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1.
Medicine (Baltimore) ; 100(47): e27980, 2021 Nov 24.
Article in English | MEDLINE | ID: covidwho-1604285

ABSTRACT

RATIONALE: Pulmonary fibrosis is an infamous sequela of coronavirus disease 2019 (COVID-19) pneumonia leading to long-lasting respiratory problems and activity limitations. Pulmonary rehabilitation is beneficial to improve the symptoms of lung fibrosis. We experienced a post-COVID-19 pulmonary fibrosis patient who received a structured exercise-based pulmonary rehabilitation program. PATIENT CONCERNS: This article presents a case of successful pulmonary rehabilitation of a patient with post-COVID-19 pulmonary fibrosis. The patient could not cut off the oxygen supplement even after a successful recovery from COVID-19. DIAGNOSIS: Diagnosis of COVID-19 was based on the reverse transcription-polymerase chain reaction (RT-PCR). Pulmonary fibrosis was diagnosed by patient's complaint, clinical appearance, and computed tomography (CT) on chest. INTERVENTION: The patient underwent ten sessions of exercise-based rehabilitation program according to Consensus Document on Pulmonary Rehabilitation in Korea, 2015. OUTCOME: On the 8th day, he could cut off the oxygen supplementation and complete the one-hour exercise without oxygen. He was discharged after completing the 10-session program without any activity limitations. LESSONS: Exercise-based pulmonary rehabilitation will help the post-COVID-19 pulmonary fibrosis patients. This case suggested the importance of pulmonary rehabilitation program to the post-COVID-19 pulmonary fibrosis patient.


Subject(s)
COVID-19/complications , Lung/diagnostic imaging , Pulmonary Fibrosis/rehabilitation , COVID-19/diagnosis , COVID-19 Testing , Humans , Lung/pathology , Male , Middle Aged , Oxygen , Pulmonary Fibrosis/diagnostic imaging , Pulmonary Fibrosis/etiology , Reverse Transcriptase Polymerase Chain Reaction , SARS-CoV-2 , Tomography, X-Ray Computed
2.
BMC Pulm Med ; 22(1): 1, 2022 Jan 03.
Article in English | MEDLINE | ID: covidwho-1608729

ABSTRACT

BACKGROUND: Quantitative evaluation of radiographic images has been developed and suggested for the diagnosis of coronavirus disease 2019 (COVID-19). However, there are limited opportunities to use these image-based diagnostic indices in clinical practice. Our aim in this study was to evaluate the utility of a novel visually-based classification of pulmonary findings from computed tomography (CT) images of COVID-19 patients with the following three patterns defined: peripheral, multifocal, and diffuse findings of pneumonia. We also evaluated the prognostic value of this classification to predict the severity of COVID-19. METHODS: This was a single-center retrospective cohort study of patients hospitalized with COVID-19 between January 1st and September 30th, 2020, who presented with suspicious findings on CT lung images at admission (n = 69). We compared the association between the three predefined patterns (peripheral, multifocal, and diffuse), admission to the intensive care unit, tracheal intubation, and death. We tested quantitative CT analysis as an outcome predictor for COVID-19. Quantitative CT analysis was performed using a semi-automated method (Thoracic Volume Computer-Assisted Reading software, GE Health care, United States). Lungs were divided by Hounsfield unit intervals. Compromised lung (%CL) volume was the sum of poorly and non-aerated volumes (- 500, 100 HU). We collected patient clinical data, including demographic and clinical variables at the time of admission. RESULTS: Patients with a diffuse pattern were intubated more frequently and for a longer duration than patients with a peripheral or multifocal pattern. The following clinical variables were significantly different between the diffuse pattern and peripheral and multifocal groups: body temperature (p = 0.04), lymphocyte count (p = 0.01), neutrophil count (p = 0.02), c-reactive protein (p < 0.01), lactate dehydrogenase (p < 0.01), Krebs von den Lungen-6 antigen (p < 0.01), D-dimer (p < 0.01), and steroid (p = 0.01) and favipiravir (p = 0.03) administration. CONCLUSIONS: Our simple visual assessment of CT images can predict the severity of illness, a resulting decrease in respiratory function, and the need for supplemental respiratory ventilation among patients with COVID-19.


