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Rajagopal S, Bogaard HJ, Elbaz MSM, Freed BH, Remy-Jardin M, van Beek EJR, Gopalan D, Kiely DG. Emerging multimodality imaging techniques for the pulmonary circulation. Eur Respir J 2024:2401128. [PMID: 39209480 DOI: 10.1183/13993003.01128-2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Accepted: 06/11/2024] [Indexed: 09/04/2024]
Abstract
Pulmonary hypertension (PH) remains a challenging condition to diagnose, classify and treat. Current approaches to the assessment of PH include echocardiography, ventilation/perfusion scintigraphy, cross-sectional imaging using computed tomography and magnetic resonance imaging, and right heart catheterisation. However, these approaches only provide an indirect readout of the primary pathology of the disease: abnormal vascular remodelling in the pulmonary circulation. With the advent of newer imaging techniques, there is a shift toward increased utilisation of noninvasive high-resolution modalities that offer a more comprehensive cardiopulmonary assessment and improved visualisation of the different components of the pulmonary circulation. In this review, we explore advances in imaging of the pulmonary vasculature and their potential clinical translation. These include advances in diagnosis and assessing treatment response, as well as strategies that allow reduced radiation exposure and implementation of artificial intelligence technology. These emerging modalities hold the promise of developing a deeper understanding of pulmonary vascular disease and the impact of comorbidities. They also have the potential to improve patient outcomes by reducing time to diagnosis, refining classification, monitoring treatment response and improving our understanding of disease mechanisms.
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Affiliation(s)
| | - Harm J Bogaard
- Department of Pulmonology, Amsterdam University Medical Center, Location VU Medical Center, Amsterdam, The Netherlands
| | - Mohammed S M Elbaz
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Benjamin H Freed
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Edwin J R van Beek
- Edinburgh Imaging, Queens Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Deepa Gopalan
- Department of Radiology, Imperial College Healthcare NHS Trust, London, UK
| | - David G Kiely
- Sheffield Pulmonary Vascular Disease Unit and NIHR Biomedical Research Centre Sheffield, Royal Hallamshire Hospital, Sheffield, UK
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2
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Becerra-Muñoz VM, Gómez Sáenz JT, Escribano Subías P. [The importance of data in Pulmonary Arterial Hypertension: from international registries to Machine Learning]. Med Clin (Barc) 2024; 162:591-598. [PMID: 38383269 DOI: 10.1016/j.medcli.2023.12.010] [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: 09/20/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 02/23/2024]
Abstract
Real-world registries have been critical to building the scientific knowledge of rare diseases, including Pulmonary Arterial Hypertension (PAH). In the past 4 decades, a considerable number of registries on this condition have allowed to improve the pathology and its subgroupś definition, to advance in the understanding of its pathophysiology, to elaborate prognostic scales and to check the transferability of the results from clinical trials to clinical practice. However, in a moment where a huge amount of data from multiple sources is available, they are not always taken into account by the registries. For that reason, Machine Learning (ML) offer a unique opportunity to manage all these data and, finally, to obtain tools that may help to get an earlier diagnose, to help to deduce the prognosis and, in the end, to advance in Personalized Medicine. Thus, we present a narrative revision with the aims of, in one hand, summing up the aspects in which data extraction is important in rare diseases -focusing on the knowledge gained from PAH real-world registries- and, on the other hand, describing some of the achievements and the potential use of the ML techniques on PAH.
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Affiliation(s)
- Víctor Manuel Becerra-Muñoz
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, España; Servicio de Cardiología, Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, España; Hospital Universitario Virgen de la Victoria, Universidad de Málaga (UMA), Málaga, España.
