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1.
BMC Pulm Med ; 22(1): 1, 2022 Jan 03.
Article in English | MEDLINE | ID: covidwho-1608729

ABSTRACT

BACKGROUND: Quantitative evaluation of radiographic images has been developed and suggested for the diagnosis of coronavirus disease 2019 (COVID-19). However, there are limited opportunities to use these image-based diagnostic indices in clinical practice. Our aim in this study was to evaluate the utility of a novel visually-based classification of pulmonary findings from computed tomography (CT) images of COVID-19 patients with the following three patterns defined: peripheral, multifocal, and diffuse findings of pneumonia. We also evaluated the prognostic value of this classification to predict the severity of COVID-19. METHODS: This was a single-center retrospective cohort study of patients hospitalized with COVID-19 between January 1st and September 30th, 2020, who presented with suspicious findings on CT lung images at admission (n = 69). We compared the association between the three predefined patterns (peripheral, multifocal, and diffuse), admission to the intensive care unit, tracheal intubation, and death. We tested quantitative CT analysis as an outcome predictor for COVID-19. Quantitative CT analysis was performed using a semi-automated method (Thoracic Volume Computer-Assisted Reading software, GE Health care, United States). Lungs were divided by Hounsfield unit intervals. Compromised lung (%CL) volume was the sum of poorly and non-aerated volumes (- 500, 100 HU). We collected patient clinical data, including demographic and clinical variables at the time of admission. RESULTS: Patients with a diffuse pattern were intubated more frequently and for a longer duration than patients with a peripheral or multifocal pattern. The following clinical variables were significantly different between the diffuse pattern and peripheral and multifocal groups: body temperature (p = 0.04), lymphocyte count (p = 0.01), neutrophil count (p = 0.02), c-reactive protein (p < 0.01), lactate dehydrogenase (p < 0.01), Krebs von den Lungen-6 antigen (p < 0.01), D-dimer (p < 0.01), and steroid (p = 0.01) and favipiravir (p = 0.03) administration. CONCLUSIONS: Our simple visual assessment of CT images can predict the severity of illness, a resulting decrease in respiratory function, and the need for supplemental respiratory ventilation among patients with COVID-19.


Subject(s)
COVID-19/classification , COVID-19/diagnostic imaging , Tomography, X-Ray Computed , Adult , Aged , Amides/therapeutic use , Antiviral Agents/therapeutic use , Body Temperature , C-Reactive Protein/metabolism , COVID-19/drug therapy , COVID-19/physiopathology , Female , Fibrin Fibrinogen Degradation Products/metabolism , Humans , L-Lactate Dehydrogenase/blood , Lung/diagnostic imaging , Lymphocyte Count , Male , Middle Aged , Mucin-1/blood , Neutrophils , Predictive Value of Tests , Prognosis , Pyrazines/therapeutic use , Radiographic Image Interpretation, Computer-Assisted , Retrospective Studies , SARS-CoV-2 , Steroids/therapeutic use
2.
J Med Virol ; 94(1): 357-365, 2022 01.
Article in English | MEDLINE | ID: covidwho-1544349

ABSTRACT

COVID-19 is a serious respiratory disease. The ever-increasing number of cases is causing heavier loads on the health service system. Using 38 blood test indicators on the first day of admission for the 422 patients diagnosed with COVID-19 (from January 2020 to June 2021) to construct different machine learning (ML) models to classify patients into either mild or severe cases of COVID-19. All models show good performance in the classification between COVID-19 patients into mild and severe disease. The area under the curve (AUC) of the random forest model is 0.89, the AUC of the naive Bayes model is 0.90, the AUC of the support vector machine model is 0.86, and the AUC of the KNN model is 0.78, the AUC of the Logistic regression model is 0.84, and the AUC of the artificial neural network model is 0.87, among which the naive Bayes model has the best performance. Different ML models can classify patients into mild and severe cases based on 38 blood test indicators taken on the first day of admission for patients diagnosed with COVID-19.