Subject(s)
COVID-19/classification , COVID-19/diagnostic imaging , Tomography, X-Ray Computed , Adult , Aged , Amides/therapeutic use , Antiviral Agents/therapeutic use , Body Temperature , C-Reactive Protein/metabolism , COVID-19/drug therapy , COVID-19/physiopathology , Female , Fibrin Fibrinogen Degradation Products/metabolism , Humans , L-Lactate Dehydrogenase/blood , Lung/diagnostic imaging , Lymphocyte Count , Male , Middle Aged , Mucin-1/blood , Neutrophils , Predictive Value of Tests , Prognosis , Pyrazines/therapeutic use , Radiographic Image Interpretation, Computer-Assisted , Retrospective Studies , SARS-CoV-2 , Steroids/therapeutic use
3.
J Korean Med Sci ; 36(44): e309, 2021 Nov 15.
Article in English | MEDLINE | ID: covidwho-1593105

ABSTRACT

BACKGROUND: We assessed maternal and neonatal outcomes of critically ill pregnant and puerperal patients in the clinical course of coronavirus disease 2019 (COVID-19). METHODS: Records of pregnant and puerperal women with polymerase chain reaction positive COVID-19 virus who were admitted to our intensive care unit (ICU) from March 2020 to August 2021 were investigated. Demographic, clinical and laboratory data, pharmacotherapy, and neonatal outcomes were analyzed. These outcomes were compared between patients that were discharged from ICU and patients who died in ICU. RESULTS: Nineteen women were included in this study. Additional oxygen was required in all cases (100%). Eight patients (42%) were intubated and mechanically ventilated. All patients that were mechanically ventilated have died. Increased levels of C-reactive protein (CRP) was seen in all patients (100%). D-dimer values increased in 15 patients (78.9%); interleukin-6 (IL-6) increased in 16 cases (84.2%). Sixteen patients used antiviral drugs. Eleven patients were discharged from the ICU and eight patients have died due to complications of COVID-19 showing an ICU mortality rate of 42.1%. Mean number of hospitalized days in ICU was significantly lower in patients that were discharged (P = 0.037). Seventeen patients underwent cesarean-section (C/S) (89.4%). Mean birth week was significantly lower in patients who died in ICU (P = 0.024). Eleven preterm (57.8%) and eight term deliveries (42.1%) occurred. CONCLUSION: High mortality rate was detected among critically ill pregnant/parturient patients followed in the ICU. Main predictors of mortality were the need of invasive mechanical ventilation and higher number of days hospitalized in ICU. Rate of C/S operations and preterm delivery were high. Pleasingly, the rate of neonatal death was low and no neonatal COVID-19 occurred.


Subject(s)
COVID-19/mortality , Pregnancy Complications, Infectious/mortality , Puerperal Disorders/mortality , SARS-CoV-2 , Adult , Antiviral Agents/therapeutic use , COVID-19/blood , COVID-19/diagnostic imaging , COVID-19/therapy , Cesarean Section , Combined Modality Therapy , Critical Illness/mortality , Delivery, Obstetric/statistics & numerical data , Female , Hospital Mortality , Humans , Infant, Newborn , Intensive Care Units/statistics & numerical data , Length of Stay/statistics & numerical data , Lung/diagnostic imaging , Oxygen Inhalation Therapy , Pregnancy , Pregnancy Outcome , Respiration, Artificial , Retrospective Studies , Treatment Outcome , Young Adult
4.
Am J Physiol Heart Circ Physiol ; 321(6): H1103-H1105, 2021 12 01.
Article in English | MEDLINE | ID: covidwho-1590548
5.
PLoS One ; 16(12): e0261307, 2021.
Article in English | MEDLINE | ID: covidwho-1598199

ABSTRACT

Medical images commonly exhibit multiple abnormalities. Predicting them requires multi-class classifiers whose training and desired reliable performance can be affected by a combination of factors, such as, dataset size, data source, distribution, and the loss function used to train deep neural networks. Currently, the cross-entropy loss remains the de-facto loss function for training deep learning classifiers. This loss function, however, asserts equal learning from all classes, leading to a bias toward the majority class. Although the choice of the loss function impacts model performance, to the best of our knowledge, we observed that no literature exists that performs a comprehensive analysis and selection of an appropriate loss function toward the classification task under study. In this work, we benchmark various state-of-the-art loss functions, critically analyze model performance, and propose improved loss functions for a multi-class classification task. We select a pediatric chest X-ray (CXR) dataset that includes images with no abnormality (normal), and those exhibiting manifestations consistent with bacterial and viral pneumonia. We construct prediction-level and model-level ensembles to improve classification performance. Our results show that compared to the individual models and the state-of-the-art literature, the weighted averaging of the predictions for top-3 and top-5 model-level ensembles delivered significantly superior classification performance (p < 0.05) in terms of MCC (0.9068, 95% confidence interval (0.8839, 0.9297)) metric. Finally, we performed localization studies to interpret model behavior and confirm that the individual models and ensembles learned task-specific features and highlighted disease-specific regions of interest. The code is available at https://github.com/sivaramakrishnan-rajaraman/multiloss_ensemble_models.


