[Use of thoracic and lung ultrasound in general anesthesia]. / A mellkas és a tüdo ultrahangvizsgálatának szerepe az általános érzéstelenítés során.

The use of ultrasound became an essential tool in the everyday practice of anesthesiology and intensive care as an indispensable prerequisite for the precise guidance of invasive procedures and also as a point-of-care diagnostic method. Despite the limitations of imaging the lung and thoracic structures, the COVID-19 pandemic and recent advances made this technology an evolving field. The intensive therapy applies these methods with important experience for differential diagnosis and assessment of disease severity or prognosis. Minor modifications of these results make the method beneficial for anesthesia and perioperative medicine. In the present review, the authors accentuate the most important imaging artefacts of lung ultrasonography and the principles of lung ultrasound diagnostic steps. Methods and artefacts of high importance supported by evidence for the assessment of airway management, attuning of intraoperative mechanical ventilation, respiratory disorders during surgery, and postoperative prognosis are articulated. This review intends to focus on evolving subfields in which technological or scientific novelties are expected. Orv Hetil. 2023; 164(22): 864-870.

Remora Namib Beetle Optimization Enabled Deep Learning for Severity of COVID-19 Lung Infection Identification and Classification Using CT Images.

Coronavirus disease 2019 (COVID-19) has seen a crucial outburst for both females and males worldwide. Automatic lung infection detection from medical imaging modalities provides high potential for increasing the treatment for patients to tackle COVID-19 disease. COVID-19 detection from lung CT images is a rapid way of diagnosing patients. However, identifying the occurrence of infectious tissues and segmenting this from CT images implies several challenges. Therefore, efficient techniques termed as Remora Namib Beetle Optimization_ Deep Quantum Neural Network (RNBO_DQNN) and RNBO_Deep Neuro Fuzzy Network (RNBO_DNFN) are introduced for the identification as well as classification of COVID-19 lung infection. Here, the pre-processing of lung CT images is performed utilizing an adaptive Wiener filter, whereas lung lobe segmentation is performed employing the Pyramid Scene Parsing Network (PSP-Net). Afterwards, feature extraction is carried out wherein features are extracted for the classification phase. In the first level of classification, DQNN is utilized, tuned by RNBO. Furthermore, RNBO is designed by merging the Remora Optimization Algorithm (ROA) and Namib Beetle Optimization (NBO). If a classified output is COVID-19, then the second-level classification is executed using DNFN for further classification. Additionally, DNFN is also trained by employing the newly proposed RNBO. Furthermore, the devised RNBO_DNFN achieved maximum testing accuracy, with TNR and TPR obtaining values of 89.4%, 89.5% and 87.5%.

Pediatric Point-of-Care Lung Ultrasonography: A Narrative Review.

Point-of-care lung ultrasonography is an evidence-based application that may play a vital role in the care of critically ill pediatric patients. Lung ultrasonography has the advantage of being available at the patient's bedside with results superior to chest radiography and comparable to chest computed tomography for most lung pathologies. It has a steep learning curve. It can be readily performed in both advanced healthcare systems and resource-scarce settings. The purpose of this review is to discuss the basic principles of lung ultrasonography and its applications in the evaluation and treatment of critically ill pediatric patients.

The role of Lung Ultrasound in the diagnosis of SARS-COV-2 disease in pregnant women.

AIM: To evaluate the role of lung ultrasound (LUS) in recognizing lung abnormalities in pregnant women affected by COVID-19 pneumonia. MATERIALS AND METHODS: An observational study analyzing LUS patterns in 60 consecutively enrolled pregnant women affected by COVID-19 infection was performed. LUS was performed by using a standardized protocol by Soldati et al. The scoring system of LUS findings ranged from 0 to 3 in increasing alteration severity. The highest score obtained from each landmark was reported and the sum of the 12 zones examined was calculated. RESULTS: Patients were divided into two groups: 26 (43.3%) patients with respiratory symptoms and 32 (53.3%) patients without respiratory symptoms; 2 patients were asymptomatic (3.3%). Among the patients with respiratory symptoms 3 (12.5%) had dyspnea that required a mild Oxygen therapy. A significant correlation was found between respiratory symptoms and LUS score (p < 0.001) and between gestational weeks and respiratory symptoms (p = 0.023). Regression analysis showed that age and respiratory symptoms were risk factors for highest LUS score (p < 0.005). DISCUSSION: LUS can affect the clinical decision course and can help in stratifying patients according to its findings. The lack of ionizing radiation and its repeatability makes it a reliable diagnostic tool in the management of pregnant women.

