Comparison of COVID-19 and Non-COVID-19 Pneumonia in Down Syndrome.
Ontology highlight
ABSTRACT:
SUBMITTER: Real de Asua D
PROVIDER: S-EPMC8397064 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundThe COVID-19 pandemic has caused the relocation of huge financial resources to departments dedicated to infected patients, at the expense of those suffering from other pathologies.AimTo compare clinical features and outcomes in COVID-19 pneumonia and non-COVID-19 pneumonia patients.Patients and methods53 patients (35 males, mean age 61.5 years) with COVID-19 pneumonia and 50 patients (32 males, mean age 72.7 years) with non-COVID-19 pneumonia, consecutively admitted between March and May 2020 were included. Clinical, laboratory and radiological data at admission were analyzed. Duration of hospitalization and mortality rates were evaluated.ResultsAmong the non-COVID patients, mean age, presence of comorbidities (neurological diseases, chronic kidney disease and chronic obstructive pulmonary disease), Charlson Comorbidity Index and risk factors (tobacco use and protracted length of stay in geriatric healthcare facilities) were higher than in COVID patients. The non-COVID-19 pneumonia group showed a higher (24% vs. 17%), although not statistically significant in-hospital mortality rate; the average duration of hospitalization was longer for COVID patients (30 vs. 9 days, p = .0001).ConclusionsIn the early stages of the COVID pandemic, our centre noted no statistical difference in unadjusted in-hospital mortality between COVID and non-COVID patients. Non-COVID patients had higher Charlson Comorbidity Scores, reflecting a greater disease burden in this population.Key MessagesIn March 2020, the COVID-19 disease was declared a pandemic, with enormous consequences for the organization of health systems and in terms of human lives; this has caused the relocation of huge financial resources to departments dedicated to infected patients, at the expense of those suffering from other pathologies.Few published reports have compared COVID-19 and non-COVID-19 pneumonia. In our study, performed in a geographic area with a low prevalence of SARS-CoV-2 infection, we found few statistically significant differences in terms of clinical characteristics between the two groups analyzed.In the early stages of the COVID pandemic, our centre noted no statistical difference in unadjusted in-hospital mortality between COVID and non-COVID patients. Non-COVID patients had higher Charlson Comorbidity Scores, reflecting a greater disease burden in this population.
| S-EPMC8648033 | biostudies-literature
Project description:Risk of severe disease and death due to coronavirus 2019 (COVID-19) is increased in certain patient demographic groups, including those who are of advanced age, male sex, and obese body mass index. Investigations of the biological variations that contribute to this risk have been hampered by heterogeneous severity, with immunologic features of critical disease potentially obscuring differences between risk groups. To examine immune heterogeneity related to demographic risk factors, we enrolled 38 patients hospitalized with clinically homogeneous COVID-19 pneumonia – defined as oxygen saturation <94% on room air without respiratory failure, septic shock, or multiple organ dysfunction – and performed single-cell RNA-sequencing of leukocytes collected at admission. Examination of individual risk factors identified strong shifts within neutrophil and monocyte/dendritic cell compartments, revealing altered immune cell type-specific responses in higher-risk COVID-19 patient subgroups. Specifically, we found transcriptional evidence of altered neutrophil maturation in aged versus young patients and enhanced cytokine responses in monocytes/dendritic cells of male versus female patients. Such innate immune cell alterations may contribute to outcome differences linked to these risk factors. They also highlight the importance of diverse patient cohorts in studies of therapies targeting the immune response in COVID-19.
2023-10-11 | GSE236177 | GEO
Project description:Platelets may be a target of bacteria and viruses, which can directly or indirectly activate them so promoting thrombosis. In accordance with this, community-acquired pneumonia (CAP) is complicated by ischemia-related vascular disease (myocardial infarction and stroke) in roughly 10% of patients while the incidence of venous thrombosis is uncertain. In CAP platelet biosynthesis of TxA2 is augmented and associated with myocardial infarction; however, a cause-effect relationship is still unclear as unclear is if platelet activation promotes thrombosis or functional changes of coronary tree such vasospasm. Retrospective studies suggested a potential role of aspirin in reducing mortality but the impact on vascular disease is still unknown. Coronavirus disease 2019 (Covid-19) is complicated by thrombosis in roughly 20% of patients with an almost equivalent localization in arterial and venous circulation. Platelet activation seems to have a pivot role in the thrombotic process in Covid-19 as consistently evidenced by its involvement in promoting Tissue Factor up-regulation via leucocyte interaction. Until now, antiplatelet treatment has been scarcely considered for the treatment of Covid-19; interventional trials, however, are in progress to explore this issue. The aim of this review is 1) to compare the type of vascular diseases complicating CAP and Covid-19 2) to assess the different role of platelets in both diseases and 3) to discuss if antiplatelet treatment is potentially useful to improve clinical outcomes.