Subject(s)
Algorithms , Diagnostic Imaging , Image Processing, Computer-Assisted/classification , Area Under Curve , Entropy , Humans , Lung/diagnostic imaging , ROC Curve , Thorax/diagnostic imaging , X-Rays
6.
Front Public Health ; 9: 663076, 2021.
Article in English | MEDLINE | ID: covidwho-1597195

ABSTRACT

Background: In Pakistan, the cases of COVID-19 have declined from 6000 per day in June to 600 in September 2020. A significant number of patients continue to recover from the disease, however, little is known about the lung function capacity among survivors. We aim to determine the long-term impact on lung function capacity in patients who have survived moderate or severe COVID-19 disease in a resource-poor setting. Methods: This prospective cohort study will be conducted at Aga Khan University Hospital (AKUH), Karachi Pakistan. Patients 15 years and above who have survived an episode of moderate or severe COVID-19, have reverse transcriptase-polymerase chain reaction (RT-PCR) positive for COVID 19 (nasopharyngeal or oropharyngeal) will be included. Patients with a pre-existing diagnosis of obstructive or interstitial lung disease, lung fibrosis, lung cancers, connective tissue disorders, autoimmune conditions affecting the lungs, underlying heart disease, history of syncope and those who refuse to participate will be excluded from the study. Pulmonary function will be assessed using spirometry and diffusion lung capacity for carbon monoxide (DLCO) at 3- and 6-months interval from the time of discharge from the hospital. Additionally, a chest X-ray and CT-chest will be performed if clinically indicated after consultation with the study pulmonologist or Infectious Disease (ID) physician. Echocardiogram (ECHO) will be performed to look for pulmonary hypertension at the 3 month visit and repeated at 6 months in case any abnormality is identified in the initial ECHO. Data analysis will be performed using standard statistical software. The study was approved by the Ethical Review Committee (ERC) of the institution (ERC reference number 2020-4735-11311). Strengths and Limitations of the Study: This cohort study will provide evidence on the long-term impact on lung function among COVID-19 survivors with moderate to severe disease. Such data will be key in understanding the impact of the disease on vital functions and will help devise rehabilitative strategies to best overcome the effects of disease. However, this will be a single-center, study recruiting only a limited number of COVID-19 survivors.


Subject(s)
COVID-19 , Cohort Studies , Humans , Lung/diagnostic imaging , Prospective Studies , SARS-CoV-2
7.
Curr Med Imaging ; 17(12): 1487-1495, 2021.
Article in English | MEDLINE | ID: covidwho-1595319

ABSTRACT

PURPOSE: The purpose of this study was to investigate the influencing factors for chest CT hysteresis and severity of coronavirus disease 2019 (COVID-19). METHODS: The chest CT data of patients with confirmed COVID-19 in 4 hospitals were retrospectively analyzed. An independent assessment was performed by one clinician using the DEXIN FACT Workstation Analysis System, and the assessment results were reviewed by another clinician. Furthermore, the mean hysteresis time was calculated according to the median time from progression to the most serious situation to improve chest CT in patients after fever relief. The optimal scaling regression analysis was performed by including variables with statistical significance in univariate analysis. In addition, a multivariate regression model was established to investigate the relationship of the percentage of lesion/total lung volume with lymphocyte and other variables. RESULTS: In the included 166 patients with COVID-19, the average value of the most serious percentage of lesion/total lung volume was 6.62, of which 90 patients with fever had an average hysteresis time of 4.5 days after symptom relief, with a similar trend observed in those without fever. Multivariate analysis revealed that lymphocyte count in peripheral blood and transcutaneous oxygen saturation decreased with the increase of the percentage of lesion/total lung volume. CONCLUSION: There is a hysteresis effect in the improvement of chest CT image relative to fever relief in patients with COVID-19. The pulmonary lesions may be related to the severity as well as decreased lymphocyte count or percutaneous oxygen saturation.