One-year follow-up evaluation of radiological and respiratory findings and functional capacity in COVID-19 survivors without comorbidities.

The aim of this study was to assess clinical findings, radiological data, pulmonary functions and physical capacity change over time and to investigate factors associated with radiological abnormalities after coronavirus disease 2019 (COVID-19) in non-comorbid patients. This prospective cohort study was conducted between April 2020 and June 2020. A total of 62 symptomatic in non-comorbid patients with COVID-19 pneumonia were included in the study. At baseline and the 2nd, 5th and 12th months, patients were scheduled for follow-up. Males represented 51.6% of the participants and overall mean age was 51.60 ±â€…12.45 years. The percentage of patients with radiological abnormalities at 2 months was significantly higher than at 5 months (P < .001). At 12 months, dyspnea frequency (P = .008), 6-minute walk test (6MWT) distance (P = .045), BORG-dyspnea (P < .001) and BORG-fatigue (P < .001) scores was significantly lower, while median SpO2 after 6MWT (P < .001) was significantly higher compared to results at 2 months. The presence of radiological abnormalities at 2 months was associated with the following values measured at 5 months: advanced age (P = .006), lung involvement at baseline (P = .046), low forced expiratory volume in 1 second (P = .018) and low forced vital capacity (P = .006). Even in COVID-19 patients without comorbidities, control computed tomography at 2 months and pulmonary rehabilitation may be beneficial, especially in COVID-19 patients with advanced age and greater baseline lung involvement.

[Predictors of severe course of new coronavirus infection (COVID-19): study design].

AIM: To identify predictors of the severe course of a new coronavirus infection. MATERIALS AND METHODS: A retrospective analysis of 120 clinical case histories of patients hospitalized in hospitals in Tyumen with a confirmed diagnosis of COVID-19 within one year (01.08.2020-01.08.2021) was carried out. The patients were divided into two groups: 1st - with a favorable outcome (n=96), 2nd - with an unfavorable (fatal) outcome (n=24). For a more complete analysis, scales for assessing the clinical condition of patients (SHOCK-COVID), severity assessment (NEWS2) were used. Information processing was carried out in the IBM.SPSS.Statistics-19 program (USA). RESULTS: As a result of the study, the median age for the 1st group was significantly lower (58 years) than for patients of the 2nd group (69 years; p=0.029). A certain set of laboratory parameters for group 2 patients deviate significantly from the reference values (C-reactive protein - CRP - 7.6 [4.7; 15.2] mg/dl, D-dimer - 1.89 [1.36; 5.3] mcg/ml, ferritin - 605 [446.7; 792] ng/ml). When analyzed in groups, taking into account the main markers of the severity of the disease, using the V.Yu. Mareev CCAS-COVID (Clinical Condition Assessment Scale) scale, for the 1st group, the sum of the set of parameters was 6 [2; 7] points, which corresponds to the average severity of coronavirus infection, for the 2nd group 13 [9; 16] points - severe course. For patients of the 2nd group, a significant increase in the indicators of an unfavorable prognosis was revealed in comparison with the 1st group. CONCLUSION: Thus, in this study, the level of CRP, ferritin, D-dimer, the percentage of lung tissue damage according to computed tomography results, SaO2 were significantly associated with an unfavorable prognosis.

Ultrahigh-Resolution Photon-Counting Detector CT of the Lungs: Association of Reconstruction Kernel and Slice Thickness With Image Quality.