| | - José Tomás Gómez Sáenz
- Centro de Salud de Nájera, La Rioja, España; Sociedad Española de Médicos de Atención Primaria (SEMERGEN), Madrid, España
| | - Pilar Escribano Subías
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, España; Hospital Universitario 12 de Octubre, Madrid, España
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Gawlitza J, Endres S, Fries P, Graf M, Wilkens H, Stroeder J, Buecker A, Massmann A, Ziegelmayer S. Machine learning assisted feature identification and prediction of hemodynamic endpoints using computed tomography in patients with CTEPH. Int J Cardiovasc Imaging 2024; 40:569-577. [PMID: 38143250 PMCID: PMC10950991 DOI: 10.1007/s10554-023-03026-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 11/24/2023] [Indexed: 12/26/2023]
Abstract
Chronic thromboembolic pulmonary hypertension (CTEPH) is a rare but potentially curable cause of pulmonary hypertension (PH). Currently PH is diagnosed by right heart catheterisation. Computed tomography (CT) is used for ruling out other causes and operative planning. This study aims to evaluate importance of different quantitative/qualitative imaging features and develop a supervised machine learning (ML) model to predict hemodynamic risk groups. 127 Patients with diagnosed CTEPH who received preoperative right heart catheterization and thoracic CTA examinations (39 ECG-gated; 88 non-ECG gated) were included. 19 qualitative/quantitative imaging features and 3 hemodynamic parameters [mean pulmonary artery pressure, right atrial pressure (RAP), pulmonary artery oxygen saturation (PA SaO2)] were gathered. Diameter-based CT features were measured in axial and adjusted multiplane reconstructions (MPR). Univariate analysis was performed for qualitative and quantitative features. A random forest algorithm was trained on imaging features to predict hemodynamic risk groups. Feature importance was calculated for all models. Qualitative and quantitative parameters showed no significant differences between ECG and non-ECG gated CTs. Depending on reconstruction plane, five quantitative features were significantly different, but mean absolute difference between parameters (MPR vs. axial) was 0.3 mm with no difference in correlation with hemodynamic parameters. Univariate analysis showed moderate to strong correlation for multiple imaging features with hemodynamic parameters. The model achieved an AUC score of 0.82 for the mPAP based risk stratification and 0.74 for the PA SaO2 risk stratification. Contrast agent retention in hepatic vein, mosaic attenuation pattern and the ratio right atrium/left ventricle were the most important features among other parameters. Quantitative and qualitative imaging features of reconstructions correlate with hemodynamic parameters in preoperative CTEPH patients-regardless of MPR adaption. Machine learning based analysis of preoperative imaging features can be used for non-invasive risk stratification. Qualitative features seem to be more important than previously anticipated.
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Affiliation(s)
- Joshua Gawlitza
- Clinic/Institute of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.
| | - Sophie Endres
- Clinic for Diagnostic and Interventional Radiology, Saarland University Medical Center, Kirrberger Strasse 100 (Building 41), 66424, Homburg, Germany
| | - Peter Fries
- Clinic for Diagnostic and Interventional Radiology, Saarland University Medical Center, Kirrberger Strasse 100 (Building 41), 66424, Homburg, Germany
| | - Markus Graf
- Clinic/Institute of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Heinrike Wilkens
- Cardiology, Angiology, Pulmonary and Intensive Care, Saarland University Medical Center, Kirrberger Strasse 100, 66424, Homburg, Germany
| | - Jonas Stroeder
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Arno Buecker
- Clinic for Diagnostic and Interventional Radiology, Saarland University Medical Center, Kirrberger Strasse 100 (Building 41), 66424, Homburg, Germany
| | - Alexander Massmann
- Department of Radiology and Nuclear Medicine, Robert-Bosch-Krankenhaus, Auerbachstr. 110, 70376, Stuttgart, Germany
| | - Sebastian Ziegelmayer
- Clinic/Institute of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
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Kim J, Villarreal M, Arya S, Hernandez A, Moreira A. Bridging the Gap: Exploring Bronchopulmonary Dysplasia through the Lens of Biomedical Informatics. J Clin Med 2024; 13:1077. [PMID: 38398389 PMCID: PMC10889493 DOI: 10.3390/jcm13041077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 02/07/2024] [Accepted: 02/12/2024] [Indexed: 02/25/2024] Open
Abstract
Bronchopulmonary dysplasia (BPD), a chronic lung disease predominantly affecting premature infants, poses substantial clinical challenges. This review delves into the promise of biomedical informatics (BMI) in reshaping BPD research and care. We commence by highlighting the escalating prevalence and healthcare impact of BPD, emphasizing the necessity for innovative strategies to comprehend its intricate nature. To this end, we introduce BMI as a potent toolset adept at managing and analyzing extensive, diverse biomedical data. The challenges intrinsic to BPD research are addressed, underscoring the inadequacies of conventional approaches and the compelling need for data-driven solutions. We subsequently explore how BMI can revolutionize BPD research, encompassing genomics and personalized medicine to reveal potential biomarkers and individualized treatment strategies. Predictive analytics emerges as a pivotal facet of BMI, enabling early diagnosis and risk assessment for timely interventions. Moreover, we examine how mobile health technologies facilitate real-time monitoring and enhance patient engagement, ultimately refining BPD management. Ethical and legal considerations surrounding BMI implementation in BPD research are discussed, accentuating issues of privacy, data security, and informed consent. In summation, this review highlights BMI's transformative potential in advancing BPD research, addressing challenges, and opening avenues for personalized medicine and predictive analytics.
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Affiliation(s)
- Jennifer Kim
- Division of Neonatology, Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA; (J.K.); (M.V.); (A.H.)
| | - Mariela Villarreal
- Division of Neonatology, Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA; (J.K.); (M.V.); (A.H.)
| | - Shreyas Arya
- Division of Neonatal-Perinatal Medicine, Dayton Children’s Hospital, Dayton, OH 45404, USA
| | - Antonio Hernandez
- Division of Neonatology, Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA; (J.K.); (M.V.); (A.H.)
| | - Alvaro Moreira
- Division of Neonatology, Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA; (J.K.); (M.V.); (A.H.)