Subject(s)
COVID-19 , Tomography, X-Ray Computed , COVID-19/diagnostic imaging , Humans , Lung/diagnostic imaging , Lung/physiopathology , Retrospective Studies , SARS-CoV-2
8.
J Acoust Soc Am ; 150(6): 4118, 2021 12.
Article in English | MEDLINE | ID: covidwho-1583239

ABSTRACT

Ultrasound in point-of-care lung assessment is becoming increasingly relevant. This is further reinforced in the context of the COVID-19 pandemic, where rapid decisions on the lung state must be made for staging and monitoring purposes. The lung structural changes due to severe COVID-19 modify the way ultrasound propagates in the parenchyma. This is reflected by changes in the appearance of the lung ultrasound images. In abnormal lungs, vertical artifacts known as B-lines appear and can evolve into white lung patterns in the more severe cases. Currently, these artifacts are assessed by trained physicians, and the diagnosis is qualitative and operator dependent. In this article, an automatic segmentation method using a convolutional neural network is proposed to automatically stage the progression of the disease. 1863 B-mode images from 203 videos obtained from 14 asymptomatic individual,14 confirmed COVID-19 cases, and 4 suspected COVID-19 cases were used. Signs of lung damage, such as the presence and extent of B-lines and white lung areas, are manually segmented and scored from zero to three (most severe). These manually scored images are considered as ground truth. Different test-training strategies are evaluated in this study. The results shed light on the efficient approaches and common challenges associated with automatic segmentation methods.


Subject(s)
COVID-19 , Humans , Image Processing, Computer-Assisted , Lung/diagnostic imaging , Pandemics , SARS-CoV-2 , Tomography, X-Ray Computed
9.
J Acoust Soc Am ; 150(6): 4151, 2021 12.
Article in English | MEDLINE | ID: covidwho-1583238

ABSTRACT

The potential of lung ultrasound (LUS) has become manifest in the light of the recent COVID-19 pandemic. The need for a point-of care, quantitative, and widely available assessment of lung condition is critical. However, conventional ultrasound imaging was never designed for lung assessment. This limits LUS to the subjective and qualitative interpretation of artifacts and imaging patterns visible on ultrasound images. A number of research groups have begun to tackle this limitation, and this special issue reports on their most recent findings. Through in silico, in vitro, and in vivo studies (preclinical animal studies and pilot clinical studies on human subjects), the research presented aims at understanding and modelling the physical phenomena involved in ultrasound propagation, and at leveraging these phenomena to extract semi-quantitative and quantitative information relevant to estimate changes in lung structure. These studies are the first steps in unlocking the full potential of lung ultrasound as a relevant tool for lung assessment.


Subject(s)
COVID-19 , Pandemics , Humans , Lung/diagnostic imaging , SARS-CoV-2 , Ultrasonography
10.
Tuberk Toraks ; 69(4): 492-498, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1580009

ABSTRACT

Introduction: To date, there is limited data on the long-term changes in the lungs of patients recovering from coronavirus (COVID-19) pneumonia. In order to evaluate pulmonary sequelae, it was planned to investigate fibrotic changes observed as sequelae in lung tissue in 3-6-month control thorax computerized tomography (CT) scans of moderate-to-severe COVID-19 pneumonia survivors. Materials and Methods: A total of 84 patients (mean age: 67.3 years ±15) with moderate-to-severe pneumonia on chest tomography at the time of diagnosis were included in the study, of which 51 (61%) were males and 33 (39%) were females. Initial and follow-up CT scans averaged 8.3 days ± 2.2 and 112.1 days ± 14.6 after symptom onset, respectively. Participants were recorded in two groups as those with and without fibrotic-like changes such as traction bronchiectasis, fibrotic - parenchymal bands, honeycomb appearance according to 3-6 months follow-up CT scans. Differences between the groups were evaluated with a two-sampled t-test. Logistic regression analyzes were performed to determine independent predictive factors of fibrotic-like sequelae changes. Result: On follow-up CTs, fibrotic-like changes were observed in 29 (35%) of the 84 participants (Group 1), while the remaining 55 (65%) showed complete radiological recovery (Group 2). With logistic regression analysis, hospital stay of 22 days or longer (OR: 4.9; 95% CI: 20, 32; p< 0.05) and a CT score of 15 or more at diagnosis (OR: 2.2; 95% CI: 13.5, 18; p< 0.05) were found to be an independent predictor for sequelae fibrotic changes in lung tissue. Conclusions: More than one-third of patients who survived COVID-19 pneumonia had fibrotic-like sequelae changes in the lung parenchyma. These changes were found to be associated with the presence of severe pneumonia at the time of diagnosis and longer hospital stay.