Subject(s)
Blood Chemical Analysis , COVID-19/classification , Neural Networks, Computer , Severity of Illness Index , Support Vector Machine , Area Under Curve , COVID-19/blood , COVID-19/diagnosis , Hematologic Tests , Humans , Logistic Models , SARS-CoV-2
3.
Front Immunol ; 12: 697622, 2021.
Article in English | MEDLINE | ID: covidwho-1518482

ABSTRACT

Objectives: The longitudinal and systematic evaluation of immunity in coronavirus disease 2019 (COVID-19) patients is rarely reported. Methods: Parameters involved in innate, adaptive, and humoral immunity were continuously monitored in COVID-19 patients from onset of illness until 45 days after symptom onset. Results: This study enrolled 27 mild, 47 severe, and 46 deceased COVID-19 patients. Generally, deceased patients demonstrated a gradual increase of neutrophils and IL-6 but a decrease of lymphocytes and platelets after the onset of illness. Specifically, sustained low numbers of CD8+ T cells, NK cells, and dendritic cells were noted in deceased patients, while these cells gradually restored in mild and severe patients. Furthermore, deceased patients displayed a rapid increase of HLA-DR expression on CD4+ T cells in the early phase, but with a low level of overall CD45RO and HLA-DR expressions on CD4+ and CD8+ T cells, respectively. Notably, in the early phase, deceased patients showed a lower level of plasma cells and antigen-specific IgG, but higher expansion of CD16+CD14+ proinflammatory monocytes and HLA-DR-CD14+ monocytic-myeloid-derived suppressor cells (M-MDSCs) than mild or severe patients. Among these immunological parameters, M-MDSCs showed the best performance in predicting COVID-19 mortality, when using a cutoff value of ≥10%. Cluster analysis found a typical immunological pattern in deceased patients on day 9 after onset, which was characterized as the increase of inflammatory markers (M-MDSCs, neutrophils, CD16+CD14+ monocytes, and IL-6) but a decrease of host immunity markers. Conclusions: This study systemically characterizes the kinetics of immunity of COVID-19, highlighting the importance of immunity in patient prognosis.


Subject(s)
COVID-19/immunology , SARS-CoV-2 , Adaptive Immunity , Aged , Aged, 80 and over , Antibodies, Viral/blood , B-Lymphocytes/immunology , COVID-19/blood , COVID-19/classification , COVID-19/physiopathology , Cytokines/blood , Dendritic Cells/immunology , Female , Humans , Immunity, Innate , Immunoglobulin G/blood , Killer Cells, Natural/immunology , Lymphocyte Count , Male , Middle Aged , SARS-CoV-2/immunology , Severity of Illness Index , T-Lymphocytes/immunology
4.
Viral Immunol ; 34(9): 639-645, 2021 11.
Article in English | MEDLINE | ID: covidwho-1517820

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may produce a systemic disease, the coronavirus disease-19 (COVID-19), with high morbidity and mortality. Even though we do not fully understand the interaction of innate and adaptive immunity in the control and complications of the viral infection, it is well recognized that SARS-CoV-2 induces an immunodepression that impairs the elimination of the virus and favors its rapid dissemination in the organism. Even less is known about the possible participation of inhibitory cells of the innate immune system, such as the myeloid-derived suppressor cells (MDSCs), or the adaptive immune system, such as the T regulatory cells (Tregs). That is why we aimed to study blood levels of MDSCs, as well as lymphocyte subpopulations, including Tregs, and activated (OX-40+) and inhibited (PD-1) T lymphocytes in patients with mild COVID-19 in comparison with data obtained from control donors. We have found that 20 hospitalized patients with COVID-19 and no health history of immunosuppression had a significant increase in the number of peripheral monocytic MDSCs (M-MDSC), but a decrease in Tregs, as well as an increase in the number of inhibited or exhausted T cells, whereas the number of activated T cells was significantly decreased compared with that from 20 healthy controls. Moreover, there was a significant negative correlation (r = 0.496) between the number of M-MDSC and the number of activated T cells. Therefore, M-MDSC rather than Tregs may contribute to the immunosuppression observed in patients with COVID-19.


