An observer comparison study to evaluate a machine learning model to quantify the infected pneumonia on lung CT images

Quantification of infected lung volume using computed tomography (CT) images can play a critical role in predicting the severity of pulmonary infectious disease. Manual segmentation of infected areas from several CT image slices, however, is not efficient and viable in clinical practice. To assist clinicians in overcoming this challenge, we developed a new method to automatically segment and quantify the percentage of the infected lung volume. First, we used a public dataset of 20 COVID-19 patients, which consists of manually annotated lung and infection masks, to train a new joint deep learning (DL) model for lung and infection segmentation. As for lung segmentation, a Mask-RCNN model was applied to the lung volume with a novel postprocessing technique. Following that, an ensemble model with a customized residual attention UNet model and feature pyramid network (FPN) models was employed for infection segmentation. Next, we assembled another set of 80 CT scans of Covid-19 patients. Two chest radiologists manually evaluated each CT scan and reported the infected lung volume percentage using a customized graphical user interface (GUI). The developed DL-model was also employed to process these CT images. Then, we compared the agreement between the radiologist (manual) and model-based (automated) percentages of diseased regions. Additionally, the GUI was used to let radiologists rate acceptance of the DL-model generated segmentation results. Analyzing the results demonstrate that the agreement between manual and automated segmentation is >95% in 28 testing cases. Furthermore, >53% of testing cases received the top assessment rating scores from two radiologists (between four-five- score). Thus, this study illustrates the feasibility of developing a DL-model based automated tool to effectively provide quantitative evaluation of infected lung regions to assist in improving the efficiency of radiologists in infection diagnosis. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

Psychological Distress and Negative Emotions in Post-COVID Infection: A Comparative Study of the COVID and NO-COVID Young Patients.

According to the literature, mental health assumed urgent relevance, and several scholars are debating on the enduring of the neurological and psychiatric symptoms in post COVID patients. Our study aimed to investigate the emotional dimensions in young population to the COVID exposition: primary endpoint was to detect the psychological distress up to 3 months in post-COVID-19. A comparative study was conducted among young adults in Italy. We also assessed dysphoria, depression, anxiety, stress symptoms, pessimism, and positive personality traits. The participants were 140 Italian young aged 18-30 years (mean = 22.1, SD ± 2.65; 65.0% female). The sample was distinguished in two groups: COVID and NO-COVID groups. The results revealed that young who have been exposed to COVID-19 infection evidenced emotional vulnerability by higher psychological distress (depression, anxiety, stress), dysphoria signs (irritability, discontent, interpersonal resentment, and feelings of renunciation/surrender) then No COVID-19 infection young. Furthermore, COVID patients showed higher negative emotions about the expected life, uncertain for future, and loss of motivation (characterized no desires) than NO-COVID infection. In conclusion, the vulnerability of young exposed to COVID infection even in mild severity should be considered as emerging unmet need of mental health recovering: urgent health policy actions to boost the psychological, biological and social strategic pillar for young generation.

Antiphospholipid Antibodies Occurrence in Acute SARS-CoV-2 Infection without Overt Thrombosis.

We sought to determine the prevalence of antiphospholipid antibodies (aPLs) and their correlation with COVID-19 severity (in terms of clinical and laboratory parameters) in patients without thrombotic events during the early phase of infection. This was a cross-sectional study with the inclusion of hospitalized COVID-19 patients from a single department during the COVID-19 pandemic (April 2020-May 2021). Previous known immune disease or thrombophilia along with long-term anticoagulation and patients with overt arterial or venous thrombosis during SARS-CoV-2 infection were excluded. In all cases, data on four criteria for aPL were collected, namely lupus anticoagulant (LA), IgM and IgG anticardiolipin antibodies (aCL), as well as IgG anti-ß2 glycoprotein I antibodies (aß2GPI). One hundred and seventy-nine COVID-19 patients were included, with a mean age of 59.6 (14.5) years and a sex ratio of 0.8 male: female. LA was positive in 41.9%, while it was strongly positive in 4.5%; aCL IgM was found in 9.5%, aCL IgG in 4.5%, and aß2GPI IgG in 1.7% of the sera tested. Clinical correlation: LA was more frequently expressed in severe COVID-19 cases than in moderate or mild cases (p = 0.027). Laboratory correlation: In univariate analysis, LA levels were correlated with D-dimer (p = 0.016), aPTT (p = 0.001), ferritin (p = 0.012), C-reactive protein (CRP) (p = 0.027), lymphocyte (p = 0.040), and platelet (p < 0.001) counts. However, in the multivariate analysis, only the CRP levels correlated with LA positivity: OR (95% CI) 1.008 (1.001-1.016), p = 0.042. LA was the most common aPL identified in the acute phase of COVID-19 and was correlated with infection severity in patients without overt thrombosis.