Subject(s)
COVID-19 , Pneumonia , Aged , Female , Follow-Up Studies , Humans , Lung/diagnostic imaging , Male , Pneumonia/diagnostic imaging , Retrospective Studies , SARS-CoV-2 , Thorax , Tomography, X-Ray Computed
11.
Adv Emerg Nurs J ; 43(4): 279-292, 2021.
Article in English | MEDLINE | ID: covidwho-1574416

ABSTRACT

Since the introduction of ultrasonography, clinicians have discovered different uses for embedding this technology in the clinical setting. The use of point-of-care ultrasonography has gained a lot of interest in the emergency department. It is a procedure that a clinician can rapidly utilize to triage, risk stratify, evaluate, and monitor the patient's condition. The COVID-19 pandemic has highlighted the significance and application of ultrasonography in identifying and managing patients presenting with lung pathology in the emergency setting.


Subject(s)
COVID-19 , Nurse Practitioners , Emergency Service, Hospital , Humans , Lung/diagnostic imaging , Pandemics , Point-of-Care Systems , SARS-CoV-2 , Ultrasonography
12.
Clin Imaging ; 77: 151-157, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1573759

ABSTRACT

As the COVID-19 pandemic impacts global populations, computed tomography (CT) lung imaging is being used in many countries to help manage patient care as well as to rapidly identify potentially useful quantitative COVID-19 CT imaging biomarkers. Quantitative COVID-19 CT imaging applications, typically based on computer vision modeling and artificial intelligence algorithms, include the potential for better methods to assess COVID-19 extent and severity, assist with differential diagnosis of COVID-19 versus other respiratory conditions, and predict disease trajectory. To help accelerate the development of robust quantitative imaging algorithms and tools, it is critical that CT imaging is obtained following best practices of the quantitative lung CT imaging community. Toward this end, the Radiological Society of North America's (RSNA) Quantitative Imaging Biomarkers Alliance (QIBA) CT Lung Density Profile Committee and CT Small Lung Nodule Profile Committee developed a set of best practices to guide clinical sites using quantitative imaging solutions and to accelerate the international development of quantitative CT algorithms for COVID-19. This guidance document provides quantitative CT lung imaging recommendations for COVID-19 CT imaging, including recommended CT image acquisition settings for contemporary CT scanners. Additional best practice guidance is provided on scientific publication reporting of quantitative CT imaging methods and the importance of contributing COVID-19 CT imaging datasets to open science research databases.


Subject(s)
COVID-19 , Pandemics , Artificial Intelligence , Biomarkers , Humans , Lung/diagnostic imaging , SARS-CoV-2 , Tomography, X-Ray Computed
14.
J Med Case Rep ; 15(1): 588, 2021 Dec 13.
Article in English | MEDLINE | ID: covidwho-1571928

ABSTRACT

INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 is the third member of the coronavirus family to cause global concern in the twenty-first century. Pregnant women are particularly at higher risk of developing severe viral pneumonia, possibly because of a partial immune suppression during their pregnancy. Under such critical and rapidly evolving circumstances, these poor findings might be helpful for the treatment of infected pregnant women with the 2019 novel coronavirus. CASE PRESENTATION: In this study, we report the case of a 33-year-old Asian pregnant woman at 25 gestational weeks with coronavirus disease 2019 who developed severe complications, including hypoxemia, acute respiratory distress syndrome, pulmonary infiltration, and bilateral pleural effusion. She died 1 month after admission to the hospital. CONCLUSION: Pregnant populations are especially at higher risk of viral pneumonia development caused by severe acute respiratory syndrome coronavirus 2. Further research on the prevention and treatment of the new coronavirus is necessary.