BACKGROUND. Prior work has shown improved image quality for photon-counting detector (PCD) CT of the lungs compared with energy-integrating detector CT. A paucity of the literature has compared PCD CT of the lungs using different reconstruction parameters. OBJECTIVE. The purpose of this study is to the compare the image quality of ultra-high-resolution (UHR) PCD CT image sets of the lungs that were reconstructed using different kernels and slice thicknesses. METHODS. This retrospective study included 29 patients (17 women and 12 men; median age, 56 years) who underwent noncontrast chest CT from February 15, 2022, to March 15, 2022, by use of a commercially available PCD CT scanner. All acquisitions used UHR mode (1024 × 1024 matrix). Nine image sets were reconstructed for all combinations of three sharp kernels (BI56, BI60, and BI64) and three slice thicknesses (0.2, 0.4, and 1.0 mm). Three radiologists independently reviewed reconstructions for measures of visualization of pulmonary anatomic structures and pathologies; reader assessments were pooled. Reconstructions were compared with the clinical reference reconstruction (obtained using the BI64 kernel and a 1.0-mm slice thickness [BI641.0-mm]). RESULTS. The median difference in the number of bronchial divisions identified versus the clinical reference reconstruction was higher for reconstructions with BI640.4-mm (0.5), BI600.4-mm (0.3), BI640.2-mm (0.5), and BI600.2-mm (0.2) (all p < .05). The median bronchial wall sharpness versus the clinical reference reconstruction was higher for reconstructions with BI640.4-mm (0.3) and BI640.2-mm (0.3) and was lower for BI561.0-mm (-0.7) and BI560.4-mm (-0.3) (all p < .05). Median pulmonary fissure sharpness versus the clinical reference reconstruction was higher for reconstructions with BI640.4-mm (0.3), BI600.4-mm (0.3), BI560.4-mm (0.5), BI640.2-mm (0.5), BI600.2-mm (0.5), and BI560.2-mm (0.3) (all p < .05). Median pulmonary vessel sharpness versus the clinical reference reconstruction was lower for reconstructions with BI561.0-mm (-0.3), BI600.4-mm (-0.3), BI560.4-mm (-0.7), BI640.2-mm (-0.7), BI600.2-mm (-0.7), and BI560.2-mm (-0.7). Median lung nodule conspicuity versus the clinical reference reconstruction was lower for reconstructions with BI561.0-mm (-0.3) and BI560.4-mm (-0.3) (both p < .05). Median conspicuity of all other pathologies versus the clinical reference reconstruction was lower for reconstructions with BI561.0 mm (-0.3), BI560.4-mm (-0.3), BI640.2-mm (-0.3), BI600.2-mm (-0.3), and BI560.2-mm (-0.3). Other comparisons among reconstructions were not significant (all p > .05). CONCLUSION. Only the reconstruction using BI640.4-mm yielded improved bronchial division identification and bronchial wall and pulmonary fissure sharpness without a loss in pulmonary vessel sharpness or conspicuity of nodules or other pathologies. CLINICAL IMPACT. The findings of this study may guide protocol optimization for UHR PCD CT of the lungs.

[Pulmonary fibrosis in patients with COVID-19: A review].