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Dwivedi K, Sharkey M, Delaney L, Alabed S, Rajaram S, Hill C, Johns C, Rothman A, Mamalakis M, Thompson AAR, Wild J, Condliffe R, Kiely DG, Swift AJ. Improving Prognostication in Pulmonary Hypertension Using AI-quantified Fibrosis and Radiologic Severity Scoring at Baseline CT. Radiology 2024; 310:e231718. [PMID: 38319169 PMCID: PMC10902594 DOI: 10.1148/radiol.231718] [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] [Received: 07/04/2023] [Revised: 11/30/2023] [Accepted: 12/22/2023] [Indexed: 02/07/2024]
Abstract
Background There is clinical need to better quantify lung disease severity in pulmonary hypertension (PH), particularly in idiopathic pulmonary arterial hypertension (IPAH) and PH associated with lung disease (PH-LD). Purpose To quantify fibrosis on CT pulmonary angiograms using an artificial intelligence (AI) model and to assess whether this approach can be used in combination with radiologic scoring to predict survival. Materials and Methods This retrospective multicenter study included adult patients with IPAH or PH-LD who underwent incidental CT imaging between February 2007 and January 2019. Patients were divided into training and test cohorts based on the institution of imaging. The test cohort included imaging examinations performed in 37 external hospitals. Fibrosis was quantified using an established AI model and radiologically scored by radiologists. Multivariable Cox regression adjusted for age, sex, World Health Organization functional class, pulmonary vascular resistance, and diffusing capacity of the lungs for carbon monoxide was performed. The performance of predictive models with or without AI-quantified fibrosis was assessed using the concordance index (C index). Results The training and test cohorts included 275 (median age, 68 years [IQR, 60-75 years]; 128 women) and 246 (median age, 65 years [IQR, 51-72 years]; 142 women) patients, respectively. Multivariable analysis showed that AI-quantified percentage of fibrosis was associated with an increased risk of patient mortality in the training cohort (hazard ratio, 1.01 [95% CI: 1.00, 1.02]; P = .04). This finding was validated in the external test cohort (C index, 0.76). The model combining AI-quantified fibrosis and radiologic scoring showed improved performance for predicting patient mortality compared with a model including radiologic scoring alone (C index, 0.67 vs 0.61; P < .001). Conclusion Percentage of lung fibrosis quantified on CT pulmonary angiograms by an AI model was associated with increased risk of mortality and showed improved performance for predicting patient survival when used in combination with radiologic severity scoring compared with radiologic scoring alone. © RSNA, 2024 Supplemental material is available for this article.
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Affiliation(s)
- Krit Dwivedi
- From the Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Glossop Rd, Sheffield S10 2JF, England (K.D., L.D., A.R., M.M., A.A.R.T., J.W., R.C., D.G.K., A.J.S.); Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (M.S., S.A., S.R., C.H., C.J.); and Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (R.C., D.G.K.)
| | - Michael Sharkey
- From the Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Glossop Rd, Sheffield S10 2JF, England (K.D., L.D., A.R., M.M., A.A.R.T., J.W., R.C., D.G.K., A.J.S.); Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (M.S., S.A., S.R., C.H., C.J.); and Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (R.C., D.G.K.)
| | - Liam Delaney
- From the Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Glossop Rd, Sheffield S10 2JF, England (K.D., L.D., A.R., M.M., A.A.R.T., J.W., R.C., D.G.K., A.J.S.); Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (M.S., S.A., S.R., C.H., C.J.); and Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (R.C., D.G.K.)
| | - Samer Alabed
- From the Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Glossop Rd, Sheffield S10 2JF, England (K.D., L.D., A.R., M.M., A.A.R.T., J.W., R.C., D.G.K., A.J.S.); Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (M.S., S.A., S.R., C.H., C.J.); and Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (R.C., D.G.K.)
| | - Smitha Rajaram
- From the Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Glossop Rd, Sheffield S10 2JF, England (K.D., L.D., A.R., M.M., A.A.R.T., J.W., R.C., D.G.K., A.J.S.); Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (M.S., S.A., S.R., C.H., C.J.); and Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (R.C., D.G.K.)
| | - Catherine Hill
- From the Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Glossop Rd, Sheffield S10 2JF, England (K.D., L.D., A.R., M.M., A.A.R.T., J.W., R.C., D.G.K., A.J.S.); Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (M.S., S.A., S.R., C.H., C.J.); and Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (R.C., D.G.K.)
| | - Christopher Johns
- From the Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Glossop Rd, Sheffield S10 2JF, England (K.D., L.D., A.R., M.M., A.A.R.T., J.W., R.C., D.G.K., A.J.S.); Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (M.S., S.A., S.R., C.H., C.J.); and Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (R.C., D.G.K.)
| | - Alexander Rothman
- From the Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Glossop Rd, Sheffield S10 2JF, England (K.D., L.D., A.R., M.M., A.A.R.T., J.W., R.C., D.G.K., A.J.S.); Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (M.S., S.A., S.R., C.H., C.J.); and Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (R.C., D.G.K.)