Subject(s)
COVID-19 , Pregnancy Complications, Infectious , Premature Birth , Adult , Female , Humans , Infectious Disease Transmission, Vertical , Lung/diagnostic imaging , Pregnancy , Pregnancy Complications, Infectious/drug therapy , Pregnancy Outcome , Pregnant Women , SARS-CoV-2
15.
BMC Med Imaging ; 21(1): 192, 2021 12 13.
Article in English | MEDLINE | ID: covidwho-1571744

ABSTRACT

AIM: This study is to compare the lung image quality between shelter hospital CT (CT Ark) and ordinary CT scans (Brilliance 64) scans. METHODS: The patients who received scans with CT Ark or Brilliance 64 CT were enrolled. Their lung images were divided into two groups according to the scanner. The objective evaluation methods of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were used. The subjective evaluation methods including the evaluation of the fine structure under the lung window and the evaluation of the general structure under the mediastinum window were compared. Kappa method was used to assess the reliability of the subjective evaluation. The subjective evaluation results were analyzed using the Wilcoxon rank sum test. SNR and CNR were tested using independent sample t tests. RESULTS: There was no statistical difference in somatotype of enrolled subjects. The Kappa value between the two observers was between 0.68 and 0.81, indicating good consistency. For subjective evaluation results, the rank sum test P value of fine structure evaluation and general structure evaluation by the two observers was ≥ 0.05. For objective evaluation results, SNR and CNR between the two CT scanners were significantly different (P<0.05). Notably, the absolute values ​​of SNR and CNR of the CT Ark were larger than Brilliance 64 CT scanner. CONCLUSION: CT Ark is fully capable of scanning the lungs of the COVID-19 patients during the epidemic in the shelter hospital.


Subject(s)
COVID-19/diagnostic imaging , Lung/diagnostic imaging , Mobile Health Units/standards , Tomography, X-Ray Computed/instrumentation , Tomography, X-Ray Computed/standards , Adult , Aged , COVID-19/epidemiology , China/epidemiology , Female , Humans , Male , Middle Aged , Observer Variation , Pandemics , SARS-CoV-2 , Signal-To-Noise Ratio
16.
Lancet Respir Med ; 9(5): 487-497, 2021 05.
Article in English | MEDLINE | ID: covidwho-1537196

ABSTRACT

BACKGROUND: Lung transplantation is a life-saving treatment for patients with end-stage lung disease; however, it is infrequently considered for patients with acute respiratory distress syndrome (ARDS) attributable to infectious causes. We aimed to describe the course of disease and early post-transplantation outcomes in critically ill patients with COVID-19 who failed to show lung recovery despite optimal medical management and were deemed to be at imminent risk of dying due to pulmonary complications. METHODS: We established a multi-institutional case series that included the first consecutive transplants for severe COVID-19-associated ARDS known to us in the USA, Italy, Austria, and India. De-identified data from participating centres-including information relating to patient demographics and pre-COVID-19 characteristics, pretransplantation disease course, perioperative challenges, pathology of explanted lungs, and post-transplantation outcomes-were collected by Northwestern University (Chicago, IL, USA) and analysed. FINDINGS: Between May 1 and Sept 30, 2020, 12 patients with COVID-19-associated ARDS underwent bilateral lung transplantation at six high-volume transplant centres in the USA (eight recipients at three centres), Italy (two recipients at one centre), Austria (one recipient), and India (one recipient). The median age of recipients was 48 years (IQR 41-51); three of the 12 patients were female. Chest imaging before transplantation showed severe lung damage that did not improve despite prolonged mechanical ventilation and extracorporeal membrane oxygenation. The lung transplant procedure was technically challenging, with severe pleural adhesions, hilar lymphadenopathy, and increased intraoperative transfusion requirements. Pathology of the explanted lungs showed extensive, ongoing acute lung injury with features of lung fibrosis. There was no recurrence of SARS-CoV-2 in the allografts. All patients with COVID-19 could be weaned off extracorporeal support and showed short-term survival similar to that of transplant recipients without COVID-19. INTERPRETATION: The findings from our report show that lung transplantation is the only option for survival in some patients with severe, unresolving COVID-19-associated ARDS, and that the procedure can be done successfully, with good early post-transplantation outcomes, in carefully selected patients. FUNDING: National Institutes of Health. VIDEO ABSTRACT.