The viral infectious disease pandemic caused by SARS-CoV-2 has affected over 500 million people and killed over 6 million. This is the official data provided by the WHO as of the end of May 2022. Among people who have recovered from COVID-19, post-COVID syndrome is quite common. Scattered epidemiological studies on post-COVID syndrome, however, indicate its high relevance. One of the manifestations of post-COVID syndrome is the development of pulmonary fibrosis (PF). This article is devoted to the analysis of literature data on epidemiology, immunomorphology, as well as X-ray morphological and functional characteristics of PF in patients with post-COVID syndrome. Attention is drawn to the various phenotypes of the post-COVID syndrome and the incidence of PF, which, as clinical practice shows, is most common in people who have had severe COVID-19. This article discusses in detail the molecular biological and immunological mechanisms of PF development. The fibrotic process of the lung parenchyma is not an early manifestation of the disease; as a rule, radiomorphological signs of this pathological process develop after four weeks from the onset of acute manifestations of a viral infection. The characteristic signs of PF include those that indicate the process of remodulation of the lung tissue: volumetric decrease in the lungs, "cellular" degeneration of the lung parenchyma, bronchiectasis and traction bronchiolectasis. The process of remodulating the lung tissue, in the process of fibrosis, is accompanied by a violation of the lung function; a particularly sensitive test of functional disorders is a decrease in the diffusion capacity of the lung tissue. Therefore, in the process of monitoring patients with post-COVID syndrome, a dynamic study of the ventilation function of the lungs is recommended. The main clinical manifestation of PF is dyspnea that occurs with minimal exertion. Shortness of breath also reflects another important aspect of fibrous remodulation of the lung parenchyma - oxygen dissociation is disturbed, which reflects a violation of the gas exchange function of the lungs. There are no generally accepted treatments for PF in post-COVID syndrome. The literature considers such approaches as the possibility of prescribing antifibrotic therapy, hyaluronidase, and medical gases: thermal helium, nitric oxide, and atomic hydrogen. The article draws attention to the unresolved issues of post-covid PF in people who have had COVID-19.

Lung Ultrasound in Critical Care and Emergency Medicine: Clinical Review.

Lung ultrasound has become a part of the daily examination of physicians working in intensive, sub-intensive, and general medical wards. The easy access to hand-held ultrasound machines in wards where they were not available in the past facilitated the widespread use of ultrasound, both for clinical examination and as a guide to procedures; among point-of-care ultrasound techniques, the lung ultrasound saw the greatest spread in the last decade. The COVID-19 pandemic has given a boost to the use of ultrasound since it allows to obtain a wide range of clinical information with a bedside, not harmful, repeatable examination that is reliable. This led to the remarkable growth of publications on lung ultrasounds. The first part of this narrative review aims to discuss basic aspects of lung ultrasounds, from the machine setting, probe choice, and standard examination to signs and semiotics for qualitative and quantitative lung ultrasound interpretation. The second part focuses on how to use lung ultrasound to answer specific clinical questions in critical care units and in emergency departments.

Artificial neural network based prediction of the lung tissue involvement as an independent in-hospital mortality and mechanical ventilation risk factor in COVID-19.

INTRODUCTION: During COVID-19 pandemic, artificial neural network (ANN) systems have been providing aid for clinical decisions. However, to achieve optimal results, these models should link multiple clinical data points to simple models. This study aimed to model the in-hospital mortality and mechanical ventilation risk using a two step approach combining clinical variables and ANN-analyzed lung inflammation data. METHODS: A data set of 4317 COVID-19 hospitalized patients, including 266 patients requiring mechanical ventilation, was analyzed. Demographic and clinical data (including the length of hospital stay and mortality) and chest computed tomography (CT) data were collected. Lung involvement was analyzed using a trained ANN. The combined data were then analyzed using unadjusted and multivariate Cox proportional hazards models. RESULTS: Overall in-hospital mortality associated with ANN-assigned percentage of the lung involvement (hazard ratio [HR]: 5.72, 95% confidence interval [CI]: 4.4-7.43, p < 0.001 for the patients with >50% of lung tissue affected by COVID-19 pneumonia), age category (HR: 5.34, 95% CI: 3.32-8.59 for cases >80 years, p < 0.001), procalcitonin (HR: 2.1, 95% CI: 1.59-2.76, p < 0.001, C-reactive protein level (CRP) (HR: 2.11, 95% CI: 1.25-3.56, p = 0.004), glomerular filtration rate (eGFR) (HR: 1.82, 95% CI: 1.37-2.42, p < 0.001) and troponin (HR: 2.14, 95% CI: 1.69-2.72, p < 0.001). Furthermore, the risk of mechanical ventilation is also associated with ANN-based percentage of lung inflammation (HR: 13.2, 95% CI: 8.65-20.4, p < 0.001 for patients with >50% involvement), age, procalcitonin (HR: 1.91, 95% CI: 1.14-3.2, p = 0.14, eGFR (HR: 1.82, 95% CI: 1.2-2.74, p = 0.004) and clinical variables, including diabetes (HR: 2.5, 95% CI: 1.91-3.27, p < 0.001), cardiovascular and cerebrovascular disease (HR: 3.16, 95% CI: 2.38-4.2, p < 0.001) and chronic pulmonary disease (HR: 2.31, 95% CI: 1.44-3.7, p < 0.001). CONCLUSIONS: ANN-based lung tissue involvement is the strongest predictor of unfavorable outcomes in COVID-19 and represents a valuable support tool for clinical decisions.