| | - Michail Mamalakis
- From the Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Glossop Rd, Sheffield S10 2JF, England (K.D., L.D., A.R., M.M., A.A.R.T., J.W., R.C., D.G.K., A.J.S.); Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (M.S., S.A., S.R., C.H., C.J.); and Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (R.C., D.G.K.)
| | - A. A. Roger Thompson
- From the Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Glossop Rd, Sheffield S10 2JF, England (K.D., L.D., A.R., M.M., A.A.R.T., J.W., R.C., D.G.K., A.J.S.); Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (M.S., S.A., S.R., C.H., C.J.); and Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (R.C., D.G.K.)
| | - Jim Wild
- From the Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Glossop Rd, Sheffield S10 2JF, England (K.D., L.D., A.R., M.M., A.A.R.T., J.W., R.C., D.G.K., A.J.S.); Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (M.S., S.A., S.R., C.H., C.J.); and Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (R.C., D.G.K.)
| | - Robin Condliffe
- From the Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Glossop Rd, Sheffield S10 2JF, England (K.D., L.D., A.R., M.M., A.A.R.T., J.W., R.C., D.G.K., A.J.S.); Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (M.S., S.A., S.R., C.H., C.J.); and Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (R.C., D.G.K.)
| | - David G. Kiely
- From the Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Glossop Rd, Sheffield S10 2JF, England (K.D., L.D., A.R., M.M., A.A.R.T., J.W., R.C., D.G.K., A.J.S.); Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (M.S., S.A., S.R., C.H., C.J.); and Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (R.C., D.G.K.)
| | - Andrew J. Swift
- From the Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Glossop Rd, Sheffield S10 2JF, England (K.D., L.D., A.R., M.M., A.A.R.T., J.W., R.C., D.G.K., A.J.S.); Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (M.S., S.A., S.R., C.H., C.J.); and Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, England (R.C., D.G.K.)
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Torbicki A, Kurzyna M. The Diagnostic Approach to Pulmonary Hypertension. Semin Respir Crit Care Med 2023; 44:728-737. [PMID: 37487526 DOI: 10.1055/s-0043-1770116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
The clinical presentation of pulmonary hypertension (PH) is nonspecific, resulting in significant delays in its detection. In the majority of cases, PH is a marker of the severity of other cardiopulmonary diseases. Differential diagnosis aimed at the early identification of patients with pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH) who do require specific and complex therapies is as important as PH detection itself. Despite all efforts aimed at the noninvasive assessment of pulmonary arterial pressure, the formal confirmation of PH still requires catheterization of the right heart and pulmonary artery. The current document will give an overview of strategies aimed at the early diagnosis of PAH and CTEPH, while avoiding their overdiagnosis. It is not intended to be a replica of the recently published European Society of Cardiology (ESC) and European Respiratory Society (ERS) Guidelines on Diagnosis and Treatment of Pulmonary Hypertension, freely available at the Web sites of both societies. While promoting guidelines' recommendations, including those on new definitions of PH, we will try to bring them closer to everyday clinical practice, benefiting from our personal experience in managing patients with suspected PH.
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Affiliation(s)
- Adam Torbicki
- Department of Pulmonary Circulation, Thromboembolic Diseases and Cardiology, Centre for Postgraduate Medical Education at ECZ-Otwock, Otwock, Poland
| | - Marcin Kurzyna
- Department of Pulmonary Circulation, Thromboembolic Diseases and Cardiology, Centre for Postgraduate Medical Education at ECZ-Otwock, Otwock, Poland
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Handa T. The potential role of artificial intelligence in the clinical practice of interstitial lung disease. Respir Investig 2023; 61:702-710. [PMID: 37708636 DOI: 10.1016/j.resinv.2023.08.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/26/2023] [Accepted: 08/09/2023] [Indexed: 09/16/2023]
Abstract
Artificial intelligence (AI) is being widely applied in the field of medicine, in areas such as drug discovery, diagnostic support, and assistance with medical practice. Among these, medical imaging is an area where AI is expected to make a significant contribution. In Japan, as of November 2022, 23 AI medical devices have received regulatory approval; all these devices are related to image analysis. In interstitial lung diseases, technologies have been developed that use AI to analyze high-resolution computed tomography and pathological images, and gene expression patterns in tissue taken from transbronchial lung biopsies to assist in the diagnosis of idiopathic pulmonary fibrosis. Some of these technologies are already being used in clinical practice in the United States. AI is expected to reduce the burden on physicians, improve reproducibility, and advance personalized medicine. Obtaining sufficient data for diseases with a small number of patients is difficult. Additionally, certain issues must be addressed in order for AI to be applied in healthcare. These issues include taking responsibility for the AI results output, updating software after the launch of technology, and adapting to new imaging technologies. Establishing research infrastructures such as large-scale databases and common platforms is important for the development of AI technology: their use requires an understanding of the characteristics and limitations of the systems. CLINICAL TRIAL REGISTRATION: Not applicable.
