Development of prognostic models to estimate the probability of lung injury one year after COVID-19-related hospitalization-a prospective study.

Background: Long-term effects of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) infection still under study. The objectives of this study were to identify persistent pulmonary lesions 1 year after coronavirus disease 2019 (COVID-19) hospitalization and assess whether it is possible to estimate the probability that a patient develops these complications in the future. Methods: A prospective study of ≥18 years old patients hospitalized for SARS-COV-2 infection who develop persistent respiratory symptoms, lung function abnormalities or have radiological findings 6-8 weeks after hospital discharge. Logistic regression models were used to identify prognostic factors associated with a higher risk of developing respiratory problems. Models performance was assessed in terms of calibration and discrimination. Results: A total of 233 patients [median age 66 years [interquartile range (IQR): 56, 74]; 138 (59.2%) male] were categorized into two groups based on whether they stayed in the critical care unit (79 cases) or not (154). At the end of follow-up, 179 patients (76.8%) developed persistent respiratory symptoms, and 22 patients (9.4%) showed radiological fibrotic lesions with pulmonary function abnormalities (post-COVID-19 fibrotic pulmonary lesions). Our prognostic models created to predict persistent respiratory symptoms [post-COVID-19 functional status at initial visit (the higher the score, the higher the risk), and history of bronchial asthma] and post-COVID-19 fibrotic pulmonary lesions [female; FVC% (the higher the FVC%, the lower the probability); and critical care unit stay] one year after infection showed good (AUC 0.857; 95% CI: 0.799-0.915) and excellent performance (AUC 0.901; 95% CI: 0.837-0.964), respectively. Conclusions: Constructed models show good performance in identifying patients at risk of developing lung injury one year after COVID-19-related hospitalization.

Explainable Machine Learning for COVID-19 Pneumonia Classification With Texture-Based Features Extraction in Chest Radiography.

Both reverse transcription-PCR (RT-PCR) and chest X-rays are used for the diagnosis of the coronavirus disease-2019 (COVID-19). However, COVID-19 pneumonia does not have a defined set of radiological findings. Our work aims to investigate radiomic features and classification models to differentiate chest X-ray images of COVID-19-based pneumonia and other types of lung patterns. The goal is to provide grounds for understanding the distinctive COVID-19 radiographic texture features using supervised ensemble machine learning methods based on trees through the interpretable Shapley Additive Explanations (SHAP) approach. We use 2,611 COVID-19 chest X-ray images and 2,611 non-COVID-19 chest X-rays. After segmenting the lung in three zones and laterally, a histogram normalization is applied, and radiomic features are extracted. SHAP recursive feature elimination with cross-validation is used to select features. Hyperparameter optimization of XGBoost and Random Forest ensemble tree models is applied using random search. The best classification model was XGBoost, with an accuracy of 0.82 and a sensitivity of 0.82. The explainable model showed the importance of the middle left and superior right lung zones in classifying COVID-19 pneumonia from other lung patterns.

A Review of Crucial Radiological Investigations in the Management of COVID-19 Cases.

Chest X-ray, chest CT, and lung ultrasound are the most common radiological interventions used in the diagnosis and management of coronavirus disease 2019 (COVID-19) patients. The purpose of this literature review, which was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, is to determine which radiological investigation is crucial for that purpose. PubMed, Medline, American Journal of Radiology (AJR), Public Library of Science (PLOS), Elsevier, National Center for Biotechnology Information (NCBI), and ScienceDirect were explored. Seventy-two articles were reviewed for potential inclusion, including 50 discussing chest CT, 15 discussing chest X-ray, five discussing lung ultrasound, and two discussing COVID-19 epidemiology. The reported sensitivities and specificities for chest CT ranged from 64 to 98% and 25 to 88%, respectively. The reported sensitivities and specificities for chest X-rays ranged from 33 to 89% and 11.1 to 88.9%, respectively. The reported sensitivities and specificities for lung ultrasound ranged from 93 to 96.8% and 21.3 to 95%, respectively. The most common findings on chest CT include ground glass opacities and consolidation. The most common findings on chest X-rays include opacities, consolidation, and pleural effusion. The data indicate that chest CT is the most effective radiological tool for the diagnosis and management of COVID-19 patients. The authors support the continued use of reverse transcription polymerase chain reaction (RT-PCR), along with physical examination and contact history, for such diagnosis. Chest CT could be more appropriate in emergency situations when quick triage of patients is necessary before RT-PCR results are available. CT can also be used to visualize the progression of COVID-19 pneumonia and to identify potential false positive RT-PCR results. Chest X-ray and lung ultrasound are acceptable in situations where chest CT is unavailable or contraindicated.

