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Tonelli R, Smit MR, Castaniere I, Casa GD, Andrisani D, Gozzi F, Bruzzi G, Cerri S, Samarelli AV, Raineri G, Spagnolo P, Rizzoni R, Ball L, Paulus F, Bos LDJ, Clini E, Marchioni A. Quantitative CT-analysis of over aerated lung tissue and correlation with fibrosis extent in patients with idiopathic pulmonary fibrosis. Respir Res 2024; 25:359. [PMID: 39369240 PMCID: PMC11453093 DOI: 10.1186/s12931-024-02970-4] [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: 07/02/2024] [Accepted: 09/06/2024] [Indexed: 10/07/2024] Open
Abstract
INTRODUCTION The usual interstitial pneumonia (UIP) pattern, hallmark of idiopathic pulmonary fibrosis (IPF), may induce harmful local overdistension during mechanical ventilation given the juxtaposition of different tissue elasticities. Mechanotransduction, linking mechanical stress and strain to molecular pro-fibrotic pathways, likely contributes to fibrosis progression. Understanding the mechanical forces and aeration patterns in the lungs of IPF patients is crucial for unraveling potential mechanisms of disease progression. Quantitative lung computed tomography (CT) can accurately assess the air content of lung regions, thus informing on zonal distension. This study aims to investigate radiological evidence of lung over aeration in spontaneously breathing UIP patients compared to healthy controls during maximal inspiration. METHODS Patients with IPF diagnosis referred to the Center for Rare Lung Diseases of the University Hospital of Modena (Italy) in the period 2020-2023 who underwent High Resolution Computed Tomography (HRCT) scans at residual volume (RV) and total lung capacity (TLC) using standardized protocols were retrospectively considered eligible. Patients with no signs of lung disease at HRCT performed with the same image acquisition protocol nor at pulmonary function test (PFTs) served as controls. Lung segmentation and quantitative analysis were performed using 3D Slicer software. Lung volumes were measured, and specific density thresholds defined over aerated and fibrotic regions. Comparison between over aerated lung at RV and TLC in the two groups and according to lung lobes was sought. Further, the correlation between aerated lung and the extent of fibrosis was assessed and compared at RV and TLC. RESULTS IPF patients (N = 20) exhibited higher over aerated lung proportions than controls (N = 15) both at RV and TLC (4.5% vs. 0.7%, p < 0.0001 and 13.8% vs. 7%, p < 0.0001 respectively). Over aeration increased significantly from RV to TLC in both groups, with no intergroup difference (p = 0.67). Sensitivity analysis revealed significant variations in over aerated lung areas among lobes when passing from RV to TLC with no difference within lobes (p = 0.28). Correlation between over aeration and fibrosis extent was moderate at RV (r = 0.62, p < 0.0001) and weak at TLC (r = 0.27, p = 0.01), being the two significantly different at interpolation analysis (p < 0.0001). CONCLUSIONS This study provides the first evidence of radiological signs of lung over aeration in patients with UIP-pattern patients when passing from RV to TLC. These findings offer new insights into the complex interplay between mechanical forces, lung structure, and fibrosis and warrant larger and longitudinal investigations.
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Affiliation(s)
- Roberto Tonelli
- Respiratory Diseases Unit, Department of Medical and Surgical Sciences, University Hospital of Modena, University of Modena Reggio Emilia, Modena, Italy
- Laboratory of Cell Therapies and Respiratory Medicine, Department of Medical and Surgical Sciences for Children and Adults, University Hospital of Modena, Modena, Italy
| | - Marry R Smit
- Intensive Care Unit, Amsterdam University Medical Centers, Location University of Amsterdam, Amsterdam, Netherlands
| | - Ivana Castaniere
- Respiratory Diseases Unit, Department of Medical and Surgical Sciences, University Hospital of Modena, University of Modena Reggio Emilia, Modena, Italy
- Laboratory of Cell Therapies and Respiratory Medicine, Department of Medical and Surgical Sciences for Children and Adults, University Hospital of Modena, Modena, Italy
| | - Giovanni Della Casa
- Radiology Unit, University Hospital of Modena, University of Modena Reggio Emilia, Modena, Italy
| | - Dario Andrisani
- Respiratory Diseases Unit, Department of Medical and Surgical Sciences, University Hospital of Modena, University of Modena Reggio Emilia, Modena, Italy
- Laboratory of Cell Therapies and Respiratory Medicine, Department of Medical and Surgical Sciences for Children and Adults, University Hospital of Modena, Modena, Italy
| | - Filippo Gozzi
- Respiratory Diseases Unit, Department of Medical and Surgical Sciences, University Hospital of Modena, University of Modena Reggio Emilia, Modena, Italy
- Laboratory of Cell Therapies and Respiratory Medicine, Department of Medical and Surgical Sciences for Children and Adults, University Hospital of Modena, Modena, Italy
- Clinical and Experimental Medicine PhD Program, University of Modena Reggio Emilia, Modena, Italy
| | - Giulia Bruzzi
- Respiratory Diseases Unit, Department of Medical and Surgical Sciences, University Hospital of Modena, University of Modena Reggio Emilia, Modena, Italy
- Laboratory of Cell Therapies and Respiratory Medicine, Department of Medical and Surgical Sciences for Children and Adults, University Hospital of Modena, Modena, Italy
- Clinical and Experimental Medicine PhD Program, University of Modena Reggio Emilia, Modena, Italy
| | - Stefania Cerri
- Respiratory Diseases Unit, Department of Medical and Surgical Sciences, University Hospital of Modena, University of Modena Reggio Emilia, Modena, Italy
- Laboratory of Cell Therapies and Respiratory Medicine, Department of Medical and Surgical Sciences for Children and Adults, University Hospital of Modena, Modena, Italy
- ERN-Lung, Frankfurt Am Main, Germany
| | - Anna Valeria Samarelli
- Respiratory Diseases Unit, Department of Medical and Surgical Sciences, University Hospital of Modena, University of Modena Reggio Emilia, Modena, Italy
- Laboratory of Cell Therapies and Respiratory Medicine, Department of Medical and Surgical Sciences for Children and Adults, University Hospital of Modena, Modena, Italy
| | - Giulia Raineri
- Respiratory Diseases Unit, Department of Medical and Surgical Sciences, University Hospital of Modena, University of Modena Reggio Emilia, Modena, Italy
- Laboratory of Cell Therapies and Respiratory Medicine, Department of Medical and Surgical Sciences for Children and Adults, University Hospital of Modena, Modena, Italy
| | - Paolo Spagnolo
- Respiratory Diseases Unit, University Hospital of Padova, University of Padova, Padua, Italy
| | - Raffella Rizzoni
- Department of Engineering, University of Ferrara, Ferrara, Italy
| | - Lorenzo Ball
- Department of Surgical Sciences and Integrated Diagnostics, University of Genoa, Genoa, Italy
| | - Frederique Paulus
- Intensive Care Unit, Amsterdam University Medical Centers, Location University of Amsterdam, Amsterdam, Netherlands
| | - Lieuwe D J Bos
- Intensive Care Unit, Amsterdam University Medical Centers, Location University of Amsterdam, Amsterdam, Netherlands
| | - Enrico Clini
- Respiratory Diseases Unit, Department of Medical and Surgical Sciences, University Hospital of Modena, University of Modena Reggio Emilia, Modena, Italy.
- Laboratory of Cell Therapies and Respiratory Medicine, Department of Medical and Surgical Sciences for Children and Adults, University Hospital of Modena, Modena, Italy.
| | - Alessandro Marchioni
- Respiratory Diseases Unit, Department of Medical and Surgical Sciences, University Hospital of Modena, University of Modena Reggio Emilia, Modena, Italy
- Laboratory of Cell Therapies and Respiratory Medicine, Department of Medical and Surgical Sciences for Children and Adults, University Hospital of Modena, Modena, Italy
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Joussellin V, Meneyrol E, Lederlin M, Jouneau S, Terzi N, Tadié JM, Gacouin A. Admission chest CT scan of intensive care patients with interstitial lung disease: Unveiling its limited predictive value through visual and automated analyses in a retrospective study (ILDICTO). Respir Med Res 2024; 86:101140. [PMID: 39357461 DOI: 10.1016/j.resmer.2024.101140] [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: 02/28/2024] [Revised: 08/30/2024] [Accepted: 09/05/2024] [Indexed: 10/04/2024]
Abstract
BACKGROUND Clinical course prediction of patients with interstitial lung disease (ILD) admitted to the intensive care unit (ICU) for acute respiratory failure (ARF) can be challenging. This study aimed to characterize the prognostic value of admission chest CT-scan in this situation. METHODS We retrospectively included ILD patients admitted to a French ICU for acute respiratory failure requiring oxygen. Patients with lymphangitis carcinomatosis and ANCA vasculitis were excluded. We analyzed every admission chest CT-scan using two different approaches: a visual analysis (grading the extent of traction bronchiectasis, ground glass and honeycomb) and an automated analysis (grading the extent of ground glass and consolidation with a dedicated software). The primary outcome was ICU mortality. RESULTS Between January 2014 and October 2020, 81 patients presented an acute respiratory failure with ILD on the admission chest CT-scan. In univariate analysis, only the main pulmonary artery diameter differed between patients who survived and those who died in ICU (30 vs 32 mm, p = 0.021). In multivariate analysis, none of the radiological funding was associated with ICU mortality. Visual and automated analyses did not yield different results, with a strong correlation between the two methods. However, the identification of an UIP pattern (and the presence of honeycomb) was associated with a poorer response to corticosteroid therapy. CONCLUSION Our study showed that the extent of radiological findings and the severity of fibrosis indices on admission chest CT scans of ILD patients admitted to the ICU for ARF were not associated with subsequent deterioration.
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Affiliation(s)
- Vincent Joussellin
- CHU Rennes, Maladies Infectieuses et Réanimation Médicale, F-35033 Rennes, France; Université Rennes1, Faculté de Médecine, Biosit, F-35043 Rennes, France.
| | - Eric Meneyrol
- CHU Rennes, Maladies Infectieuses et Réanimation Médicale, F-35033 Rennes, France; Université Rennes1, Faculté de Médecine, Biosit, F-35043 Rennes, France
| | - Mathieu Lederlin
- Department of Radiology, CHU Rennes, Univ Rennes, 5 LTSI, INSERM U1099 Rennes, France
| | - Stéphane Jouneau
- Department of Respiratory Medicine, Reference Centre for Rare Pulmonary Diseases, CHU Rennes, Univ Rennes, Rennes, France; IRSET UMR1085, Univ Rennes, Rennes, France
| | - Nicolas Terzi
- CHU Rennes, Maladies Infectieuses et Réanimation Médicale, F-35033 Rennes, France; Université Rennes1, Faculté de Médecine, Biosit, F-35043 Rennes, France; Inserm-CIC-1414, Faculté de Médecine, Université Rennes I, IFR 140, F-35033 Rennes, France
| | - Jean-Marc Tadié
- CHU Rennes, Maladies Infectieuses et Réanimation Médicale, F-35033 Rennes, France; Université Rennes1, Faculté de Médecine, Biosit, F-35043 Rennes, France; Inserm-CIC-1414, Faculté de Médecine, Université Rennes I, IFR 140, F-35033 Rennes, France
| | - Arnaud Gacouin
- CHU Rennes, Maladies Infectieuses et Réanimation Médicale, F-35033 Rennes, France; Université Rennes1, Faculté de Médecine, Biosit, F-35043 Rennes, France; Inserm-CIC-1414, Faculté de Médecine, Université Rennes I, IFR 140, F-35033 Rennes, France
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Krisht AAH, Grapin K, de Beauchene RC, Bonnet B, Cassagnes L, Evrard B, Adda M, Souweine B, Dupuis C. SARS-CoV2 pneumonia patients admitted to the ICU: Analysis according to clinical and biological parameters and the extent of lung parenchymal lesions on chest CT scan, a monocentric observational study. PLoS One 2024; 19:e0308014. [PMID: 39298399 DOI: 10.1371/journal.pone.0308014] [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: 02/06/2024] [Accepted: 07/16/2024] [Indexed: 09/21/2024] Open
Abstract
BACKGROUND CT-scan and inflammatory and coagulation biomarkers could help in prognostication of COVID-19 in patients on ICU admission. OBJECTIVE The objectives of this study were to measure the prognostic value of the extent of lung parenchymal lesions on computed tomography (CT) and of several coagulation and inflammatory biomarkers, and to explore the characteristics of the patients depending on the extent of lung parenchymal lesions. DESIGN Retrospective monocentric observational study achieved on a dataset collected prospectively. SETTING Medical ICU of the university hospital of Clermont-Ferrand, France. PATIENTS All consecutive adult patients aged ≥18 years admitted between 20 March, 2020 and 31 August, 2021 for COVID-19 pneumonia. INTERVENTIONS Characteristics at baseline and during ICU stay, and outcomes at day 60 were recorded. The extent of lung parenchyma lesions observed on the chest CT performed on admission was established by artificial intelligence software. MEASUREMENTS Several clinical characteristics and laboratory features were collected on admission including plasma interleukin-6, HLA-DR monocytic-expression rate (mHLA-DR), and the extent of lung parenchymal lesions. Factors associated with day-60 mortality were investigated by uni- and multivariate survival analyses. RESULTS 270 patients were included. Inflammation biomarkers including the levels of neutrophils, CRP, ferritin and Il10 were the indices the most associated with the severity of the extent of the lung lesions. Patients with more extensive lung parenchymal lesions (≥ 75%) on admission had higher CRP serum levels. The extent of lung parenchymal lesions was associated with a decrease in the PaO2/FiO2 ratio(p<0.01), fewer ventilatory-free days (p = 0.03), and a higher death rate at day 60(p = 0.01). Extent of the lesion of more than 75% was independently associated with day-60 mortality (aHR = 1.72[1.06; 2.78], p = 0.03). The prediction of death at day 60 was improved when considering simultaneously biological and radiological markers obtained on ICU admission (AUC = 0.78). CONCLUSIONS The extent of lung parenchyma lesions on CT was associated with inflammation, and the combination of coagulation and inflammatory biomarkers and the extent of the lesions predicted the poorest outcomes.
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Affiliation(s)
- Abed Al Hadi Krisht
- CHU Clermont-Ferrand, Service de Médecine Intensive et Réanimation, Clermont-Ferrand, France
| | - Kévin Grapin
- CHU Clermont-Ferrand, Service de Médecine Intensive et Réanimation, Clermont-Ferrand, France
| | | | - Benjamin Bonnet
- CHU Clermont-Ferrand, Service d'Immunologie, Clermont-Ferrand, France
- Université Clermont Auvergne, Laboratoire d'Immunologie, ECREIN, UMR1019 UNH, UFR Médecine de Clermont-Ferrand, Clermont-Ferrand, France
| | - Lucie Cassagnes
- CHU Clermont-Ferrand, Service de Radiologie, Clermont-Ferrand, France
- Université Clermont Auvergne, Unité de Nutrition Humaine, INRAe, CRNH Auvergne, Clermont Ferrand, France
| | - Bertrand Evrard
- CHU Clermont-Ferrand, Service d'Immunologie, Clermont-Ferrand, France
- Université Clermont Auvergne, Laboratoire d'Immunologie, ECREIN, UMR1019 UNH, UFR Médecine de Clermont-Ferrand, Clermont-Ferrand, France
| | - Mireille Adda
- CHU Clermont-Ferrand, Service de Médecine Intensive et Réanimation, Clermont-Ferrand, France
| | - Bertrand Souweine
- CHU Clermont-Ferrand, Service de Médecine Intensive et Réanimation, Clermont-Ferrand, France
- Université Clermont Auvergne, CNRS, LMGE, Clermont-Ferrand, France
| | - Claire Dupuis
- CHU Clermont-Ferrand, Service de Médecine Intensive et Réanimation, Clermont-Ferrand, France
- Université Clermont Auvergne, Unité de Nutrition Humaine, INRAe, CRNH Auvergne, Clermont Ferrand, France
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Iwao Y, Kawata N, Sekiguchi Y, Haneishi H. Nonrigid registration method for longitudinal chest CT images in COVID-19. Heliyon 2024; 10:e37272. [PMID: 39286087 PMCID: PMC11403531 DOI: 10.1016/j.heliyon.2024.e37272] [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: 03/15/2023] [Revised: 08/22/2024] [Accepted: 08/30/2024] [Indexed: 09/19/2024] Open
Abstract
Rationale and objectives To analyze morphological changes in patients with COVID-19-associated pneumonia over time, a nonrigid registration technique is required that reduces differences in respiratory phase and imaging position and does not excessively deform the lesion region. A nonrigid registration method using deep learning was applied for lung field alignment, and its practicality was verified through quantitative evaluation, such as image similarity of whole lung region and image similarity of lesion region, as well as visual evaluation by a physician. Materials and methods First, the lung field positions and sizes of the first and second CT images were roughly matched using a classical registration method based on iterative calculations as a preprocessing step. Then, voxel-by-voxel transformation was performed using VoxelMorph, a nonrigid deep learning registration method. As an objective evaluation, the similarity of the images was calculated. To evaluate the invariance of image features in the lesion site, primary statistics and 3D shape features were calculated and statistically analyzed. Furthermore, as a subjective evaluation, the similarity of images and whether nonrigid transformation caused unnatural changes in the shape and size of the lesion region were visually evaluated by a pulmonologist. Results The proposed method was applied to 509 patient data points with high image similarity. The variances in histogram characteristics before and after image deformation were confirmed. Visual evaluation confirmed the agreement between the shape and internal structure of the lung field and the natural deformation of the lesion region. Conclusion The developed nonrigid registration method was shown to be effective for quantitative time series analysis of the lungs.
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Affiliation(s)
- Yuma Iwao
- Center for Frontier Medical Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba-shi, Chiba, 263-8522, Japan
- Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, 4-9-1, Anagawa, Inage-ku, Chiba-shi, Chiba, 263-8555, Japan
| | - Naoko Kawata
- Department of Respirology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-ku, Chiba-shii, Chiba, 260-8677, Japan
- Graduate School of Science and Engineering, Chiba University, Chiba, 263-8522, Japan
- Medical Mycology Research Center (MMRC), Chiba University, Japan
| | - Yuki Sekiguchi
- Graduate School of Science and Engineering, Chiba University, Chiba, 263-8522, Japan
| | - Hideaki Haneishi
- Center for Frontier Medical Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba-shi, Chiba, 263-8522, Japan
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Xing H, Gu S, Li Z, Wei XE, He L, Liu Q, Feng H, Wang N, Huang H, Fan Y. Incorporation of Chest Computed Tomography Quantification to Predict Outcomes for Patients on Hemodialysis with COVID-19. KIDNEY DISEASES (BASEL, SWITZERLAND) 2024; 10:284-294. [PMID: 39131882 PMCID: PMC11309758 DOI: 10.1159/000539568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 05/26/2024] [Indexed: 08/13/2024]
Abstract
Introduction Patients undergoing maintenance hemodialysis are vulnerable to coronavirus disease 2019 (COVID-19), exhibiting a high risk of hospitalization and mortality. Thus, early identification and intervention are important to prevent disease progression in these patients. Methods This was a two-center retrospective observational study of patients on hemodialysis diagnosed with COVID-19 at the Lingang and Xuhui campuses of Shanghai Sixth People's Hospital. Patients were randomized into the training (130) and validation cohorts (54), while 59 additional patients served as an independent external validation cohort. Artificial intelligence-based parameters of chest computed tomography (CT) were quantified, and a nomogram for patient outcomes at 14 and 28 days was created by screening quantitative CT measures, clinical data, and laboratory examination items, using univariate and multivariate Cox regression models. Results The median dialysis duration was 48 (interquartile range, 24-96) months. Age, diabetes mellitus, serum phosphorus level, lymphocyte count, and chest CT score were identified as independent prognostic indicators and included in the nomogram. The concordance index values were 0.865, 0.914, and 0.885 in the training, internal validation, and external validation cohorts, respectively. Calibration plots showed good agreement between the expected and actual outcomes. Conclusion This is the first study in which a reliable nomogram was developed to predict short-term outcomes and survival probabilities in patients with COVID-19 on hemodialysis. This model may be helpful to clinicians in treating COVID-19, managing serum phosphorus, and adjusting the dialysis strategies for these vulnerable patients to prevent disease progression in the context of COVID-19 and continuous emergence of novel viruses.