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Affiliation(s)
- Tomohiro Handa
- Department of Advanced Medicine for Respiratory Failure and Graduate School of Medicine, Kyoto University, Kyoto, Japan; Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
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8
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Olsson KM, Corte TJ, Kamp JC, Montani D, Nathan SD, Neubert L, Price LC, Kiely DG. Pulmonary hypertension associated with lung disease: new insights into pathomechanisms, diagnosis, and management. THE LANCET. RESPIRATORY MEDICINE 2023; 11:820-835. [PMID: 37591300 DOI: 10.1016/s2213-2600(23)00259-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 06/26/2023] [Accepted: 06/28/2023] [Indexed: 08/19/2023]
Abstract
Patients with chronic lung diseases, particularly interstitial lung disease and chronic obstructive pulmonary disease, frequently develop pulmonary hypertension, which results in clinical deterioration, worsening of oxygen uptake, and an increased mortality risk. Pulmonary hypertension can develop and progress independently from the underlying lung disease. The pulmonary vasculopathy is distinct from that of other forms of pulmonary hypertension, with vascular ablation due to loss of small pulmonary vessels being a key feature. Long-term tobacco exposure might contribute to this type of pulmonary vascular remodelling. The distinct pathomechanisms together with the underlying lung disease might explain why treatment options for this condition remain scarce. Most drugs approved for pulmonary arterial hypertension have shown no or sometimes harmful effects in pulmonary hypertension associated with lung disease. An exception is inhaled treprostinil, which improves exercise capacity in patients with interstitial lung disease and pulmonary hypertension. There is a pressing need for safe, effective treatment options and for reliable, non-invasive diagnostic tools to detect and characterise pulmonary hypertension in patients with chronic lung disease.
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Affiliation(s)
- Karen M Olsson
- Department of Respiratory Medicine and Infectious Diseases, Hannover Medical School, Hannover, Germany; Biomedical Research in Endstage and Obstructive Lung Disease Hanover (BREATH), German Center for Lung Research, Hannover, Germany.
| | - Tamera J Corte
- Department of Respiratory Medicine, Royal Prince Alfred Hospital and University of Sydney, Sydney, NSW, Australia
| | - Jan C Kamp
- Department of Respiratory Medicine and Infectious Diseases, Hannover Medical School, Hannover, Germany; Biomedical Research in Endstage and Obstructive Lung Disease Hanover (BREATH), German Center for Lung Research, Hannover, Germany
| | - David Montani
- Department of Respiratory and Intensive Care Medicine, Hôpital Bicêtre, Assistance Publique-Hôpitaux de Paris, INSERM Unité Mixte de Recherche 999, Université Paris-Saclay, Paris, France
| | - Steven D Nathan
- Advanced Lung Disease and Transplant Program, Inova Fairfax Hospital, Falls Church, VA, USA
| | - Lavinia Neubert
- Institute of Pathology, Hannover Medical School, Hannover, Germany; Biomedical Research in Endstage and Obstructive Lung Disease Hanover (BREATH), German Center for Lung Research, Hannover, Germany
| | - Laura C Price
- National Heart and Lung Institute, Imperial College London, London, UK; National Pulmonary Hypertension Service, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - David G Kiely
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK; NIHR Biomedical Research Centre, Sheffield, UK
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9
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Piccari L, Allwood B, Antoniou K, Chung JH, Hassoun PM, Nikkho SM, Saggar R, Shlobin OA, Vitulo P, Nathan SD, Wort SJ. Pathogenesis, clinical features, and phenotypes of pulmonary hypertension associated with interstitial lung disease: A consensus statement from the Pulmonary Vascular Research Institute's Innovative Drug Development Initiative - Group 3 Pulmonary Hypertension. Pulm Circ 2023; 13:e12213. [PMID: 37025209 PMCID: PMC10071306 DOI: 10.1002/pul2.12213] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 03/03/2023] [Accepted: 03/21/2023] [Indexed: 04/08/2023] Open
Abstract
Pulmonary hypertension (PH) is a frequent complication of interstitial lung disease (ILD). Although PH has mostly been described in idiopathic pulmonary fibrosis, it can manifest in association with many other forms of ILD. Associated pathogenetic mechanisms are complex and incompletely understood but there is evidence of disruption of molecular and genetic pathways, with panvascular histopathologic changes, multiple pathophysiologic sequelae, and profound clinical ramifications. While there are some recognized clinical phenotypes such as combined pulmonary fibrosis and emphysema and some possible phenotypes such as connective tissue disease associated with ILD and PH, the identification of further phenotypes of PH in ILD has thus far proven elusive. This statement reviews the current evidence on the pathogenesis, recognized patterns, and useful diagnostic tools to detect phenotypes of PH in ILD. Distinct phenotypes warrant recognition if they are characterized through either a distinct presentation, clinical course, or treatment response. Furthermore, we propose a set of recommendations for future studies that might enable the recognition of new phenotypes.