The comparison of clinical, laboratory, and radiological findings in immunocompromised and immunocompetent patients with COVID-19: A case-control study.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19) with significant morbidity and mortality. We reported and compared the clinical and para-clinical findings of immunocompromised and immunocompetent COVID-19 patients in a case-control study at the Imam Khomeini hospital in Tehran, Iran. METHODS: In this study, 107 immunocompromised COVID-19 patients were recruited as the case group, and 107 immunocompetent COVID-19 patients as the control group. The participants were matched based on age and sex. The patients' information was retrieved from the hospital records in an information sheet. Associations between clinical and para-clinical findings with the immune status were assessed using bivariate and multivariate analyses. RESULTS: The initial pulse rate and recovery time were significantly higher in immunocompromised patients (p < .05). Myalgia, nausea/vomiting, loss of appetite, headache, and dizziness were more frequently reported by the control group (p < .05). Regarding the prescribed medications' duration, Sofosbovir was used longer in the case group, while Ribavirin was used longer in the control groups (p < .05). The most common complication in the case group was acute respiratory distress syndrome, although no major complications were observed in the control group. According to the multivariate analysis, recovery time and Lopinavir/Ritonavir (Kaletra) prescription were significantly higher in the immunocompromised compared to the immunocompetent group. CONCLUSION: Recovery time was significantly longer in the immunocompromised compared to the immunocompetent group, which emphasizes the necessity of prolonged care in these high-risk patients. Also, it is recommended to investigate the effect of novel therapeutic interventions to reduce the recovery time in addition to improving the prognosis of immunodeficient patients with COVID-19.

Review on Diagnostic Methods for SARS-CoV-2

Background: In December 2019, a large number of patients with a novel coronavirus were identified in Wuhan, China. The novel coronavirus (COVID-19) is highly contagious and in-creasing the rate of mortality day by day. The World Health Organization declared COVID-19 as a worldwide pandemic on March 11, 2020. Early diagnosis of SARS-CoV-2 can restrict the COVID-19 pandemic. Objective(s): We aim to study the currently available diagnostic methods for COVID-19. Method(s): World Health Organisation portal, Centre for Disease Control and Prevention portal, Indian Council of Medical Research portal, Chinese Centre for Disease Control and Prevention por-tal, Science Direct, Google Scholar, Research Gate, etc. were searched for obtaining data. Result(s): Rapid diagnosis and prompt treatment can reduce the number of prospective cases. The diagnostic strategy encompasses the screening of the virus with nucleic acid amplification test (NAAT) such as real-time reverse-transcription polymerase chain reaction (RT-PCR) assays. Sero-logical testing is a diagnostic procedure used for identifying the presence of immune responses. Radiological findings in individuals with COVID-19 are characterised by multiple areas of consolida-tion in the chest. Rapid antigen tests are in-vitro diagnostics that have been designed to give results within 10-20 min. Conclusion(s): Rapid, simple, and safe diagnosis of COVID-19 has a great impact on deciding clinical and epidemiological factors. RT-PCR results often require 5 to 6 hours. Diagnosis by serologi-cal testing is not suitable but important epidemiologically. At present, the best radiological strategy remains undefined. Rapid antigen tests have limitations on sensitivity.Copyright © 2021 Bentham Science Publishers.

Augmentation of literature review of COVID-19 radiology.