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Affiliation(s)
- Haifan Xing
- Department of Nephrology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Sijie Gu
- Department of Nephrology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ze Li
- Department of Nephrology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiao-er Wei
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Li He
- Department of Nephrology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qiye Liu
- Department of Nephrology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haoran Feng
- Department of Nephrology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Niansong Wang
- Department of Nephrology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hengye Huang
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Fan
- Department of Nephrology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Obe -A- Ndzem Holenn SE, Mazoba TK, Mukanga DY, Zokere TB, Lungela D, Makulo JR, Ahuka S, Mbongo AT, Molua AA. Interest of Chest CT to Assess the Prognosis of SARS-CoV-2 Pneumonia: An In-Hospital-Based Experience in Sub-Saharan Africa. Pulm Med 2024; 2024:5520174. [PMID: 38699403 PMCID: PMC11065491 DOI: 10.1155/2024/5520174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/24/2024] [Accepted: 04/06/2024] [Indexed: 05/05/2024] Open
Abstract
Methods We included all patients with respiratory symptoms (dyspnea, fever, and cough) and/or respiratory failure admitted to the SOS Médecins de nuit SARL hospital, DR Congo, during the 2nd and 3rd waves of the COVID-19 pandemic. The diagnosis of COVID-19 was established based on RT-PCR anti-SARS-CoV-2 tests (G1 (RT-PCR positive) vs. G2 (RT-PCR negative)), and all patients had a chest CT on the day of admission. We retrieved the digital files of patients, precisely the clinical, biological, and chest CT parameters of the day of admission as well as the vital outcome (survival or death). Chest CT were read by a very high-definition console using Advantage Windows software and exported to the hospital network using the RadiAnt DICOM viewer. To determine the threshold for the percentage of lung lesions associated with all-cause mortality, we used ROC curves. Factors associated with death, including chest CT parameters, were investigated using logistic regression analysis. Results The study included 200 patients (average age 56.2 ± 15.2 years; 19% diabetics and 4.5% obese), and COVID-19 was confirmed among 56% of them (G1). Chest CT showed that ground glass (72.3 vs. 39.8%), crazy paving (69.6 vs. 17.0%), and consolidation (83.9 vs. 22.7%), with bilateral and peripheral locations (68.8 vs. 30.7%), were more frequent in G1 vs. G2 (p < 0.001). No case of pulmonary embolism and fibrosis had been documented. The lung lesions affecting 30% of the parenchyma were informative in predicting death (area under the ROC curve at 0.705, p = 0.017). In multivariate analysis, a percentage of lesions affecting 50% of the lung parenchyma increased the risk of dying by 7.194 (1.656-31.250). Conclusion The chest CT demonstrated certain characteristic lesions more frequently in patients in whom the diagnosis of COVID-19 was confirmed. The extent of lesions affecting at least half of the lung parenchyma from the first day of admission to hospital increases the risk of death by a factor of 7.
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Affiliation(s)
- Serge Emmanuel Obe -A- Ndzem Holenn
- Department of Radiology and Medical Imaging, Hôpital Médecins de nuit SARL, Kinshasa, Democratic Republic of the Congo
- Department of Radiology and Medical Imaging, Cliniques Universitaires de Kinshasa, Kinshasa, Democratic Republic of the Congo
- Intensive Care Unit, Cliniques Universitaires de Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Tacite Kpanya Mazoba
- Department of Radiology and Medical Imaging, Cliniques Universitaires de Kinshasa, Kinshasa, Democratic Republic of the Congo
- Interdisciplinary Center for Research in Medical Imaging (CIRIMED), University of Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Désiré Yaya Mukanga
- Department of Radiology and Medical Imaging, Hôpital Médecins de nuit SARL, Kinshasa, Democratic Republic of the Congo
| | - Tyna Bongosepe Zokere
- Department of Radiology and Medical Imaging, Hôpital Médecins de nuit SARL, Kinshasa, Democratic Republic of the Congo
| | - Djo Lungela
- Intensive Care Unit, Hôpital Médecins de nuit SARL, Kinshasa, Democratic Republic of the Congo
| | - Jean-Robert Makulo
- COVID-19 Treatment Center, Cliniques Universitaires de Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Steve Ahuka
- Department of Microbiology, Cliniques Universitaires de Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Angèle Tanzia Mbongo
- Department of Radiology and Medical Imaging, Cliniques Universitaires de Kinshasa, Kinshasa, Democratic Republic of the Congo
- Interdisciplinary Center for Research in Medical Imaging (CIRIMED), University of Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Antoine Aundu Molua
- Department of Radiology and Medical Imaging, Cliniques Universitaires de Kinshasa, Kinshasa, Democratic Republic of the Congo
- Interdisciplinary Center for Research in Medical Imaging (CIRIMED), University of Kinshasa, Kinshasa, Democratic Republic of the Congo
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Gökcan MK, Kurtuluş DF, Aypak A, Köksal M, Ökten SR. Insights from 3D modeling and fluid dynamics in COVID-19 pneumonia. Med Biol Eng Comput 2024; 62:621-636. [PMID: 37980307 DOI: 10.1007/s11517-023-02958-0] [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: 02/13/2023] [Accepted: 10/25/2023] [Indexed: 11/20/2023]
Abstract
We address the lack of research regarding aerodynamic events behind respiratory distress at COVID-19. The use of chest CT enables quantification of pneumonia extent; however, there is a paucity of data regarding the impact of airflow changes. We reviewed 31 COVID-19 patients who were admitted in March 2020 with varying severity of pulmonary disease. Lung volumes were segmented and measured on CT images and patient-specific models of the lungs were created. Incompressible, laminar, and three-dimensional Navier-Stokes equations were used for the fluid dynamics (CFD) analyses of ten patients (five mild, five pneumonia). Of 31 patients, 17 were female, 18 had pneumonia, and 2 were deceased. Effective lung volume decreased in the general group, but the involvement of the right lung was prominent in dyspnea patients. CFD analyses revealed that the mass flow distribution was significantly distorted in pneumonia cases with diminished flow rate towards the right lung. In addition, the distribution of flow parameters showed mild group had less airway resistance with higher velocity (1.228 m/s vs 1.572 m/s) and higher static pressure values at airway branches (1.5112 Pa vs 1.3024 Pa). Therefore, we conclude that airway resistance and mass flow rate distribution are as important as the radiological involvement degree in defining the disease severity.
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Affiliation(s)
- M Kürşat Gökcan
- Otorhinolaryngology, Head and Neck Surgery Department, Ankara University Medical School, Ankara, Turkey.
- Ankara Üniversitesi KBB Hastalıkları Anabilim Dalı, İbni Sina Hastanesi Ek bina K-2, 06100, Sıhhiye, Ankara, Turkey.
| | - D Funda Kurtuluş
- Department of Aerospace Engineering, Faculty of Engineering, Middle East Technical University, Ankara, Turkey
| | - Adalet Aypak
- Department of Infectious Diseases and Clinical Microbiology, Ankara Bilkent City Hospital, Ankara, Turkey
| | - Murathan Köksal
- Department of Radiology, Ankara Bilkent City Hospital, Ankara, Turkey
| | - Sarper R Ökten
- Department of Radiology, Ankara Bilkent City Hospital, Ankara, Turkey
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Grapin K, De Bauchene R, Bonnet B, Mirand A, Cassagnes L, Calvet L, Thouy F, Bouzgarrou R, Henquell C, Evrard B, Adda M, Souweine B, Dupuis C. Severe Acute Respiratory Syndrome Coronavirus 2 Pneumonia in Critically Ill Patients: A Cluster Analysis According to Baseline Characteristics, Biological Features, and Chest CT Scan on Admission. Crit Care Med 2024; 52:e38-e46. [PMID: 37889095 DOI: 10.1097/ccm.0000000000006105] [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: 10/28/2023]
Abstract
BACKGROUND Inconsistent results from COVID-19 studies raise the issue of patient heterogeneity. OBJECTIVE The objective of this study was to identify homogeneous subgroups of patients (clusters) using baseline characteristics including inflammatory biomarkers and the extent of lung parenchymal lesions on CT, and to compare their outcomes. DESIGN Retrospective single-center study. SETTING Medical ICU of the University Hospital of Clermont-Ferrand, France. PATIENTS All consecutive adult patients aged greater than or equal to 18 years, admitted between March 20, 2020, and August 31, 2021, for COVID-19 pneumonia. INTERVENTIONS Characteristics at baseline, during ICU stay, and outcomes at day 60 were recorded. On the chest CT performed at admission the extent of lung parenchyma lesions was established by artificial intelligence software. MEASUREMENTS AND MAIN RESULTS Clusters were determined by hierarchical clustering on principal components using principal component analysis of admission characteristics including plasma interleukin-6, human histocompatibility leukocyte antigen-DR expression rate on blood monocytes (HLA-DR) monocytic-expression rate (mHLA-DR), and the extent of lung parenchymal lesions. Factors associated with day 60 mortality were investigated by univariate survival analysis. Two hundred seventy patients were included. Four clusters were identified and three were fully described. Cluster 1 (obese patients, with moderate hypoxemia, moderate extent of lung parenchymal lesions, no inflammation, and no down-regulation of mHLA-DR) had a better prognosis at day 60 (hazard ratio [HR] = 0.27 [0.15-0.46], p < 0.01), whereas cluster 2 (older patients with comorbidities, moderate extent of lung parenchyma lesions but significant hypoxemia, inflammation, and down-regulation of mHLA-DR) and cluster 3 (patients with severe parenchymal disease, hypoxemia, inflammatory reaction, and down-regulation of mHLA-DR) had an increased risk of mortality (HR = 2.07 [1.37-3.13], p < 0.01 and HR = 1.52 [1-2.32], p = 0.05, respectively). In multivariate analysis, only clusters 1 and 2 were independently associated with day 60 death. CONCLUSIONS Three clusters with distinct characteristics and outcomes were identified. Such clusters could facilitate the identification of targeted populations for the next trials.
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Affiliation(s)
- Kévin Grapin
- CHU Clermont-Ferrand, Service de Médecine intensive et réanimation, Clermont-Ferrand, France
| | | | - Benjamin Bonnet
- CHU Clermont-Ferrand, Service d'Immunologie, Clermont-Ferrand, France
- Université Clermont Auvergne, Laboratoire d'Immunologie, ECREIN, UMR1019, UNH, UFR Médecine de Clermont-Ferrand, Clermont-Ferrand, France
| | - Audrey Mirand
- CHU Clermont-Ferrand, 3IHP, Service de virologie, Clermont-Ferrand, France
- Université Clermont Auvergne, UMR CNRS 6023, LMGE, Clermont-Ferrand, France
| | - Lucie Cassagnes
- CHU Clermont-Ferrand, Service de Radiologie, Clermont-Ferrand, France
- Université Clermont Auvergne, ASMS, UMR 1019, UNH, INRAe, CRNH Auvergne, Clermont-Ferrand, France
| | - Laure Calvet
- CHU Clermont-Ferrand, Service de Médecine intensive et réanimation, Clermont-Ferrand, France
| | - François Thouy
- CHU Clermont-Ferrand, Service de Médecine intensive et réanimation, Clermont-Ferrand, France
| | - Radhia Bouzgarrou
- CHU Clermont-Ferrand, Service de Médecine intensive et réanimation, Clermont-Ferrand, France
| | - Cécile Henquell
- CHU Clermont-Ferrand, 3IHP, Service de virologie, Clermont-Ferrand, France
- Université Clermont Auvergne, UMR CNRS 6023, LMGE, Clermont-Ferrand, France
| | - Bertrand Evrard
- CHU Clermont-Ferrand, Service d'Immunologie, Clermont-Ferrand, France
- Université Clermont Auvergne, Laboratoire d'Immunologie, ECREIN, UMR1019, UNH, UFR Médecine de Clermont-Ferrand, Clermont-Ferrand, France
| | - Mireille Adda
- CHU Clermont-Ferrand, Service de Médecine intensive et réanimation, Clermont-Ferrand, France
| | - Bertrand Souweine
- CHU Clermont-Ferrand, Service de Médecine intensive et réanimation, Clermont-Ferrand, France
- Université Clermont Auvergne, UMR CNRS 6023, LMGE, Clermont-Ferrand, France
| | - Claire Dupuis
- CHU Clermont-Ferrand, Service de Médecine intensive et réanimation, Clermont-Ferrand, France
- Université Clermont Auvergne, ASMS, UMR 1019, UNH, INRAe, CRNH Auvergne, Clermont-Ferrand, France
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9
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Ciarmiello A, Tutino F, Giovannini E, Milano A, Barattini M, Yosifov N, Calvi D, Setti M, Sivori M, Sani C, Bastreri A, Staffiere R, Stefanini T, Artioli S, Giovacchini G. Multivariable Risk Modelling and Survival Analysis with Machine Learning in SARS-CoV-2 Infection. J Clin Med 2023; 12:7164. [PMID: 38002776 PMCID: PMC10672177 DOI: 10.3390/jcm12227164] [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: 10/10/2023] [Revised: 11/03/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023] Open
Abstract
AIM To evaluate the performance of a machine learning model based on demographic variables, blood tests, pre-existing comorbidities, and computed tomography(CT)-based radiomic features to predict critical outcome in patients with acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS We retrospectively enrolled 694 SARS-CoV-2-positive patients. Clinical and demographic data were extracted from clinical records. Radiomic data were extracted from CT. Patients were randomized to the training (80%, n = 556) or test (20%, n = 138) dataset. The training set was used to define the association between severity of disease and comorbidities, laboratory tests, demographic, and CT-based radiomic variables, and to implement a risk-prediction model. The model was evaluated using the C statistic and Brier scores. The test set was used to assess model prediction performance. RESULTS Patients who died (n = 157) were predominantly male (66%) over the age of 50 with median (range) C-reactive protein (CRP) = 5 [1, 37] mg/dL, lactate dehydrogenase (LDH) = 494 [141, 3631] U/I, and D-dimer = 6.006 [168, 152.015] ng/mL. Surviving patients (n = 537) had median (range) CRP = 3 [0, 27] mg/dL, LDH = 484 [78, 3.745] U/I, and D-dimer = 1.133 [96, 55.660] ng/mL. The strongest risk factors were D-dimer, age, and cardiovascular disease. The model implemented using the variables identified using the LASSO Cox regression analysis classified 90% of non-survivors as high-risk individuals in the testing dataset. In this sample, the estimated median survival in the high-risk group was 9 days (95% CI; 9-37), while the low-risk group did not reach the median survival of 50% (p < 0.001). CONCLUSIONS A machine learning model based on combined data available on the first days of hospitalization (demographics, CT-radiomics, comorbidities, and blood biomarkers), can identify SARS-CoV-2 patients at risk of serious illness and death.
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Affiliation(s)
- Andrea Ciarmiello
- Nuclear Medicine Unit, Ospedale Civile Sant’Andrea, Via Vittorio Veneto 170, 19124 La Spezia, Italy; (F.T.); (E.G.); (N.Y.); (G.G.)
| | - Francesca Tutino
- Nuclear Medicine Unit, Ospedale Civile Sant’Andrea, Via Vittorio Veneto 170, 19124 La Spezia, Italy; (F.T.); (E.G.); (N.Y.); (G.G.)
| | - Elisabetta Giovannini
- Nuclear Medicine Unit, Ospedale Civile Sant’Andrea, Via Vittorio Veneto 170, 19124 La Spezia, Italy; (F.T.); (E.G.); (N.Y.); (G.G.)
| | - Amalia Milano
- Oncology Unit, Ospedale Civile Sant’Andrea, 19124 La Spezia, Italy;
| | - Matteo Barattini
- Radiology Unit, Ospedale Civile Sant’Andrea, 19124 La Spezia, Italy; (M.B.); (T.S.)
| | - Nikola Yosifov
- Nuclear Medicine Unit, Ospedale Civile Sant’Andrea, Via Vittorio Veneto 170, 19124 La Spezia, Italy; (F.T.); (E.G.); (N.Y.); (G.G.)
| | - Debora Calvi
- Infectius Diseases Unit, Ospedale Civile Sant’Andrea, 19124 La Spezia, Italy; (D.C.); (S.A.)
| | - Maurizo Setti
- Internal Medicine Unit, Ospedale San Bartolomeo, 19138 Sarzana, Italy;
| | | | - Cinzia Sani
- Intensive Care Unit, Ospedale Civile Sant’Andrea, 19124 La Spezia, Italy;
| | - Andrea Bastreri
- Emergency Department, Ospedale Civile Sant’Andrea, 19124 La Spezia, Italy;
| | | | - Teseo Stefanini
- Radiology Unit, Ospedale Civile Sant’Andrea, 19124 La Spezia, Italy; (M.B.); (T.S.)
| | - Stefania Artioli
- Infectius Diseases Unit, Ospedale Civile Sant’Andrea, 19124 La Spezia, Italy; (D.C.); (S.A.)
| | - Giampiero Giovacchini
- Nuclear Medicine Unit, Ospedale Civile Sant’Andrea, Via Vittorio Veneto 170, 19124 La Spezia, Italy; (F.T.); (E.G.); (N.Y.); (G.G.)
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10
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Keicher M, Burwinkel H, Bani-Harouni D, Paschali M, Czempiel T, Burian E, Makowski MR, Braren R, Navab N, Wendler T. Multimodal graph attention network for COVID-19 outcome prediction. Sci Rep 2023; 13:19539. [PMID: 37945590 PMCID: PMC10636061 DOI: 10.1038/s41598-023-46625-8] [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: 07/03/2023] [Accepted: 11/03/2023] [Indexed: 11/12/2023] Open
Abstract
When dealing with a newly emerging disease such as COVID-19, the impact of patient- and disease-specific factors (e.g., body weight or known co-morbidities) on the immediate course of the disease is largely unknown. An accurate prediction of the most likely individual disease progression can improve the planning of limited resources and finding the optimal treatment for patients. In the case of COVID-19, the need for intensive care unit (ICU) admission of pneumonia patients can often only be determined on short notice by acute indicators such as vital signs (e.g., breathing rate, blood oxygen levels), whereas statistical analysis and decision support systems that integrate all of the available data could enable an earlier prognosis. To this end, we propose a holistic, multimodal graph-based approach combining imaging and non-imaging information. Specifically, we introduce a multimodal similarity metric to build a population graph that shows a clustering of patients. For each patient in the graph, we extract radiomic features from a segmentation network that also serves as a latent image feature encoder. Together with clinical patient data like vital signs, demographics, and lab results, these modalities are combined into a multimodal representation of each patient. This feature extraction is trained end-to-end with an image-based Graph Attention Network to process the population graph and predict the COVID-19 patient outcomes: admission to ICU, need for ventilation, and mortality. To combine multiple modalities, radiomic features are extracted from chest CTs using a segmentation neural network. Results on a dataset collected in Klinikum rechts der Isar in Munich, Germany and the publicly available iCTCF dataset show that our approach outperforms single modality and non-graph baselines. Moreover, our clustering and graph attention increases understanding of the patient relationships within the population graph and provides insight into the network's decision-making process.