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Affiliation(s)
- Lucilla Piccari
- Department of Pulmonary Medicine Hospital del Mar Barcelona Spain
| | - Brian Allwood
- Department of Medicine, Division of Pulmonology Stellenbosch University & Tygerberg Hospital Cape Town South Africa
| | - Katerina Antoniou
- Department of Thoracic Medicine University of Crete School of Medicine Heraklion Crete Greece
| | - Jonathan H Chung
- Department of Radiology The University of Chicago Medicine Chicago Illinois USA
| | - Paul M Hassoun
- Department of Medicine, Division of Pulmonary and Critical Care Medicine Johns Hopkins University Baltimore Maryland USA
| | | | - Rajan Saggar
- Lung & Heart-Lung Transplant and Pulmonary Hypertension Programs University of California Los Angeles David Geffen School of Medicine Los Angeles California USA
| | - Oksana A Shlobin
- Advanced Lung Disease and Transplant Program, Inova Health System Falls Church Virginia USA
| | - Patrizio Vitulo
- Department of Pulmonary Medicine IRCCS Mediterranean Institute for Transplantation and Advanced Specialized Therapies Palermo Sicilia Italy
| | - Steven D Nathan
- Advanced Lung Disease and Transplant Program, Inova Health System Falls Church Virginia USA
| | - Stephen John Wort
- National Pulmonary Hypertension Service at the Royal Brompton Hospital London UK
- National Heart and Lung Institute, Imperial College London UK
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10
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Mamalakis M, Dwivedi K, Sharkey M, Alabed S, Kiely D, Swift AJ. A transparent artificial intelligence framework to assess lung disease in pulmonary hypertension. Sci Rep 2023; 13:3812. [PMID: 36882484 PMCID: PMC9990015 DOI: 10.1038/s41598-023-30503-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 02/24/2023] [Indexed: 03/09/2023] Open
Abstract
Recent studies have recognized the importance of characterizing the extent of lung disease in pulmonary hypertension patients by using Computed Tomography. The trustworthiness of an artificial intelligence system is linked with the depth of the evaluation in functional, operational, usability, safety and validation dimensions. The safety and validation of an artificial tool is linked to the uncertainty estimation of the model's prediction. On the other hand, the functionality, operation and usability can be achieved by explainable deep learning approaches which can verify the learning patterns and use of the network from a generalized point of view. We developed an artificial intelligence framework to map the 3D anatomical models of patients with lung disease in pulmonary hypertension. To verify the trustworthiness of the framework we studied the uncertainty estimation of the network's prediction, and we explained the learning patterns of the network. Therefore, a new generalized technique combining local explainable and interpretable dimensionality reduction approaches (PCA-GradCam, PCA-Shape) was developed. Our open-source software framework was evaluated in unbiased validation datasets achieving accurate, robust and generalized results.
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Affiliation(s)
- Michail Mamalakis
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Beech Hill Rd, Sheffield, S10 2RX, UK.
- Department of Computer Science, University of Sheffield, 211 Portobello, Sheffield, S1 4DP, UK.
- Insigneo Institute for in silico Medicine, University of Sheffield, The Pam Liversidge Building, Sheffield, S1 3JD, UK.
| | - Krit Dwivedi
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Beech Hill Rd, Sheffield, S10 2RX, UK
- Insigneo Institute for in silico Medicine, University of Sheffield, The Pam Liversidge Building, Sheffield, S1 3JD, UK
| | - Michael Sharkey
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Beech Hill Rd, Sheffield, S10 2RX, UK
| | - Samer Alabed
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Beech Hill Rd, Sheffield, S10 2RX, UK
- Insigneo Institute for in silico Medicine, University of Sheffield, The Pam Liversidge Building, Sheffield, S1 3JD, UK
| | - David Kiely
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Beech Hill Rd, Sheffield, S10 2RX, UK
- Department of Cardiology, University of Sheffield, Sheffield Teaching Hospitals Sheffield, Sheffield, S5 7AU, UK
| | - Andrew J Swift
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Beech Hill Rd, Sheffield, S10 2RX, UK.
- Insigneo Institute for in silico Medicine, University of Sheffield, The Pam Liversidge Building, Sheffield, S1 3JD, UK.
