific literature on markers of BOS after lung transplant. We performed a systematic review to find all available data on potential prognostic and diagnostic markers of BOS.
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Fu A, Vasileva A, Hanafi N, Belousova N, Wu J, Rajyam SS, Ryan CM, Hantos Z, Chow CW. Characterization of chronic lung allograft dysfunction phenotypes using spectral and intrabreath oscillometry. Front Physiol 2022; 13:980942. [PMID: 36277208 PMCID: PMC9582781 DOI: 10.3389/fphys.2022.980942] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Chronic lung allograft dysfunction (CLAD) is the major cause of death beyond 2 years after lung transplantation and develops in 50% of all patients by 5 years post-transplant. CLAD is diagnosed on the basis of a sustained drop of 20% for at least 3 months in the forced expiratory volume (FEV1), compared to the best baseline value achieved post-transplant. CLAD presents as two main phenotypes: bronchiolitis obliterans syndrome (BOS) is more common and has better prognosis than restrictive allograft syndrome (RAS). Respiratory oscillometry is a different modality of lung function testing that is highly sensitive to lung mechanics. The current study investigated whether spectral and intrabreath oscillometry can differentiate between CLAD-free, BOS- and RAS-CLAD at CLAD onset, i.e., at the time of the initial 20% drop in the FEV1. Methods: A retrospective, cross-sectional analysis of 263 double lung transplant recipients who underwent paired testing with oscillometry and spirometry at the Toronto General Pulmonary Function Laboratory from 2017 to 2022 was conducted. All pulmonary function testing and CLAD diagnostics were performed following international guidelines. Statistical analysis was conducted using multiple comparisons. Findings: The RAS (n = 6) spectral oscillometry pattern differs from CLAD-free (n = 225) by right-ward shift of reactance curve similar to idiopathic pulmonary fibrosis whereas BOS (n = 32) has a pattern similar to obstructive lung disease. Significant differences were found in most spectral and intrabreath parameters between BOS, RAS, and time-matched CLAD-free patients. Post-hoc analysis revealed these differences were primarily driven by BOS instead of RAS. While no differences were found between CLAD-free and RAS patients with regards to spectral oscillometry, the intrabreath metric of reactance at end-inspiration (XeI) was significantly different (p < 0.05). BOS and RAS were differentiated by spectral oscillometry measure R5, and intrabreath resistance at end expiration, ReE (p < 0.05 for both). Conclusion: Both spectral and intrabreath oscillometry can differentiate BOS-CLAD from CLAD-free states while intrabreath oscillometry, specifically XeI, can uniquely distinguish RAS-CLAD from CLAD-free. Spectral and intrabreath oscillometry offer complementary information regarding lung mechanics in CLAD patients to help distinguish the two phenotypes and could prove useful in prognostication.
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Affiliation(s)
- Anne Fu
- Division of Respirology, Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Anastasiia Vasileva
- Division of Respirology, Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Nour Hanafi
- Division of Respirology, Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Natalia Belousova
- Division of Respirology, Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Toronto Lung Transplant Program, Ajmera Multi-Organ Transplant Unit, University Health Network, Toronto, ON, Canada
| | - Joyce Wu
- Division of Respirology, Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Toronto General Pulmonary Function Laboratory, University Health Network, Toronto, ON, Canada
| | - Sarada Sriya Rajyam
- Division of Respirology, Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Clodagh M. Ryan
- Division of Respirology, Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Toronto General Pulmonary Function Laboratory, University Health Network, Toronto, ON, Canada
| | - Zoltán Hantos
- Department of Anesthesiology and Intensive Therapy, Semmelweis University, Budapest, Hungary
| | - Chung-Wai Chow
- Division of Respirology, Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Toronto Lung Transplant Program, Ajmera Multi-Organ Transplant Unit, University Health Network, Toronto, ON, Canada
- Toronto General Pulmonary Function Laboratory, University Health Network, Toronto, ON, Canada
- *Correspondence: Chung-Wai Chow,
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Quantitative CT Correlates with Local Inflammation in Lung of Patients with Subtypes of Chronic Lung Allograft Dysfunction. Cells 2022; 11:cells11040699. [PMID: 35203345 PMCID: PMC8870691 DOI: 10.3390/cells11040699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 02/08/2022] [Accepted: 02/11/2022] [Indexed: 02/03/2023] Open
Abstract
Chronic rejection of lung allografts has two major subtypes, bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS), which present radiologically either as air trapping with small airways disease or with persistent pleuroparenchymal opacities. Parametric response mapping (PRM), a computed tomography (CT) methodology, has been demonstrated as an objective readout of BOS and RAS and bears prognostic importance, but has yet to be correlated to biological measures. Using a topological technique, we evaluate the distribution and arrangement of PRM-derived classifications of pulmonary abnormalities from lung transplant recipients undergoing redo-transplantation for end-stage BOS (N = 6) or RAS (N = 6). Topological metrics were determined from each PRM classification and compared to structural and biological markers determined from microCT and histopathology of lung core samples. Whole-lung measurements of PRM-defined functional small airways disease (fSAD), which serves as a readout of BOS, were significantly elevated in BOS versus RAS patients (p = 0.01). At the core-level, PRM-defined parenchymal disease, a potential readout of RAS, was found to correlate to neutrophil and collagen I levels (p < 0.05). We demonstrate the relationship of structural and biological markers to the CT-based distribution and arrangement of PRM-derived readouts of BOS and RAS.
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13
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Novel biomarkers of chronic lung allograft dysfunction: is there anything reliable? Curr Opin Organ Transplant 2022; 27:1-6. [PMID: 34939958 DOI: 10.1097/mot.0000000000000944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE OF REVIEW Chronic lung allograft dysfunction (CLAD) remains a major barrier preventing long-term survival following lung transplantation. As our clinical knowledge regarding its definition and presentation has significantly improved over the last years, adequate biomarkers to predict development of CLAD, phenotype of CLAD or prognosis post-CLAD diagnosis are definitely needed. RECENT FINDINGS Radiological and physiological markers are gradually entering routine clinical practice. In-depth investigation of biological samples including broncho-alveolar lavage, biopsy and serum has generated potential biomarkers involved in fibrogenesis, airway injury and inflammation but none of these are universally accepted or implemented although progress has been made, specifically regarding donor-derived cell-free DNA and donor-specific antibodies. SUMMARY Although a lot of promising biomarkers have been put forward, a very limited number has made it to routine clinical practice. Nevertheless, a biomarker that leads to earlier detection or more adequate disease phenotyping would advance the field enormously.
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Rozenberg D, McInnis M, Chow CW. Utilizing Automated Radiographic Signatures to Prognosticate Chronic Lung Allograft Dysfunction: What Does the Future Hold? Am J Respir Crit Care Med 2021; 204:883-885. [PMID: 34384039 PMCID: PMC8534617 DOI: 10.1164/rccm.202107-1726ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- Dmitry Rozenberg
- University Health Network, 7989, Medicine, Respirology and Lung Transplantation , Toronto, Ontario, Canada;
| | - Micheal McInnis
- University Health Network, 7989, Joint Department of Medical Imaging, Toronto, Ontario, Canada
| | - Chung-Wai Chow
- University of Toronto, 7938, Medicine, Toronto, Ontario, Canada
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