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Affiliation(s)
- Matthias Keicher
- Computer Aided Medical Procedures and Augmented Reality, School of Computation, Information and Technology, Technical University of Munich, Boltzmannstr. 3, 85748, Garching, Germany.
| | - Hendrik Burwinkel
- Computer Aided Medical Procedures and Augmented Reality, School of Computation, Information and Technology, Technical University of Munich, Boltzmannstr. 3, 85748, Garching, Germany
| | - David Bani-Harouni
- Computer Aided Medical Procedures and Augmented Reality, School of Computation, Information and Technology, Technical University of Munich, Boltzmannstr. 3, 85748, Garching, Germany
| | - Magdalini Paschali
- Computer Aided Medical Procedures and Augmented Reality, School of Computation, Information and Technology, Technical University of Munich, Boltzmannstr. 3, 85748, Garching, Germany
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94304, USA
| | - Tobias Czempiel
- Computer Aided Medical Procedures and Augmented Reality, School of Computation, Information and Technology, Technical University of Munich, Boltzmannstr. 3, 85748, Garching, Germany
| | - Egon Burian
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Marcus R Makowski
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Rickmer Braren
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Nassir Navab
- Computer Aided Medical Procedures and Augmented Reality, School of Computation, Information and Technology, Technical University of Munich, Boltzmannstr. 3, 85748, Garching, Germany
| | - Thomas Wendler
- Computer Aided Medical Procedures and Augmented Reality, School of Computation, Information and Technology, Technical University of Munich, Boltzmannstr. 3, 85748, Garching, Germany
- Department of Diagnostic and Interventional Radiology and Neuroradiology, Clinical Computational Medical Imaging Research, University Hospital Augsburg, Stenglinstr. 2, 86156, Augsburg, Germany
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11
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Ghafoori M, Hamidi M, Modegh RG, Aziz-Ahari A, Heydari N, Tavafizadeh Z, Pournik O, Emdadi S, Samimi S, Mohseni A, Khaleghi M, Dashti H, Rabiee HR. Predicting survival of Iranian COVID-19 patients infected by various variants including omicron from CT Scan images and clinical data using deep neural networks. Heliyon 2023; 9:e21965. [PMID: 38058649 PMCID: PMC10696006 DOI: 10.1016/j.heliyon.2023.e21965] [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: 10/12/2022] [Revised: 10/26/2023] [Accepted: 11/01/2023] [Indexed: 12/08/2023] Open
Abstract
Purpose: The rapid spread of the COVID-19 omicron variant virus has resulted in an overload of hospitals around the globe. As a result, many patients are deprived of hospital facilities, increasing mortality rates. Therefore, mortality rates can be reduced by efficiently assigning facilities to higher-risk patients. Therefore, it is crucial to estimate patients' survival probability based on their conditions at the time of admission so that the minimum required facilities can be provided, allowing more opportunities to be available for those who need them. Although radiologic findings in chest computerized tomography scans show various patterns, considering the individual risk factors and other underlying diseases, it is difficult to predict patient prognosis through routine clinical or statistical analysis. Method: In this study, a deep neural network model is proposed for predicting survival based on simple clinical features, blood tests, axial computerized tomography scan images of lungs, and the patients' planned treatment. The model's architecture combines a Convolutional Neural Network and a Long Short Term Memory network. The model was trained using 390 survivors and 108 deceased patients from the Rasoul Akram Hospital and evaluated 109 surviving and 36 deceased patients infected by the omicron variant. Results: The proposed model reached an accuracy of 87.5% on the test data, indicating survival prediction possibility. The accuracy was significantly higher than the accuracy achieved by classical machine learning methods without considering computerized tomography scan images (p-value <= 4E-5). The images were also replaced with hand-crafted features related to the ratio of infected lung lobes used in classical machine-learning models. The highest-performing model reached an accuracy of 84.5%, which was considerably higher than the models trained on mere clinical information (p-value <= 0.006). However, the performance was still significantly less than the deep model (p-value <= 0.016). Conclusion: The proposed deep model achieved a higher accuracy than classical machine learning methods trained on features other than computerized tomography scan images. This proves the images contain extra information. Meanwhile, Artificial Intelligence methods with multimodal inputs can be more reliable and accurate than computerized tomography severity scores.
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Affiliation(s)
- Mahyar Ghafoori
- Radiology Department, Hazrat Rasoul Akram Hospital, School of Medicine, Iran University of Medical Sciences, Hemmat, Tehran, 14535, Iran
| | - Mehrab Hamidi
- BCB Lab, Department of Computer Engineering, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
- AI-Med Group, AI Innovation Center, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
| | - Rassa Ghavami Modegh
- Data science and Machine learning Lab, Department of Computer Engineering, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
- BCB Lab, Department of Computer Engineering, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
- AI-Med Group, AI Innovation Center, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
| | - Alireza Aziz-Ahari
- Radiology Department, Hazrat Rasoul Akram Hospital, School of Medicine, Iran University of Medical Sciences, Hemmat, Tehran, 14535, Iran
| | - Neda Heydari
- Radiology Department, Hazrat Rasoul Akram Hospital, School of Medicine, Iran University of Medical Sciences, Hemmat, Tehran, 14535, Iran
| | - Zeynab Tavafizadeh
- Radiology Department, Hazrat Rasoul Akram Hospital, School of Medicine, Iran University of Medical Sciences, Hemmat, Tehran, 14535, Iran
| | - Omid Pournik
- Radiology Department, Hazrat Rasoul Akram Hospital, School of Medicine, Iran University of Medical Sciences, Hemmat, Tehran, 14535, Iran
| | - Sasan Emdadi
- AI-Med Group, AI Innovation Center, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
| | - Saeed Samimi
- AI-Med Group, AI Innovation Center, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
| | - Amir Mohseni
- BCB Lab, Department of Computer Engineering, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
- AI-Med Group, AI Innovation Center, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
| | - Mohammadreza Khaleghi
- Radiology Department, Hazrat Rasoul Akram Hospital, School of Medicine, Iran University of Medical Sciences, Hemmat, Tehran, 14535, Iran
| | - Hamed Dashti
- AI-Med Group, AI Innovation Center, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
| | - Hamid R. Rabiee
- Data science and Machine learning Lab, Department of Computer Engineering, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
- BCB Lab, Department of Computer Engineering, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
- AI-Med Group, AI Innovation Center, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
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12
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Colombi D, Petrini M, Risoli C, Mangia A, Milanese G, Silva M, Franco C, Sverzellati N, Michieletti E. Quantitative CT at Follow-Up of COVID-19 Pneumonia: Relationship with Pulmonary Function Tests. Diagnostics (Basel) 2023; 13:3328. [PMID: 37958224 PMCID: PMC10648873 DOI: 10.3390/diagnostics13213328] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/20/2023] [Accepted: 10/26/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND The role of quantitative chest computed tomography (CT) is controversial in the follow-up of patients with COVID-19 pneumonia. The aim of this study was to test during the follow-up of COVID-19 pneumonia the association between pulmonary function tests (PFTs) and quantitative parameters extrapolated from follow-up (FU) CT scans performed at least 6 months after COVID-19 onset. METHODS The study included patients older than 18 years old, admitted to the emergency department of our institution between 29 February 2020 and 31 December 2020, with a diagnosis of COVID-19 pneumonia, who underwent chest CT at admission and FU CT at least 6 months later; PFTs were performed within 6 months of FU CT. At FU CT, quantitative parameters of well-aerated lung and pneumonia extent were identified both visually and by software using CT density thresholds. The association between PFTs and quantitative parameters was tested by the calculation of the Spearman's coefficient of rank correlation (rho). RESULTS The study included 40 patients (38% females; median age 63 years old, IQR, 56-71 years old). A significant correlation was identified between low attenuation areas% (%LAAs) <950 Hounsfield units (HU) and both forced expiratory volume in 1s/forced vital capacity (FEV1/FVC) ratio (rho -0.410, 95% CIs -0.639--0.112, p = 0.008) and %DLCO (rho -0.426, 95% CIs -0.678--0.084, p = 0.017). The remaining quantitative parameters failed to demonstrate a significant association with PFTs (p > 0.05). CONCLUSIONS At follow-up, CT scans performed at least 6 months after COVID-19 pneumonia onset showed %LAAs that were inversely associated with %DLCO and could be considered a marker of irreversible lung damage.
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Affiliation(s)
- Davide Colombi
- Radiology Unit, Department of Radiological Functions, AUSL Piacenza, Via Taverna 49, 29121 Piacenza, Italy; (M.P.); (C.R.)
| | - Marcello Petrini
- Radiology Unit, Department of Radiological Functions, AUSL Piacenza, Via Taverna 49, 29121 Piacenza, Italy; (M.P.); (C.R.)
| | - Camilla Risoli
- Radiology Unit, Department of Radiological Functions, AUSL Piacenza, Via Taverna 49, 29121 Piacenza, Italy; (M.P.); (C.R.)
| | - Angelo Mangia
- Pulmonology Unit, Department of Emergency, AUSL Piacenza, Via Taverna 49, 29121 Piacenza, Italy; (A.M.); (C.F.)
| | - Gianluca Milanese
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Via Gramsci 14, 43126 Parma, Italy; (G.M.); (M.S.)
| | - Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Via Gramsci 14, 43126 Parma, Italy; (G.M.); (M.S.)
| | - Cosimo Franco
- Pulmonology Unit, Department of Emergency, AUSL Piacenza, Via Taverna 49, 29121 Piacenza, Italy; (A.M.); (C.F.)
| | - Nicola Sverzellati
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Via Gramsci 14, 43126 Parma, Italy; (G.M.); (M.S.)
| | - Emanuele Michieletti
- Radiology Unit, Department of Radiological Functions, AUSL Piacenza, Via Taverna 49, 29121 Piacenza, Italy; (M.P.); (C.R.)
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13
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Suzuki M, Fujii Y, Nishimura Y, Yasui K, Fujisawa H. Quantitative analysis of chest computed tomography of COVID-19 pneumonia using a software widely used in Japan. PLoS One 2023; 18:e0287953. [PMID: 37871048 PMCID: PMC10593239 DOI: 10.1371/journal.pone.0287953] [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: 07/07/2023] [Accepted: 10/04/2023] [Indexed: 10/25/2023] Open
Abstract
This study aimed to determine the optimal conditions to measure the percentage of the area considered as pneumonia (pneumonia volume ratio [PVR]) and the computed tomography (CT) score due to coronavirus disease 2019 (COVID-19) using the Ziostation2 image analysis software (Z2; Ziosoft, Tokyo, Japan), which is popular in Japan, and to evaluate its usefulness for assessing the clinical severity. We included 53 patients (41 men and 12 women, mean age: 61.3 years) diagnosed with COVID-19 using polymerase chain reaction who had undergone chest CT and were hospitalized between January 2020 and January 2021. Based on the COVID-19 infection severity, the patients were classified as mild (n = 38) or severe (n = 15). For 10 randomly selected samples, the PVR and CT scores by Z2 under different conditions and the visual simple PVR and CT scores were compared. The conditions with the highest statistical agreement were determined. The usefulness of the clinical severity assessment based on the PVR and CT scores using Z2 under the determined conditions was statistically evaluated. The best agreement with the visual measurement was achieved by the Z2 measurement condition of ≥-600 HU. The areas under the receiver operating characteristic curves, Youden's index, and the sensitivity, specificity, and p-values of the PVR and CT scores by Z2 were as follows: PVR: 0.881, 18.69, 66.7, 94.7, and <0.001; CT score: 0.77, 7.5, 40, 74, and 0.002, respectively. We determined the optimal condition for assessing the PVR of COVID-19 pneumonia using Z2 and demonstrated that the AUC of the PVR was higher than that of CT scores in the assessment of clinical severity. The introduction of new technologies is time-consuming and expensive; our method has high clinical utility and can be promptly used in any facility where Z2 has been introduced.
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Affiliation(s)
- Minako Suzuki
- Department of Radiology, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Yoshimi Fujii
- Department of Radiology, Fujisawa City Hospital, Fujisawa, Kanagawa, Japan
| | - Yurie Nishimura
- Department of Radiology, Fujisawa City Hospital, Fujisawa, Kanagawa, Japan
| | - Kazuma Yasui
- Department of Radiology, Fujisawa City Hospital, Fujisawa, Kanagawa, Japan
| | - Hidefumi Fujisawa
- Department of Radiology, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
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14
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Tanaka H, Maetani T, Chubachi S, Tanabe N, Shiraishi Y, Asakura T, Namkoong H, Shimada T, Azekawa S, Otake S, Nakagawara K, Fukushima T, Watase M, Terai H, Sasaki M, Ueda S, Kato Y, Harada N, Suzuki S, Yoshida S, Tateno H, Yamada Y, Jinzaki M, Hirai T, Okada Y, Koike R, Ishii M, Hasegawa N, Kimura A, Imoto S, Miyano S, Ogawa S, Kanai T, Fukunaga K. Clinical utilization of artificial intelligence-based COVID-19 pneumonia quantification using chest computed tomography - a multicenter retrospective cohort study in Japan. Respir Res 2023; 24:241. [PMID: 37798709 PMCID: PMC10552312 DOI: 10.1186/s12931-023-02530-2] [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: 07/18/2023] [Accepted: 09/04/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND Computed tomography (CT) imaging and artificial intelligence (AI)-based analyses have aided in the diagnosis and prediction of the severity of COVID-19. However, the potential of AI-based CT quantification of pneumonia in assessing patients with COVID-19 has not yet been fully explored. This study aimed to investigate the potential of AI-based CT quantification of COVID-19 pneumonia to predict the critical outcomes and clinical characteristics of patients with residual lung lesions. METHODS This retrospective cohort study included 1,200 hospitalized patients with COVID-19 from four hospitals. The incidence of critical outcomes (requiring the support of high-flow oxygen or invasive mechanical ventilation or death) and complications during hospitalization (bacterial infection, renal failure, heart failure, thromboembolism, and liver dysfunction) was compared between the groups of pneumonia with high/low-percentage lung lesions, based on AI-based CT quantification. Additionally, 198 patients underwent CT scans 3 months after admission to analyze prognostic factors for residual lung lesions. RESULTS The pneumonia group with a high percentage of lung lesions (N = 400) had a higher incidence of critical outcomes and complications during hospitalization than the low percentage group (N = 800). Multivariable analysis demonstrated that AI-based CT quantification of pneumonia was independently associated with critical outcomes (adjusted odds ratio [aOR] 10.5, 95% confidence interval [CI] 5.59-19.7), as well as with oxygen requirement (aOR 6.35, 95% CI 4.60-8.76), IMV requirement (aOR 7.73, 95% CI 2.52-23.7), and mortality rate (aOR 6.46, 95% CI 1.87-22.3). Among patients with follow-up CT scans (N = 198), the multivariable analysis revealed that the pneumonia group with a high percentage of lung lesions on admission (aOR 4.74, 95% CI 2.36-9.52), older age (aOR 2.53, 95% CI 1.16-5.51), female sex (aOR 2.41, 95% CI 1.13-5.11), and medical history of hypertension (aOR 2.22, 95% CI 1.09-4.50) independently predicted persistent residual lung lesions. CONCLUSIONS AI-based CT quantification of pneumonia provides valuable information beyond qualitative evaluation by physicians, enabling the prediction of critical outcomes and residual lung lesions in patients with COVID-19.
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Affiliation(s)
- Hiromu Tanaka
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Tomoki Maetani
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Shotaro Chubachi
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
| | - Naoya Tanabe
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan.
| | - Yusuke Shiraishi
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Takanori Asakura
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
- Department of Clinical Medicine (Laboratory of Bioregulatory Medicine), Kitasato University School of Pharmacy, Tokyo, Japan
- Department of Respiratory Medicine, Kitasato University, Kitasato Institute Hospital, Tokyo, Japan
| | - Ho Namkoong
- Department of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan
| | - Takashi Shimada
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Shuhei Azekawa
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Shiro Otake
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Kensuke Nakagawara
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Takahiro Fukushima
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Mayuko Watase
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Hideki Terai
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Mamoru Sasaki
- Department of Respiratory Medicine, JCHO (Japan Community Health care Organization), Saitama Medical Center, Saitama, Japan
| | - Soichiro Ueda
- Department of Respiratory Medicine, JCHO (Japan Community Health care Organization), Saitama Medical Center, Saitama, Japan
| | - Yukari Kato
- Department of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
| | - Norihiro Harada
- Department of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
| | - Shoji Suzuki
- Department of Pulmonary Medicine, Saitama City Hospital, Saitama, Japan
| | - Shuichi Yoshida
- Department of Pulmonary Medicine, Saitama City Hospital, Saitama, Japan
| | - Hiroki Tateno
- Department of Pulmonary Medicine, Saitama City Hospital, Saitama, Japan
| | - Yoshitake Yamada
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Masahiro Jinzaki
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Toyohiro Hirai
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Ryuji Koike
- Health Science Research and Development Center (HeRD), Tokyo Medical and Dental University, Tokyo, Japan
| | - Makoto Ishii
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Naoki Hasegawa
- Department of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan
| | - Akinori Kimura
- Institute of Research, Tokyo Medical and Dental University, Tokyo, Japan
| | - Seiya Imoto
- Division of Health Medical Intelligence, Human Genome Center, the Institute of Medical Science, the University of Tokyo, Tokyo, Japan
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan
| | - Takanori Kanai
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Koichi Fukunaga
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
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Iwasawa T, Matsushita S, Hirayama M, Baba T, Ogura T. Quantitative Analysis for Lung Disease on Thin-Section CT. Diagnostics (Basel) 2023; 13:2988. [PMID: 37761355 PMCID: PMC10528918 DOI: 10.3390/diagnostics13182988] [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: 08/01/2023] [Revised: 08/30/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Thin-section computed tomography (CT) is widely employed not only for assessing morphology but also for evaluating respiratory function. Three-dimensional images obtained from thin-section CT provide precise measurements of lung, airway, and vessel volumes. These volumetric indices are correlated with traditional pulmonary function tests (PFT). CT also generates lung histograms. The volume ratio of areas with low and high attenuation correlates with PFT results. These quantitative image analyses have been utilized to investigate the early stages and disease progression of diffuse lung diseases, leading to the development of novel concepts such as pre-chronic obstructive pulmonary disease (pre-COPD) and interstitial lung abnormalities. Quantitative analysis proved particularly valuable during the COVID-19 pandemic when clinical evaluations were limited. In this review, we introduce CT analysis methods and explore their clinical applications in the context of various lung diseases. We also highlight technological advances, including images with matrices of 1024 × 1024 and slice thicknesses of 0.25 mm, which enhance the accuracy of these analyses.
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Affiliation(s)
- Tae Iwasawa
- Department of Radiology, Kanagawa Cardiovascular & Respiratory Center, 6-16-1 Tomioka-higashi, Kanazawa-ku, Yokohama 236-0051, Japan; (S.M.); (M.H.)
| | - Shoichiro Matsushita
- Department of Radiology, Kanagawa Cardiovascular & Respiratory Center, 6-16-1 Tomioka-higashi, Kanazawa-ku, Yokohama 236-0051, Japan; (S.M.); (M.H.)
| | - Mariko Hirayama
- Department of Radiology, Kanagawa Cardiovascular & Respiratory Center, 6-16-1 Tomioka-higashi, Kanazawa-ku, Yokohama 236-0051, Japan; (S.M.); (M.H.)
| | - Tomohisa Baba
- Department of Respiratory Medicine, Kanagawa Cardiovascular & Respiratory Center, 6-16-1 Tomioka-higashi, Kanazawa-ku, Yokohama 236-0051, Japan; (T.B.); (T.O.)
| | - Takashi Ogura
- Department of Respiratory Medicine, Kanagawa Cardiovascular & Respiratory Center, 6-16-1 Tomioka-higashi, Kanazawa-ku, Yokohama 236-0051, Japan; (T.B.); (T.O.)
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16
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Wu G, Zhu Y, Qiu X, Yuan X, Mi X, Zhou R. Application of clinical and CT imaging features in the evaluation of disease progression in patients with COVID-19. BMC Pulm Med 2023; 23:329. [PMID: 37674193 PMCID: PMC10481600 DOI: 10.1186/s12890-023-02613-2] [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: 05/24/2023] [Accepted: 08/26/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND The Corona Virus Disease 2019(COVID-19) pandemic has strained healthcare systems worldwide, necessitating the early prediction of patients requiring critical care. This study aimed to analyze the laboratory examination indicators, CT features, and prognostic risk factors in COVID-19 patients. METHODS A retrospective study was conducted on 90 COVID-19 patients at the First Affiliated Hospital of Gannan Medical University between December 17, 2022, and March 17, 2023. Clinical data, laboratory examination results, and computed tomography (CT) imaging data were collected. Logistic multivariate regression analysis was performed to identify independent risk factors, and the predictive ability of each risk factor was assessed using the area under the receiver operating characteristic (ROC) curve. RESULTS Multivariate logistic regression analysis revealed that comorbid diabetes (odds ratio [OR] = 526.875, 95%CI = 1.384-1960.84, P = 0.053), lymphocyte count reduction (OR = 8.773, 95%CI = 1.432-53.584, P = 0.064), elevated D-dimer level (OR = 362.426, 95%CI = 1.228-984.995, P = 0.023), and involvement of five lung lobes (OR = 0.926, 95%CI = 0.026-0.686, P = 0.025) were risk factors for progression to severe COVID-19. ROC curve analysis showed the highest predictive value for 5 lung lobes (AUC = 0.782). Oxygen saturation was positively correlated with normally aerated lung volume and the proportion of normally aerated lung volume (P < 0.05). CONCLUSIONS The study demonstrated that comorbid diabetes, lymphocyte count reduction, elevated D-dimer levels, and involvement of the five lung lobes are significant risk factors for severe COVID-19. In CT lung volume quantification, normal aerated lung volume and the proportion of normal aerated lung volume correlated with blood oxygen saturation.
