Radiological Findings as Predictors of COVID-19 Lung Sequelae: A Systematic Review and Meta-Analysis

Background This systematic review and meta-analysis aimed to investigate the radiological predictors of post- coronavirus disease 19 (COVID-19) pulmonary fibrosis and incomplete absorption of pulmonary lesions. Method We systematically searched PubMed, EMBASE, and Web of Science for studies reporting the predictive value of radiological findings in patients with post-COVID-19 lung residuals published through November 11, 2022. The pooled odds ratios with a 95% confidence interval (CI) were assessed. The random-effects model was used due to the heterogeneity of the true effect sizes. Results We included 11 studies. There were 1777 COVID-19-positive patients, and 1014 (57 %) were male. All studies used chest computed tomography (CT) as a radiologic tool. Moreover, chest X-ray (CXR) and lung ultrasound were used in two studies, along with a CT scan. CT severity score, Radiographic Assessment of Lung Edema score (RALE), interstitial score, lung ultrasound score (LUS), patchy opacities, abnormal CXR, pleural traction, and subpleural abnormalities were found to be predictors of post-COVID-19 sequels. CT severity score (CTSS) and consolidations were the most common predictors among included studies. Pooled analysis revealed that pulmonary residuals in patients with initial consolidation are about four times more likely than in patients without this finding (OR: 3.830;95% CI: 1.811-8.102, I2: 4.640). Conclusion Radiological findings can predict the long-term pulmonary sequelae of COVID-19 patients. CTSS is an important predictor of lung fibrosis and COVID-19 mortality. Lung fibrosis can be diagnosed and tracked using the LUS. Changes in RALE score during hospitalization can be used as an independent predictor of mortality.

Impact of admission viral load on respiratory outcomes in hospitalized SARS-CoV-2 infected patients with cancer and without cancer: A 2-, 4- and 6-months follow-up prospective study.

BACKGROUND: This prospective follow-up study aimed to determine the temporal changes in respiratory outcomes over 6 months period in patients with and without cancer hospitalized for severe COVID-19 and to determine the associated risk factors based on admission viral load. METHODS: All adult patients hospitalized with a confirmed diagnosis of severe SARS-CoV-2 infection were investigated using rRT-PCR on nasopharyngeal swab specimens. Patients were divided into three arbitrary groups according to their cycle threshold (CT) values obtained at admission as high (CT<25.0), medium (CT between 25.0 and 30.0), and low (CT>30.0) viral load. Patients had pulmonary function tests, chest high-resolution computed tomography (HRCT), and a 6-minute walking time distance measured at each follow-up visit. RESULTS: This follow-up study had a total of 112 participants, of which 75 were cancer-free and 37 had active cancer. Overall, 29.5% had a low viral load, compared to 48.2% who had a high viral load, and 22.3% had a medium viral load. For patients who did not have cancer, the mean age was 57.3 (SD 15.4) and for those who had cancer, it was 62.3 (SD 18.4). Most patients had overall better temporal changes in pulmonary function and tolerance, as well as exercise capacity, even though severe and chronic respiratory abnormalities persisted in a fraction of the patients. In patients without cancer who had a high viral load, we have seen a substantial reduction in diffusion capacity of the lungs for carbon monoxide (DLCO) predicted value with a median of 65 (IQR 63-70) while in patients with cancer, it was 60 (IQR 56-67) at 2 months. At 4 and 6 months, the predicted DLCO values for patients without cancer were 65 (IQR 61-70), whereas the predicted DLCO values for patients with active cancer were 62 (IQR 60-67) and 67 (59-73). Importantly, radiological abnormalities persisted in 22 (29%) non-cancer patients and 16 (43%) cancer patients. Multivariate regression analysis showed an increased odds ratio of impaired HRCT associated with a high viral load of 3.04 (95% CI:1.68-6.14; p < 0.001) for patients without cancer and 5.07 (95% CI: 4.04-10.8; p < 0.0001) for patients with cancer. The CT pneumonia score at hospitalization was 2.25 (95% CI:1.76-3.08; p = 0.041) and 2.85 (95% CI:1.89-5.14; p = 0.031) for non-cancer and cancer patients respectively. CONCLUSIONS: The evidence of persistent pulmonary abnormalities and radiographic changes was found in both patient groups who had high viral load at hospital admission and suggesting that SARS-CoV-2 viral load might serve as a useful indicator to predict the development of respiratory complications in patients with COVID-19.