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11
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Chamberlin JH, Smith C, Schoepf UJ, Nance S, Elojeimy S, O'Doherty J, Baruah D, Burt JR, Varga-Szemes A, Kabakus IM. A deep convolutional neural network ensemble for composite identification of pulmonary nodules and incidental findings on routine PET/CT. Clin Radiol 2023; 78:e368-e376. [PMID: 36863883 DOI: 10.1016/j.crad.2023.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 10/19/2022] [Accepted: 01/30/2023] [Indexed: 02/18/2023]
Abstract
AIM To evaluate primary and secondary pathologies of interest using an artificial intelligence (AI) platform, AI-Rad Companion, on low-dose computed tomography (CT) series from integrated positron-emission tomography (PET)/CT to detect CT findings that might be overlooked. MATERIALS AND METHODS One hundred and eighty-nine sequential patients who had undergone PET/CT were included. Images were evaluated using an ensemble of convolutional neural networks (AI-Rad Companion, Siemens Healthineers, Erlangen, Germany). The primary outcome was detection of pulmonary nodules for which the accuracy, identity, and intra-rater reliability was calculated. For secondary outcomes (binary detection of coronary artery calcium, aortic ectasia, vertebral height loss), accuracy and diagnostic performance were calculated. RESULTS The overall per-nodule accuracy for detection of lung nodules was 0.847. The overall sensitivity and specificity for detection of lung nodules was 0.915 and 0.781. The overall per-patient accuracy for AI detection of coronary artery calcium, aortic ectasia, and vertebral height loss was 0.979, 0.966, and 0.840, respectively. The sensitivity and specificity for coronary artery calcium was 0.989 and 0.969. The sensitivity and specificity for aortic ectasia was 0.806 and 1. CONCLUSION The neural network ensemble accurately assessed the number of pulmonary nodules and presence of coronary artery calcium and aortic ectasia on low-dose CT series of PET/CT. The neural network was highly specific for the diagnosis of vertebral height loss, but not sensitive. The use of the AI ensemble can help radiologists and nuclear medicine physicians to catch CT findings that might be overlooked.
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Affiliation(s)
- J H Chamberlin
- Division of Thoracic Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - C Smith
- Division of Thoracic Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - U J Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - S Nance
- Division of Thoracic Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - S Elojeimy
- Division of Nuclear Medicine, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - J O'Doherty
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA; Siemens Medical Solutions, Malvern, PA, USA
| | - D Baruah
- Division of Thoracic Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - J R Burt
- Division of Thoracic Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - A Varga-Szemes
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - I M Kabakus
- Division of Thoracic Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA; Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA; Division of Nuclear Medicine, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
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12
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Alkhanfar D, Shahin Y, Alandejani F, Dwivedi K, Alabed S, Johns C, Lawrie A, Thompson AAR, Rothman AMK, Tschirren J, Uthoff JM, Hoffman E, Condliffe R, Wild JM, Kiely DG, Swift AJ. Severe pulmonary hypertension associated with lung disease is characterised by a loss of small pulmonary vessels on quantitative computed tomography. ERJ Open Res 2022; 8:00503-2021. [PMID: 35586449 PMCID: PMC9108962 DOI: 10.1183/23120541.00503-2021] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 02/10/2022] [Indexed: 11/28/2022] Open
Abstract
Background Pulmonary hypertension (PH) in patients with chronic lung disease (CLD) predicts reduced functional status, clinical worsening and increased mortality, with patients with severe PH-CLD (≥35 mmHg) having a significantly worse prognosis than mild to moderate PH-CLD (21-34 mmHg). The aim of this cross-sectional study was to assess the association between computed tomography (CT)-derived quantitative pulmonary vessel volume, PH severity and disease aetiology in CLD. Methods Treatment-naïve patients with CLD who underwent CT pulmonary angiography, lung function testing and right heart catheterisation were identified from the ASPIRE registry between October 2012 and July 2018. Quantitative assessments of total pulmonary vessel and small pulmonary vessel volume were performed. Results 90 patients had PH-CLD including 44 associated with COPD/emphysema and 46 with interstitial lung disease (ILD). Patients with severe PH-CLD (n=40) had lower small pulmonary vessel volume compared to patients with mild to moderate PH-CLD (n=50). Patients with PH-ILD had significantly reduced small pulmonary blood vessel volume, compared to PH-COPD/emphysema. Higher mortality was identified in patients with lower small pulmonary vessel volume. Conclusion Patients with severe PH-CLD, regardless of aetiology, have lower small pulmonary vessel volume compared to patients with mild-moderate PH-CLD, and this is associated with a higher mortality. Whether pulmonary vessel changes quantified by CT are a marker of remodelling of the distal pulmonary vasculature requires further study.