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Affiliation(s)
- Guobin Wu
- Respiratory and Critical Care Medicine, The First Affiliated Hospital of Gannan Medical University, No. 23 Qingnian Road, Zhanggong District, Ganzhou, 341000 Jiangxi China
| | - Yunya Zhu
- General Medicine, The First Affiliated Hospital of Gannan Medical University, No. 23 Qingnian Road, Zhanggong District, Ganzhou, 341000 Jiangxi China
| | - Xingting Qiu
- Radiology, The First Affiliated Hospital of Gannan Medical University, No. 23 Qingnian Road, Zhanggong District, Ganzhou, 341000 Jiangxi China
| | - Xiaoliang Yuan
- Respiratory and Critical Care Medicine, The First Affiliated Hospital of Gannan Medical University, No. 23 Qingnian Road, Zhanggong District, Ganzhou, 341000 Jiangxi China
| | - Xiaojing Mi
- Respiratory and Critical Care Medicine, The First Affiliated Hospital of Gannan Medical University, No. 23 Qingnian Road, Zhanggong District, Ganzhou, 341000 Jiangxi China
| | - Rong Zhou
- Respiratory and Critical Care Medicine, The First Affiliated Hospital of Gannan Medical University, No. 23 Qingnian Road, Zhanggong District, Ganzhou, 341000 Jiangxi China
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17
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Musso G, Taliano C, Paschetta E, De Iuliis M, Fonti C, Vianou IS, Druetta M, Riedo F, Ferraris A, Tirabassi G. Mechanical Power Delivered by Noninvasive Ventilation Contributes to Physio-Anatomical and Clinical Responses to Early Versus Late Proning in COVID-19 Pneumonia. Crit Care Med 2023; 51:1185-1200. [PMID: 37232709 DOI: 10.1097/ccm.0000000000005915] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
OBJECTIVES To study: 1) the effect of prone position (PP) on noninvasive ventilation (NIV)-delivered mechanical power (MP) and 2) the impact of MP on physio-anatomical and clinical responses to early versus late PP in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia. DESIGN Nonrandomized trial with inverse probability of treatment weighted-matched groups. SETTING HUMANITAS Gradenigo Sub-ICU. PATIENTS One hundred thirty-eight SARS-CoV-2 pneumonia patients with moderate-to-severe acute hypoxemic respiratory failure (Pa o2 /F io2 ratio < 200 mm Hg) receiving NIV from September 1, 2020, to February 28, 2021 (Ethics approval: ISRCTN23016116). INTERVENTIONS Early PP or late PP or supine position. MEASUREMENTS AND MAIN RESULTS Respiratory parameters were hourly recorded. Time-weighted average MP values were calculated for each ventilatory session. Gas exchange parameters and ventilatory ratio (VR) were measured 1 hour after each postural change. Lung ultrasonographic scores and circulating biomarkers were assessed daily. MP delivered during the initial 24 hours of NIV (MP [first 24 hr]) was the primary exposure variable. Primary outcomes: 28-day endotracheal intubation and death. Secondary outcomes: oxygen-response, C o2 -response, ultrasonographic, and systemic inflammatory biomarker responses after 24 hours of NIV. Fifty-eight patients received early PP + NIV, 26 late PP + NIV, and 54 supine NIV. Early PP group had lower 28-day intubation and death than late PP (hazard ratio [HR], 0.35; 95% CI, 0.19-0.69 and HR, 0.26; 95% CI, 0.07-0.67, respectively) and supine group. In Cox multivariate analysis, (MP [first 24 hr]) predicted 28-day intubation (HR, 1.70; 95% CI, 1.25-2.09; p = 0.009) and death (HR, 1.51; 95% CI, 1.19-1.91; p = 0.007). Compared with supine position, PP was associated with a 35% MP reduction. VR, ultrasonographic scores, and inflammatory biomarkers improved after 24 hours of NIV in the early PP, but not in late PP or supine group. A MP (first 24 hr) greater than or equal to 17.9 J/min was associated with 28-day death (area under the curve, 0.92; 95% CI, 0.88-0.96; p < 0.001); cumulative hours of MP greater than or equal to 17.9 J/min delivered before PP initiation attenuated VR, ultrasonographic, and biomarker responses to PP. CONCLUSIONS MP delivered by NIV during initial 24 hours predicts clinical outcomes. PP curtails MP, but cumulative hours of NIV with MP greater than or equal to 17.9 J/min delivered before PP initiation attenuate the benefits of PP.
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Affiliation(s)
- Giovanni Musso
- Emergency Medicine Department, San Luigi Gonzaga Hospital, Orbassano, Turin, Italy
| | - Claudio Taliano
- Emergency Medicine Department, Humanitas Gradenigo, Turin, Italy
| | - Elena Paschetta
- Emergency Medicine Department, Humanitas Gradenigo, Turin, Italy
| | | | - Caterina Fonti
- Emergency Medicine Department, San Luigi Gonzaga Hospital, Orbassano, Turin, Italy
| | | | - Marta Druetta
- Emergency Medicine Department, Humanitas Gradenigo, Turin, Italy
| | - Federica Riedo
- Emergency Medicine Department, Humanitas Gradenigo, Turin, Italy
| | | | - Gloria Tirabassi
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
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18
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Grodecki K, Killekar A, Simon J, Lin A, Cadet S, McElhinney P, Chan C, Williams MC, Pressman BD, Julien P, Li D, Chen P, Gaibazzi N, Thakur U, Mancini E, Agalbato C, Munechika J, Matsumoto H, Menè R, Parati G, Cernigliaro F, Nerlekar N, Torlasco C, Pontone G, Maurovich-Horvat P, Slomka PJ, Dey D. Artificial intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systems. Br J Radiol 2023; 96:20220180. [PMID: 37310152 PMCID: PMC10461277 DOI: 10.1259/bjr.20220180] [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: 02/11/2022] [Revised: 05/15/2023] [Accepted: 05/30/2023] [Indexed: 06/14/2023] Open
Abstract
OBJECTIVE We aimed to evaluate the effectiveness of utilizing artificial intelligence (AI) to quantify the extent of pneumonia from chest CT scans, and to determine its ability to predict clinical deterioration or mortality in patients admitted to the hospital with COVID-19 in comparison to semi-quantitative visual scoring systems. METHODS A deep-learning algorithm was utilized to quantify the pneumonia burden, while semi-quantitative pneumonia severity scores were estimated through visual means. The primary outcome was clinical deterioration, the composite end point including admission to the intensive care unit, need for invasive mechanical ventilation, or vasopressor therapy, as well as in-hospital death. RESULTS The final population comprised 743 patients (mean age 65 ± 17 years, 55% men), of whom 175 (23.5%) experienced clinical deterioration or death. The area under the receiver operating characteristic curve (AUC) for predicting the primary outcome was significantly higher for AI-assisted quantitative pneumonia burden (0.739, p = 0.021) compared with the visual lobar severity score (0.711, p < 0.001) and visual segmental severity score (0.722, p = 0.042). AI-assisted pneumonia assessment exhibited lower performance when applied for calculation of the lobar severity score (AUC of 0.723, p = 0.021). Time taken for AI-assisted quantification of pneumonia burden was lower (38 ± 10 s) compared to that of visual lobar (328 ± 54 s, p < 0.001) and segmental (698 ± 147 s, p < 0.001) severity scores. CONCLUSION Utilizing AI-assisted quantification of pneumonia burden from chest CT scans offers a more accurate prediction of clinical deterioration in patients with COVID-19 compared to semi-quantitative severity scores, while requiring only a fraction of the analysis time. ADVANCES IN KNOWLEDGE Quantitative pneumonia burden assessed using AI demonstrated higher performance for predicting clinical deterioration compared to current semi-quantitative scoring systems. Such an AI system has the potential to be applied for image-based triage of COVID-19 patients in clinical practice.
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Affiliation(s)
| | - Aditya Killekar
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | | | - Andrew Lin
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sebastien Cadet
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Priscilla McElhinney
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Cato Chan
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Michelle C. Williams
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Barry D. Pressman
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Peter Julien
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Peter Chen
- Department of Medicine, Women’s Guild Lung Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Nicola Gaibazzi
- Cardiology, Azienda Ospedaliero-Universitaria di Parma, Parma, Italy
| | | | | | - Cecilia Agalbato
- Centro Cardiologico Monzino IRCCS, University of Milan, Milan, Italy
| | - Jiro Munechika
- Division of Radiology, Showa University School of Medicine, Tokyo, Japan
| | - Hidenari Matsumoto
- Division of Cardiology, Showa University School of Medicine, Tokyo, Japan
| | | | | | | | | | | | - Gianluca Pontone
- Centro Cardiologico Monzino IRCCS, University of Milan, Milan, Italy
| | | | - Piotr J. Slomka
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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19
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Frish N, Israel A, Ashkenazi S, Vinker S, Green I, Golan-Cohen A, Merzon E. The Association of Weight Reduction and Other Variables after Bariatric Surgery with the Likelihood of SARS-CoV-2 Infection. J Clin Med 2023; 12:4054. [PMID: 37373747 DOI: 10.3390/jcm12124054] [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: 03/27/2023] [Revised: 06/06/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND AND AIMS Although obesity has been confirmed as a risk factor for SARS-CoV-2 infection and its severity, the role of post-bariatric surgery (BS) variables and the infection is unclear. We, therefore, aimed to study comprehensively the relationship between the extent of weight reduction after surgery and other demographic, clinical, and laboratory variables with the rates of SARS-CoV-2 infection. METHODS A population-based cross-sectional study was performed, utilizing advanced tracking methodologies on the computerized database of a nation-wide health maintenance organization (HMO). The study population included all HMO members aged ≥18 years that had been tested at least once for SARS-CoV-2 during the study period and underwent BS at least one year before their testing. RESULTS Of the total 3038 individuals who underwent BS, 2697 (88.78%) were positive for SARS-CoV-2 infection and 341 (11.22%) were negative. Multivariate regression analysis demonstrated that the body mass index and the amount of weight reduction after the BS were not related to the likelihood of SARS-CoV-2 infection. Post-operative low socioeconomic status (SES) and vitamin D3 deficiency were associated with significant and independent increased rates of SARS-CoV-2 infection (odds ratio [OR] 1.56, 95% confidence interval [CI], 1.19-2.03, p < 0.001; and OR 1.55, 95% CI, 1.18-2.02, p < 0.001; respectively). Post-operative physical activity > 3 times/week was associated with a significant and independent reduced rate of SARS-CoV-2 infection (OR 0.51, 95% CI, 0.35-0.73, p < 0.001). CONCLUSION Post-BS vitamin D3 deficiency, SES, and physical activity, but not the amount of weight reduction, were significantly associated with the rates of SARS-CoV-2 infection. Healthcare workers should be aware of these associations after BS and intervene accordingly.
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Affiliation(s)
- Noam Frish
- Adelson School of Medicine, Ariel University, Ariel 40700, Israel
| | - Ariel Israel
- Leumit Health Services, Tel Aviv 64738, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Shai Ashkenazi
- Adelson School of Medicine, Ariel University, Ariel 40700, Israel
| | - Shlomo Vinker
- Leumit Health Services, Tel Aviv 64738, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Ilan Green
- Leumit Health Services, Tel Aviv 64738, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Avivit Golan-Cohen
- Leumit Health Services, Tel Aviv 64738, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Eugene Merzon
- Adelson School of Medicine, Ariel University, Ariel 40700, Israel
- Leumit Health Services, Tel Aviv 64738, Israel
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20
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Musso G, Taliano C, De Iuliis M, Paschetta E, Fonti C, Ferraris A, Druetta M, Vianou IS, Ranghino F, Riedo F, Deangelis D, Tirabassi G. Mechanical power normalized to aerated lung predicts noninvasive ventilation failure and death and contributes to the benefits of proning in COVID-19 hypoxemic respiratory failure. EPMA J 2023:1-39. [PMID: 37359998 PMCID: PMC10256581 DOI: 10.1007/s13167-023-00325-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 05/15/2023] [Indexed: 06/28/2023]
Abstract
Background Concern exists that noninvasive ventilation (NIV) may promote ventilation-induced lung injury(VILI) and worsen outcome in acute hypoxemic respiratory failure (AHRF). Different individual ventilatory variables have been proposed to predict clinical outcomes, with inconsistent results.Mechanical power (MP), a measure of the energy transfer rate from the ventilator to the respiratory system during mechanical ventilation, might provide solutions for this issue in the framework of predictive, preventive and personalized medicine (PPPM). We explored (1) the impact of ventilator-delivered MP normalized to well-aerated lung (MPWAL) on physio-anatomical and clinical responses to NIV in COVID-19-related AHRF and (2) the effect of prone position(PP) on MPWAL. Methods We analyzed 216 noninvasively ventilated COVID-19 patients (108 patients receiving PP + NIV and 108 propensity score-matched patients receiving supine NIV) with moderate-to-severe(paO2/FiO2 ratio < 200) AHRF enrolled in the PRO-NIV controlled non-randomized study (ISRCTN23016116).Quantification of differentially aerated lung volumes by lung ultrasonography (LUS) was validated against CT scans. Respiratory parameters were hourly recorded, ABG were performed 1 h after each postural change. Time-weighed average values of ventilatory variables, including MPWAL, and gas exchange parameters (paO2/FiO2 ratio, dead space indices) were calculated for each ventilatory session. LUS and circulating biomarkers were assessed daily. Results Compared with supine position, PP was associated with a 34% MPWAL reduction, attributable largely to an absolute MP reduction and secondly to an enhanced lung reaeration.Patients receiving a high MPWAL during the 1st 24 h of NIV [MPWAL(day 1)] had higher 28-d NIV failure (HR = 4.33,95%CI:3.09 - 5.98) and death (HR = 5.17,95%CI: 3.01 - 7.35) risks than those receiving a low MPWAL(day 1).In Cox multivariate analyses, MPWAL(day 1) remained independently associated with 28-d NIV failure (HR = 1.68,95%CI:1.15-2.41) and death (HR = 1.69,95%CI:1.22-2.32).MPWAL(day 1) outperformed other power measures and ventilatory variables as predictor of 28-d NIV failure (AUROC = 0.89;95%CI:0.85-0.93) and death (AUROC = 0.89;95%CI:0.85-0.94).MPWAL(day 1) predicted also gas exchange, ultrasonographic and inflammatory biomarker responses, as markers of VILI, on linear multivariate analysis. Conclusions In the framework of PPPM, early bedside MPWAL calculation may provide added value to predict response to NIV and guide subsequent therapeutic choices i.e. prone position adoption during NIV or upgrading to invasive ventilation, to reduce hazardous MPWAL delivery, prevent VILI progression and improve clinical outcomes in COVID-19-related AHRF. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-023-00325-5.
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Affiliation(s)
- Giovanni Musso
- Emergency Medicine Department, San Luigi Gonzaga Hospital, Regione Gonzole 10, Orbassano, 10043 Turin, TO Italy
| | - Claudio Taliano
- Emergency Medicine Department, HUMANITAS Gradenigo, Turin, Italy
| | | | - Elena Paschetta
- Emergency Medicine Department, HUMANITAS Gradenigo, Turin, Italy
| | - Caterina Fonti
- Emergency Medicine Department, San Luigi Gonzaga Hospital, Regione Gonzole 10, Orbassano, 10043 Turin, TO Italy
| | | | - Marta Druetta
- Emergency Medicine Department, HUMANITAS Gradenigo, Turin, Italy
| | | | | | - Federica Riedo
- Emergency Medicine Department, HUMANITAS Gradenigo, Turin, Italy
| | - Davide Deangelis
- Emergency Medicine Department, HUMANITAS Gradenigo, Turin, Italy
| | - Gloria Tirabassi
- Department of Biomedical Sciences, HUMANITAS University, Via Rita Levi Montalcini 4, 20072 Milan, Pieve Emanuele Italy
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21
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Salehi Z, Motlagh Ghoochani BFN, Hasani Nourian Y, Jamalkandi SA, Ghanei M. The controversial effect of smoking and nicotine in SARS-CoV-2 infection. ALLERGY, ASTHMA, AND CLINICAL IMMUNOLOGY : OFFICIAL JOURNAL OF THE CANADIAN SOCIETY OF ALLERGY AND CLINICAL IMMUNOLOGY 2023; 19:49. [PMID: 37264452 PMCID: PMC10234254 DOI: 10.1186/s13223-023-00797-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 04/18/2023] [Indexed: 06/03/2023]
Abstract
The effects of nicotine and cigarette smoke in many diseases, notably COVID-19 infection, are being debated more frequently. The current basic data for COVID-19 is increasing and indicating the higher risk of COVID-19 infections in smokers due to the overexpression of corresponding host receptors to viral entry. However, current multi-national epidemiological reports indicate a lower incidence of COVID-19 disease in smokers. Current data indicates that smokers are more susceptible to some diseases and more protective of some other. Interestingly, nicotine is also reported to play a dual role, being both inflammatory and anti-inflammatory. In the present study, we tried to investigate the effect of pure nicotine on various cells involved in COVID-19 infection. We followed an organ-based systematic approach to decipher the effect of nicotine in damaged organs corresponding to COVID-19 pathogenesis (12 related diseases). Considering that the effects of nicotine and cigarette smoke are different from each other, it is necessary to be careful in generalizing the effects of nicotine and cigarette to each other in the conducted researches. The generalization and the undifferentiation of nicotine from smoke is a significant bias. Moreover, different doses of nicotine stimulate different effects (dose-dependent response). In addition to further assessing the role of nicotine in COVID-19 infection and any other cases, a clever assessment of underlying diseases should also be considered to achieve a guideline for health providers and a personalized approach to treatment.
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Affiliation(s)
- Zahra Salehi
- Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | | | - Yazdan Hasani Nourian
- Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Sadegh Azimzadeh Jamalkandi
- Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
| | - Mostafa Ghanei
- Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
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22
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Szabó M, Kardos Z, Kostyál L, Tamáska P, Oláh C, Csánky E, Szekanecz Z. The importance of chest CT severity score and lung CT patterns in risk assessment in COVID-19-associated pneumonia: a comparative study. Front Med (Lausanne) 2023; 10:1125530. [PMID: 37265487 PMCID: PMC10229788 DOI: 10.3389/fmed.2023.1125530] [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: 12/16/2022] [Accepted: 05/02/2023] [Indexed: 06/03/2023] Open
Abstract
Introduction Chest computed tomography (CT) is suitable to assess morphological changes in the lungs. Chest CT scoring systems (CCTS) have been developed and use in order to quantify the severity of pulmonary involvement in COVID-19. CCTS has also been correlated with clinical outcomes. Here we wished to use a validated, relatively simple CTSS to assess chest CT patterns and to correlate CTSS with clinical outcomes in COVID-19. Patients and methods Altogether 227 COVID-19 cases underwent chest CT scanning using a 128 multi-detector CT scanner (SOMATOM Go Top, Siemens Healthineers, Germany). Specific pathological features, such as ground-glass opacity (GGO), crazy-paving pattern, consolidation, fibrosis, subpleural lines, pleural effusion, lymphadenopathy and pulmonary embolism were evaluated. CTSS developed by Pan et al. (CTSS-Pan) was applied. CTSS and specific pathologies were correlated with demographic, clinical and laboratory data, A-DROP scores, as well as outcome measures. We compared CTSS-Pan to two other CT scoring systems. Results The mean CTSS-Pan in the 227 COVID-19 patients was 14.6 ± 6.7. The need for ICU admission (p < 0.001) and death (p < 0.001) were significantly associated with higher CTSS. With respect to chest CT patterns, crazy-paving pattern was significantly associated with ICU admission. Subpleural lines exerted significant inverse associations with ICU admission and ventilation. Lymphadenopathy was associated with all three outcome parameters. Pulmonary embolism led to ICU admission. In the ROC analysis, CTSS>18.5 significantly predicted admission to ICU (p = 0.026) and CTSS>19.5 was the cutoff for increased mortality (p < 0.001). CTSS-Pan and the two other CTSS systems exerted similar performance. With respect to clinical outcomes, CTSS-Pan might have the best performance. Conclusion CTSS may be suitable to assess severity and prognosis of COVID-19-associated pneumonia. CTSS and specific chest CT patterns may predict the need for ventilation, as well as mortality in COVID-19. This can help the physician to guide treatment strategies in COVID-19, as well as other pulmonary infections.