We suggest an augmentation of the excellent comprehensive review article titled "Comprehensive literature review on the radiographic findings, imaging modalities, and the role of radiology in the coronavirus disease 2019 (COVID-19) pandemic" under the following categories: (1) "Inclusion of additional radiological features, related to pulmonary infarcts and to COVID-19 pneumonia"; (2) "Amplified discussion of cardiovascular COVID-19 manifestations and the role of cardiac magnetic resonance imaging in monitoring and prognosis"; (3) "Imaging findings related to fluorodeoxyglucose positron emission tomography, optical, thermal and other imaging modalities/devices, including 'intelligent edge' and other remote monitoring devices"; (4) "Artificial intelligence in COVID-19 imaging"; (5) "Additional annotations to the radiological images in the manuscript to illustrate the additional signs discussed"; and (6) "A minor correction to a passage on pulmonary destruction".

Hematological Markers in the Assessment of Severity of COVID-19 Infection - A Retrospective Cross-Sectional Study

Background: Patients with COVID-19 (coronavirus disease 2019) present varying disease severity;with such heterogeneity in clinical presentations, it can be challenging to assess the severity and progression of the disease. In addition, no specific markers have been identified that would indicate the diagnosis or prognosis of the disease. Therefore, this study was aimed to determine whether a panel of hematological and inflammatory biomarkers were indicative of disease severity in the assessment and the prognosis of COVID-19. Methods: The retrospective cross-sectional study was carried out in a university hospital in South India between May 2020 and September 2020. The participants were 997 patients with COVID-19, confirmed by real-time reverse transcriptasepolymerase chain reaction (RT-PCR). Information regarding demographics and laboratory tests was obtained from medical records. Association analysis was conducted using SPSS, version 16, and a P-value <0.05 was considered statistically significant. Results: Inflammatory markers such as C-reactive protein (CRP) and D-dimer, calculated inflammatory ratios, and hemoglobin were significantly increased in cases of severe COVID-19. Leucocytosis with increased absolute neutrophil count and decreased absolute lymphocyte count were observed. Conclusion: Haematological and inflammatory markers may indicate the severity of the disease. The severity of COVID-19 was indicated by elevated total white cells, increased neutrophillymphocyte, and platelet-lymphocyte ratios. Increasing levels of CRP indicated a severe prognosis of the disease. D-dimer elevations may indicate the incidence of thromboembolic episodes. Therefore, hematological indices were considered applicable in assessing the progression of the disease and for the risk stratification of the disease. © 2022 Shriaz University of Medical Sciences

Integrating patient symptoms, clinical readings, and radiologist feedback with computer-aided diagnosis system for detection of infectious pulmonary disease: a feasibility study.

Automatic computer-aided diagnosis (CAD) system has been widely used as an assisting tool for mass screening and risk assessment of infectious pulmonary diseases (PDs). However, such a system still lacks clinical acceptability and trust due to the integration gap between the patient's metadata, radiologist feedback, and the CAD system. This paper proposed three integration frameworks, namely-direct integration (DI), rule-based integration (RBI), and weight-based integration (WBI). The proposed framework helps clinicians diagnose lung inflammation and provide an end-to-end robust diagnostic system. Initially, the feasibility of integrating patients' symptoms, clinical pathologies, and radiologist feedback with CAD system to improve the classification performance is investigated. Subsequently, the patient's metadata and radiologist feedback are integrated with the CAD system using the proposed integration frameworks. The proposed method's performance is evaluated using a private dataset consisting of 70 chest X-ray (CXR) images (31 COVID-19, 14 other diseases, and 25 normal). The obtained results reveal that the proposed WBI achieved the highest classification performance (accuracy = 98.18%, F1 score = 97.73%, and Matthew's correlation coefficient = 0.969) compared to DI and RI. The generalization capability of the proposed framework is also verified from an external validation set. Furthermore, the Friedman average ranking and Shaffer and Holm post hoc statistical methods reveal the obtained results' statistical significance. Methodological diagram of proposed integration frameworks.