Predicting COVID-19 Severity Integrating RNA-Seq Data Using Machine Learning Techniques

A fundamental challenge in the fight against COVID-19 is the development of reliable and accurate tools to predict disease progression in a patient. This information can be extremely useful in distinguishing hospitalized patients at higher risk for needing UCI from patients with low severity. How SARS-CoV-2 infection will evolve is still unclear. Method(s): A novel pipeline was developed that can integrate RNA-Seq data from different databases to obtain a genetic biomarker COVID-19 severity index using an artificial intelligence algorithm. Our pipeline ensures robustness through multiple cross-validation processes in different steps. Result(s): CD93, RPS24, PSCA, and CD300E were identified as COVID-19 severity gene signatures. Furthermore, using the obtained gene signature, an effective multi-class classifier capable of discrimi-nating between control, outpatient, inpatient, and ICU COVID-19 patients was optimized, achieving an accuracy of 97.5%. Conclusion(s): In summary, during this research, a new intelligent pipeline was implemented to develop a specific gene signature that can detect the severity of patients suffering COVID-19. Our approach to clinical decision support systems achieved excellent results, even when processing unseen samples. Our system can be of great clinical utility for the strategy of planning, organizing and managing human and material resources, as well as for automatically classifying the severity of patients affected by COVID-19.Copyright © 2023 Bentham Science Publishers.

The impact of lncRNA-GAS5/miRNA-200/ACE2 molecular pathway on the severity of COVID-19.

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is responsible for coronavirus disease (COVID-19), potentially has severe adverse effects, leading to public health crises worldwide. In COVID-19, deficiency of ACE-2 is linked to increased inflammation and cytokine storms via increased angiotensin II levels and decreased ACE-2/Mas receptor axis activity. MiRNAs are small sequences of noncoding RNAs that regulate gene expression by binding to the targeted mRNAs. MiR-200 dysfunction has been linked to the development of ARDS following acute lung injury and has been proposed as a key regulator of ACE2 expression. LncRNA growth arrest-specific transcript 5 (GAS5) has been recently studied for its modulatory effect on the miRNA-200/ACE2 axis. OBJECTIVE: The current study aims to investigate the role of lncRNA GAS5, miRNA-200, and ACE2 as new COVID-19 diagnostic markers capable of predicting the severity of SARS-CoV-2 complications. METHODS: A total of 280 subjects were classified into three groups: COVID-19-negative controls (n=80), and COVID-19 patients (n=200) who required hospitalization were classified into two groups: group (2) moderate cases (n=112) and group (3) severe cases (n = 88). RESULTS: The results showed that the serum GAS5 expression was significantly down-expressed in COVID-19 patients; as a consequence, the expression of miR-200 was reported to be overexpressed and its targeted ACE2 was down-regulated. The ROC curve was drawn to examine the diagnostic abilities of GAS5, miR-200, and ACE2, yielding high diagnostic accuracy with high sensitivity and specificity. CONCLUSION: lncRNA-GAS5, miRNA-200, and ACE2 panels presented great diagnostic potential as they demonstrated the highest diagnostic accuracy for discriminating moderate COVID-19 and severe COVID-19 cases.

Understanding the Experience of Long COVID Symptoms in Hospitalized and Non-Hospitalized Individuals: A Random, Cross-Sectional Survey Study.

The relationship between initial COVID-19 infection and the development of long COVID remains unclear. The purpose of this study was to compare the experience of long COVID in previously hospitalized and non-hospitalized adults in a community-based, cross-sectional telephone survey. Participants included persons with positive COVID-19 test results between 21 March 2021 and 21 October 2021 in Alberta, Canada. The survey included 330 respondents (29.1% response rate), which included 165 previously hospitalized and 165 non-hospitalized individuals. Significantly more previously hospitalized respondents self-reported long COVID symptoms (81 (49.1%)) compared to non-hospitalized respondents (42 (25.5%), p < 0.0001). Most respondents in both groups experienced these symptoms for more than 6 months (hospitalized: 66 (81.5%); non-hospitalized: 25 (59.5), p = 0.06). Hospitalized respondents with long COVID symptoms reported greater limitations on everyday activities from their symptoms compared to non-hospitalized respondents (p < 0.0001) and tended to experience a greater impact on returning to work (unable to return to work-hospitalized: 20 (19.1%); non-hospitalized: 6 (4.5%), p < 0.0001). No significant differences in self-reported long COVID symptoms were found between male and female respondents in both groups (p > 0.05). This study provides novel data to further support that individuals who were hospitalized for COVID-19 appear more likely to experience long COVID symptoms.