Primary SARS-CoV-2 Pneumonia Screening in Adults: Analysis of the Correlation between High-Resolution Computed Tomography Pulmonary Patterns and Initial Oxygen Saturation Levels.

BACKGROUND: Chest high-resolution computed tomography (HRCT) is mandatory for patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and a high respiratory rate (RR) because sublobar consolidation is the likely pathological pattern in addition to ground glass opacities (GGOs). OBJECTIVE: The present study determined the correlation between the percentage extent of typical pulmonary lesions on HRCT, as a representation of severity, and the RR and peripheral oxygen saturation level (SpO2), as measured through pulse oximetry, in patients with reverse transcriptase polymerase chain reaction (RT-PCR)-confirmed primary (noncomplicated) SARS-CoV-2 pneumonia. METHODS: The present retrospective study was conducted in 332 adult patients who presented withzdyspnea and hypoxemia and were admitted to Prince Mohammed bin Abdulaziz Hospital, Riyadh, Saudi Arabia between May 15, 2020 and December 15, 2020. All the patients underwent chest HRCT. Of the total, 198 patients with primary noncomplicated SARS-CoV-2 pneumonia were finally selected based on the typical chest HRCT patterns. The main CT patterns, GGO and sublobar consolidation, were individually quantified as a percentage of the total pulmonary involvement through algebraic summation of the percentage of the 19 pulmonary segments affected. Additionally, the statistical correlation strength between the total percentage pulmonary involvement and the age, initial RR, and percentage SpO2 of the patients was determined. RESULTS: The mean ± standard deviation (SD) age of the 198 patients was 48.9 ± 11.4 years. GGO magnitude alone exhibited a significant weak positive correlation with patients' age (r = 0.2; p = 0.04). Sublobar consolidation extent exhibited a relatively stronger positive correlation with RR than GGO magnitude (r = 0.23; p = 0.002). A relatively stronger negative correlation was observed between the GGO extent and SpO2 (r = - 0.38; p = 0.002) than that between sublobar consolidation and SpO2 (r = - 0.2; p = 0.04). An increase in the correlation strength was demonstrated with increased case segregation with GGO extent (r = - 0.34; p = 0.01). CONCLUSION: The correlation between the magnitudes of typical pulmonary lesion patterns, particularly GGO, which exhibited an incremental correlation pattern on chest HRCT, and the SpO2 percentage, may allow the establishment of an artificial intelligence program to differentiate primary SARS-CoV-2 pneumonia from other complications and associated pathology influencing SpO2.

kNN-SVM with Deep Features for COVID-19 Pneumonia Detection from Chest X-ray

Most attention has been paid to chest Computed Tomography (CT) in this burgeoning crisis because many cases of COVID-19 demonstrate respiratory illness clinically resembling viral pneumonia which persists in prominent visual signatures on high-resolution CT befitting of viruses that damage lungs. However, CT is very expensive, time-consuming, and inaccessible in remote hospitals. As an important complement, this research proposes a novel kNN-regularized Support Vector Machine (kNN-SVM) algorithm for identifying COVID-induced pneumonia from inexpensive and simple frontal chest X-ray (CXR). To compute the deep features, we used transfer learning on the standard VGG16 model. Then the autoencoder algorithm is used for dimensionality reduction. Finally, a novel kNN-regularized Support Vector Machine algorithm is developed and implemented which can successfully classify the three classes: Normal, Pneumonia, and COVID-19 on a benchmark chest X-ray dataset. kNN-SVM combines the properties of two well-known formalisms: k-Nearest Neighbors (kNN) and Support Vector Machines (SVMs). Our approach extends the total-margin SVM, which considers the distance of all points from the margin;each point is weighted based on its k nearest neighbors. The intuition is that examples that are mostly surrounded by similar neighbors, i.e., of their own class, are given more priority to minimize the influence of drastic outliers and improve generalization and robustness. Thus, our approach combines the local sensitivity of kNN with the global stability of the total-margin SVM. Extensive experimental results demonstrate that the proposed kNN-SVM can detect COVID-19-induced pneumonia from chest X-ray with greater or comparable accuracy relative to human radiologists. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Detection of COVID-19 in High Resolution Computed Tomography Using Vision Transformer