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Affiliation(s)
- Miklós Szabó
- Department of Pulmonology, Borsod Academic County Hospital, Miskolc, Hungary
| | - Zsófia Kardos
- Department of Rheumatology, Borsod Academic County Hospital, Miskolc, Hungary
- Faculty of Health Sciences, University of Miskolc, Miskolc, Hungary
| | - László Kostyál
- Department of Radiology, Borsod Academic County Hospital, Miskolc, Hungary
| | - Péter Tamáska
- Department of Radiology, Borsod Academic County Hospital, Miskolc, Hungary
| | - Csaba Oláh
- Department of Radiology, Borsod Academic County Hospital, Miskolc, Hungary
| | - Eszter Csánky
- Department of Pulmonology, Borsod Academic County Hospital, Miskolc, Hungary
| | - Zoltán Szekanecz
- Department of Rheumatology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
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23
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Calandriello L, De Lorenzis E, Cicchetti G, D'Abronzo R, Infante A, Castaldo F, Del Ciello A, Farchione A, Gremese E, Marano R, Natale L, D'Agostino MA, Bosello SL, Larici AR. Extension of Lung Damage at Chest Computed Tomography in Severely Ill COVID-19 Patients Treated with Interleukin-6 Receptor Blockers Correlates with Inflammatory Cytokines Production and Prognosis. Tomography 2023; 9:981-994. [PMID: 37218940 DOI: 10.3390/tomography9030080] [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: 04/06/2023] [Revised: 04/27/2023] [Accepted: 04/30/2023] [Indexed: 05/24/2023] Open
Abstract
Elevated inflammatory markers are associated with severe coronavirus disease 2019 (COVID-19), and some patients benefit from Interleukin (IL)-6 pathway inhibitors. Different chest computed tomography (CT) scoring systems have shown a prognostic value in COVID-19, but not specifically in anti-IL-6-treated patients at high risk of respiratory failure. We aimed to explore the relationship between baseline CT findings and inflammatory conditions and to evaluate the prognostic value of chest CT scores and laboratory findings in COVID-19 patients specifically treated with anti-IL-6. Baseline CT lung involvement was assessed in 51 hospitalized COVID-19 patients naive to glucocorticoids and other immunosuppressants using four CT scoring systems. CT data were correlated with systemic inflammation and 30-day prognosis after anti-IL-6 treatment. All the considered CT scores showed a negative correlation with pulmonary function and a positive one with C-reactive protein (CRP), IL-6, IL-8, and Tumor Necrosis Factor α (TNF-α) serum levels. All the performed scores were prognostic factors, but the disease extension assessed by the six-lung-zone CT score (S24) was the only independently associated with intensive care unit (ICU) admission (p = 0.04). In conclusion, CT involvement correlates with laboratory inflammation markers and is an independent prognostic factor in COVID-19 patients representing a further tool to implement prognostic stratification in hospitalized patients.
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Affiliation(s)
- Lucio Calandriello
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Diagnostic Imaging Area, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, L.go Agostino Gemelli 8, 00168 Rome, Italy
| | - Enrico De Lorenzis
- Unit of Rheumatology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, L.go Agostino Gemelli 8, 00168 Rome, Italy
| | - Giuseppe Cicchetti
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Diagnostic Imaging Area, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, L.go Agostino Gemelli 8, 00168 Rome, Italy
| | - Rosa D'Abronzo
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Diagnostic Imaging Area, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, L.go Agostino Gemelli 8, 00168 Rome, Italy
| | - Amato Infante
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Diagnostic Imaging Area, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, L.go Agostino Gemelli 8, 00168 Rome, Italy
| | - Federico Castaldo
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Diagnostic Imaging Area, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, L.go Agostino Gemelli 8, 00168 Rome, Italy
| | - Annemilia Del Ciello
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Diagnostic Imaging Area, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, L.go Agostino Gemelli 8, 00168 Rome, Italy
| | - Alessandra Farchione
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Diagnostic Imaging Area, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, L.go Agostino Gemelli 8, 00168 Rome, Italy
| | - Elisa Gremese
- Division of Clinical Immunology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, L.go Agostino Gemelli 8, 00168 Rome, Italy
- Department of Geriatric and Orthopaedic Sciences, Università Cattolica del Sacro Cuore, L.go Francesco Vito 1, 00168 Rome, Italy
| | - Riccardo Marano
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Diagnostic Imaging Area, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, L.go Agostino Gemelli 8, 00168 Rome, Italy
- Department of Radiological and Hematological Sciences, Section of Radiology, Università Cattolica del Sacro Cuore, L.go Francesco Vito 1, 00168 Rome, Italy
| | - Luigi Natale
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Diagnostic Imaging Area, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, L.go Agostino Gemelli 8, 00168 Rome, Italy
- Department of Radiological and Hematological Sciences, Section of Radiology, Università Cattolica del Sacro Cuore, L.go Francesco Vito 1, 00168 Rome, Italy
| | - Maria Antonietta D'Agostino
- Unit of Rheumatology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, L.go Agostino Gemelli 8, 00168 Rome, Italy
- Department of Geriatric and Orthopaedic Sciences, Università Cattolica del Sacro Cuore, L.go Francesco Vito 1, 00168 Rome, Italy
| | - Silvia Laura Bosello
- Unit of Rheumatology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, L.go Agostino Gemelli 8, 00168 Rome, Italy
- Department of Geriatric and Orthopaedic Sciences, Università Cattolica del Sacro Cuore, L.go Francesco Vito 1, 00168 Rome, Italy
| | - Anna Rita Larici
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Diagnostic Imaging Area, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, L.go Agostino Gemelli 8, 00168 Rome, Italy
- Department of Radiological and Hematological Sciences, Section of Radiology, Università Cattolica del Sacro Cuore, L.go Francesco Vito 1, 00168 Rome, Italy
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24
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Colombi D, Petrini M, Morelli N, Silva M, Milanese G, Sverzellati N, Michieletti E. Are Interstitial Lung Abnormalities a Prognostic Factor of Worse Outcome in COVID-19 Pneumonia? J Thorac Imaging 2023; 38:137-144. [PMID: 36917514 DOI: 10.1097/rti.0000000000000704] [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: 03/15/2023]
Abstract
PURPOSE To assess the association between interstitial lung abnormalities (ILAs) and worse outcome in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease (COVID-19)-related pneumonia. MATERIALS AND METHODS The study included patients older than 18 years, who were admitted at the emergency department between February 29 and April 30, 2020 with findings of COVID-19 pneumonia at chest computed tomography (CT), with positive reverse-transcription polymerase chain reaction nasal-pharyngeal swab for SARS-CoV-2, and with the availability of prepandemic chest CT. Prepandemic CTs were reviewed for the presence of ILAs, categorized as fibrotic in cases with associated architectural distortion, bronchiectasis, or honeycombing. Worse outcome was defined as intensive care unit (ICU) admission or death. Cox proportional hazards regression analysis was used to test the association between ICU admission/death and preexisting ILAs. RESULTS The study included 147 patients (median age 73 y old; 95% CIs: 71-76-y old; 29% females). On prepandemic CTs, ILA were identified in 33/147 (22%) of the patients, 63% of which were fibrotic ILAs. Fibrotic ILAs were associated with higher risk of ICU admission or death in patients with COVID-19 pneumonia (hazard ratios: 2.73, 95% CIs: 1.50-4.97, P =0.001). CONCLUSIONS In patients affected by COVID-19 pneumonia, preexisting fibrotic ILAs were an independent predictor of worse prognosis, with a 2.7 times increased risk of ICU admission or death. Chest CT scans obtained before the diagnosis of COVID-19 pneumonia should be carefully reviewed for the presence and characterization of ILAs.
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Affiliation(s)
- Davide Colombi
- Department of Radiological Functions, Azienda USL Piacenza, Piacenza
| | - Marcello Petrini
- Department of Radiological Functions, Azienda USL Piacenza, Piacenza
| | - Nicola Morelli
- Department of Radiological Functions, Azienda USL Piacenza, Piacenza
| | - Mario Silva
- Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Gianluca Milanese
- Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Nicola Sverzellati
- Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
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25
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Di Napoli A, Tagliente E, Pasquini L, Cipriano E, Pietrantonio F, Ortis P, Curti S, Boellis A, Stefanini T, Bernardini A, Angeletti C, Ranieri SC, Franchi P, Voicu IP, Capotondi C, Napolitano A. 3D CT-Inclusive Deep-Learning Model to Predict Mortality, ICU Admittance, and Intubation in COVID-19 Patients. J Digit Imaging 2023; 36:603-616. [PMID: 36450922 PMCID: PMC9713092 DOI: 10.1007/s10278-022-00734-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 10/08/2022] [Accepted: 10/30/2022] [Indexed: 12/02/2022] Open
Abstract
Chest CT is a useful initial exam in patients with coronavirus disease 2019 (COVID-19) for assessing lung damage. AI-powered predictive models could be useful to better allocate resources in the midst of the pandemic. Our aim was to build a deep-learning (DL) model for COVID-19 outcome prediction inclusive of 3D chest CT images acquired at hospital admission. This retrospective multicentric study included 1051 patients (mean age 69, SD = 15) who presented to the emergency department of three different institutions between 20th March 2020 and 20th January 2021 with COVID-19 confirmed by real-time reverse transcriptase polymerase chain reaction (RT-PCR). Chest CT at hospital admission were evaluated by a 3D residual neural network algorithm. Training, internal validation, and external validation groups included 608, 153, and 290 patients, respectively. Images, clinical, and laboratory data were fed into different customizations of a dense neural network to choose the best performing architecture for the prediction of mortality, intubation, and intensive care unit (ICU) admission. The AI model tested on CT and clinical features displayed accuracy, sensitivity, specificity, and ROC-AUC, respectively, of 91.7%, 90.5%, 92.4%, and 95% for the prediction of patient's mortality; 91.3%, 91.5%, 89.8%, and 95% for intubation; and 89.6%, 90.2%, 86.5%, and 94% for ICU admission (internal validation) in the testing cohort. The performance was lower in the validation cohort for mortality (71.7%, 55.6%, 74.8%, 72%), intubation (72.6%, 74.7%, 45.7%, 64%), and ICU admission (74.7%, 77%, 46%, 70%) prediction. The addition of the available laboratory data led to an increase in sensitivity for patient's mortality (66%) and specificity for intubation and ICU admission (50%, 52%, respectively), while the other metrics maintained similar performance results. We present a deep-learning model to predict mortality, ICU admittance, and intubation in COVID-19 patients. KEY POINTS: • 3D CT-based deep learning model predicted the internal validation set with high accuracy, sensibility and specificity (> 90%) mortality, ICU admittance, and intubation in COVID-19 patients. • The model slightly increased prediction results when laboratory data were added to the analysis, despite data imbalance. However, the model accuracy dropped when CT images were not considered in the analysis, implying an important role of CT in predicting outcomes.
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Affiliation(s)
- Alberto Di Napoli
- Radiology Department, Castelli Hospital, 00040, Ariccia, Italy
- NESMOS Department, Neuroradiology Unit, Sant'Andrea Hospital, Sapienza University, Via Grottarossa 1035, 00189, 00165, Rome, Italy
| | - Emanuela Tagliente
- Medical Physics Department, Bambino Gesù Children's Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), 00165, Rome, Italy
| | - Luca Pasquini
- NESMOS Department, Neuroradiology Unit, Sant'Andrea Hospital, Sapienza University, Via Grottarossa 1035, 00189, 00165, Rome, Italy.
- Radiology Department, Neuroradiology Service, Memorial Sloan Kettering Cancer Center, New York, NY, 1275, USA.
| | - Enrica Cipriano
- COVID Medicine Department, Castelli Hospital, 00040, Ariccia, Italy
| | | | - Piermaria Ortis
- COVID Intensive Care Unit, Castelli Hospital, 00040, Ariccia, Italy
| | - Simona Curti
- Emergency Department, Castelli Hospital, 00040, Ariccia, Italy
| | - Alessandro Boellis
- Radiology Department, Sant'Andrea Civil Hospital, 19121, La Spezia, Italy
| | - Teseo Stefanini
- Radiology Department, Sant'Andrea Civil Hospital, 19121, La Spezia, Italy
| | - Antonio Bernardini
- Radiology Department, Giuseppe Mazzini Civil Hospital, 64100, Teramo, Italy
| | - Chiara Angeletti
- Anestesiology, Intensive Care and Pain Medicine, Emergency Department, Giuseppe Mazzini Civil Hospital, 64100, Teramo, Italy
| | | | - Paola Franchi
- Radiology Department, Giuseppe Mazzini Civil Hospital, 64100, Teramo, Italy
| | - Ioan Paul Voicu
- Radiology Department, Giuseppe Mazzini Civil Hospital, 64100, Teramo, Italy
| | - Carlo Capotondi
- Radiology Department, Castelli Hospital, 00040, Ariccia, Italy
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children's Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), 00165, Rome, Italy
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26
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Serum and Urinary Biomarkers in COVID-19 Patients with or without Baseline Chronic Kidney Disease. J Pers Med 2023; 13:jpm13030382. [PMID: 36983566 PMCID: PMC10051063 DOI: 10.3390/jpm13030382] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 02/20/2023] [Accepted: 02/21/2023] [Indexed: 02/25/2023] Open
Abstract
In a prospective, observational, non-interventional, single-center study, we assessed various plasma and urinary biomarkers of kidney injury (neutrophil gelatinase-associated Lipocain [NGAL], kidney-injury molecule-1 [KIM-1], and interleukin-18 [IL-18]); inflammation (IL-6, C-reactive protein [CRP]); plus angiotensin converting enzyme 2 (ACE2) in 120 COVID-19 patients (of whom 70 had chronic kidney disease (CKD) at emergency-department (ED) admission). Our aim was to correlate the biomarkers with the outcomes (death, acute kidney injury [AKI]). All patients had received a chest-CT scan at admission to calculate the severity score (0–5). Biomarkers were also assessed in healthy volunteers and non-COVID-19-CKD patients. These biomarkers statistically differed across subgroups, i.e., they were significantly increased in COVID-19 patients, except for urinary (u)KIM1 and uIL-18. Amongst the biomarkers, only IL-6 was independently associated with mortality, along with AKI and not using remdesivir. Regarding the prediction of AKI, only IL-6 and uKIM1 were significantly elevated in patients presenting with AKI. However, AKI could not be predicted. Having high baseline IL-6 levels was associated with subsequent ventilation requirement and death. The mortality rate was almost 90% when the chest CT-scan severity score was 3 or 4 vs. 6.8% when the severity score was 0–2 (p < 0.0001).
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27
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Association of subpleural ground-glass opacities with respiratory failure and RNAemia in COVID-19. Eur Radiol 2023:10.1007/s00330-023-09427-0. [PMID: 36735038 PMCID: PMC9896440 DOI: 10.1007/s00330-023-09427-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 11/03/2022] [Accepted: 01/02/2023] [Indexed: 02/04/2023]
Abstract
OBJECTIVES To examine the radiological patterns specifically associated with hypoxemic respiratory failure in patients with coronavirus disease (COVID-19). METHODS We enrolled patients with COVID-19 confirmed by qPCR in this prospective observational cohort study. We explored the association of clinical, radiological, and microbiological data with the development of hypoxemic respiratory failure after COVID-19 onset. Semi-quantitative CT scores and dominant CT patterns were retrospectively determined for each patient. The microbiological evaluation included checking the SARS-CoV-2 viral load by qPCR using nasal swab and serum specimens. RESULTS Of the 214 eligible patients, 75 developed hypoxemic respiratory failure and 139 did not. The CT score was significantly higher in patients who developed hypoxemic respiratory failure than in those did not (median [interquartile range]: 9 [6-14] vs 0 [0-3]; p < 0.001). The dominant CT patterns were subpleural ground-glass opacities (GGOs) extending beyond the segmental area (n = 44); defined as "extended GGOs." Multivariable analysis showed that hypoxemic respiratory failure was significantly associated with extended GGOs (odds ratio [OR] 29.6; 95% confidence interval [CI], 9.3-120; p < 0.001), and a CT score > 4 (OR 12.7; 95% CI, 5.3-33; p < 0.001). The incidence of RNAemia was significantly higher in patients with extended GGOs (58.3%) than in those without any pulmonary lesion (14.7%; p < 0.001). CONCLUSIONS Extended GGOs along the subpleural area were strongly associated with hypoxemia and viremia in patients with COVID-19. KEY POINTS • Extended ground-glass opacities (GGOs) along the subpleural area and a CT score > 4, in the early phase of COVID-19, were independently associated with the development of hypoxemic respiratory failure. • The absence of pulmonary lesions on CT in the early phase of COVID-19 was associated with a lower risk of developing hypoxemic respiratory failure. • Compared to patients with other CT findings, the extended GGOs and a higher CT score were also associated with a higher incidence of RNAemia.
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28
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Rizzetto F, Gnocchi G, Travaglini F, Di Rocco G, Rizzo A, Carbonaro LA, Vanzulli A. Impact of COVID-19 Pandemic on the Workload of Diagnostic Radiology: A 2-Year Observational Study in a Tertiary Referral Hospital. Acad Radiol 2023; 30:276-284. [PMID: 35781400 PMCID: PMC9186449 DOI: 10.1016/j.acra.2022.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 05/28/2022] [Accepted: 06/02/2022] [Indexed: 01/11/2023]
Abstract
RATIONALE AND OBJECTIVES To evaluate the impact of COVID-19 pandemic on diagnostic imaging workload in a tertiary referral hospital. MATERIALS AND METHODS Radiological examinations performed in pre-pandemic period (2015-2019) and in pandemic period (2020-2021) were retrospectively included. Based on epidemiological data and restriction measures, four pandemic waves were identified. For each of them, the relative change (RC) in workload was calculated and compared to the 5-year averaged workload in the corresponding pre-COVID-19 periods. Workload variations were also assessed according to technique (radiographs, CT, MRI, ultrasounds), body district (chest, abdomen, breast, musculoskeletal, head/neck, brain/spine, cardiovascular) and care setting (inpatient, outpatient, emergency imaging, pre-admission imaging). RESULTS A total of 1384380 examinations were included. In 2020 imaging workload decreased (RC = -11%) compared to the average of the previous 5 years, while in 2021 only a minimal variation (RC = +1%) was observed. During first wave, workload was reduced for all modalities, body regions and types of care setting (RC from -86% to -10%), except for CT (RC = +3%). In subsequent waves, workload increased only for CT (mean RC = +18%) and, regarding body districts, for breast (mean RC = +23%) and cardiovascular imaging (mean RC = +23%). For all other categories, a workload comparable to pre-pandemic period was almost only restored in the fourth wave. In all pandemics periods workload decrease was mainly due to reduced outpatient activity (p < 0.001), while inpatient and emergency imaging was increased (p < 0.001). CONCLUSION Evaluating imaging workload changes throughout COVID-19 pandemic helps to understand the response dynamics of radiological services and to improve institutional preparedness to face extreme contingency.