CT features of pulmonary embolism in patients with COVID-19 pneumonia.

OBJECTIVE: The aim of this study was to describe the Computed Tomography (CT) features of pulmonary embolism in patients hospitalized for acute COVID-19 pneumonia and to evaluate the prognostic significance of these features. PATIENTS AND METHODS: This retrospective study included 110 consecutive patients who were hospitalized for acute COVID-19 pneumonia and underwent pulmonary computed tomography angiography (BTPA) on the basis of clinical suspicion. The diagnosis of COVID-19 infection was determined by CT findings typical of COVID-19 pneumonia and/or a positive result of a reverse transcriptase-polymerase chain reaction test. RESULTS: Of the 110 patients, 30 (27.3%) had acute pulmonary embolism and 71 (64.5%) had CT features of chronic pulmonary embolism. Of the 14 (12.7%) patients who died despite receiving therapeutic doses of heparin, 13 (92.9%) had CT features of chronic pulmonary embolism and 1 (7.1%) of acute pulmonary embolism. CT features of chronic pulmonary embolism were more common in deceased patients than in surviving patients (92.9% vs. 60.4%, p=0.01, respectively). Low oxygen saturation and high urine microalbumin creatinine ratio at admission in COVID-19 patients are important determinants of mortality after adjusting for sex and age in logistic procedures. CONCLUSIONS: CT features of chronic pulmonary embolism are common in COVID-19 patients undergoing Computed Tomography Pulmonary Angiography (CTPA) in the hospital. The coexistence of albuminuria, low oxygen saturation and CT features of chronic pulmonary embolism at admission in COVID-19 patients may herald fatal outcomes.

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.

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.

The fatal trajectory of pulmonary COVID-19 is driven by lobular ischemia and fibrotic remodelling.

BACKGROUND: COVID-19 is characterized by a heterogeneous clinical presentation, ranging from mild symptoms to severe courses of disease. 9-20% of hospitalized patients with severe lung disease die from COVID-19 and a substantial number of survivors develop long-COVID. Our objective was to provide comprehensive insights into the pathophysiology of severe COVID-19 and to identify liquid biomarkers for disease severity and therapy response. METHODS: We studied a total of 85 lungs (n = 31 COVID autopsy samples; n = 7 influenza A autopsy samples; n = 18 interstitial lung disease explants; n = 24 healthy controls) using the highest resolution Synchrotron radiation-based hierarchical phase-contrast tomography, scanning electron microscopy of microvascular corrosion casts, immunohistochemistry, matrix-assisted laser desorption ionization mass spectrometry imaging, and analysis of mRNA expression and biological pathways. Plasma samples from all disease groups were used for liquid biomarker determination using ELISA. The anatomic/molecular data were analyzed as a function of patients' hospitalization time. FINDINGS: The observed patchy/mosaic appearance of COVID-19 in conventional lung imaging resulted from microvascular occlusion and secondary lobular ischemia. The length of hospitalization was associated with increased intussusceptive angiogenesis. This was associated with enhanced angiogenic, and fibrotic gene expression demonstrated by molecular profiling and metabolomic analysis. Increased plasma fibrosis markers correlated with their pulmonary tissue transcript levels and predicted disease severity. Plasma analysis confirmed distinct fibrosis biomarkers (TSP2, GDF15, IGFBP7, Pro-C3) that predicted the fatal trajectory in COVID-19. INTERPRETATION: Pulmonary severe COVID-19 is a consequence of secondary lobular microischemia and fibrotic remodelling, resulting in a distinctive form of fibrotic interstitial lung disease that contributes to long-COVID. FUNDING: This project was made possible by a number of funders. The full list can be found within the Declaration of interests / Acknowledgements section at the end of the manuscript.