In the current pandemic, precise and early diagnose of COVID-19 patient remained a crucial task for control of the spread of the COVID-19 virus in the healthcare sector. Due to the unexpected spike in COVID-19 cases, the majority of countries have experienced scarcity and poor testing rate. Chest X-rays and CT scans have been discussed in the literature as a viable source of testing for COVID-19 disease in patients. However, manually reviewing the CT and x-ray images is time-consuming and prone to error. Taking account into these constraints and the improvements in data science, this research proposed a Vision Transformer-based deep learning pipeline for COVID-19 diagnose from CT-based imaging. Due to the scarcity of large data sets, three open-source datasets of CT scans are pooled to generate 27370 images of covid and non- covid individuals. The proposed vision transformer-based model accurately diagnoses COVID-19 from normal chest CT images with an accuracy of 98 percent. This research would assist the practitioner, radiologist and doctors in early and accurate diagnose of COVID-19. © 2022 IEEE.

Lung Ultrasound (LUS) in COVID-19 Pneumonia: Usefulness in Two Atypical Cases.

Clinical experience and scientific articles have shown that patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can be paucisymptomatic or asymptomatic at the time of diagnosis. In this paper, we will discuss two paucisymptomatic patients with blood tests suggestive for SARS-CoV-2 infection but with repeated negative nasopharyngeal swabs and without typical features of COVID-19 pneumonia on chest high-resolution computed tomography. In these cases, lung ultrasound helped to raise clinical suspicion of COVID-19 pneumonia and facilitate diagnosis. LEARNING POINTS: During the current COVID-19 pandemic, lung ultrasound (LUS) is being used extensively to evaluate and monitor lung damage in infected patients.Several patients have been described with negative PCR swabs who tested positive for SARS-CoV-2 in bronchoalveolar lavage fluid.Typical signs of interstitial pneumonia on LUS strongly indicate COVID-19 pneumonia, thus suggesting further investigation and invasive tests to confirm the diagnosis.

Isolated cerebral mucormycosis in post-COVID-19 pneumonia

Background: Rhino-orbital-cerebral and isolated cerebral involvement of basal ganglia by mucormycosis are two different manifestations of CNS mucormycosis. The former variant caused by inhaled fungal spores and is common with immunosuppressive conditions. The latter form is caused by intravascular inoculation of spores as seen in intravenous drug abusers. Case report: Here we describe a case of young, non-addict patient with a history of recent mild COVID-19 pneumonia who presented with isolated cerebral mucormycosis involving bilateral basal ganglia. Discussion(s): The pulmonary vasculitis associated with COVID-19 is probably the cause of direct intravascular entry of inhaled fungal spores leading to direct isolated cerebral involvement. Such condition may rapidly turn fatal. Conclusion(s): This is the first reported case of isolated cerebral mucormycosis following post-COVID-19 infection. Early tissue diagnosis and intravenous amphotericin B is the key management.Copyright © 2022

Pulmonary embolism presenting as delirium: an acute confusional state in an elderly patient-a case report.

Background: Large numbers of elderly patients are admitted to hospitals in acute confusional states. In many, the underlying causes are easily found; in some, correct diagnosis is difficult. Pulmonary embolism (PE), the most serious clinical presentation of venous thromboembolism, is often misdiagnosed because of its non-specific features including delirium. Case presentation: A 73-year-old woman was admitted to our hospital in a confused state with no obvious risk factors of PE. D-dimer levels were elevated and contrast-enhanced high-resolution computed tomography (HRCT) of the chest confirmed the diagnosis of PE. She was treated with enoxaparin and discharged on dabigatran. Her symptoms had resolved at the time of discharge, and she has been stable for over three month's follow-up visit. Conclusion: PE should be regarded as a differential in elderly patients with an acute confusional state despite the absence of obvious risk factors. Investigating for and treating when confirmed may save a life.

Pattern of Lung Involvement in Predicting Severity and Sequelae in Patients With COVID-19.