Subject(s)
COVID-19 , Critical Illness/therapy , Lung Transplantation/methods , Lung , Respiratory Distress Syndrome , Blood Transfusion/methods , COVID-19/complications , COVID-19/diagnosis , COVID-19/physiopathology , COVID-19/surgery , Critical Care/methods , Extracorporeal Membrane Oxygenation/methods , Female , Humans , Intraoperative Care/methods , Lung/diagnostic imaging , Lung/pathology , Male , Middle Aged , Outcome and Process Assessment, Health Care , Pulmonary Fibrosis/etiology , Pulmonary Fibrosis/pathology , Respiration, Artificial/methods , Respiratory Distress Syndrome/etiology , Respiratory Distress Syndrome/surgery , SARS-CoV-2/pathogenicity
17.
Gac Med Mex ; 157(3): 251-256, 2021.
Article in English | MEDLINE | ID: covidwho-1535081

ABSTRACT

INTRODUCTION: Lung ultrasound (LUS) implementation in patients with COVID-19 can help to establish the degree of pulmonary involvement, evaluate treatment response and estimate in-hospital outcome. OBJECTIVE: To evaluate the application of a LUS protocol in patients with COVID-19 infection to predict in-hospital mortality. METHODS: The study was carried out from April 1 to August 1, 2020 in patients with COVID-19 infection admitted to the Intensive Care Unit. Lung evaluation was carried out by physicians trained in critical care ultrasonography. RESULTS: Most patients were males, median age was 56 years, and 59 % required mechanical ventilation. In-hospital mortality was 39.4 %, and in those with a LUS score ≥ 19, mortality was higher (50 %). The multiple logistic regression model showed that a LUS score ≥ 19 was significantly associated with mortality (hazard ratio = 2.55, p = 0.01). CONCLUSIONS: LUS is a safe and fast clinical tool that can be applied at bedside in patients with COVID-19 infection to establish the degree of parenchymal involvement and predict mortality.


Subject(s)
COVID-19/complications , Hospital Mortality , Intensive Care Units , Lung/diagnostic imaging , Ultrasonography , Aged , COVID-19/mortality , Critical Care , Female , Hospitalization , Humans , Male , Middle Aged , Point-of-Care Testing , Respiration, Artificial/statistics & numerical data
18.
J Infect ; 83(5): e6-e9, 2021 11.
Article in English | MEDLINE | ID: covidwho-1527752

ABSTRACT

PURPOSE: To describe the relationship between the severity of lung damage and cytokine levels in sputum, bronchoalveolar lavage fluid (BALF), serum. METHOD: Eight severe patients infected with coronavirus disease 2019 (COVID-19) were admitted and their cytokines and chest computed tomography (CT) were analyzed. RESULTS: Compared with in serum, IL-6 and TNF-α in sputum and in BALF show more directly reflect the severity of COVID-19 critical patients. The gradient ratio of IL-6 levels may predict the prognosis of severe patients. CONCLUSION: Cytokine levels in the sputum may be more helpful for indicating lung damage. Local intervention through the respiratory tract is expected to benefit patients with severe COVID-19.


Subject(s)
COVID-19 , Cytokines , Bronchoalveolar Lavage Fluid , Humans , Lung/diagnostic imaging , Prognosis , SARS-CoV-2 , Sputum
19.
Turk J Med Sci ; 51(4): 1665-1674, 2021 08 30.
Article in English | MEDLINE | ID: covidwho-1526879

ABSTRACT

Background/aim: Coronavirus disease 2019 (COVID-19) is a disease with a high rate of progression to critical illness. However, the predictors of mortality in critically ill patients admitted to the intensive care unit (ICU) are not yet well understood. In this study, we aimed to investigate the risk factors associated with ICU mortality in our hospital. Materials and methods: In this single-centered retrospective study, we enrolled 86 critically ill adult patients with COVID-19 admitted to ICU of Dokuz Eylül University Hospital (Izmir, Turkey) between 18 March 2020 and 31 October 2020. Data on demographic information, preexisting comorbidities, treatments, the laboratory findings at ICU admission, and clinical outcomes were collected. The chest computerized tomography (CT) of the patients were evaluated specifically for COVID-19 and CT score was calculated. Data of the survivors and nonsurvivors were compared with survival analysis to identify risk factors of mortality in the ICU. Results: The mean age of the patients was 71.1 ± 14.1 years. The patients were predominantly male. The most common comorbidity in patients was hypertension. ICU mortality was 62.8%. Being over 60 years old, CT score > 15, acute physiology and chronic health evaluation (APACHE) II score ≥ 15, having dementia, treatment without favipiravir, base excess in blood gas analysis ≤ ­2.0, WBC > 10,000/mm3, D-dimer > 1.6 µg/mL, troponin > 24 ng/L, Na ≥ 145 mmol/L were considered to link with ICU mortality according to Kaplan­Meier curves (log-rank test, p < 0.05). The APACHE II score (HR: 1.055, 95% CI: 1.021­1.090) and chest CT score (HR: 2.411, 95% CI:1.193­4.875) were associated with ICU mortality in the cox proportional-hazard regression model adjusted for age, dementia, favipiravir treatment and troponin. Howewer, no difference was found between survivors and nonsurvivors in terms of intubation timing. Conclusions: COVID-19 patients have a high ICU admission and mortality rate. Studies in the ICU are also crucial in this respect. In our study, we investigated the ICU mortality risk factors of COVID-19 patients. We determined a predictive mortality model consisting of APACHE II score and chest CT score. It was thought that this feasible and practical model would assist in making clinical decisions.