A Brief Analysis of a New Device to Prevent Early Intubation in Hypoxemic Patients: An Observational Study

Featured Application A new device to support oxygen therapy for patients diagnosed with severe COVID-19. The need for mechanical ventilation is one of the main concerns related to the care of patients with COVID-19. The aim of this study is to evaluate the efficacy of a bubble device for oxygen supplementation. This device was implemented for the selected patients hospitalized with severe COVID-19 pneumonia with persistent low oxygen saturation. Patients were selected in three major COVID-19 hospitals of Bahia state in Brazil from July to November 2020, where they remained with the device for seven days and were monitored for different factors, such as vital signs, oximetry evaluation, and arterial blood gasometry. Among the 51 patients included in the study, 68.63% successfully overcame hypoxemia without the necessity to be transferred to mechanical ventilation, whereas 31.37% required tracheal intubation (p value < 0.05). There was no difference of note on the analysis of the clinical data, chemistry, and hematological evaluation, with the exception of the SpO(2) on follow-up days. Multivariate analysis revealed that the independent variable, male sex, SpO(2), and non-inhaled mask, was associated with the necessity of requiring early mechanical ventilation. We concluded that this bubble device should be a prior step to be utilized before indication of mechanical ventilation in patients with persistent hypoxemia of severe COVID-19 pneumonia.

A Two-Step Radiologist-Like Approach for Covid-19 Computer-Aided Diagnosis from Chest X-Ray Images

Thanks to the rapid increase in computational capability during the latest years, traditional and more explainable methods have been gradually replaced by more complex deep-learning-based approaches, which have in fact reached new state-of-the-art results for a variety of tasks. However, for certain kinds of applications performance alone is not enough. A prime example is represented by the medical field, in which building trust between the physicians and the AI models is fundamental. Providing an explainable or trustful model, however, is not a trivial task, considering the black-box nature of deep-learning based methods. While some existing methods, such as gradient or saliency maps, try to provide insights about the functioning of deep neural networks, they often provide limited information with regards to clinical needs. We propose a two-step diagnostic approach for the detection of Covid-19 infection from Chest X-Ray images. Our approach is designed to mimic the diagnosis process of human radiologists: it detects objective radiological findings in the lungs, which are then employed for making a final Covid-19 diagnosis. We believe that this kind of structural explainability can be preferable in this context. The proposed approach achieves promising performance in Covid-19 detection, compatible with expert human radiologists. Moreover, despite this work being focused Covid-19, we believe that this approach could be employed for many different CXR-based diagnosis. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Differential diagnosis of COVID-19 at the chest computed tomography scan: A review with special focus on cancer patients.

BACKGROUND: Given the several radiological features shared by coronavirus disease 2019 pneumonia and other infective or non-infective diseases with lung involvement, the differential diagnosis is often tricky, and no unequivocal tool exists to help the radiologist in the proper diagnosis. Computed tomography is considered the gold standard in detecting pulmonary illness caused by severe acute respiratory syndrome coronavirus 2. AIM: To conduct a systematic review including the available studies evaluating computed tomography similarities and discrepancies between coronavirus disease 2019 pneumonia and other pulmonary illness, then providing a discussion focus on cancer patients. METHODS: Using pertinent keywords, we performed a systematic review using PubMed to select relevant studies published until October 30, 2020. RESULTS: Of the identified 133 studies, 18 were eligible and included in this review. CONCLUSION: Ground-glass opacity and consolidations are the most common computed tomography lesions in coronavirus disease 2019 pneumonia and other respiratory diseases. Only two studies included cancer patients, and the differential diagnosis with early lung cancer and radiation pneumonitis was performed. A single lesion associated with pleural effusion and lymphadenopathies in lung cancer and the onset of the lesions in the radiation field in the case of radiation pneumonitis allowed the differential diagnosis. Nevertheless, the studies were heterogeneous, and the type and prevalence of lesions, distributions, morphology, evolution, and additional signs, together with epidemiological, clinical, and laboratory findings, are crucial to help in the differential diagnosis.

Systematic Review: Clinical Symptoms and Laboratory and Radiology Findings in Children with COVID-19.