Background During the COVID pandemic, high-resolution CT scan has played a pivotal role in detecting lung involvement and severity based on the segments of the lung involved. The pattern of involvement was not considered, and our aim is to observe the pattern of lung involvement in predicting severity and guiding management protocol in patients with COVID-19. Methodology It was a prospective observational study conducted with 151 patients admitted with COVID-19 with a positive reverse transcriptase polymerase chain reaction test (RT-PCR) in a single tertiary care hospital in south India. Patients with pre-existing lung pathologies were excluded from the study. Eligible patients were then divided into mild, moderate, and severe categories based on Indian Council of Medical Research (ICMR) guidelines, and high-resolution computed tomography (HRCT) chest was done, findings of which were then categorized based on lung involvement; into ground glass opacities (GGO), interstitial involvement and mixture of both. These were then analyzed to determine their importance with respect to the duration of stay and severity of the disease. Results The data collected was analyzed by IBM SPSS software version 23.0 (IBM Corp., Armonk, NY, USA). The study population included 114 males (75.5%) and 37 females (24.5%). HRCT chest was done which showed 62.3% of patients had GGO, 14.6% had interstitial lung involvement, 18.5% had a mixture of both and 4.6% had normal lung findings. These findings, when compared to clinical categories of severity, showed a significant co-relation between pattern of involvement of the lung and the severity of the disease. It also showed significant co-relation with the duration of stay. Conclusion HRCT chest has proven to be useful in the determination of patient's severity and can guide with management. We suggest earlier initiation of steroids and anticoagulants in patients with interstitial involvement even for the patients not on oxygen therapy yet. It can be used as a triage modality for screening due to the advantage of presenting with immediate results as opposed to RT-PCR which might take hours and can delay treatment which can prevent worsening.

CO RADS grade of HRCT Thorax and RT PCR testing for the diagnosis of coronavirus disease 2019 (COVID 19): A descriptive hospital based study of asymptomatic planned surgery cases.

Background: The coronavirus disease 2019 (COVID-19) reporting and data system (CO-RADS) grade of high-resolution computed tomography (HRCT)-thorax scan investigation is an innovative tool for the diagnosis of COVID-19 patients. By this tool, majority of moderate-to-severe COVID-19 patients are screened to detect lung pathologies. Hardly any study has explored its use vis-a-vis reverse transcriptase-polymerase chain reaction (RT-PCR) in asymptomatic patients. Objectives: (1) The objective of the study is to assess the frequency COVID-19 patients among asymptomatic subjects who were admitted in the hospital for planned surgery, (2) estimate the sensitivity and specificity of CO-RADS grade of HRCT-thorax investigation for the diagnosis of COVID-19 patients where RT-PCR test was considered as "Gold Standard" test. Methodology: A descriptive retrospective study was conducted by studying the records in the case files of 150 patients who were admitted in the Department of General Surgery, Man Mohini Health Clinic, Murshidabad, West Bengal for minor surgical procedures between September 1 and December 31, 2020. Data were collected from hospital records. The CO-RADS grade of HRCT-thorax investigation and RT-PCR test were performed for the diagnosis of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) virus. The MS-excel application was applied for data analysis. Results: The mean age of the participants was 42.58 ± 14.29 years. A total of 17 (11%) and 39 (26%) of the patients were diagnosed with COVID-19 by HRCT-thorax and RT-PCR test, respectively. The sensitivity and specificity of CO-RADS grade of HRCT-thorax investigation for diagnosis of COVID-19 patients were 43.58% and 100%, respectively. The positive and negative predictive values of CO-RADS grade of HRCT-thorax investigation were 100% and 83.45%, respectively. Conclusions: The frequency of asymptomatic patients with COVID-19 that was missed by HRCT thorax was high, compared to the gold standard RT-PCR, reflecting its low sensitivity and low negative predictive value in the diagnosis of SARS-CoV-2 virus infection. Hence, it is difficult to conclude in favor of HRCT thorax as first-line screening modality in all individuals.