| S-EPMC8204311 | biostudies-literature
Project description:Pneumonia is a common cause of morbidity and mortality and is most often caused by bacterial pathogens. COVID-19 is characterized by lung infection with potential progressive organ failure. The systemic consequences of both disease on the systemic blood metabolome are not fully understood. The aim of this study was to compare the blood metabolome of both diseases and we hypothesize that plasma metabolomics may help to identify the systemic effects of these diseases. Therefore, we profiled the plasma metabolome of 43 cases of COVID-19 pneumonia, 23 cases of non-COVID-19 pneumonia, and 26 controls using a non-targeted approach. Metabolic alterations differentiating the three groups were detected, with specific metabolic changes distinguishing the two types of pneumonia groups. A comparison of venous and arterial blood plasma samples from the same subjects revealed the distinct metabolic effects of pulmonary pneumonia. In addition, a machine learning signature of four metabolites was predictive of the disease outcome of COVID-19 subjects with an area under the curve (AUC) of 86 ± 10 %. Overall, the results of this study uncover systemic metabolic changes that could be linked to the etiology of COVID-19 pneumonia and non-COVID-19 pneumonia.
| S-EPMC9693035 | biostudies-literature
Project description:BackgroundTo develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spreading coronavirus disease 2019 (COVID-19).MethodsIn this retrospective study, a total of 326 chest CT exams from 134 patients (63 confirmed COVID-19 patients and 71 non-COVID-19 patients) were collected from January 20 to February 8, 2020. A semi-automatic segmentation procedure was used to delineate the volume of interest (VOI), and radiomic features were extracted. The Support Vector Machine (SVM) model was built on the combination of 4 groups of features, including radiomic features, traditional radiological features, quantifying features, and clinical features. By repeating cross-validation procedure, the performance on the time-independent testing cohort was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.ResultsFor the SVM model built on the combination of 4 groups of features (integrated model), the per-exam AUC was 0.925 (95% CI 0.856 to 0.994) for differentiating COVID-19 on the testing cohort, and the sensitivity and specificity were 0.816 (95% CI 0.651 to 0.917) and 0.923 (95% CI 0.621 to 0.996), respectively. As for the SVM models built on radiomic features, radiological features, quantifying features, and clinical features, individually, the AUC on the testing cohort reached 0.765, 0.818, 0.607, and 0.739, respectively, significantly lower than the integrated model, except for the radiomic model.ConclusionThe machine learning-based CT radiomics models may accurately classify COVID-19, helping clinicians and radiologists to identify COVID-19 positive cases.
| S-EPMC8424152 | biostudies-literature
Project description:The current SARS-CoV-2 outbreak, which causes COVID-19, is particularly devastating for individuals with chronic medical conditions, in particular those with Down Syndrome (DS) who often exhibit a higher prevalence of respiratory tract infections, immune dysregulation and potential complications. The incidence of Alzheimer's disease (AD) is much higher in DS than in the general population, possibly increasing further the risk of COVID-19 infection and its complications. Here we provide a biological overview with regard to specific susceptibility of individuals with DS to SARS-CoV-2 infection as well as data from a recent survey on the prevalence of COVID-19 among them. We see an urgent need to protect people with DS, especially those with AD, from COVID-19 and future pandemics and focus on developing protective measures, which also include interventions by health systems worldwide for reducing the negative social effects of long-term isolation and increased periods of hospitalization.