Disease-Modifying Therapies, Outcomes, Risk Factors and Severity of COVID-19 in Multiple Sclerosis: A MENACTRIMS Registry Based Study

Background: Multiple sclerosis (MS) patients have been considered a higher-risk population for COVID-19 due to the high prevalence of disability and disease-modifying therapy use;however, there is little data in our Middle East and North Africa region (MENA) identifying clinical characteristics of MS associated with worse COVID-19 outcomes. Material(s) and Method(s): This a nationwide, multicenter, retrospective cohort study conducted between March 2020 and February 2021 and included MS patients with a suspected or confirmed COVID-19. Using data collected from the MENACTRIMS registry and local COVID-19 registries, the association of patient demographics, MS disease characteristics, and use of disease-modifying therapies with outcomes and severity of COVID-19 illness were evaluated by multivariate logistic models. Result(s): A total of 600 MS patients with suspected (n=58) or confirmed (n=542) COVID-19 (mean age: 36.4 +/- 10.16 years;414 (69%) females;mean disease duration: 8.3+/- 6.6 years) were analyzed. Seventy-three patients (12.2%) had a COVID-19 severity score of 3 or more, and 15 patients (2.5%) died of COVID-19. The median EDSS was 2.0 (range, 0-9.5), and 559 patients (93.2%) were receiving disease-modifying therapy (DMT). There was a higher proportion of patients with a COVID-19 severity score of 3 or more among patients treated with DMTs relative to untreated patients (82.9% vs 17.1%;P < .001), from whom the majority (n=117;19.7%) were maintained on anti-CD20 therapies such as ocrelizumab and rituximab. Comorbidities mainly hypertension and cardiovascular diseases, progressive MS, disease duration, and EDSS were associated with severe or worse COVID-19 disease outcome. Multivariate logistic regression analysis showed that older age (odds ratio per 10 years, 1.5 [95%CI, 1.1-2.0]), male gender (OR, 2.1 [95%CI. 1.2-3.8]), obesity (OR, 2.8 [95%CI, 1.3-5.8]), and treatment ocrelizumab/rituximab (OR for ocrelizumab, 4.6 [95%CI. 1.2-17.7], OR for rituximab, 14.1 [95%CI, 4.8-41.3]) or off-label immunosuppressive medications such as azathioprine or mycophenolate mofetil (OR, 8.8 [95%CI. 1.7-44.0]) were risk factors for moderate to severe COVID-19 requiring hospitalization. Surprisingly, smoking and diabetes were not identified as risk factors for severe COVID-19 disease in our cohort. Conclusion(s): In this registry-based cohort study of patients with MS, age, sex, EDSS, obesity, progressive MS were independent risk factors for severe COVID-19. Moreover, there was an association found between exposure to anti-CD20 DMTs and COVID-19 severity. Knowledge of these risk factors may help improve the clinical management of MS patients with COVID-19 infection.Copyright © 2022

Association Between Diabetes and Mortality Among Adult Patients Hospitalized With COVID-19: A Cohort Study of Hospitalized Adults in Ontario, Canada, and Copenhagen, Denmark.