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Affiliation(s)
- Francesco Rizzetto
- Department of Radiology (F.R., G.G., F.T., G.D.R., A.R., L.A.C., A.V.), ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy; Postgraduation School of Diagnostic and Interventional Radiology (F.R., G.G., G.D.R., A.R.), University of Milan, via Festa del Perdono 7, 20122, Milan, Italy; Department of Oncology and Hemato-Oncology (L.A.C., A.V.), University of Milan, via Festa del Perdono 7, 20122, Milan, Italy.
| | - Giulia Gnocchi
- Department of Radiology (F.R., G.G., F.T., G.D.R., A.R., L.A.C., A.V.), ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy; Postgraduation School of Diagnostic and Interventional Radiology (F.R., G.G., G.D.R., A.R.), University of Milan, via Festa del Perdono 7, 20122, Milan, Italy; Department of Oncology and Hemato-Oncology (L.A.C., A.V.), University of Milan, via Festa del Perdono 7, 20122, Milan, Italy
| | - Francesca Travaglini
- Department of Radiology (F.R., G.G., F.T., G.D.R., A.R., L.A.C., A.V.), ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy; Postgraduation School of Diagnostic and Interventional Radiology (F.R., G.G., G.D.R., A.R.), University of Milan, via Festa del Perdono 7, 20122, Milan, Italy; Department of Oncology and Hemato-Oncology (L.A.C., A.V.), University of Milan, via Festa del Perdono 7, 20122, Milan, Italy
| | - Gabriella Di Rocco
- Department of Radiology (F.R., G.G., F.T., G.D.R., A.R., L.A.C., A.V.), ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy; Postgraduation School of Diagnostic and Interventional Radiology (F.R., G.G., G.D.R., A.R.), University of Milan, via Festa del Perdono 7, 20122, Milan, Italy; Department of Oncology and Hemato-Oncology (L.A.C., A.V.), University of Milan, via Festa del Perdono 7, 20122, Milan, Italy
| | - Aldo Rizzo
- Department of Radiology (F.R., G.G., F.T., G.D.R., A.R., L.A.C., A.V.), ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy; Postgraduation School of Diagnostic and Interventional Radiology (F.R., G.G., G.D.R., A.R.), University of Milan, via Festa del Perdono 7, 20122, Milan, Italy; Department of Oncology and Hemato-Oncology (L.A.C., A.V.), University of Milan, via Festa del Perdono 7, 20122, Milan, Italy
| | - Luca Alessandro Carbonaro
- Department of Radiology (F.R., G.G., F.T., G.D.R., A.R., L.A.C., A.V.), ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy; Postgraduation School of Diagnostic and Interventional Radiology (F.R., G.G., G.D.R., A.R.), University of Milan, via Festa del Perdono 7, 20122, Milan, Italy; Department of Oncology and Hemato-Oncology (L.A.C., A.V.), University of Milan, via Festa del Perdono 7, 20122, Milan, Italy
| | - Angelo Vanzulli
- Department of Radiology (F.R., G.G., F.T., G.D.R., A.R., L.A.C., A.V.), ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy; Postgraduation School of Diagnostic and Interventional Radiology (F.R., G.G., G.D.R., A.R.), University of Milan, via Festa del Perdono 7, 20122, Milan, Italy; Department of Oncology and Hemato-Oncology (L.A.C., A.V.), University of Milan, via Festa del Perdono 7, 20122, Milan, Italy
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Cereser L, Passarotti E, Tullio A, Patruno V, Monterubbiano L, Apa P, Zuiani C, Girometti R. Can a chest HRCT-based crash course on COVID-19 cases make inexperienced thoracic radiologists readily available to face the next pandemic? Clin Imaging 2023; 94:1-8. [PMID: 36434939 PMCID: PMC9678839 DOI: 10.1016/j.clinimag.2022.11.010] [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: 08/08/2022] [Revised: 11/01/2022] [Accepted: 11/14/2022] [Indexed: 11/23/2022]
Abstract
OBJECTIVE To test the inter-reader agreement in assessing lung disease extent, HRCT signs, and Radiological Society of North America (RSNA) categorization between a chest-devoted radiologist (CR) and two HRCT-naïve radiology residents (RR1 and RR2) after the latter attended a COVID-19-based chest high-resolution computed tomography (HRCT) "crash course". METHODS The course was built by retrospective inclusion of 150 patients who underwent HRCT for COVID-19 pneumonia between November 2020 and January 2021. During a first 10-days-long "training phase", RR1 and RR2 read a pool of 100/150 HRCTs, receiving day-by-day access to CR reports as feedback. In the subsequent 2-days-long "test phase", they were asked to report 50/150 HRCTs with no feedback. Test phase reports of RR1/RR2 were then compared with CR using unweighted or linearly-weighted Cohen's kappa (k) statistic and intraclass correlation coefficient (ICC). RESULTS We observed almost perfect agreement in assessing disease extent between RR1-CR (k = 0.83, p < 0.001) and RR2-CR (k = 0.88, p < 0.001). The agreement between RR1-CR and RR2-CR on consolidation, crazy paving pattern, organizing pneumonia (OP) pattern, and pulmonary artery (PA) diameter was substantial (k = 0.65 and k = 0.68), moderate (k = 0.42 and k = 0.51), slight (k = 0.10 and k = 0.20), and good-to-excellent (ICC = 0.87 and ICC = 0.91), respectively. The agreement in providing RSNA categorization was moderate for R1 versus CR (k = 0.56) and substantial for R2 versus CR (k = 0.67). CONCLUSION HRCT-naïve readers showed an acceptable overall agreement with CR, supporting the hypothesis that a crash course can be a tool to readily make non-subspecialty radiologists available to cooperate in reading high burden of HRCT examinations during a pandemic/epidemic.
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Affiliation(s)
- Lorenzo Cereser
- Institute of Radiology, Department of Medicine, University of Udine, University Hospital “S. Maria della Misericordia”, p.le S. Maria della Misericordia, 15, 33100 Udine, Italy,Corresponding author
| | - Emanuele Passarotti
- Institute of Radiology, Department of Medicine, University of Udine, University Hospital “S. Maria della Misericordia”, p.le S. Maria della Misericordia, 15, 33100 Udine, Italy
| | - Annarita Tullio
- Institute of Hygiene and Clinical Epidemiology, University Hospital “S. Maria della Misericordia”, p.le S. Maria della Misericordia, 15, 33100 Udine, Italy
| | - Vincenzo Patruno
- Pulmonology Department, “S. Maria della Misericordia” University Hospital, p.le S. Maria della Misericordia, 15, 33100 Udine, Italy
| | - Leonardo Monterubbiano
- Institute of Radiology, Department of Medicine, University of Udine, University Hospital “S. Maria della Misericordia”, p.le S. Maria della Misericordia, 15, 33100 Udine, Italy
| | - Pierpaolo Apa
- Institute of Radiology, Department of Medicine, University of Udine, University Hospital “S. Maria della Misericordia”, p.le S. Maria della Misericordia, 15, 33100 Udine, Italy
| | - Chiara Zuiani
- Institute of Radiology, Department of Medicine, University of Udine, University Hospital “S. Maria della Misericordia”, p.le S. Maria della Misericordia, 15, 33100 Udine, Italy
| | - Rossano Girometti
- Institute of Radiology, Department of Medicine, University of Udine, University Hospital “S. Maria della Misericordia”, p.le S. Maria della Misericordia, 15, 33100 Udine, Italy
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Zorzi G, Berta L, Rizzetto F, De Mattia C, Felisi MMJ, Carrazza S, Nerini Molteni S, Vismara C, Scaglione F, Vanzulli A, Torresin A, Colombo PE. Artificial intelligence for differentiating COVID-19 from other viral pneumonias on CT: comparative analysis of different models based on quantitative and radiomic approaches. Eur Radiol Exp 2023; 7:3. [PMID: 36690869 PMCID: PMC9870776 DOI: 10.1186/s41747-022-00317-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 12/15/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND To develop a pipeline for automatic extraction of quantitative metrics and radiomic features from lung computed tomography (CT) and develop artificial intelligence (AI) models supporting differential diagnosis between coronavirus disease 2019 (COVID-19) and other viral pneumonia (non-COVID-19). METHODS Chest CT of 1,031 patients (811 for model building; 220 as independent validation set (IVS) with positive swab for severe acute respiratory syndrome coronavirus-2 (647 COVID-19) or other respiratory viruses (384 non-COVID-19) were segmented automatically. A Gaussian model, based on the HU histogram distribution describing well-aerated and ill portions, was optimised to calculate quantitative metrics (QM, n = 20) in both lungs (2L) and four geometrical subdivisions (GS) (upper front, lower front, upper dorsal, lower dorsal; n = 80). Radiomic features (RF) of first (RF1, n = 18) and second (RF2, n = 120) order were extracted from 2L using PyRadiomics tool. Extracted metrics were used to develop four multilayer-perceptron classifiers, built with different combinations of QM and RF: Model1 (RF1-2L); Model2 (QM-2L, QM-GS); Model3 (RF1-2L, RF2-2L); Model4 (RF1-2L, QM-2L, GS-2L, RF2-2L). RESULTS The classifiers showed accuracy from 0.71 to 0.80 and area under the receiving operating characteristic curve (AUC) from 0.77 to 0.87 in differentiating COVID-19 versus non-COVID-19 pneumonia. Best results were associated with Model3 (AUC 0.867 ± 0.008) and Model4 (AUC 0.870 ± 0.011. For the IVS, the AUC values were 0.834 ± 0.008 for Model3 and 0.828 ± 0.011 for Model4. CONCLUSIONS Four AI-based models for classifying patients as COVID-19 or non-COVID-19 viral pneumonia showed good diagnostic performances that could support clinical decisions.
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Affiliation(s)
- Giulia Zorzi
- Postgraduate School of Medical Physics, Università degli Studi di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
- Department of Physics, INFN Sezione di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
| | - Luca Berta
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy.
| | - Francesco Rizzetto
- Postgraduate School of Diagnostic and Interventional Radiology, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy.
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy.
| | - Cristina De Mattia
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Marco Maria Jacopo Felisi
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Stefano Carrazza
- Department of Physics, INFN Sezione di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
- Department of Physics, Università degli Studi di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
| | - Silvia Nerini Molteni
- Chemical-Clinical and Microbiological Analyses, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Chiara Vismara
- Chemical-Clinical and Microbiological Analyses, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Francesco Scaglione
- Chemical-Clinical and Microbiological Analyses, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy
| | - Angelo Vanzulli
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy
| | - Alberto Torresin
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
- Department of Physics, INFN Sezione di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
- Department of Physics, Università degli Studi di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
| | - Paola Enrica Colombo
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
- Department of Physics, Università degli Studi di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
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Software-Based Assessment of Well-Aerated Lung at CT for Quantification of Predicted Pulmonary Function in Resected NSCLC. Life (Basel) 2023; 13:life13010198. [PMID: 36676147 PMCID: PMC9862480 DOI: 10.3390/life13010198] [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: 12/17/2022] [Revised: 12/27/2022] [Accepted: 01/03/2023] [Indexed: 01/11/2023] Open
Abstract
Background: To test the agreement between postoperative pulmonary function tests 12 months after surgery (mpo-PFTs) for non-small cell lung cancer (NSCLC) and predicted lung function based on the quantification of well-aerated lung (WAL) at staging CT (sCT). Methods: We included patients with NSCLC who underwent lobectomy or segmentectomy without a history of thoracic radiotherapy or chemotherapy treatment with the availability of PFTs at 12 months follow-up. Postoperative predictive (ppo) lung function was calculated using the resected lobe WAL (the lung volume between −950 and −750 HU) at sCT. The Spearman correlation coefficient (rho) and intraclass correlation coefficient (ICC) were used to the test the agreement between WAL ppo-PFTs and mpo-PFTs. Results: the study included 40 patients (68 years-old, IQR 62−74 years-old; 26/40, 65% males). The WAL ppo-forced expiratory volume in 1 s (FEV1) and the ppo-diffusing capacity of the lung for carbon monoxide (%DLCO) were significantly correlated with corresponding mpo-PFTs (rho = 0.842 and 0.717 respectively; p < 0.001). The agreement with the corresponding mpo-PFTs of WAL ppo-FEV1 was excellent (ICC 0.904), while it was good (ICC 0.770) for WAL ppo-%DLCO. Conclusions: WAL ppo-FEV1 and WAL ppo-%DLCO at sCT showed, respectively, excellent and good agreement with corresponding mpo-PFTs measured 12 months after surgery for NSCLC. WAL is an easy parameter obtained by staging CT that can be used to estimate post-resection lung function for patients with borderline pulmonary function undergoing lung surgery.
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Walston SL, Matsumoto T, Miki Y, Ueda D. Artificial intelligence-based model for COVID-19 prognosis incorporating chest radiographs and clinical data; a retrospective model development and validation study. Br J Radiol 2022; 95:20220058. [PMID: 36193755 PMCID: PMC9733620 DOI: 10.1259/bjr.20220058] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 08/19/2022] [Accepted: 08/23/2022] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVES The purpose of this study was to develop an artificial intelligence-based model to prognosticate COVID-19 patients at admission by combining clinical data and chest radiographs. METHODS This retrospective study used the Stony Brook University COVID-19 dataset of 1384 inpatients. After exclusions, 1356 patients were randomly divided into training (1083) and test datasets (273). We implemented three artificial intelligence models, which classified mortality, ICU admission, or ventilation risk. Each model had three submodels with different inputs: clinical data, chest radiographs, and both. We showed the importance of the variables using SHapley Additive exPlanations (SHAP) values. RESULTS The mortality prediction model was best overall with area under the curve, sensitivity, specificity, and accuracy of 0.79 (0.72-0.86), 0.74 (0.68-0.79), 0.77 (0.61-0.88), and 0.74 (0.69-0.79) for the clinical data-based model; 0.77 (0.69-0.85), 0.67 (0.61-0.73), 0.81 (0.67-0.92), 0.70 (0.64-0.75) for the image-based model, and 0.86 (0.81-0.91), 0.76 (0.70-0.81), 0.77 (0.61-0.88), 0.76 (0.70-0.81) for the mixed model. The mixed model had the best performance (p value < 0.05). The radiographs ranked fourth for prognostication overall, and first of the inpatient tests assessed. CONCLUSIONS These results suggest that prognosis models become more accurate if AI-derived chest radiograph features and clinical data are used together. ADVANCES IN KNOWLEDGE This AI model evaluates chest radiographs together with clinical data in order to classify patients as having high or low mortality risk. This work shows that chest radiographs taken at admission have significant COVID-19 prognostic information compared to clinical data other than age and sex.
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Affiliation(s)
| | | | - Yukio Miki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University,1-4-3 Asahi-machi, Abeno-ku, Osaka, Japan
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Hefeda MM, Elsharawy DE, Dawoud TM. Correlation between the initial CT chest findings and short-term prognosis in Egyptian patients with COVID-19 pneumonia. EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [PMCID: PMC8727045 DOI: 10.1186/s43055-021-00685-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background The recent pandemic of COVID‐19 has thrown the world into chaos due to its high rate of transmissions. This study aimed to highlight the encountered CT findings in 910 patients with COVID-19 pneumonia in Egypt including the mean severity score and also correlation between the initial CT finding and the short-term prognosis in 320 patients. Results All patients had confirmed COVID-19 infection. Non-contrast CT chest was performed for all cases; in addition, the correlation between each CT finding and disease severity or the short-term prognosis was reported. The mean age was higher for patients with unfavorable prognosis (P < 0.01). The patchy pattern was the most common, found in 532/910 patients (58.4%), the nodular pattern was the least common 123/910 (13.5%). The diffuse pattern was reported in 124 (13.6%). The ground glass density was the most common reported density in the study 512/910 (56.2%). The crazy pavement sign was reported more frequently in patients required hospitalization or ICU and was reported in 53 (56.9%) of patients required hospitalization and in 29 (40.2%) patients needed ICU, and it was reported in 11 (39.2%) deceased patients. Air bronchogram was reported more frequently in patients with poor prognosis than patients with good prognosis (16/100; 26% Vs 12/220; 5.4%). The mean CT severity score for patients with poor prognosis was 15.2. The mean CT severity score for patients with good prognosis 8.7., with statistically significant difference (P = 0.001).
Conclusion Our results confirm the important role of the initial CT findings in the prediction of clinical outcome and short-term prognosis. Some signs like subpleural lines, halo sign, reversed halo sign and nodular shape of the lesions predict mild disease and favorable prognosis. The crazy paving sign, dense vessel sign, consolidation, diffuse shape and high severity score predict more severe disease and probably warrant early hospitalization. The high severity score is most important in prediction of unfavorable prognosis. The nodular shape of the lesions is the most important predictor of good prognosis.
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Messineo L, Fanfulla F, Pedroni L, Pini F, Borghesi A, Golemi S, Vailati G, Kerlin K, Malhotra A, Corda L, Sands S. Breath-holding physiology, radiological severity and adverse outcomes in COVID-19 patients: A prospective validation study. Respirology 2022; 27:1073-1082. [PMID: 35933689 PMCID: PMC9539071 DOI: 10.1111/resp.14336] [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: 04/27/2022] [Accepted: 07/18/2022] [Indexed: 12/13/2022]
Abstract
BACKGROUND AND OBJECTIVE COVID-19 remains a major cause of respiratory failure, and means to identify future deterioration is needed. We recently developed a prediction score based on breath-holding manoeuvres (desaturation and maximal duration) to predict incident adverse COVID-19 outcomes. Here we prospectively validated our breath-holding prediction score in COVID-19 patients, and assessed associations with radiological scores of pulmonary involvement. METHODS Hospitalized COVID-19 patients (N = 110, three recruitment centres) performed breath-holds at admission to provide a prediction score (Messineo et al.) based on mean desaturation (20-s breath-holds) and maximal breath-hold duration, plus baseline saturation, body mass index and cardiovascular disease. Odds ratios for incident adverse outcomes (composite of bi-level ventilatory support, ICU admission and death) were described for patients with versus without elevated scores (>0). Regression examined associations with chest x-ray (Brixia score) and computed tomography (CT; 3D-software quantification). Additional comparisons were made with the previously-validated '4C-score'. RESULTS Elevated prediction score was associated with adverse COVID-19 outcomes (N = 12/110), OR[95%CI] = 4.54[1.17-17.83], p = 0.030 (positive predictive value = 9/48, negative predictive value = 59/62). Results were diminished with removal of mean desaturation from the prediction score (OR = 3.30[0.93-11.72]). The prediction score rose linearly with Brixia score (β[95%CI] = 0.13[0.02-0.23], p = 0.026, N = 103) and CT-based quantification (β = 1.02[0.39-1.65], p = 0.002, N = 45). Mean desaturation was also associated with both radiological assessment. Elevated 4C-scores (≥high-risk category) had a weaker association with adverse outcomes (OR = 2.44[0.62-9.56]). CONCLUSION An elevated breath-holding prediction score is associated with almost five-fold increased adverse COVID-19 outcome risk, and with pulmonary deficits observed in chest imaging. Breath-holding may identify COVID-19 patients at risk of future respiratory failure.
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Affiliation(s)
- Ludovico Messineo
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham & Women’s Hospital & Harvard Medical School, Boston, Massachusetts, USA
| | - Francesco Fanfulla
- Sleep Medicine and Respiratory Function Unit, Maugeri Clinical and Scientific Institutes IRCCS, Pavia, Italy
| | - Leonardo Pedroni
- Respiratory Medicine and Sleep Laboratory, Department of Experimental and Clinical Sciences, University of Brescia and Spedali Civili, Brescia, Italy
| | - Floriana Pini
- Department of General Medicine, Mellini Hospital, Chiari, Italy
| | - Andrea Borghesi
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Salvatore Golemi
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Guido Vailati
- Respiratory Medicine and Sleep Laboratory, Department of Experimental and Clinical Sciences, University of Brescia and Spedali Civili, Brescia, Italy
| | - Kayla Kerlin
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham & Women’s Hospital & Harvard Medical School, Boston, Massachusetts, USA
| | - Atul Malhotra
- University of California San Diego, La Jolla, CA, USA
| | - Luciano Corda
- Respiratory Medicine and Sleep Laboratory, Department of Experimental and Clinical Sciences, University of Brescia and Spedali Civili, Brescia, Italy,Department of Internal Medicine, Spedali Civili, Brescia, Italy
| | - Scott Sands
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham & Women’s Hospital & Harvard Medical School, Boston, Massachusetts, USA,Department of Allergy Immunology and Respiratory Medicine and Central Clinical School, The Alfred and Monash University, Melbourne, Victoria, Australia
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Chen L, Wu F, Huang J, Yang J, Fan W, Nie Z, Jiang H, Wang J, Xia W, Yang F. Well-Aerated Lung and Mean Lung Density Quantified by CT at Discharge to Predict Pulmonary Diffusion Function 5 Months after COVID-19. Diagnostics (Basel) 2022; 12:diagnostics12122921. [PMID: 36552928 PMCID: PMC9776504 DOI: 10.3390/diagnostics12122921] [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: 10/17/2022] [Revised: 11/12/2022] [Accepted: 11/21/2022] [Indexed: 11/25/2022] Open
Abstract
Background: The aim of this study was to explore the predictive values of quantitative CT indices of the total lung and lung lobe tissue at discharge for the pulmonary diffusion function of coronavirus disease 2019 (COVID-19) patients at 5 months after symptom onset. Methods: A total of 90 patients with moderate and severe COVID-19 underwent CT scans at discharge, and pulmonary function tests (PFTs) were performed 5 months after symptom onset. The differences in quantitative CT and PFT results between Group 1 (patients with abnormal diffusion function) and Group 2 (patients with normal diffusion function) were compared by the chi-square test, Fisher’s exact test or Mann−Whitney U test. Univariate analysis, stepwise linear regression and logistic regression were used to determine the predictors of diffusion function in convalescent patients. Results: A total of 37.80% (34/90) of patients presented diffusion dysfunction at 5 months after symptom onset. The mean lung density (MLD) of the total lung tissue in Group 1 was higher than that in Group 2, and the percentage of the well-aerated lung (WAL) tissue volume (WAL%) of Group 1 was lower than that of Group 2 (all p < 0.05). Multiple stepwise linear regression identified only WAL and WAL% of the left upper lobe (LUL) as parameters that positively correlated with the percent of the predicted value of diffusion capacity of the lungs for carbon monoxide (WAL: p = 0.002; WAL%: p = 0.004), and multiple stepwise logistic regression identified MLD and MLDLUL as independent predictors of diffusion dysfunction (MLD: OR (95%CI): 1.011 (1.001, 1.02), p = 0.035; MLDLUL: OR (95%CI): 1.016 (1.004, 1.027), p = 0.008). Conclusion: At five months after symptom onset, more than one-third of moderate and severe COVID-19 patients presented with diffusion dysfunction. The well-aerated lung and mean lung density quantified by CT at discharge could be predictors of diffusion function in convalesce.