Résumé Contexte: Le système de notification et de données sur la maladie à coronavirus 2019 (COVID 19) (CO RADS) la tomographie (HRCT)­exploration du thorax est un outil innovant pour le diagnostic des patients COVID 19. Par cet outil, la majorité des Les patients COVID 19 modérés à sévères sont dépistés pour détecter les pathologies pulmonaires. Pratiquement aucune étude n'a exploré son utilisation vis-à-vis réaction en chaîne par transcriptase polymérase (RT PCR) chez des patients asymptomatiques. Objectifs: (1) L'objectif de l'étude est d'évaluer la fréquence Patients COVID 19 parmi les sujets asymptomatiques qui ont été admis à l'hôpital pour une chirurgie planifiée, (2) estimer la sensibilité et la spécificité de grade CO-RADS de l'investigation HRCT-thorax pour le diagnostic des patients COVID-19 où le test RT-PCR a été considéré comme "Gold Standard" test. Méthodologie: Une étude rétrospective descriptive a été menée en étudiant les dossiers des dossiers de 150 patients admis dans le département de chirurgie générale, clinique de santé Man Mohini, Murshidabad, Bengale occidental pour des interventions chirurgicales mineures entre septembre 1 et 31 décembre 2020. Les données ont été recueillies à partir des dossiers hospitaliers. Le grade CO RADS de l'examen HRCT thorax et du test RT PCR était réalisée pour le diagnostic du virus du coronavirus 2 lié au syndrome respiratoire aigu sévère (SRAS CoV 2). L'application MS Excel a été appliquée pour l'analyse des données. Résultats: L'âge moyen des participants était de 42,58 ± 14,29 ans. Au total, 17 (11 %) et 39 (26 %) des patients ont été diagnostiqués avec COVID 19 par HRCT thorax et test RT PCR, respectivement. La sensibilité et la spécificité du grade CO-RADS de l'investigation HRCT-thorax pour le diagnostic des patients COVID-19 étaient de 43,58 % et 100 %, respectivement. Les valeurs prédictives positives et négatives du grade CO RADS de L'investigation HRCT-thorax était de 100 % et 83,45 %, respectivement. Conclusions: La fréquence des patients asymptomatiques atteints de COVID 19 qui manqué par HRCT thorax était élevé, par rapport à la RT-PCR de référence, reflétant sa faible sensibilité et sa faible valeur prédictive négative dans le diagnostic d'infection par le virus SARS CoV 2. Par conséquent, il est difficile de conclure en faveur de HRCT thorax comme modalité de dépistage de première ligne chez tous les individus. Mots-clés: personnes asymptomatiques, tomodensitométrie haute résolution - thorax, transcriptase inverse-réaction en chaîne par polymérase maladie à coronavirus 2019.

Comparison of Imaging Severity Between Vaccinated and Unvaccinated COVID-19 Patients: Perspective of an Indian District.

BACKGROUND: Extensive vaccination drives undertaken globally helped in the fight against the coronavirus disease 2019 (COVID-19) pandemic, but different nations adopted different vaccination policies to tackle the disease. The vaccination drive in India began with the administration of two different vaccines: Covishield and Covaxin. We assessed the effect of vaccination status on imaging severity in patients with positive COVID-19 reverse transcription-polymerase chain reaction (RT-PCR)/antigen tests. METHOD: This was a single-center retrospective observation analysis carried out over three months between March 1, 2021, to May 31, 2021. Data access was provided by the District Hospital Review Board (DHRB) and the Department of Health (DOH), District Ambala, Haryana. Appropriate statistical tools were used to analyze the data. Statistical Package for Social Sciences (SPSS) 26.0 and Python 3.9 were used for statistical analysis and visualization, and a p-value of less than 0.05 was considered statistically significant. RESULTS: The total sample size of the study was 1,316, out of which 371 (28.2%) were vaccinated and 945 (71.8%) were not vaccinated. The mean age of the study participants was 49.6 ± 15.7 years. Seven hundred ninety-seven (60.6%) participants were male, while 519 (39.4%) participants were female. A statistically significant reduction was observed in the computed tomography severity score (CTSS) of the vaccinated population compared to the non-vaccinated group (χ2 = 74.3, p < 0.001). Vaccination led to a statistically significant decrease in mean CTSS across all lung lobes. CONCLUSION: Emerging COVID-19 variants challenge the effect of available vaccines, with different nations adopting different vaccination strategies to deal with the ongoing health problem. CTSS was employed as an objective marker to study the disease severity and effect of vaccination. Vaccination resulted in a significant reduction in CTSS seen on high-resolution computed tomography (HRCT) chest scans. There was a significant decrease in the incidence of severe COVID-19 pneumonia among vaccinated individuals. We need more observational data to corroborate the efficacy of vaccines presented in the randomized trials. Sharing such data between different nations can help us adopt a unifying vaccination strategy and decrease the impact of COVID-19 in subsequent disease waves.

