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Garg I, Vidholia A, Garg A, Singh S, Agahi A, Laroia A, Saboo SS, Comellas AP, Hoffman EA, Nagpal P. E-cigarette or vaping product use-associated lung injury: A review of clinico-radio-pathological characteristics. Respir Investig 2022; 60:738-749. [PMID: 35963780 DOI: 10.1016/j.resinv.2022.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 06/01/2022] [Accepted: 06/16/2022] [Indexed: 01/12/2023]
Abstract
Aggressive, albeit false marketing of electronic nicotine delivery systems (ENDS) or vaping devices as safer alternatives to cigarette smoking, combined with lack of regulations, has led to its mass adoption, especially among youth. A sudden increase in acute lung injuries was noted in 2019 which was linked to ENDS. It was termed by the Centers for Disease Control and Prevention (CDC) as electronic cigarette or vaping product use-associated lung injury (EVALI). Analysis of bronchoalveolar lavage fluid samples linked EVALI to vitamin E acetate (VEA), which is used as a diluting agent for marijuana oils. Patients with EVALI present with a combination of non-specific respiratory, gastrointestinal, and systemic symptoms. Laboratory results may show elevated inflammatory biomarkers. EVALI is a diagnosis of exclusion and must meet the following criteria: i) history of vaping within last 90 days, ii) abnormal chest imaging, iii) negative evaluation for infection, and iv) no other plausible diagnosis. A spectrum of computed tomography (CT) chest findings has been reported in EVALI, ranging from diffuse alveolar damage to organizing pneumonia, characterized by bilateral ground-glass opacities, consolidation, and septal thickening. A similar spectrum is seen on histopathology, characterized by lipid-laden alveolar macrophages, with varying degrees of infiltrative inflammatory cells and fibrin deposition. Early and accurate identification of the EVALI pattern can help optimize patient care. For example, in diffuse alveolar damage (DAD), a lower threshold for ventilation support and corticosteroid may improve outcomes. Here, we review the etiopathogenesis, clinical management, histopathology, and imaging features of EVALI.
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Affiliation(s)
- Ishan Garg
- Pathology, Arkana Laboratories, 10810 Executive Center Dr #100, Little Rock, AR 72211, USA
| | - Aditi Vidholia
- Department of Pathology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Ankita Garg
- Department of Anesthesiology, Dr Ram Manohar Lohia Hospital & Post Graduate Institute of Medical Education and Research, Baba Kharak Singh Rd, Connaught Place, New Delhi 110001, India
| | - Shuchita Singh
- Shanti Hospital, Rajeev Nagar, Agra, Uttar Pradesh 282005, India
| | - Alireza Agahi
- Department of Pathology, Mayo Clinic, 201 1(st) St SW, Rochester, MN 55905, USA
| | - Archana Laroia
- Department of Radiology, University of Iowa Hospitals and Clinics, 200 Hawkins Dr, Iowa City, IA 52242, USA
| | - Sachin S Saboo
- Radiology, South Texas Radiology Group, San Antonio, TX 78230, USA
| | - Alejandro P Comellas
- Department of Internal Medicine, University of Iowa Carver College of Medicine, 375 Newton Rd, Iowa City, IA 52242, USA
| | - Eric A Hoffman
- Department of Radiology, University of Iowa Hospitals and Clinics, 200 Hawkins Dr, Iowa City, IA 52242, USA; Department of Internal Medicine, University of Iowa Carver College of Medicine, 375 Newton Rd, Iowa City, IA 52242, USA; Roy J. Carver Department of Biomedical Engineering, University of Iowa College of Engineering, 375 Newton Rd, Iowa City, IA 52242, USA
| | - Prashant Nagpal
- Department of Radiology, University of Iowa Hospitals and Clinics, 200 Hawkins Dr, Iowa City, IA 52242, USA; Department of Radiology, University of Wisconsin-Madison, Madison, WI 53706, USA.