Coronavirus disease 2019 (COVID-19) caused by the new [novel] coronavirus, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a pandemic with exceeding 72 million cases and 1.2 million deaths by the end of November 2020. We aimed to evaluate clinical, laboratory, and radiology findings of COVID-19 in children as reported worldwide and thereby to increase the clinical knowledge about the disease. Bibliographic searches were conducted in December 2020 using PubMed and Google Scholar. The search was limited to children [below 18 years of age]. The search strategy yielded a total of 336 potential articles but finally a total of 25 valid studies covering a total of 2446 (China: 1109, Europe: 663, North America: 674) pediatric patients. In the studies covered by this review, it was observed that the median age was calculated at various values between the ages of 1 and 7 years. In the studies, overall rate of the asymptomatic patients was 24.8% (ranging between 10.7 and 56.6). Acute upper respiratory tract infection (URTI) [mild disease] was observed in 40.7 (ranging between 22 and 50.6%), mild pneumonia in 27% (ranging between 9.5 and 40.6%), and severe pneumonia in 5.3% (ranging between 1.9 and 10.6%). A total of 3% (ranging between 0.7 and 5.1%) of the patients had critical severity. Among the most common clinical symptoms and findings; 61.7% (ranging between 57.4 and 64.3%) of the patients had fever, 53.2% (ranging between 30.6 and 75.1%) had cough, 16.8% (ranging between 4.6 and 27.2%) had diarrhea or nausea, and 15% had lymphopenia. Abnormal radiological findings were detected in 47.2 of the children with COVID-19 and ground glass opacity was in 22.2%. COVID-19 manifests milder and the clinical signs and symptoms vary widely in children. Laboratory and radiological findings of COVID-19 in pediatric patients are not mostly disease-specific, except lymphopenia may have a limited value, and ground glass opacity may have a significant diagnostic value.

Mediastinal lymphadenopathy may predict 30-day mortality in patients with COVID-19.

PURPOSE: There is scarce data on the impact of the presence of mediastinal lymphadenopathy on the prognosis of coronavirus-disease 2019 (COVID-19). We aimed to investigate whether its presence is associated with increased risk for 30-day mortality in a large group of patients with COVID-19. METHOD: In this retrospective cross-sectional study, 650 adult laboratory-confirmed hospitalized COVID-19 patients were included. Patients with comorbidities that may cause enlarged mediastinal lymphadenopathy were excluded. Demographics, clinical characteristics, vital and laboratory findings, and outcome were obtained from electronic medical records. Computed tomography scans were evaluated by two blinded radiologists. Univariate and multivariate logistic regression analyses were performed to determine independent predictive factors of 30-day mortality. RESULTS: Patients with enlarged mediastinal lymphadenopathy (n = 60, 9.2%) were older and more likely to have at least one comorbidity than patients without enlarged mediastinal lymphadenopathy (p = 0.03, p = 0.003). There were more deaths in patients with enlarged mediastinal lymphadenopathy than in those without (11/60 vs 45/590, p = 0.01). Older age (OR:3.74, 95% CI: 2.06-6.79; p < 0.001), presence of consolidation pattern (OR:1.93, 95% CI: 1.09-3.40; p = 0.02) and enlarged mediastinal lymphadenopathy (OR:2.38, 95% CI:1.13-4.98; p = 0.02) were independently associated with 30-day mortality. CONCLUSION: In this large group of hospitalized patients with COVID-19, we found that in addition to older age and consolidation pattern on CT scan, enlarged mediastinal lymphadenopathy were independently associated with increased mortality. Mediastinal evaluation should be performed in all patients with COVID-19.

The outlook for diagnostic purposes of the 2019-novel coronavirus disease.

At the end of December 2019, a novel acute respiratory syndrome coronavirus 2 (SARS-CoV2) appeared as the third unheard of outbreak of human coronavirus infection in the 21st century. First, in Wuhan, China, the novel SARS-CoV2 was named by the World Health Organization (WHO), as 2019-nCOV (COVID-19), and spread extremely all over the world. SARS-CoV2 is transmitted to individuals by human-to-human transmission leading to severe viral pneumonia and respiratory system injury. SARS-CoV2 elicits infections from the common cold to severe conditions accompanied by lung injury, acute respiratory distress syndrome, and other organ destruction. There is a possibility of virus transmission from asymptomatic cases as active carriers, in addition to symptomatic ones, which is a crucial crisis of COVID-19 that should be considered. Hence, paying more attention to the accurate and immediate diagnosis of suspected and infected cases can be a great help in preventing the rapid spread of the virus, improving the disease prognosis, and controlling the pandemic. In this review, we provide a comprehensive and up-to-date overview of the different types of Clinical and Para-clinical diagnostic methods and their practical features, which can help understand better the applications and capacities of various diagnostic approaches for COVID-19 infected cases.