Chest HRCT Assessment of COVID-19 Patients in Vaccinated versus Nonvaccinated Patients: A Comparative Study in a Tertiary Care Hospital.

Coronavirus disease 2019 (COVID-19) has turned out to be the most devastating viral disease that the world has encountered for the past century. The World Health Organization (WHO) declared it a pandemic on March 11, 2020. The disease mainly spreads through respiratory droplets which makes social distancing a primary tool of prevention. Many variant strains have emerged up since the pandemic started and the Delta variant is responsible for recent surge of cases in second wave of COVID-19 in India. Mass vaccination is the most efficacious precautionary measure that can be applied to stop the transmission and generate herd immunity. Vaccination does not give 100% prevention from infection, but it halts the severity of infection. Vaccine is the boon amidst the mayhem. Our study highlights that those vaccinated (particularly two doses) had clinically mild symptoms and mild computed tomography severity score (CTSS) with a speedy recovery. Those unvaccinated had moderate to severe symptoms with moderate to severe CTSS (>8) often requiring hospital admission and having poor prognosis. Thus, vaccine helps reduce the health burden of the already strained health care system. Immunization visit can also be used as an opportunity to disseminate message to encourage behavior, to reduce transmission risk of COVID-19 virus, to identify the signs and symptoms of disease, and to provide guidance on what to do.

COVID-19 Diagnosis with HRCT Images Using Deep Transfer Learning

High-resolution computed tomography (HRCT) is a way of diagnosing, in which X-rays are used to acquire the high resolution images. It is one of the types of Computed Tomography(CT) which is more clear and accurate in giving precise results. The HRCT scan covers the whole lung tissue which helps to find the cause of any abnormalities in scanned images. The present study is undertaken to investigate COVID-19 disease on HRCT images with Deep Transfer Learning models. In this paper, we are proposing Deep Learning model on HRCT Images for predicting whether a patient is affected or not. The Proposed model is an automatic classification of images by considering Mobile Net, Inception Net, VGG16, Resnet50, CNN deep learning models. The results are obtained from Inception Net with classification mean accuracy of 99%. Our model demonstrates the use of InceptionNet deep transfer learning model for diagnosing Covid-19 as an alternate way of testing the infection. © 2022 IEEE.

Role of High Resolution Computed Tomography (HRCT) in predicting critical disease in corona virus disease

Introduction: In the present study, we correlate the oxygen requirement of adult patients infected with COVID-19 virus with 25 CT severity score and estimate dinical outcome in the COVID-19 infected patients. Materials and methods: An observational case control study of 123 symptomatic COVID-19 positive patients presented to our hospital was collected for 3 months (August 2020 to Oct 2020). All patients underwent plain HRCT scan on TOSHIBA Activion 16 slice CT. The study was approved by Institutional Ethics Research review board and informed consents were obtained from all COVID-19 infected patients. Results: In our study, the Mean age of the patients ranged from 51-60 years (69.9% males, 30.1% females). CT severity score was correlated positively with the oxygen requirements as well as with other parameters i.e. age and sex. CT score of more than or equal to 18 was associated with an increased mortality risk and found to be predictive of death both in univariate (HR, 8.33;95% CI, 3.19-21.73;p < 0.0001) and multivariate analysis (HR, 3.74;95% CI, 1.10-12.77;p = 0.0348). Conclusion: The COVID-19 clinical severity is highly correlated with the 25-point CT severity score. Our findings imply that a chest CT grading system can help predict COVID-19 disease fate and has a strong relationship with oxygen demand and intubation.

The Role of Lung Ultrasound in SARS-CoV-19 Pneumonia Management.