Generative adversarial network for automatic quantification of Coronavirus disease 2019 pneumonia on chest radiographs.

PURPOSE: To develop a generative adversarial network (GAN) to quantify COVID-19 pneumonia on chest radiographs automatically. MATERIALS AND METHODS: This retrospective study included 50,000 consecutive non-COVID-19 chest CT scans in 2015-2017 for training. Anteroposterior virtual chest, lung, and pneumonia radiographs were generated from whole, segmented lung, and pneumonia pixels from each CT scan. Two GANs were sequentially trained to generate lung images from radiographs and to generate pneumonia images from lung images. GAN-driven pneumonia extent (pneumonia area/lung area) was expressed from 0% to 100%. We examined the correlation of GAN-driven pneumonia extent with semi-quantitative Brixia X-ray severity score (one dataset, n = 4707) and quantitative CT-driven pneumonia extent (four datasets, n = 54-375), along with analyzing a measurement difference between the GAN and CT extents. Three datasets (n = 243-1481), where unfavorable outcomes (respiratory failure, intensive care unit admission, and death) occurred in 10%, 38%, and 78%, respectively, were used to examine the predictive power of GAN-driven pneumonia extent. RESULTS: GAN-driven radiographic pneumonia was correlated with the severity score (0.611) and CT-driven extent (0.640). 95% limits of agreements between GAN and CT-driven extents were -27.1% to 17.4%. GAN-driven pneumonia extent provided odds ratios of 1.05-1.18 per percent for unfavorable outcomes in the three datasets, with areas under the receiver operating characteristic curve (AUCs) of 0.614-0.842. When combined with demographic information only and with both demographic and laboratory information, the prediction models yielded AUCs of 0.643-0.841 and 0.688-0.877, respectively. CONCLUSION: The generative adversarial network automatically quantified COVID-19 pneumonia on chest radiographs and identified patients with unfavorable outcomes.

A deep learning-based application for COVID-19 diagnosis on CT: The Imaging COVID-19 AI initiative.

BACKGROUND: Recently, artificial intelligence (AI)-based applications for chest imaging have emerged as potential tools to assist clinicians in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19). OBJECTIVES: To develop a deep learning-based clinical decision support system for automatic diagnosis of COVID-19 on chest CT scans. Secondarily, to develop a complementary segmentation tool to assess the extent of lung involvement and measure disease severity. METHODS: The Imaging COVID-19 AI initiative was formed to conduct a retrospective multicentre cohort study including 20 institutions from seven different European countries. Patients with suspected or known COVID-19 who underwent a chest CT were included. The dataset was split on the institution-level to allow external evaluation. Data annotation was performed by 34 radiologists/radiology residents and included quality control measures. A multi-class classification model was created using a custom 3D convolutional neural network. For the segmentation task, a UNET-like architecture with a backbone Residual Network (ResNet-34) was selected. RESULTS: A total of 2,802 CT scans were included (2,667 unique patients, mean [standard deviation] age = 64.6 [16.2] years, male/female ratio 1.3:1). The distribution of classes (COVID-19/Other type of pulmonary infection/No imaging signs of infection) was 1,490 (53.2%), 402 (14.3%), and 910 (32.5%), respectively. On the external test dataset, the diagnostic multiclassification model yielded high micro-average and macro-average AUC values (0.93 and 0.91, respectively). The model provided the likelihood of COVID-19 vs other cases with a sensitivity of 87% and a specificity of 94%. The segmentation performance was moderate with Dice similarity coefficient (DSC) of 0.59. An imaging analysis pipeline was developed that returned a quantitative report to the user. CONCLUSION: We developed a deep learning-based clinical decision support system that could become an efficient concurrent reading tool to assist clinicians, utilising a newly created European dataset including more than 2,800 CT scans.