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Li F, Zhang X, Comellas AP, Hoffman EA, Yang T, Lin CL. Contrastive learning and subtyping of post-COVID-19 lung computed tomography images. Front Physiol 2022; 13:999263. [PMID: 36304574 PMCID: PMC9593072 DOI: 10.3389/fphys.2022.999263] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 09/27/2022] [Indexed: 11/30/2022] Open
Abstract
Patients who recovered from the novel coronavirus disease 2019 (COVID-19) may experience a range of long-term symptoms. Since the lung is the most common site of the infection, pulmonary sequelae may present persistently in COVID-19 survivors. To better understand the symptoms associated with impaired lung function in patients with post-COVID-19, we aimed to build a deep learning model which conducts two tasks: to differentiate post-COVID-19 from healthy subjects and to identify post-COVID-19 subtypes, based on the latent representations of lung computed tomography (CT) scans. CT scans of 140 post-COVID-19 subjects and 105 healthy controls were analyzed. A novel contrastive learning model was developed by introducing a lung volume transform to learn latent features of disease phenotypes from CT scans at inspiration and expiration of the same subjects. The model achieved 90% accuracy for the differentiation of the post-COVID-19 subjects from the healthy controls. Two clusters (C1 and C2) with distinct characteristics were identified among the post-COVID-19 subjects. C1 exhibited increased air-trapping caused by small airways disease (4.10%, p = 0.008) and diffusing capacity for carbon monoxide %predicted (DLCO %predicted, 101.95%, p < 0.001), while C2 had decreased lung volume (4.40L, p < 0.001) and increased ground glass opacity (GGO%, 15.85%, p < 0.001). The contrastive learning model is able to capture the latent features of two post-COVID-19 subtypes characterized by air-trapping due to small airways disease and airway-associated interstitial fibrotic-like patterns, respectively. The discovery of post-COVID-19 subtypes suggests the need for different managements and treatments of long-term sequelae of patients with post-COVID-19.
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Affiliation(s)
- Frank Li
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States
- IIHR-Hydroscience and Engineering, University of Iowa, Iowa City, IA, United States
| | - Xuan Zhang
- IIHR-Hydroscience and Engineering, University of Iowa, Iowa City, IA, United States
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA, United States
| | | | - Eric A. Hoffman
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States
- Department of Radiology, University of Iowa, Iowa City, IA, United States
| | - Tianbao Yang
- Department of Computer Science, University of Iowa, Iowa City, IA, United States
| | - Ching-Long Lin
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States
- IIHR-Hydroscience and Engineering, University of Iowa, Iowa City, IA, United States
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA, United States
- Department of Radiology, University of Iowa, Iowa City, IA, United States
- *Correspondence: Ching-Long Lin,
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Hoffman EA. Origins of and lessons from quantitative functional X-ray computed tomography of the lung. Br J Radiol 2022; 95:20211364. [PMID: 35193364 PMCID: PMC9153696 DOI: 10.1259/bjr.20211364] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/20/2022] [Accepted: 01/27/2022] [Indexed: 12/16/2022] Open
Abstract
Functional CT of the lung has emerged from quantitative CT (qCT). Structural details extracted at multiple lung volumes offer indices of function. Additionally, single volumetric images, if acquired at standardized lung volumes and body posture, can be used to model function by employing such engineering techniques as computational fluid dynamics. With the emergence of multispectral CT imaging including dual energy from energy integrating CT scanners and multienergy binning using the newly released photon counting CT technology, function is tagged via use of contrast agents. Lung disease phenotypes have previously been lumped together by the limitations of spirometry and plethysmography. QCT and its functional embodiment have been imbedded into studies seeking to characterize chronic obstructive pulmonary disease, severe asthma, interstitial lung disease and more. Reductions in radiation dose by an order of magnitude or more have been achieved. At the same time, we have seen significant increases in spatial and density resolution along with methodologic validations of extracted metrics. Together, these have allowed attention to turn towards more mild forms of disease and younger populations. In early applications, clinical CT offered anatomic details of the lung. Functional CT offers regional measures of lung mechanics, the assessment of functional small airways disease, as well as regional ventilation-perfusion matching (V/Q) and more. This paper will focus on the use of quantitative/functional CT for the non-invasive exploration of dynamic three-dimensional functioning of the breathing lung and beating heart within the unique negative pressure intrathoracic environment of the closed chest.