OBJECTIVES: Diabetes has been reported to be associated with an increased risk of death among patients with COVID-19. However, the available studies lack detail on COVID-19 illness severity and measurement of relevant comorbidities. METHODS: We conducted a multicentre, retrospective cohort study of patients 18 years of age and older who were hospitalized with COVID-19 between January 1, 2020, and November 30, 2020, in Ontario, Canada, and Copenhagen, Denmark. Chart abstraction emphasizing comorbidities and disease severity was performed by trained research personnel. The association between diabetes and death was measured using Poisson regression. The main outcome measure was in-hospital 30-day risk of death. RESULTS: Our study included 1,133 hospitalized patients with COVID-19 in Ontario and 305 in Denmark, of whom 405 and 75 patients, respectively, had pre-existing diabetes. In both Ontario and Denmark, patients with diabetes were more likely to be older; have chronic kidney disease, cardiovascular disease, and higher troponin levels; and be receiving antibiotics, when compared with adults without diabetes. In Ontario, 24% (n=96) of adults with diabetes died compared with 15% (n=109) of adults without diabetes. In Denmark, 16% (n=12) of adults with diabetes died in hospital compared with 13% (n=29) of those without diabetes. In Ontario, the crude mortality ratio among patients with diabetes was 1.60 (95% confidence interval [CI], 1.24 to 2.07) and in the adjusted regression model it was 1.19 (95% CI, 0.86 to 1.66). In Denmark, the crude mortality ratio among patients with diabetes was 1.27 (95% CI, 0.68 to 2.36) and in the adjusted model it was 0.87 (95% CI, 0.49 to 1.54). Meta-analysis of the 2 rate ratios from each region resulted in a crude mortality ratio of 1.55 (95% CI, 1.22 to 1.96) and an adjusted mortality ratio of 1.11 (95% CI, 0.84 to 1.47). CONCLUSION: The presence of diabetes was not strongly associated with in-hospital COVID-19 mortality independent of illness severity and other comorbidities.

Clinical and radiological imaging as prognostic predictors in COVID-19 patients

BackgroundSince the announcement of COVID-19 as a pandemic infection, several studies have been performed to discuss the clinical picture, laboratory finding, and imaging features of this disease. The aim of this study is to demarcate the imaging features of novel coronavirus infected pneumonia (NCIP) in different age groups and outline the relation between radiological aspect, including CT severity, and clinical aspect, including age, oxygen saturation, and fatal outcome. We implemented a prospective observational study enrolled 299 laboratory-confirmed COVID-19 patients (169 males and 130 females;age range = 2–91 years;mean age = 38.4 ± 17.2). All patients were submitted to chest CT with multi-planar reconstruction. The imaging features of NCIP in different age groups were described. The relations between CT severity and age, oxygen saturation, and fatal outcome were evaluated.ResultsThe most predominant CT features were bilateral (75.4%), posterior (66.3%), pleural-based (93.5%), lower lobe involvement (89.8%), and ground-glass opacity (94.7%). ROC curve analysis revealed that the optimal cutoff age that was highly exposed to moderate and severe stages of NCIP was 38 years old (AUC = 0.77, p < 0.001). NCIP was noted in 42.6% below 40-year-old age group compared to 84% above 40-year-old age group. The CT severity was significantly related to age and fatal outcome (p < 0.001). Anterior, centrilobular, hilar, apical, and middle lobe involvements had a significant relation to below 90% oxygen saturation. A significant negative correlation was found between CT severity and oxygen saturation (r = − 0.49, p < 0.001). Crazy-paving pattern, anterior aspect, hilar, centrilobular involvement, and moderate and severe stages had a statistically significant relation to higher mortality.ConclusionThe current study confirmed the value of CT as a prognostic predictor in NCIP through demonstration of the strong relation between CT severity and age, oxygen saturation, and the fatal outcome. In the era of COVID-19 pandemic, this study is considered to be an extension to other studies discussing chest CT features of COVID-19 in different age groups with demarcation of the relation of chest CT severity to different pattern and distribution of NCIP, age, oxygen saturation, and mortality rate.

Associations between Interleukin-4 and COVID-19 severity: A systematic review and meta-analysis

Introduction: This systematic review and meta-analysis aimed to determine the correlation between IL-4 concentrations and COVID-19 severity. Methods: This study was designed as a systematic review and meta-analysis and was performed in accordance to the PRISMA statement. Titles, abstracts, and full texts of articles were independently reviewed by at least 2 authors. Continuous variables were compared by the mean difference (MD) with 95% confidence interval (CI). Results: Thirty-three studies reported IL-4 levels among severe versus non-severe COVID-19 patients. Pooled analysis showed that levels of IL-4 among those groups varied and amounted to 2.72 ± 3.76 pg/mL vs 3.08 ± 4.14 pg/mL (MD =-0.26;95%CI:-0.43 to-0.10;p = 0.002. In addition, eight studies reported levels of IL-4 among COVID-19 patients who survived vs deceased and was 2.61 ± 0.49 pg/mL vs (3.44 ± 16.4 pg/mL, respectively (MD = 0.22;95%CI: 0.08 to 0.37;p = 0.002). Discussion: This detailed systematic review and meta-analysis revealed that the plasma concentration of IL-4 is a potential risk factor for COVID-19 severity and mortality. Specifically, old age and male gender were associated with high IL-4 levels. Lung damage could result from the change in IL-4 concentration, thus making critical and severe COVID-19 cases at a very high risk of dying, thereby reducing their quality of life. Therefore, strategies such as using monoclonal antibodies to inhibit Th2 cytokines could be explored in developing an effective treatment regimen for COVID-19 patients. Take-home message: An independent risk factor for the severity and fatality of COVID-19 is the plasma levels of IL-4. High IL-4 levels are specifically related to old age and male gender. Lung damage may be a result of the change in IL-4 concentration, placing COVID-19 critically and severely ill at a high risk of dying. © 2022 by the authors.