Contemporary Concise Review 2022: Interstitial lung disease.

Novel genetic associations for idiopathic pulmonary fibrosis (IPF) risk have been identified. Common genetic variants associated with IPF are also associated with chronic hypersensitivity pneumonitis. The characterization of underlying mechanisms, such as pathways involved in myofibroblast differentiation, may reveal targets for future treatments. Newly identified circulating biomarkers are associated with disease progression and mortality. Deep learning and machine learning may increase accuracy in the interpretation of CT scans. Novel treatments have shown benefit in phase 2 clinical trials. Hospitalization with COVID-19 is associated with residual lung abnormalities in a substantial number of patients. Inequalities exist in delivering and accessing interstitial lung disease specialist care.

Chronic lung disease in children due to SARS-CoV-2 pneumonia: Case series.

The reported prevalence of chronic lung disease (CLD) due to coronavirus 2 (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2)]) pneumonia with the severe acute respiratory syndrome in children is unknown and rarely reported in English literature. In contrast to most other respiratory viruses, children generally have less severe symptoms when infected with SARS-CoV-2. Although only a minority of children with SARS-CoV-2 infection require hospitalization, severe cases have been reported. More severe SARS-CoV-2 respiratory disease in infants has been reported in low- and middle-income countries (LMICs) compared to high-income countries (HICs). We describe our experience of five cases of CLD in children due to SARS-CoV-2 collected between April 2020 and August 2022. We included children who had a history of a positive SARS-CoV-2 polymerase chain reaction (PCR) or antigen test or a positive antibody test in the serum. Three patterns of CLD related to SARS-CoV-2 were identified: (1) CLD in infants postventilation for severe pneumonia (n = 3); (2) small airway disease with bronchiolitis obliterans picture (n = 1) and (3) adolescent with adult-like post-SARS-CoV-2 disease (n = 1). Chest computerized tomography scans showed airspace disease and ground-glass opacities involving both lungs with the development of coarse interstitial markings seen in four patients, reflecting the long-term fibrotic consequences of diffuse alveolar damage that occur in children post-SARS-CoV-2 infection. Children with SARS-CoV-2 infection mostly have mild symptoms with little to no long-term sequelae, but the severe long-term respiratory disease can develop.

A unilateral whiteout lung in child with multisystem inflammatory syndrome associated with COVID-19 due to SARS-CoV-2: one case report of a boy.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a relatively new and rare complication of COVID-19. This complication seems to develop after the infection rather than during the acute phase of COVID-19. This report aims to describe a case of MIS-C in an 8-year-old Thai boy who presented with unilateral lung consolidation. Unilateral whiteout lung is not a common pediatric chest radiograph finding in MIS-C, but this is attributed to severe acute respiratory failure. CASE PRESENTATION: An 8-year-old boy presented with persistent fever for seven days, right cervical lymphadenopathy, and dyspnea for 12 h. The clinical and biochemical findings were compatible with MIS-C. Radiographic features included total opacity of the right lung and CT chest found consolidation and ground-glass opacities of the right lung. He was treated with intravenous immunoglobulin and methylprednisolone, and he dramatically responded to the treatment. He was discharged home in good condition after 8 days of treatment. CONCLUSION: Unilateral whiteout lung is not a common pediatric chest radiographic finding in MIS-C, but when it is encountered, a timely and accurate diagnosis is required to avoid delays and incorrect treatment. We describe a pediatric patient with unilateral lung consolidation from the inflammatory process.

Comments on "Post severe COVID-19 infection lung damages study. The experience of early three months multidisciplinary follow-up" by De Michele et al.

Dear Editor, we read the original study by De Michele et al. titled "Post severe COVID-19 infection lung damages study. The experience of early three months multidisciplinary follow-up" with great interest...