| S-EPMC7929736 | biostudies-literature
Project description:(1) Background: Data on different steroid compounds for the treatment of hospitalized COVID-19 (coronavirus disease 2019) patients are still limited. The aim of this study was to compare COVID-19 patients admitted to non-intensive units and treated with methylprednisolone or dexamethasone. (2) Methods: This was a single-center retrospective study that included consecutive patients with COVID-19 hospitalized in medical wards during the second wave of the pandemic. Thirty-day mortality and the need for intensive or semi-intensive care were the main clinical outcomes analyzed in patients receiving methylprednisolone (60 mg/day) compared with dexamethasone (6 mg/day). Secondary outcomes included complication rates, length of hospital stay, and time to viral clearance. (3) Results: Two-hundred-forty-six patients were included in the analysis, 110 treated with dexamethasone and 136 with methylprednisolone. No statistically significant differences were found between the two groups of patients regarding 30-day mortality (OR 1.35, CI95% 0.71-2.56, p = 0.351) and the need for intensive or semi-intensive care (OR 1.94, CI95% 0.81-4.66, p = 0.136). The complication rates, length of hospital stay, and time to viral clearance did not significantly differ between the two groups. (4) Conclusions: In patients hospitalized for COVID-19 in non-intensive units, the choice of different steroid compounds, such as dexamethasone or methylprednisolone, did not affect the main clinical outcomes.
| S-EPMC8703463 | biostudies-literature
Project description:Temporal changes in fecal microbiota of COVID-19 patients: a longitudinal cohort
| PRJEB61723 | ENA
Project description:PurposeThe aim of this study was to determine the effectiveness of two different lung ultrasonography (LUS) methods that can be used in the diagnosis of coronavirus disease 2019 (COVID-19) and to investigate their correlations with computed tomography (CT).MethodsIn this prospective, randomized, and single-blind study, 60 patients with COVID-19 were included. The patients were randomized to either the 12-zone LUS group (n=30) or the 14-zone LUS group (n=30). The correlation between LUS and thoracic CT scores was evaluated. As a secondary outcome measure, the characteristic features of the findings of thoracic CT and LUS were examined.ResultsThe study was completed with a total of 59 patients. Moderate and high correlations were found between the total CT and LUS scores in the 12-zone and 14-zone study groups. There were no statistically significant differences in the lesion types detected in patients using LUS and CT (P>0.05). The left lung lower lobe CT scores were statistically significantly lower in the 14-zone study group than in the 12-zone group (P=0.019). The left lower lobe CT and LUS scores were highly correlated in the 14-zone group (P<0.001, r=0.902).ConclusionThe results of our study indicated that the two different LUS examination methods performed in different patients had similar findings in terms of the diagnosis and their correlations with CT results.
| S-EPMC8696151 | biostudies-literature
Project description:BackgroundCOVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive cases through quick and accurate testing. Since artificial intelligence (AI) offers the powerful mechanism to automatically extract the tissue features and characterise the disease, we therefore hypothesise that AI-based strategies can provide quick detection and classification, especially for radiological computed tomography (CT) lung scans.MethodologySix models, two traditional machine learning (ML)-based (k-NN and RF), two transfer learning (TL)-based (VGG19 and InceptionV3), and the last two were our custom-designed deep learning (DL) models (CNN and iCNN), were developed for classification between COVID pneumonia (CoP) and non-COVID pneumonia (NCoP). K10 cross-validation (90% training: 10% testing) protocol on an Italian cohort of 100 CoP and 30 NCoP patients was used for performance evaluation and bispectrum analysis for CT lung characterisation.ResultsUsing K10 protocol, our results showed the accuracy in the order of DL > TL > ML, ranging the six accuracies for k-NN, RF, VGG19, IV3, CNN, iCNN as 74.58 ± 2.44%, 96.84 ± 2.6, 94.84 ± 2.85%, 99.53 ± 0.75%, 99.53 ± 1.05%, and 99.69 ± 0.66%, respectively. The corresponding AUCs were 0.74, 0.94, 0.96, 0.99, 0.99, and 0.99 (p-values < 0.0001), respectively. Our Bispectrum-based characterisation system suggested CoP can be separated against NCoP using AI models. COVID risk severity stratification also showed a high correlation of 0.7270 (p < 0.0001) with clinical scores such as ground-glass opacities (GGO), further validating our AI models.ConclusionsWe prove our hypothesis by demonstrating that all the six AI models successfully classified CoP against NCoP due to the strong presence of contrasting features such as ground-glass opacities (GGO), consolidations, and pleural effusion in CoP patients. Further, our online system takes < 2 s for inference.
| S-EPMC7854027 | biostudies-literature