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Affiliation(s)
- Eric A Hoffman
- Departments of Radiology, Internal Medicine and Biomedical Engineering University of Iowa, Iowa, United States
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Stammes MA, Lee JH, Meijer L, Naninck T, Doyle-Meyers LA, White AG, Borish HJ, Hartman AL, Alvarez X, Ganatra S, Kaushal D, Bohm RP, le Grand R, Scanga CA, Langermans JAM, Bontrop RE, Finch CL, Flynn JL, Calcagno C, Crozier I, Kuhn JH. Medical imaging of pulmonary disease in SARS-CoV-2-exposed non-human primates. Trends Mol Med 2022; 28:123-142. [PMID: 34955425 PMCID: PMC8648672 DOI: 10.1016/j.molmed.2021.12.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 12/01/2021] [Accepted: 12/01/2021] [Indexed: 12/11/2022]
Abstract
Chest X-ray (CXR), computed tomography (CT), and positron emission tomography-computed tomography (PET-CT) are noninvasive imaging techniques widely used in human and veterinary pulmonary research and medicine. These techniques have recently been applied in studies of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-exposed non-human primates (NHPs) to complement virological assessments with meaningful translational readouts of lung disease. Our review of the literature indicates that medical imaging of SARS-CoV-2-exposed NHPs enables high-resolution qualitative and quantitative characterization of disease otherwise clinically invisible and potentially provides user-independent and unbiased evaluation of medical countermeasures (MCMs). However, we also found high variability in image acquisition and analysis protocols among studies. These findings uncover an urgent need to improve standardization and ensure direct comparability across studies.
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Affiliation(s)
- Marieke A Stammes
- Biomedical Primate Research Centre (BPRC), 2288 GJ, Rijswijk, The Netherlands.
| | - Ji Hyun Lee
- Integrated Research Facility at Fort Detrick (IRF-Frederick), Division of Clinical Research (DCR), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Fort Detrick, Frederick, MD 21702, USA
| | - Lisette Meijer
- Biomedical Primate Research Centre (BPRC), 2288 GJ, Rijswijk, The Netherlands
| | - Thibaut Naninck
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, 92260 Fontenay-aux-Roses, France
| | - Lara A Doyle-Meyers
- Tulane National Primate Research Center, Covington, LA 70433, USA; Department of Medicine, Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - Alexander G White
- Department of Microbiology and Molecular Genetics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - H Jacob Borish
- Department of Microbiology and Molecular Genetics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Amy L Hartman
- Center for Vaccine Research, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Infectious Diseases and Microbiology, School of Public Health, University of Pittsburgh, Pitt Public Health, Pittsburgh, PA 15261, USA
| | - Xavier Alvarez
- Texas Biomedical Research Institute, San Antonio, TX 78227, USA
| | | | - Deepak Kaushal
- Texas Biomedical Research Institute, San Antonio, TX 78227, USA
| | - Rudolf P Bohm
- Tulane National Primate Research Center, Covington, LA 70433, USA; Department of Medicine, Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - Roger le Grand
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, 92260 Fontenay-aux-Roses, France
| | - Charles A Scanga
- Department of Microbiology and Molecular Genetics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Center for Vaccine Research, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Jan A M Langermans
- Biomedical Primate Research Centre (BPRC), 2288 GJ, Rijswijk, The Netherlands; Department Population Health Sciences, Division of Animals in Science and Society, Faculty of Veterinary Medicine, Utrecht University, 3584 CL, Utrecht, The Netherlands
| | - Ronald E Bontrop
- Biomedical Primate Research Centre (BPRC), 2288 GJ, Rijswijk, The Netherlands; Department of Biology, Theoretical Biology and Bioinformatics, Utrecht University, 3584 CH, Utrecht, The Netherlands
| | - Courtney L Finch
- Integrated Research Facility at Fort Detrick (IRF-Frederick), Division of Clinical Research (DCR), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Fort Detrick, Frederick, MD 21702, USA
| | - JoAnne L Flynn
- Department of Microbiology and Molecular Genetics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Center for Vaccine Research, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Claudia Calcagno
- Integrated Research Facility at Fort Detrick (IRF-Frederick), Division of Clinical Research (DCR), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Fort Detrick, Frederick, MD 21702, USA
| | - Ian Crozier
- Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA
| | - Jens H Kuhn
- Integrated Research Facility at Fort Detrick (IRF-Frederick), Division of Clinical Research (DCR), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Fort Detrick, Frederick, MD 21702, USA
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