PURPOSE: We aimed to assess the role of lung ultrasound (LUS) in the diagnosis and prognosis of SARS-CoV-2 pneumonia, by comparing it with High Resolution Computed Tomography (HRCT). PATIENTS AND METHODS: All consecutive patients with laboratory-confirmed SARS-CoV-2 infection and hospitalized in COVID Centers were enrolled. LUS and HRCT were carried out on all patients by expert operators within 48-72 h of admission. A four-level scoring system computed in 12 regions of the chest was used to categorize the ultrasound imaging, from 0 (absence of visible alterations with ultrasound) to 3 (large consolidation and cobbled pleural line). Likewise, a semi-quantitative scoring system was used for HRCT to estimate pulmonary involvement, from 0 (no involvement) to 5 (>75% involvement for each lobe). The total CT score was the sum of the individual lobar scores and ranged from 0 to 25. LUS scans were evaluated according to a dedicated scoring system. CT scans were assessed for typical findings of COVID-19 pneumonia (bilateral, multi-lobar lung infiltration, posterior peripheral ground glass opacities). Oxygen requirement and mortality were also recorded. RESULTS: Ninety-nine patients were included in the study (male 68.7%, median age 71). 40.4% of patients required a Venturi mask and 25.3% required non-invasive ventilation (C-PAP/Bi-level). The overall mortality rate was 21.2% (median hospitalization 30 days). The median ultrasound thoracic score was 28 (IQR 20-36). For the CT evaluation, the mean score was 12.63 (SD 5.72), with most of the patients having LUS scores of 2 (59.6%). The bivariate correlation analysis displayed statistically significant and high positive correlations between both the CT and composite LUS scores and ventilation, lactates, COVID-19 phenotype, tachycardia, dyspnea, and mortality. Moreover, the most relevant and clinically important inverse proportionality in terms of P/F, i.e., a decrease in P/F levels, was indicative of higher LUS/CT scores. Inverse proportionality P/F levels and LUS and TC scores were evaluated by univariate analysis, with a P/F-TC score correlation coefficient of -0.762, p < 0.001, and a P/F-LUS score correlation coefficient of -0.689, p < 0.001. CONCLUSIONS: LUS and HRCT show a synergistic role in the diagnosis and disease severity evaluation of COVID-19.

Correlation of Radiological Findings on High-Resolution Computed Tomography Chest in Vaccinated and Unvaccinated COVID-19 Patients from Eastern India

Background: In this coronavirus disease-2019 (COVID-19) pandemic, safe and effective preventative vaccines are essential to contain the pandemic, which has had severe medical, economic, and societal consequences, despite some people still becoming infected after receiving immunisation. Methods: A total of 200 patients were examined and split into two groups: (1) 100 consecutive COVID-19-positive cases who had been vaccinated and (2) 100 consecutive COVID-19-positive patients with no vaccination. At the time of the scan, the patient's vaccination status was noted. Results: The computed tomography severity score (CTSS) of unvaccinated individuals was found to be considerably greater than that of partly or fully vaccinated patients (median 13 vs. 7, P < 0.001). Completely vaccinated individuals had a considerably lower median CTSS than partly vaccinated patients (6 vs. 9, P < 0.001). Conclusions: Individuals should be thoroughly vaccinated to avoid major lung disease. As a result, stronger dedication and motivating efforts should be made worldwide to improve the COVID-19 vaccination program.

Lung decortication for spontaneous hydropneumothorax in post COVID patients: A case series.

Hydropneumothorax is an abnormal collection of air and fluid in the pleural space. As it is a rare complication of COVID-19 pneumonia, we report a case series of spontaneous hydropneumothorax converted to pus collection that was resistant to medical management and treated as decortication and pleurectomy.

Factors Affecting the Risk of Interstitial Lung Disease Development in Hospitalized Patients With COVID-19 Pneumonia.