AI-MIA: COVID-19 Detection and Severity Analysis Through Medical Imaging

This paper presents the baseline approach for the organized 2nd Covid-19 Competition, occurring in the framework of the AIMIA Workshop in the European Conference on Computer Vision (ECCV 2022). It presents the COV19-CT-DB database which is annotated for COVID-19 detection, consisting of about 7,700 3-D CT scans. Part of the database consisting of Covid-19 cases is further annotated in terms of four Covid-19 severity conditions. We have split the database and the latter part of it in training, validation and test datasets. The former two datasets are used for training and validation of machine learning models, while the latter is used for evaluation of the developed models. The baseline approach consists of a deep learning approach, based on a CNN-RNN network and report its performance on the COVID19-CT-DB database. The paper presents the results of both Challenges organised in the framework of the Competition, also compared to the performance of the baseline scheme. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19 Severity Index (SI): is it a sensitive predictor score for disease outcome in hospitalized patients?

Introduction. The COVID-19 pandemic showed the wide ranging of coronavirus disease prognosing, hence clinical identification of patients who are at risk of poor outcomes is a priority. But there is no proven prognostic scoring system yet. COVID-19 SI was developed as a triage tool, that could be used by healthcare personnel to identify highrisk patients. 1 Aim. To estimate whether COVID-19 SI could predict the disease outcome in hospitalized patients with coronavirus disease already on admission? Methods. The study was a single-center retrospective analysis based on data of 632 COVID-19 patients admitted to the City Hospital No 4 (Dnipro) from August to October 2021. The patients' SI on admission and disease outcome were analyzed and statistically processed. Results. Distribution of survivors and nonsurvivors regarding clinical risk in accordance with SI is presented in Table 1. The sensitivity of SI as prognostic score totaled 37 %;specificity - 52,7 %. Conclusions. The study confirmed COVID-19 SI as a good triage tool on admission, but it has low sensitivity and specificity as prognostic score. (Table Presented).

Boosting COVID-19 Severity Detection with Infection-Aware Contrastive Mixup Classification

This paper presents our solution for the 2nd COVID-19 Severity Detection Competition. This task aims to distinguish the Mild, Moderate, Severe, and Critical grades in COVID-19 chest CT images. In our approach, we devise a novel infection-aware 3D Contrastive Mixup Classification network for severity grading. Specifically, we train two segmentation networks to first extract the lung region and then the inner lesion region. The lesion segmentation mask serves as complementary information for the original CT slices. To relieve the issue of imbalanced data distribution, we further improve the advanced Contrastive Mixup Classification network by weighted cross-entropy loss. On the COVID-19 severity detection leaderboard, our approach won the first place with a Macro F1 Score of 51.76%. It significantly outperforms the baseline method by over 11.46%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Effects of Antidepressants on COVID-19 Outcomes: Retrospective Study on Large-Scale Electronic Health Record Data.