Subject(s)
COVID-19/diagnostic imaging , COVID-19/mortality , Critical Care/methods , Hospital Mortality , Intubation, Intratracheal/methods , Tomography, X-Ray Computed/methods , Adolescent , Adult , Aged , Aged, 80 and over , Female , Humans , Intensive Care Units , Intubation, Intratracheal/statistics & numerical data , Lung/diagnostic imaging , Male , Middle Aged , Retrospective Studies , Risk Factors , SARS-CoV-2 , Survival Analysis , Time Factors , Turkey/epidemiology , Young Adult
20.
PLoS One ; 16(10): e0257892, 2021.
Article in English | MEDLINE | ID: covidwho-1526682

ABSTRACT

BACKGROUND: Coronavirus Disease 2019 (COVID-19) is a respiratory viral illness causing pneumonia and systemic disease. Abnormalities in pulmonary function tests (PFT) after COVID-19 infection have been described. The determinants of these abnormalities are unclear. We hypothesized that inflammatory biomarkers and CT scan parameters at the time of infection would be associated with abnormal gas transfer at short term follow-up. METHODS: We retrospectively studied subjects who were hospitalized for COVID-19 pneumonia and discharged. Serum inflammatory biomarkers, CT scan and clinical characteristics were assessed. CT images were evaluated by Functional Respiratory Imaging with automated tissue segmentation algorithms of the lungs and pulmonary vasculature. Volumes of the pulmonary vessels that were ≤5mm (BV5), 5-10mm (BV5_10), and ≥10mm (BV10) in cross sectional area were analyzed. Also the amount of opacification on CT (ground glass opacities). PFT were performed 2-3 months after discharge. The diffusion capacity of carbon monoxide (DLCO) was obtained. We divided subjects into those with a DLCO <80% predicted (Low DLCO) and those with a DLCO ≥80% predicted (Normal DLCO). RESULTS: 38 subjects were included in our cohort. 31 out of 38 (81.6%) subjects had a DLCO<80% predicted. The groups were similar in terms of demographics, body mass index, comorbidities, and smoking status. Hemoglobin, inflammatory biomarkers, spirometry and lung volumes were similar between groups. CT opacification and BV5 were not different between groups, but both Low and Normal DLCO groups had lower BV5 measures compared to healthy controls. BV5_10 and BV10 measures were higher in the Low DLCO group compared to the normal DLCO group. Both BV5_10 and BV10 in the Low DLCO group were greater compared to healthy controls. BV5_10 was independently associated with DLCO<80% in multivariable logistic regression (OR 1.29, 95% CI 1.01, 1.64). BV10 negatively correlated with DLCO% predicted (r = -0.343, p = 0.035). CONCLUSIONS: Abnormalities in pulmonary vascular volumes at the time of hospitalization are independently associated with a low DLCO at follow-up. There was no relationship between inflammatory biomarkers during hospitalization and DLCO. Pulmonary vascular abnormalities during hospitalization for COVID-19 may serve as a biomarker for abnormal gas transfer after COVID-19 pneumonia.


Subject(s)
COVID-19/diagnostic imaging , Lung/blood supply , Lung/diagnostic imaging , SARS-CoV-2/metabolism , Tomography, X-Ray Computed , Adult , Aged , Biomarkers/metabolism , COVID-19/metabolism , COVID-19/therapy , Female , Follow-Up Studies , Hospitalization , Humans , Lung/metabolism , Lung/virology , Male , Middle Aged , Retrospective Studies
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