BACKGROUND: Coronavirus disease 2019 (COVID-19) related chronic lung changes secondary to severe disease have become well known. The aim of this study was to determine the risk factors that affect the development of interstitial lung disease in subjects with COVID-19 pneumonia who were hospitalized. METHODS: Patients hospitalized with COVID-19 pneumonia between June 2020 and March 2021 were retrospectively analyzed. Smoking histories, comorbidities, reverse transcriptase polymerase chain reaction test results, laboratory parameters at the time of the diagnosis, oxygen support, the use of corticosteroids with dosage and duration data, the need for ICU care were recorded. High-resolution computed tomographies (HRCT) were obtained for study population in their 3-6 months follow-up visit. The subjects were classified as having residual parenchymal lung disease if a follow-up HRCT revealed parenchymal abnormalities except pure ground-glass opacities (the residual disease group). The control group consisted of the subjects with normal chest radiograph or HRCT in their follow-up visit or the presence of pure ground-glass opacities. Two groups were compared for their demographic and clinical abnormalities, laboratory parameters, treatment regimens, and the need for ICU care. RESULTS: The study included 446 subjects. The mean ± SD age was 58.4 ± 13.87 years, with 257 men (57.6%). Although 55 subjects had normal HRCT features on their follow-up HRCT, 157 had abnormal lung parenchymal findings. Univariate logistic regression analysis revealed statistically significant results for age, sex, corticosteroid treatment, and the need for ICU care for predicting interstitial lung disease development (P < .001, P = .003, P < .001, and P < .001, respectively). Also, the residual disease group had significantly higher leukocyte and neutrophil counts and lower lymphocyte counts (P < .001, P < .001, P = .004, respectively). Correlated with these findings, neutrophil-to-lymphocyte ratios and platelet-to-lymphocyte ratios were significantly higher in the residual disease group (P < .001 and P = .008, respectively). CONCLUSIONS: Residual parenchymal disease was observed 3-6 months after discharge in one third of the subjects hospitalized with COVID-19 pneumonia. It was observed that interstitial lung disease developed more frequently in older men and in those subjects with more-severe disease parameters.

Spectrum of High Resolution Computed tomography chest findings in PCR positive COVID-19 patients according to duration of infection and CT severity score assessment

Objective: To evaluate the spectrum of HRCT findings of COVID-19 in RT-PCR positive patients according to duration of infection and severity of disease. Methods: This retrospective study was conducted at Radiology department of Lahore General Hospital, Lahore from May to July 2020. Total 40 COVID-19 patients were reviewed for clinical features, HRCT chest findings based on time from symptom onset and CT conduction. Chi-square and fissure exact test were used for measuring association with severity of COVID-19, p value ≤0.05 was reported significant. Mean CT scores were calculated. ROC curve analysis showed threshold values of CT-SS for severe disease. Results: Of total 40 patients with age ranged from 22-83 years, 22(55%) were males and 18(45%) females. The hallmark of COVID-19 was combined GGO and consolidation, GGO alone and consolidation alone in bilateral, sub pleural and posterior distribution. Early stage had normal CT or GGO alone, intermediate and late stage had both GGO and consolidation. Septal lines/bands and crazy paving pattern were prevalent in late stage. Clinically, 24 (60%) were in severe group and 16(40%) in mild group. Severity of COVID-19 was associated with GGO alone (p=0.05), GGO and consolidation (p=0.01), crazy paving (p=0.01) and lung scores (p≤0.05). The threshold values of CT-SS for identifying severe disease by two radiologists were 18.50 and 20.50. Conclusion: HRCT manifestations along with CT-SS aids in predicting disease severity. Staging according to duration of infection is effective in understanding variation in pattern of chest findings in coronavirus disease.

Association between Periodontitis and COVID-19 Based on Severity Scores of HRCT Chest Scans.

BACKGROUND: A relationship between periodontitis and COVID-19 may exist, as highlighted by several hypothetical models. However, the evidence is limited. Hence, the present study was conducted to determine whether an association exists between periodontitis and COVID-19. METHODS: A cross-sectional study was carried out with patients diagnosed with COVID-19 who were divided into three groups-mild, moderate, and severe COVID-19-based on the COVID-19 severity score of high-resolution computed tomography (HRCT) chest scans. Periodontal parameters-including the plaque index (PI), ratio of sites with gingival bleeding (BOP), pocket depth (PD), gingival recession (REC), clinical attachment loss (CAL), and mean numbers of mobile and missing teeth due to periodontitis-were recorded for all three groups. Statistical analyses were applied to the data. RESULTS: Of 294 patients with COVID-19, approximately 50.68% (n = 149) had periodontitis, and the highest percentage (87.5%) was reported in the severe COVID-19 group. Additionally, severe and advanced stages of periodontitis (stage III-IV) were found to be significantly more frequent in subjects with severe COVID-19 than in the other two groups. The HRCT severity score (CT-SS) was moderately correlated with increased levels of periodontal parameters. CONCLUSIONS: Results of logistic regression analyses showed that the probability of developing severe COVID-19 was 2.81 times higher in patients with periodontitis. An association exists between periodontitis and severe COVID-19.