BACKGROUND: Antidepressants exert an anticholinergic effect in varying degrees, and various classes of antidepressants can produce a different effect on immune function. While the early use of antidepressants has a notional effect on COVID-19 outcomes, the relationship between the risk of COVID-19 severity and the use of antidepressants has not been properly investigated previously owing to the high costs involved with clinical trials. Large-scale observational data and recent advancements in statistical analysis provide ample opportunity to virtualize a clinical trial to discover the detrimental effects of the early use of antidepressants. OBJECTIVE: We primarily aimed to investigate electronic health records for causal effect estimation and use the data for discovering the causal effects of early antidepressant use on COVID-19 outcomes. As a secondary aim, we developed methods for validating our causal effect estimation pipeline. METHODS: We used the National COVID Cohort Collaborative (N3C), a database aggregating health history for over 12 million people in the United States, including over 5 million with a positive COVID-19 test. We selected 241,952 COVID-19-positive patients (age >13 years) with at least 1 year of medical history. The study included a 18,584-dimensional covariate vector for each person and 16 different antidepressants. We used propensity score weighting based on the logistic regression method to estimate causal effects on the entire data. Then, we used the Node2Vec embedding method to encode SNOMED-CT (Systematized Nomenclature of Medicine-Clinical Terms) medical codes and applied random forest regression to estimate causal effects. We used both methods to estimate causal effects of antidepressants on COVID-19 outcomes. We also selected few negatively effective conditions for COVID-19 outcomes and estimated their effects using our proposed methods to validate their efficacy. RESULTS: The average treatment effect (ATE) of using any one of the antidepressants was -0.076 (95% CI -0.082 to -0.069; P<.001) with the propensity score weighting method. For the method using SNOMED-CT medical embedding, the ATE of using any one of the antidepressants was -0.423 (95% CI -0.382 to -0.463; P<.001). CONCLUSIONS: We applied multiple causal inference methods with novel application of health embeddings to investigate the effects of antidepressants on COVID-19 outcomes. Additionally, we proposed a novel drug effect analysis-based evaluation technique to justify the efficacy of the proposed method. This study offers causal inference methods on large-scale electronic health record data to discover the effects of common antidepressants on COVID-19 hospitalization or a worse outcome. We found that common antidepressants may increase the risk of COVID-19 complications and uncovered a pattern where certain antidepressants were associated with a lower risk of hospitalization. While discovering the detrimental effects of these drugs on outcomes could guide preventive care, identification of beneficial effects would allow us to propose drug repurposing for COVID-19 treatment.

Preoperative SARS-CoV-2 infection increases risk of early postoperative cardiovascular complications following noncardiac surgery.

As the coronavirus disease 2019 (COVID-19) pandemic progresses to an endemic phase, a greater number of patients with a history of COVID-19 will undergo surgery. Major adverse cardiovascular and cerebrovascular events (MACE) are the primary contributors to postoperative morbidity and mortality; however, studies assessing the relationship between a previous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and postoperative MACE outcomes are limited. Here, we analyzed retrospective data from 457,804 patients within the N3C Data Enclave, the largest national, multi-institutional data set on COVID-19 in the United States. However, 7.4% of patients had a history of COVID-19 before surgery. When comorbidities, age, race, and risk of surgery were controlled, patients with preoperative COVID-19 had an increased risk for 30-day postoperative MACE. MACE risk was influenced by an interplay between COVID-19 disease severity and time between surgery and infection; in those with mild disease, MACE risk was not increased even among those undergoing surgery within 4 wk following infection. In those with moderate disease, risk for postoperative MACE was mitigated 8 wk after infection, whereas patients with severe disease continued to have elevated postoperative MACE risk even after waiting for 8 wk. Being fully vaccinated decreased the risk for postoperative MACE in both patients with no history of COVID-19 and in those with breakthrough COVID-19 infection. Together, our results suggest that a thorough assessment of the severity, vaccination status, and timing of SARS-CoV-2 infection must be a mandatory part of perioperative stratification.NEW & NOTEWORTHY With an increasing proportion of patients undergoing surgery with a prior history of COVID-19, it is crucial to understand the impact of SARS-CoV-2 infection on postoperative cardiovascular/cerebrovascular risk. Our work assesses a large, national, multi-institutional cohort of patients to highlight that COVID-19 infection increases risk for postoperative major adverse cardiovascular and cerebrovascular events (MACE). MACE risk is influenced by an interplay between disease severity and time between infection and surgery, and full vaccination reduces the risk for 30-day postoperative MACE. These results highlight the importance of stratifying time-to-surgery guidelines based on disease severity.

Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists.