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Affiliation(s)
- Leqing Chen
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Feihong Wu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Jia Huang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Jinrong Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Wenliang Fan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Zhuang Nie
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Hongwei Jiang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Jiazheng Wang
- MSC Clinical & Technical Solutions, Philips Healthcare, Floor 7, Building 2, World Profit Center, Beijing 100600, China
| | - Wenfang Xia
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Correspondence: (W.X.); (F.Y.); Tel.: +86-027-85353238 (F.Y.)
| | - Fan Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
- Correspondence: (W.X.); (F.Y.); Tel.: +86-027-85353238 (F.Y.)
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Nakagawara K, Chubachi S, Namkoong H, Tanaka H, Lee H, Azekawa S, Otake S, Fukushima T, Morita A, Watase M, Sakurai K, Kusumoto T, Asakura T, Masaki K, Kamata H, Ishii M, Hasegawa N, Harada N, Ueda T, Ueda S, Ishiguro T, Arimura K, Saito F, Yoshiyama T, Nakano Y, Mutoh Y, Suzuki Y, Edahiro R, Murakami K, Sato Y, Okada Y, Koike R, Kitagawa Y, Tokunaga K, Kimura A, Imoto S, Miyano S, Ogawa S, Kanai T, Fukunaga K. Impact of upper and lower respiratory symptoms on COVID-19 outcomes: a multicenter retrospective cohort study. Respir Res 2022; 23:315. [PMID: 36380316 PMCID: PMC9665023 DOI: 10.1186/s12931-022-02222-3] [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: 07/27/2022] [Accepted: 10/12/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Respiratory symptoms are associated with coronavirus disease 2019 (COVID-19) outcomes. However, the impacts of upper and lower respiratory symptoms on COVID-19 outcomes in the same population have not been compared. The objective of this study was to characterize upper and lower respiratory symptoms and compare their impacts on outcomes of hospitalized COVID-19 patients. METHODS This was a multicenter, retrospective cohort study; the database from the Japan COVID-19 Task Force was used. A total of 3314 COVID-19 patients were included in the study, and the data on respiratory symptoms were collected. The participants were classified according to their respiratory symptoms (Group 1: no respiratory symptoms, Group 2: only upper respiratory symptoms, Group 3: only lower respiratory symptoms, and Group 4: both upper and lower respiratory symptoms). The impacts of upper and lower respiratory symptoms on the clinical outcomes were compared. The primary outcome was the percentage of patients with poor clinical outcomes, including the need for oxygen supplementation via high-flow oxygen therapy, mechanical ventilation, and extracorporeal membrane oxygenation or death. RESULTS Of the 3314 COVID-19 patients, 605, 1331, 1229, and 1149 were classified as Group 1, Group 2, Group 3, and Group 4, respectively. In univariate analysis, patients in Group 2 had the best clinical outcomes among all groups (odds ratio [OR]: 0.21, 95% confidence interval [CI]: 0.11-0.39), while patients in Group 3 had the worst outcomes (OR: 3.27, 95% CI: 2.43-4.40). Group 3 patients had the highest incidence of pneumonia, other complications due to secondary infections, and thrombosis during the clinical course. CONCLUSIONS Upper and lower respiratory tract symptoms had vastly different impacts on the clinical outcomes of COVID-19.
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Affiliation(s)
- Kensuke Nakagawara
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Shotaro Chubachi
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan.
| | - Ho Namkoong
- Department of Infectious Diseases, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan.
| | - Hiromu Tanaka
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Ho Lee
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Shuhei Azekawa
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Shiro Otake
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Takahiro Fukushima
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Atsuho Morita
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Mayuko Watase
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Kaori Sakurai
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Tatsuya Kusumoto
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Takanori Asakura
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
- Department of Clinical Medicine (Laboratory of Bioregulatory Medicine), Kitasato University School of Pharmacy, Tokyo, Japan
- Department of Respiratory Medicine, Kitasato University, Kitasato Institute Hospital, Tokyo, Japan
| | - Katsunori Masaki
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Hirofumi Kamata
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Makoto Ishii
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Naoki Hasegawa
- Department of Infectious Diseases, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Norihiro Harada
- Department of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
| | - Tetsuya Ueda
- Department of Respiratory Medicine, Osaka Saiseikai Nakatsu Hospital, Osaka, Japan
| | - Soichiro Ueda
- Department of Internal Medicine, JCHO (Japan Community Health Care Organization) Saitama Medical Center, Saitama, Japan
| | - Takashi Ishiguro
- Department of Respiratory Medicine, Saitama Cardiovascular and Respiratory Center, Kumagaya, Japan
| | - Ken Arimura
- Department of Respiratory Medicine, Tokyo Women's Medical University, Tokyo, Japan
| | - Fukuki Saito
- Department of Emergency and Critical Care Medicine, Kansai Medical University General Medical Center, Moriguchi, Japan
| | - Takashi Yoshiyama
- Respiratory Disease Center, Fukujuji Hospital, Japan Anti-Tuberculosis Association, Tokyo, Japan
| | - Yasushi Nakano
- Department of Internal Medicine, Kawasaki Municipal Ida Hospital, Kawasaki, Japan
| | - Yoshikazu Mutoh
- Department of Infectious Diseases, Tosei General Hospital, Seto, Japan
| | - Yusuke Suzuki
- Department of Respiratory Medicine, Kitasato University, Kitasato Institute Hospital, Tokyo, Japan
| | - Ryuya Edahiro
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Koji Murakami
- Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Yasunori Sato
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- The Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Ryuji Koike
- Medical Innovation Promotion Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yuko Kitagawa
- Department of Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Katsushi Tokunaga
- Genome Medical Science Project (Toyama), National Center for Global Health and Medicine, Tokyo, Japan
| | - Akinori Kimura
- Institute of Research, Tokyo Medical and Dental University, Tokyo, Japan
| | - Seiya Imoto
- Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, the University of Tokyo, Tokyo, Japan
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institute, Stockholm, Sweden
| | - Takanori Kanai
- Division of Gastroenterology and Hepatology, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Koichi Fukunaga
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
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Ufuk F, Savaş R. COVID-19 pneumonia: lessons learned, challenges, and preparing for the future. Diagn Interv Radiol 2022; 28:576-585. [PMID: 36550758 PMCID: PMC9885718 DOI: 10.5152/dir.2022.221881] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 09/16/2022] [Indexed: 12/24/2022]
Abstract
Coronavirus disease 2019 (COVID-19) is a viral disease that causes life-threatening health problems during acute illness, causing a pandemic and millions of deaths. Although computed tomography (CT) was used as a diagnostic tool for COVID-19 in the early period of the pan demic due to the inaccessibility or long duration of the polymerase chain reaction tests, cur rent studies have revealed that CT scan should not be used to diagnose COVID-19. However, radiologic findings are vital in assessing pneumonia severity and investigating complications in patients with COVID-19. Long-term symptoms, also known as long COVID, in people recovering from COVID-19 affect patients' quality of life and cause global health problems. Herein, we aimed to summarize the lessons learned in COVID-19 pneumonia, the challenges in diagnosing the disease and complications, and the prospects for future studies.
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Affiliation(s)
- Furkan Ufuk
- From the Department of Radiology (F.U. ✉ ), School of Medicine, University of Pamukkale, Denizli, Turkey Department of Radiology (R.S.), School of Medicine, University of Ege, Izmir, Turkey.
| | - Recep Savaş
- From the Department of Radiology (F.U. ✉ ), School of Medicine, University of Pamukkale, Denizli, Turkey Department of Radiology (R.S.), School of Medicine, University of Ege, Izmir, Turkey.
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Wu W, Bhatraju PK, Cobb N, Sathe NA, Duan KI, Seitz KP, Thau MR, Sung CC, Hippe DS, Reddy G, Pipavath S. Radiographic Findings and Association With Clinical Severity and Outcomes in Critically Ill Patients With COVID-19. Curr Probl Diagn Radiol 2022; 51:884-891. [PMID: 35610068 PMCID: PMC9023378 DOI: 10.1067/j.cpradiol.2022.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 03/16/2022] [Accepted: 04/18/2022] [Indexed: 01/08/2023]
Abstract
PURPOSE To describe evolution and severity of radiographic findings and assess association with disease severity and outcomes in critically ill COVID-19 patients. MATERIALS AND METHODS This retrospective study included 62 COVID-19 patients admitted to the intensive care unit (ICU). Clinical data was obtained from electronic medical records. A total of 270 chest radiographs were reviewed and qualitatively scored (CXR score) using a severity scale of 0-30. Radiographic findings were correlated with clinical severity and outcome. RESULTS The CXR score increases from a median initial score of 10 at hospital presentation to the median peak CXR score of 18 within a median time of 4 days after hospitalization, and then slowly decreases to a median last CXR score of 15 in a median time of 12 days after hospitalization. The initial and peak CXR score was independently associated with invasive MV after adjusting for age, gender, body mass index, smoking, and comorbidities (Initial, odds ratio [OR]: 2.11 per 5-point increase, confidence interval [CI] 1.35-3.32, P= 0.001; Peak, OR: 2.50 per 5-point increase, CI 1.48-4.22, P= 0.001). Peak CXR scores were also independently associated with vasopressor usage (OR: 2.28 per 5-point increase, CI 1.30-3.98, P= 0.004). Peak CXR scores strongly correlated with the duration of invasive MV (Rho = 0.62, P< 0.001), while the initial CXR score (Rho = 0.26) and the peak CXR score (Rho = 0.27) correlated weakly with the sequential organ failure assessment score. No statistically significant associations were found between radiographic findings and mortality. CONCLUSIONS Evolution of radiographic features indicates rapid disease progression and correlate with requirement for invasive MV or vasopressors but not mortality, which suggests potential nonpulmonary pathways to death in COVID-19.
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Affiliation(s)
- Wei Wu
- University of Washington School of Medicine, Department of Radiology, Seattle, WA.
| | - Pavan K Bhatraju
- University of Washington School of Medicine, Department of Internal Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Seattle, WA
| | - Natalie Cobb
- University of Washington School of Medicine, Department of Internal Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Seattle, WA
| | - Neha A Sathe
- University of Washington School of Medicine, Department of Internal Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Seattle, WA
| | - Kevin I Duan
- University of Washington School of Medicine, Department of Internal Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Seattle, WA
| | - Kevin P Seitz
- University of Washington School of Medicine, Department of Internal Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Seattle, WA
| | - Matthew R Thau
- University of Washington School of Medicine, Department of Internal Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Seattle, WA
| | - Clifford C Sung
- University of Washington School of Medicine, Department of Internal Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Seattle, WA
| | - Daniel S Hippe
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Gautham Reddy
- University of Washington School of Medicine, Department of Radiology, Seattle, WA
| | - Sudhakar Pipavath
- University of Washington School of Medicine, Department of Radiology, Seattle, WA
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Banerjee A, Mukhopadhyay O, Santra R, Bhadury A, Chaudhuri S. Clinical, laboratory and high-resolution computed tomography (HRCT) thorax profile of reverse transcription polymerase chain reaction (RT-PCR) negative COVID-19 suspects with moderate to severe disease. JOURNAL OF EDUCATION AND HEALTH PROMOTION 2022; 11:333. [PMID: 36568002 PMCID: PMC9768718 DOI: 10.4103/jehp.jehp_287_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 05/17/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Diagnostic dilemma arises when patients with clinical suspicion of COVID-19 disease having moderate-to-severe respiratory symptoms yield negative result for COVID-19 in reverse transcription polymerase chain reaction (RT-PCR). This study evaluated the clinical, laboratory and HRCT thorax findings among RT-PCR-negative COVID-19 suspects with moderate-to-severe disease. MATERIALS AND METHODS A hospital-based retrospective observational study was conducted between July 2021 to December 2021, among 60 moderate and severe symptomatic COVID-19 suspects admitted in the severe acute respiratory illness (SARI) ward and intensive care unit (ICU), who were negative for COVID-19 in RT-PCR. Data were abstracted from the medical records section of the hospital using a predesigned data abstraction form and presented by descriptive statistics. RESULTS Mean age of study participants was 55.5 years (SD 14.1 years), and majority were males (n = 43, 71.7%). Common presenting symptoms were fever (n = 60, 100%), dyspnea (n = 57, 95%), and cough (n = 54, 90%). The common laboratory findings were rise of C-reactive protein (n = 60, 100%), NLR (n = 49, 81.7%), d-dimer (n = 47, 78.3%), ferritin (n = 46, 76.7%), and LDH (n = 40, 66.7%). HRCT scan of thorax revealed ground glass opacities with or without consolidations located bilaterally with diffuse or peripheral distribution, interlobar septal thickening (n = 43, 74.1%), vascular thickening (n = 35, ≥58.3%), and sub-pleural lines (n = 32, 53.3%). Median CT-SS value was 15 (IQR 11-19), and majority (n = 56, 93.3%) belonged to CO-RADS ≥4. CONCLUSION Diagnosis of COVID-19 can be presumed in RT-PCR-negative suspected COVID-19 patients with moderate-to-severe disease, with marked rise of inflammatory markers and HRCT revealing typical findings of COVID-19 pneumonia.
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Affiliation(s)
- Arnab Banerjee
- Department of General Medicine, Deben Mahata Government Medical College and Hospital, Purulia, West Bengal, India
| | - Olivia Mukhopadhyay
- Department of Pharmacology, Bankura Sammilani Medical College and Hospital, Bankura, West Bengal, India
| | - Ranjita Santra
- Department of Pharmacology, Deben Mahata Government Medical College and Hospital, Purulia, West Bengal, India
| | - Anuran Bhadury
- Department of Pharmacology, Deben Mahata Government Medical College and Hospital, Purulia, West Bengal, India
| | - Sirshendu Chaudhuri
- Department of Public Health, Institute of Public Health, Hyderabad, Telangana, India
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Dal H, Karabulut Keklik ES, Yilmaz H, Avcil M, Yaman E, Dağtekin G, Diker S, Can S. Estimation of biochemical factors affecting survival in intensive care COVID-19 patients undergoing chest CT scoring: A retrospective cross-sectional study. Medicine (Baltimore) 2022; 101:e30407. [PMID: 36221408 PMCID: PMC9541058 DOI: 10.1097/md.0000000000030407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is a rapidly spreading deadly respiratory disease that emerged in the city of Wuhan in December 2019. As a result of its rapid and widespread transmission, the WHO declared a pandemic on March 11, 2020 and studies evaluating mortality and prognosis in COVID-19 gained importance. The aim of this study was to determine the factors affecting the survival of COVID-19 patients followed up in a tertiary intensive care unit (ICU) and undergoing chest computed tomography (CT) scoring. This retrospective cross-sectional study was conducted with the approval of Uşak University Medical Faculty Ethics Committee between July and September 2020. It included 187 symptomatic patients (67 females, 120 males) with suspected COVID-19 who underwent chest CT scans in the ICU. Demographics, acute physiology and chronic health evaluation (APACHE II), chest CT scores, COVID-19 real-time polymerase chain reaction (RT PCR) results, and laboratory parameters were recorded. SPSS 15.0 for Windows was used for the data analysis. The ages of the patients ranged from 18 to 94 and the mean age was 68.0 ± 13.9 years. The COVID-19 RT PCR test was positive in 86 (46.0%) patients and 110 patients (58.8%) died during the follow-up. ICU stay (P = .024) and total invasive mechanical ventilation time (P < .001) were longer and blood urea nitrogen (BUN) was higher (P < .001) in the nonsurvivors. Patients with an APACHE II score of 23 and above had a 1.12-fold higher mortality rate (95% CI 0.061-0.263). There was no significant difference in total chest CT score between the survivors and nonsurvivors (P = .210). Chest CT score was not significantly associated with mortality in COVID-19 patients. Our idea that COVID-19 will cause greater mortality in patients with severe chest CT findings has changed. More studies on COVID-19 are needed to reveal the markers that affect prognosis and mortality in this period when new variants are affecting the world.
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Affiliation(s)
- Hakan Dal
- Department of Intensive Care Unit, Uşak Research and Training Hospital, Uşak, Turkey
| | | | - Hakan Yilmaz
- Department of Radiology, Uşak Research and Training Hospital, Uşak, Turkey
| | - Mücahit Avcil
- Department of Emergency Medicine, Uşak Research and Training Hospital, Uşak, Turkey
| | - Eda Yaman
- Department of Emergency Medicine, Uşak Research and Training Hospital, Uşak, Turkey
- *Correspondence: Eda Yaman, Uşak Medical Faculty, Department of Emergency Medicine, Uşak, 64100, Turkey (e-mail: )
| | - Gökçe Dağtekin
- Department of Public Health, Uşak Health Directorate, Uşak, Turkey
| | - Süleyman Diker
- Deparment of Internal Medicine, Uşak Research and Training Hospital, Uşak, Turkey
| | - Sema Can
- Department of Emergency Medicine, Uşak Research and Training Hospital, Uşak, Turkey
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Lakhani A, Laturkar N, Dhok A, Mitra K. Prognostic utility of cardiovascular indices in COVID-19 infection: A single-center prospective study in India. J Family Med Prim Care 2022; 11:6297-6302. [PMID: 36618222 PMCID: PMC9810928 DOI: 10.4103/jfmpc.jfmpc_501_22] [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: 03/02/2022] [Revised: 05/22/2022] [Accepted: 06/02/2022] [Indexed: 11/10/2022] Open
Abstract
Background Cardiac signs can show illness progression and severity in a number of respiratory and cardiovascular disorders. The possible importance of CT findings in the prognosis and result of COVID-19 patients is related to the severity of lung disease and cardiac parameters. The CT-assessed cardiac indices are known for predicting the involvement of extent of diseases. Hence, the objective of this study was to correlate the extent of cardiovascular and respiratory involvement in predicting the severity of disease using CT-assessed cardiac indices in Indian population suffering from COVID-19. Methodology A total of 120 COVID-19 patients were included following the inclusion criteria for one year. The confounding factors were assessed and analyzed. The correlation between the cumulative hazard function of death and duration in hospital along with survival rate were done in terms of pulmonary artery-to-aorta ratio (PA/A), and cardiothoracic ratio (CTR). Results The analysis showed mean age of patients to be 49.5(±15.32) years in which mean females were 38(±31.7) and males were 82(±68.3). The interquartile range of CT severity was 8. The PA/A ratio in discharged patients was 0.85 when compared to deceased patients with 1.03 having statistically significant inference (P = 0.00). The CTR (P = 0.00), epicardial adipose thickness (P = 0.00), epicardial adipose density (P = 0.00), and D-dimer (P = 0.007) were showing statistically significant inference. Conclusion The predictive values of CT-assessed cardiac indices might be used for predicting the involvement of cardiovascular and respiratory involvement in COVID-19 patients. It could have an impact on improving the possibilities of survival of patients suffering from COVID-19 in India.