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Affiliation(s)
- Dheyaa Alkhanfar
- Dept of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK.,INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield, UK
| | - Yousef Shahin
- Dept of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK.,Dept of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Faisal Alandejani
- Dept of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Krit Dwivedi
- Dept of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Samer Alabed
- Dept of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK.,Dept of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Chris Johns
- Dept of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Allan Lawrie
- Dept of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - A A Roger Thompson
- Dept of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK.,Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Alexander M K Rothman
- Dept of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK.,Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | | | - Johanna M Uthoff
- Dept of Computer Science, University of Sheffield, Sheffield, UK
| | - Eric Hoffman
- Dept of Radiology, University of Iowa, Iowa City, IA, USA
| | - Robin Condliffe
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Jim M Wild
- Dept of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK.,INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield, UK
| | - David G Kiely
- INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield, UK.,Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK.,These authors contributed equally
| | - Andrew J Swift
- Dept of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK.,INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield, UK.,These authors contributed equally
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13
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Dwivedi K, Condliffe R, Sharkey M, Lewis R, Alabed S, Rajaram S, Hill C, Saunders L, Metherall P, Alandejani F, Alkhanfar D, Wild JM, Lu H, Kiely DG, Swift AJ. Computed tomography lung parenchymal descriptions in routine radiological reporting have diagnostic and prognostic utility in patients with idiopathic pulmonary arterial hypertension and pulmonary hypertension associated with lung disease. ERJ Open Res 2022; 8:00549-2021. [PMID: 35083317 PMCID: PMC8784758 DOI: 10.1183/23120541.00549-2021] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 12/07/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Patients with pulmonary hypertension (PH) and lung disease may pose a diagnostic dilemma between idiopathic pulmonary arterial hypertension (IPAH) and PH associated with lung disease (PH-CLD). The prognostic impact of common computed tomography (CT) parenchymal features is unknown. METHODS 660 IPAH and PH-CLD patients assessed between 2001 and 2019 were included. Reports for all CT scans 1 year prior to diagnosis were analysed for common lung parenchymal patterns. Cox regression and Kaplan-Meier analysis were performed. RESULTS At univariate analysis of the whole cohort, centrilobular ground-glass (CGG) changes (hazard ratio, HR 0.29) and ground-glass opacification (HR 0.53) predicted improved survival, while honeycombing (HR 2.79), emphysema (HR 2.09) and fibrosis (HR 2.38) predicted worse survival (all p<0.001). Fibrosis was an independent predictor after adjusting for baseline demographics, PH severity and diffusing capacity of the lung for carbon monoxide (HR 1.37, p<0.05). Patients with a clinical diagnosis of IPAH who had an absence of reported parenchymal lung disease (IPAH-noLD) demonstrated superior survival to patients diagnosed with either IPAH who had coexistent CT lung disease or PH-CLD (2-year survival of 85%, 60% and 46%, respectively, p<0.05). CGG changes were present in 23.3% of IPAH-noLD and 5.8% of PH-CLD patients. There was no significant difference in survival between IPAH-noLD patients with or without CGG changes. PH-CLD patients with fibrosis had worse survival than those with emphysema. INTERPRETATION Routine clinical reports of CT lung parenchymal disease identify groups of patients with IPAH and PH-CLD with significantly different prognoses. Isolated CGG changes are not uncommon in IPAH but are not associated with worse survival.
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Affiliation(s)
- Krit Dwivedi
- Dept of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Medical School, Sheffield, UK.,Dept of Radiology, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK.,Co-first authors
| | - Robin Condliffe
- Pulmonary Vascular Disease Unit, Royal Hallamshire Hospitals, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK.,Co-first authors
| | - Michael Sharkey
- Dept of Radiology, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK.,3DLab, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK
| | - Robert Lewis
- Pulmonary Vascular Disease Unit, Royal Hallamshire Hospitals, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK
| | - Samer Alabed
- Dept of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Medical School, Sheffield, UK.,Dept of Radiology, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK
| | - Smitha Rajaram
- Dept of Radiology, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK
| | - Catherine Hill
- Dept of Radiology, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK
| | - Laura Saunders
- Dept of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Medical School, Sheffield, UK
| | - Peter Metherall
- Dept of Radiology, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK.,3DLab, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK
| | - Faisal Alandejani
- Dept of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Medical School, Sheffield, UK
| | - Dheyaa Alkhanfar
- Dept of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Medical School, Sheffield, UK
| | - Jim M Wild
- Dept of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Medical School, Sheffield, UK
| | - Haiping Lu
- Dept of Computer Science, University of Sheffield, Sheffield, UK
| | - David G Kiely
- Dept of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Medical School, Sheffield, UK.,Pulmonary Vascular Disease Unit, Royal Hallamshire Hospitals, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK.,Co-senior authors
| | - Andrew J Swift
- Dept of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Medical School, Sheffield, UK.,Dept of Radiology, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK.,3DLab, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK.,Co-senior authors
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14
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Kiely DG, Condliffe R. Assessing pulmonary hypertension severity in lung disease is a key step to improving outcomes: embrace resistance and don't be pressurised to go with the flow. Eur Respir J 2021; 58:58/2/2102008. [PMID: 34446507 DOI: 10.1183/13993003.02008-2021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 07/21/2021] [Indexed: 11/05/2022]
Affiliation(s)
- David G Kiely
- Dept of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK .,Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK.,INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield, UK
| | - Robin Condliffe
- Dept of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK.,Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
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15
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Pulmonary Hypertension: Diagnosis and Management. Diagnostics (Basel) 2021; 11:diagnostics11061066. [PMID: 34207897 PMCID: PMC8229206 DOI: 10.3390/diagnostics11061066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 06/04/2021] [Indexed: 11/30/2022] Open
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