OBJECTIVE: To help improve radiologists' efficacy of disease diagnosis in reading computed tomography (CT) images, this study aims to investigate the feasibility of applying a modified deep learning (DL) method as a new strategy to automatically segment disease-infected regions and predict disease severity. METHODS: We employed a public dataset acquired from 20 COVID-19 patients, which includes manually annotated lung and infections masks, to train a new ensembled DL model that combines five customized residual attention U-Net models to segment disease infected regions followed by a Feature Pyramid Network model to predict disease severity stage. To test the potential clinical utility of the new DL model, we conducted an observer comparison study. First, we collected another set of CT images acquired from 80 COVID-19 patients and process images using the new DL model. Second, we asked two chest radiologists to read images of each CT scan and report the estimated percentage of the disease-infected lung volume and disease severity level. Third, we also asked radiologists to rate acceptance of DL model-generated segmentation results using a 5-scale rating method. RESULTS: Data analysis results show that agreement of disease severity classification between the DL model and radiologists is >90% in 45 testing cases. Furthermore, >73% of cases received a high rating score (≥4) from two radiologists. CONCLUSION: This study demonstrates the feasibility of developing a new DL model to automatically segment disease-infected regions and quantitatively predict disease severity, which may help avoid tedious effort and inter-reader variability in subjective assessment of disease severity in future clinical practice.

Age of First Overweight and Obesity, COVID-19 and Long COVID in Two British Birth Cohorts.

Longer exposure to obesity, and thus a longer period in an inflamed state, may increase susceptibility to infectious diseases and worsen severity. Previous cross-sectional work finds higher BMI is related to worse COVID-19 outcomes, but less is known about associations with BMI across adulthood. To examine this, we used body mass index (BMI) collected through adulthood in the 1958 National Child Development Study (NCDS) and the 1970 British Cohort Study (BCS70). Participants were grouped by the age they were first overweight (> 25 kg/m2) and obese (> 30 kg/m2). Logistic regression was used to assess associations with COVID-19 (self-reported and serology-confirmed), severity (hospital admission and contact with health services) and long-COVID reported at ages 62 (NCDS) and 50 (BCS70). An earlier age of obesity and overweight, compared to those who never became obese or overweight, was associated with increased odds of adverse COVID-19 outcomes, but results were mixed and often underpowered. Those with early exposure to obesity were over twice as likely in NCDS (odds ratio (OR) 2.15, 95% confidence interval (CI) 1.17-4.00) and three times as likely in BCS70 (OR 3.01, 95% CI 1.74-5.22) to have long COVID. In NCDS they were also over four times as likely to be admitted to hospital (OR 4.69, 95% CI 1.64-13.39). Most associations were somewhat explained by contemporaneous BMI or reported health, diabetes or hypertension; however, the association with hospital admission in NCDS remained. An earlier age of obesity onset is related to COVID-19 outcomes in later life, providing evidence of the long-term impact of raised BMI on infectious disease outcomes in midlife.

Severe and fatal COVID-19 is characterised by increased circulating glucagon like peptide 1 and procalcitonin modulated by type 2 diabetes.

AIMS: Endotoxemia commonly occurs in severe and fatal COVID-19, suggesting that concomitant bacterial stimuli may amplify the innate immune response induced by SARS-CoV-2. We previously demonstrated that the endogenous glucagon like peptide 1 (GLP-1) system in conjunction with increased procalcitonin (PCT) is hyperactivated in patients with severe Gram-negative sepsis and modulated by type 2 diabetes (T2D). We aimed to determine the association of COVID-19 severity with endogenous GLP-1 activation upregulated by increased specific pro-inflammatory innate immune response in patients with and without T2D. MATERIALS AND METHODS: Plasma levels of total GLP-1, IL-6, and PCT were estimated on admission and during hospitalisation in 61 patients (17 with T2D) with non-severe and severe COVID-19. RESULTS: COVID-19 patients demonstrated ten-fold increase of IL-6 levels regardless of disease severity. Increased admission GLP-1 levels (p = 0.03) accompanied by two-fold increased PCT were found in severe as compared with non-severe patients. Moreover, GLP-1 and PCT levels were significantly increased in non-survived as compared with survived patients at admission (p = 0.01 and p = 0.001, respectively) and at 5 to 6 days of hospitalisation (p = 0.05). Both non-diabetic and T2D patients demonstrated a positive correlation between GLP-1 and PCT response (r = 0.33, p = 0.03, and r = 0.54, p = 0.03, respectively), but the intensity of this joint pro-inflammatory/GLP-1 response was modulated by T2D. In addition, hypoxaemia down-regulated GLP-1 response only in T2D patients with bilateral lung damage. CONCLUSIONS: The persistent joint increase of endogenous GLP-1 and PCT in severe and fatal COVID-19 suggests a role of concomitant bacterial infection in disease exacerbation. Early elevation of endogenous GLP-1 may serve as a new biomarker of COVID-19 severity and fatal outcome.