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Affiliation(s)
- Aisha Lakhani
- Department of Radiodiagnosis and Imaging, NKP Salve Institute of Medical Sciences and Research Centre, Nagpur, Maharashtra, India
| | - Nikhil Laturkar
- Department of Radiodiagnosis and Imaging, NKP Salve Institute of Medical Sciences and Research Centre, Nagpur, Maharashtra, India
| | - Avinash Dhok
- Department of Radiodiagnosis and Imaging, NKP Salve Institute of Medical Sciences and Research Centre, Nagpur, Maharashtra, India,Address for correspondence: Dr. Avinash Dhok, Professor and Head, Department of Radiodiagnosis and Imaging, NKP Salve Institute of Medical Sciences and Research Centre, Nagpur - 440 019, Maharashtra, India. E-mail:
| | - Kajal Mitra
- Department of Radiodiagnosis and Imaging, NKP Salve Institute of Medical Sciences and Research Centre, Nagpur, Maharashtra, India
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Cheng J, Zhao W, Liu J, Xie X, Wu S, Liu L, Yue H, Li J, Wang J, Liu J. Automated Diagnosis of COVID-19 Using Deep Supervised Autoencoder With Multi-View Features From CT Images. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2723-2736. [PMID: 34351863 PMCID: PMC9647725 DOI: 10.1109/tcbb.2021.3102584] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Accurate and rapid diagnosis of coronavirus disease 2019 (COVID-19) from chest CT scans is of great importance and urgency during the worldwide outbreak. However, radiologists have to distinguish COVID-19 pneumonia from other pneumonia in a large number of CT scans, which is tedious and inefficient. Thus, it is urgently and clinically needed to develop an efficient and accurate diagnostic tool to help radiologists to fulfill the difficult task. In this study, we proposed a deep supervised autoencoder (DSAE) framework to automatically identify COVID-19 using multi-view features extracted from CT images. To fully explore features characterizing CT images from different frequency domains, DSAE was proposed to learn the latent representation by multi-task learning. The proposal was designed to both encode valuable information from different frequency features and construct a compact class structure for separability. To achieve this, we designed a multi-task loss function, which consists of a supervised loss and a reconstruction loss. Our proposed method was evaluated on a newly collected dataset of 787 subjects including COVID-19 pneumonia patients, other pneumonia patients, and normal subjects without abnormal CT findings. Extensive experimental results demonstrated that our proposed method achieved encouraging diagnostic performance and may have potential clinical application for the diagnosis of COVID-19.
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Matsunaga F, Kono Y, Kitamura H, Terashima M. The role of radiologic technologists during the COVID-19 pandemic. Glob Health Med 2022; 4:237-241. [PMID: 36119782 PMCID: PMC9420333 DOI: 10.35772/ghm.2022.01011] [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: 03/02/2022] [Revised: 06/20/2022] [Accepted: 07/05/2022] [Indexed: 06/15/2023]
Abstract
During the pandemic, stress of coronavirus disease 2019 (COVID-19) on a radiology department has caused major change in the workflow and protocol, which can inflame unnecessary anxiety among the staff. We have adapted and responded quickly however, to the volatile clinical situations owing to a close consultant in infection control. Our repeatedly revised procedures since the 2014 Ebola outbreak possess the expertise and were very useful. In-house training sessions have been held and updated accordingly. In-house networking service has now become more common in our department instead of the emergency contact network relaying the message to the person on the phone tree. Up until January 2022, we examined 10,861 chest X-rays with no in-hospital infection. We sincerely hope our chest X-ray strategies comply with infection prevention and control standards and minimize use of personal protective equipment will be embraced as a positive initiative by frontline radiologic technologists and relieve their anxiety.
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Affiliation(s)
- Futoshi Matsunaga
- Address correspondence to:Futoshi Matsunaga, Department of Radiology, National Center for Global Health and Medicine, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan. E-mail:
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Immovilli P, Schiavetti I, Cordioli C, De Mitri P, Grazioli S, Guidetti D, Sormani MP. Lung involvement correlates with disability in MS patients with COVID-19 pneumonia. Neurol Sci 2022; 43:6657-6659. [PMID: 35962215 PMCID: PMC9374582 DOI: 10.1007/s10072-022-06333-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 08/08/2022] [Indexed: 11/27/2022]
Affiliation(s)
- Paolo Immovilli
- Emergency Department, Neurology Unit, Guglielmo da Saliceto Hospital, Via Giuseppe Taverna 39, 29121, Piacenza, Italy.
| | - Irene Schiavetti
- Section of Biostatistics, Department of Health Sciences, University of Genova, Genoa, Italy
| | - Cinzia Cordioli
- Multiple Sclerosis Center, ASST Spedali Civili Di Brescia, Montichiari (Brescia), Italy
| | - Paola De Mitri
- Emergency Department, Neurology Unit, Guglielmo da Saliceto Hospital, Via Giuseppe Taverna 39, 29121, Piacenza, Italy
| | - Silvia Grazioli
- Radiology Unit, ASST Spedali Civili Di Brescia, Montichiari (Brescia), Italy
| | - Donata Guidetti
- Emergency Department, Neurology Unit, Guglielmo da Saliceto Hospital, Via Giuseppe Taverna 39, 29121, Piacenza, Italy
| | - Maria Pia Sormani
- Section of Biostatistics, Department of Health Sciences, University of Genova, Genoa, Italy
- Ospedale Policlinico San Martino IRCCS, Genoa, Italy
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Carbonell G, Del Valle DM, Gonzalez-Kozlova E, Marinelli B, Klein E, El Homsi M, Stocker D, Chung M, Bernheim A, Simons NW, Xiang J, Nirenberg S, Kovatch P, Lewis S, Merad M, Gnjatic S, Taouli B. Quantitative chest computed tomography combined with plasma cytokines predict outcomes in COVID-19 patients. Heliyon 2022; 8:e10166. [PMID: 35958514 PMCID: PMC9356575 DOI: 10.1016/j.heliyon.2022.e10166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 03/08/2022] [Accepted: 07/27/2022] [Indexed: 01/29/2023] Open
Abstract
Despite extraordinary international efforts to dampen the spread and understand the mechanisms behind SARS-CoV-2 infections, accessible predictive biomarkers directly applicable in the clinic are yet to be discovered. Recent studies have revealed that diverse types of assays bear limited predictive power for COVID-19 outcomes. Here, we harness the predictive power of chest computed tomography (CT) in combination with plasma cytokines using a machine learning and k-fold cross-validation approach for predicting death during hospitalization and maximum severity degree in COVID-19 patients. Patients (n = 152) from the Mount Sinai Health System in New York with plasma cytokine assessment and a chest CT within five days from admission were included. Demographics, clinical, and laboratory variables, including plasma cytokines (IL-6, IL-8, and TNF-α), were collected from the electronic medical record. We found that CT quantitative alone was better at predicting severity (AUC 0.81) than death (AUC 0.70), while cytokine measurements alone better-predicted death (AUC 0.70) compared to severity (AUC 0.66). When combined, chest CT and plasma cytokines were good predictors of death (AUC 0.78) and maximum severity (AUC 0.82). Finally, we provide a simple scoring system (nomogram) using plasma IL-6, IL-8, TNF-α, ground-glass opacities (GGO) to aerated lung ratio and age as new metrics that may be used to monitor patients upon hospitalization and help physicians make critical decisions and considerations for patients at high risk of death for COVID-19.
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Affiliation(s)
- Guillermo Carbonell
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology, Universidad de Murcia, Spain
- Instituto Murciano de Investigación Biosanitaria, Spain
| | - Diane Marie Del Valle
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edgar Gonzalez-Kozlova
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brett Marinelli
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Emma Klein
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Maria El Homsi
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Daniel Stocker
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Switzerland
| | - Michael Chung
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Adam Bernheim
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nicole W. Simons
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jiani Xiang
- Scientific Computing; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sharon Nirenberg
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Scientific Computing; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Patricia Kovatch
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Scientific Computing; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sara Lewis
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Miriam Merad
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sacha Gnjatic
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Oncological Sciences; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bachir Taouli
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Do TD, Skornitzke S, Merle U, Kittel M, Hofbaur S, Melzig C, Kauczor HU, Wielpütz MO, Weinheimer O. COVID-19 pneumonia: Prediction of patient outcome by CT-based quantitative lung parenchyma analysis combined with laboratory parameters. PLoS One 2022; 17:e0271787. [PMID: 35905122 PMCID: PMC9337660 DOI: 10.1371/journal.pone.0271787] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 07/07/2022] [Indexed: 12/23/2022] Open
Abstract
Objectives To evaluate the prognostic value of fully automatic lung quantification based on spectral computed tomography (CT) and laboratory parameters for combined outcome prediction in COVID-19 pneumonia. Methods CT images of 53 hospitalized COVID-19 patients including virtual monochromatic reconstructions at 40-140keV were analyzed using a fully automated software system. Quantitative CT (QCT) parameters including mean and percentiles of lung density, fibrosis index (FIBI-700, defined as the percentage of segmented lung voxels ≥-700 HU), quantification of ground-glass opacities and well-aerated lung areas were analyzed. QCT parameters were correlated to laboratory and patient outcome parameters (hospitalization, days on intensive care unit, invasive and non-invasive ventilation). Results Best correlations were found for laboratory parameters LDH (r = 0.54), CRP (r = 0.49), Procalcitonin (r = 0.37) and partial pressure of oxygen (r = 0.35) with the QCT parameter 75th percentile of lung density. LDH, Procalcitonin, 75th percentile of lung density and FIBI-700 were the strongest independent predictors of patients’ outcome in terms of days of invasive ventilation. The combination of LDH and Procalcitonin with either 75th percentile of lung density or FIBI-700 achieved a r2 of 0.84 and 1.0 as well as an area under the receiver operating characteristic curve (AUC) of 0.99 and 1.0 for the prediction of the need of invasive ventilation. Conclusions QCT parameters in combination with laboratory parameters could deliver a feasible prognostic tool for the prediction of invasive ventilation in patients with COVID-19 pneumonia.
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Affiliation(s)
- Thuy D. Do
- Clinic for Diagnostic and Interventional Radiology (DIR), University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
| | - Stephan Skornitzke
- Clinic for Diagnostic and Interventional Radiology (DIR), University Hospital Heidelberg, Heidelberg, Germany
| | - Uta Merle
- Department of Internal Medicine IV (Gastroenterology and Infectious Disease), University Hospital Heidelberg, Heidelberg, Germany
| | - Maximilian Kittel
- Institute for Clinical Chemistry, Medical Faculty Mannheim of Heidelberg University, Mannheim, Germany
| | - Stefan Hofbaur
- Clinic for Gastroenterology and Nephrology, Landshut Hospital, Landshut, Germany
| | - Claudius Melzig
- Clinic for Diagnostic and Interventional Radiology (DIR), University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
| | - Hans-Ulrich Kauczor
- Clinic for Diagnostic and Interventional Radiology (DIR), University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik, University Hospital Heidelberg, Heidelberg, Germany
| | - Mark O. Wielpütz
- Clinic for Diagnostic and Interventional Radiology (DIR), University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik, University Hospital Heidelberg, Heidelberg, Germany
| | - Oliver Weinheimer
- Clinic for Diagnostic and Interventional Radiology (DIR), University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- * E-mail:
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Yilmaz AE. How reliable are the multiple comparison methods for odds ratio? J Appl Stat 2022; 49:3141-3163. [PMID: 36035608 PMCID: PMC9415621 DOI: 10.1080/02664763.2022.2104229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
The homogeneity tests of odds ratios are used in clinical trials and epidemiological investigations as a preliminary step of meta-analysis. In recent studies, the severity or mortality of COVID-19 in relation to demographic characteristics, comorbidities, and other conditions has been popularly discussed by interpreting odds ratios and using meta-analysis. According to the homogeneity test results, a common odds ratio summarizes all of the odds ratios in a series of studies. If the aim is not to find a common odds ratio, but to find which of the sub-characteristics/groups is different from the others or is under risk, then the implementation of a multiple comparison procedure is required. In this article, the focus is placed on the accuracy and reliability of the homogeneity of odds ratio tests for multiple comparisons when the odds ratios are heterogeneous at the omnibus level. Three recently proposed multiple comparison tests and four homogeneity of odds ratios tests with six adjustment methods to control the type-I error rate are considered. The reliability and accuracy of the methods are discussed in relation to COVID-19 severity data associated with diabetes on a country-by-country basis, and a simulation study to assess the powers and type-I error rates of the tests is conducted.
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Arsava EM, Ardali Duzgun S, Durhan G, Cakan M, Akpinar E, Topcuoglu MA. Admission chest CT findings and risk assessment for stroke-associated pneumonia. Acta Neurol Belg 2022; 123:433-439. [PMID: 35879553 PMCID: PMC9312318 DOI: 10.1007/s13760-022-02043-7] [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: 01/16/2022] [Accepted: 07/18/2022] [Indexed: 11/25/2022]
Abstract
Introduction Stroke-associated pneumonia (SAP) is a significant cause of morbidity and mortality after stroke. Various factors, including dysphagia and stroke severity, are closely related to SAP risk; however, the contribution of the baseline pulmonary parenchymal status to this interplay is an understudied field. Herein, we evaluated the prognostic performance of admission chest computed tomography (CT) findings in predicting SAP. Methods We evaluated admission chest CT images, acquired as part of a COVID-19-related institutional policy, in a consecutive series of acute ischemic stroke patients. The pulmonary opacity load at baseline was quantified using automated volumetry and visual scoring algorithms. The relationship between pulmonary opacities with risk of pneumonia within 7 days of symptom onset (i.e., SAP) was evaluated by bivariate and multivariate analyses. Results Twenty-three percent of patients in our cohort (n = 100) were diagnosed with SAP. Patients with SAP were more likely to have atrial fibrillation, COPD, severe neurological deficits, and dysphagia. The visual opacity score on chest CT was significantly higher among patients who developed SAP (p = 0.014), while no such relationship was observed in terms of absolute or relative opacity volume. In multivariate analyses, admission stroke severity, presence of dysphagia and a visual opacity score of ≥ 3 (OR 6.37, 95% CI 1.61–25.16; p = 0.008) remained significantly associated with SAP risk. Conclusions Pulmonary opacity burden, as evaluated on admission chest CT, is significantly associated with development of pneumonia within initial days of stroke. This association is independent of other well-known predisposing factors for SAP, including age, stroke severity, and presence of dysphagia. Supplementary Information The online version contains supplementary material available at 10.1007/s13760-022-02043-7.
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Affiliation(s)
- Ethem Murat Arsava
- Department of Neurology, Faculty of Medicine, Hacettepe University, Sihhiye, 06100, Ankara, Turkey.
| | - Selin Ardali Duzgun
- Department of Radiology, Faculty of Medicine, Hacettepe University, Ankara, Turkey
| | - Gamze Durhan
- Department of Radiology, Faculty of Medicine, Hacettepe University, Ankara, Turkey
| | - Melike Cakan
- Department of Neurology, Faculty of Medicine, Hacettepe University, Sihhiye, 06100, Ankara, Turkey
| | - Erhan Akpinar
- Department of Radiology, Faculty of Medicine, Hacettepe University, Ankara, Turkey
| | - Mehmet Akif Topcuoglu
- Department of Neurology, Faculty of Medicine, Hacettepe University, Sihhiye, 06100, Ankara, Turkey
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Ismail A, Doghish AS, Elkhatib WF, Magdy AM, Mahmoud EE, Ahmed MI, Khalil MAF. Clinical and chest computed tomography features of patients suffering from mild and severe COVID-19 at Fayoum University Hospital in Egypt. PLoS One 2022; 17:e0271271. [PMID: 35802733 PMCID: PMC9269943 DOI: 10.1371/journal.pone.0271271] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 06/28/2022] [Indexed: 12/23/2022] Open
Abstract
Background
In pandemic COVID-19 (coronavirus disease 2019), the prognosis of patients has been determined using clinical data and CT (computed tomography) scans, but it is still unclear whether chest CT characteristics are correlated to COVID-19 severity.
Aim
To explore the potential association between clinical data and 25-point CT score and investigate their predictive significance in COVID-19-positive patients at Fayoum University Hospital in Egypt.
Methods
This study was conducted on 252 Egyptian COVID-19 patients at Fayoum University Hospital in Egypt. The patients were classified into two groups: a mild group (174 patients) and a severe group (78 patients). The results of clinical laboratory data, and CT scans of severe and mild patients, were collected, analyzed, and compared.
Results
The severe group show high significance levels of CRP, alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine, urea, ferritin, lactate dehydrogenase (LDH), neutrophil percent, and heart rate (HR) than the mild group. Lymphopenia, hypoalbuminemia, hypocalcemia, and decreased oxygen saturation (SpO2) were the most observed abnormalities in severe COVID-19 patients. Lymphopenia, low SpO2 and albumin levels, elevated serum LDH, ferritin, urea, and CRP levels were found to be significantly correlated with severity CT score (P<0.0001).
Conclusion
The clinical severity of COVID-19 and the CT score are highly correlated. Our findings indicate that the CT scoring system can help to predict COVID-19 disease outcomes and has a strong correlation with clinical laboratory testing.
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Affiliation(s)
- Ahmed Ismail
- Department of Biochemistry, Faculty of Pharmacy (Boys), Al-Azhar University, Nasr City, Cairo, Egypt
| | - Ahmed S. Doghish
- Department of Biochemistry, Faculty of Pharmacy, Badr University in Cairo (BUC), Badr City, Cairo, Egypt
- * E-mail: (ASD); , (WFE)
| | - Walid F. Elkhatib
- Microbiology and Immunology Department, Faculty of Pharmacy, Ain Shams University, African Union Organization St., Abbassia, Cairo, Egypt
- Department of Microbiology and Immunology, Faculty of Pharmacy, Galala University, New Galala City, Suez, Egypt
- * E-mail: (ASD); , (WFE)
| | - Ahmed M. Magdy
- Department of Radiology, Faculty of Medicine, Fayoum University, Fayoum, Egypt
| | - Eman E. Mahmoud
- Department of Clinical and Chemical Pathology, Faculty of Medicine, Fayoum University, Fayoum, Egypt
| | - Mona I. Ahmed
- Department of Chest Diseases, Faculty of Medicine, Fayoum University, Fayoum, Egypt
| | - Mahmoud A. F. Khalil
- Department of Microbiology and Immunology, Faculty of Pharmacy, Fayoum University, Fayoum, Egypt
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Lensink K, Lo F(J, Eddy RL, Law M, Laradji I, Haber E, Nicolaou S, Murphy D, Parker WA. A Soft Labeling Approach to Develop Automated Algorithms that Incorporate Uncertainty in Pulmonary Opacification on Chest CT using COVID-19 Pneumonia. Acad Radiol 2022; 29:994-1003. [PMID: 35490114 DOI: 10.1016/j.acra.2022.03.025] [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: 01/23/2022] [Revised: 03/15/2022] [Accepted: 03/24/2022] [Indexed: 11/24/2022]
Abstract
RATIONALE AND OBJECTIVES Hard data labels for automated algorithm training are binary and cannot incorporate uncertainty between labels. We proposed and evaluated a soft labeling methodology to quantify opacification and percent well-aerated lung (%WAL) on chest CT, that considers uncertainty in segmenting pulmonary opacifications and reduces labeling burden. MATERIALS AND METHODS We retrospectively sourced 760 COVID-19 chest CT scans from five international centers between January and June 2020. We created pixel-wise labels for >27,000 axial slices that classify three pulmonary opacification patterns: pure ground-glass, crazy-paving, consolidation. We also quantified %WAL as the total area of lung without opacifications. Inter-user hard label variability was quantified using Shannon entropy (range=0-1.39, low-high entropy/variability). We incorporated a soft labeling and modeling cycle following an initial model with hard labels and compared performance using point-wise accuracy and intersection-over-union of opacity labels with ground-truth, and correlation with ground-truth %WAL. RESULTS Hard labels annotated by 12 radiologists demonstrated large inter-user variability (3.37% of pixels achieved complete agreement). Our soft labeling approach increased point-wise accuracy from 60.0% to 84.3% (p=0.01) compared to hard labeling at predicting opacification type and area involvement. The soft label model accurately predicted %WAL (R=0.900) compared to the hard label model (R=0.856), but the improvement was not statistically significant (p=0.349). CONCLUSION Our soft labeling approach increased accuracy for automated quantification and classification of pulmonary opacification on chest CT. Although we developed the model on COVID-19, our intent is broad application for pulmonary opacification contexts and to provide a foundation for future development using soft labeling methods.
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