Subject(s)
COVID-19/immunology , Myeloid-Derived Suppressor Cells/immunology , SARS-CoV-2/immunology , T-Lymphocytes, Regulatory/immunology , Aged , COVID-19/blood , COVID-19/classification , Female , Humans , Lymphocyte Activation , Lymphocyte Count/methods , Lymphocyte Subsets , Male , Middle Aged , SARS-CoV-2/pathogenicity
5.
Comput Math Methods Med ; 2021: 9269173, 2021.
Article in English | MEDLINE | ID: covidwho-1511543

ABSTRACT

Early diagnosis of the harmful severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), along with clinical expertise, allows governments to break the transition chain and flatten the epidemic curve. Although reverse transcription-polymerase chain reaction (RT-PCR) offers quick results, chest X-ray (CXR) imaging is a more reliable method for disease classification and assessment. The rapid spread of the coronavirus disease 2019 (COVID-19) has triggered extensive research towards developing a COVID-19 detection toolkit. Recent studies have confirmed that the deep learning-based approach, such as convolutional neural networks (CNNs), provides an optimized solution for COVID-19 classification; however, they require substantial training data for learning features. Gathering this training data in a short period has been challenging during the pandemic. Therefore, this study proposes a new model of CNN and deep convolutional generative adversarial networks (DCGANs) that classify CXR images into normal, pneumonia, and COVID-19. The proposed model contains eight convolutional layers, four max-pooling layers, and two fully connected layers, which provide better results than the existing pretrained methods (AlexNet and GoogLeNet). DCGAN performs two tasks: (1) generating synthetic/fake images to overcome the challenges of an imbalanced dataset and (2) extracting deep features of all images in the dataset. In addition, it enlarges the dataset and represents the characteristics of diversity to provide a good generalization effect. In the experimental analysis, we used four distinct publicly accessible datasets of chest X-ray images (COVID-19 X-ray, COVID Chest X-ray, COVID-19 Radiography, and CoronaHack-Chest X-Ray) to train and test the proposed CNN and the existing pretrained methods. Thereafter, the proposed CNN method was trained with the four datasets based on the DCGAN synthetic images, resulting in higher accuracy (94.8%, 96.6%, 98.5%, and 98.6%) than the existing pretrained models. The overall results suggest that the proposed DCGAN-CNN approach is a promising solution for efficient COVID-19 diagnosis.


Subject(s)
Algorithms , COVID-19 Testing/methods , COVID-19/classification , COVID-19/diagnostic imaging , Deep Learning , SARS-CoV-2 , COVID-19 Testing/statistics & numerical data , Databases, Factual , Early Diagnosis , False Positive Reactions , Humans , Neural Networks, Computer , Pandemics , ROC Curve , Radiography, Thoracic/statistics & numerical data , Software Design , Tomography, X-Ray Computed/statistics & numerical data
6.
PLoS One ; 16(10): e0259179, 2021.
Article in English | MEDLINE | ID: covidwho-1496531

ABSTRACT

This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%.


Subject(s)
COVID-19/classification , COVID-19/diagnostic imaging , Image Processing, Computer-Assisted/methods , Algorithms , COVID-19/metabolism , Data Accuracy , Deep Learning , Humans , Neural Networks, Computer , ROC Curve , Radiography/methods , SARS-CoV-2/pathogenicity , Sensitivity and Specificity , Tomography, X-Ray Computed/methods
7.
Emerg Med J ; 38(12): 901-905, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1495501

ABSTRACT

OBJECTIVE: Validated clinical risk scores are needed to identify patients with COVID-19 at risk of severe disease and to guide triage decision-making during the COVID-19 pandemic. The objective of the current study was to evaluate the performance of early warning scores (EWS) in the ED when identifying patients with COVID-19 who will require intensive care unit (ICU) admission for high-flow-oxygen usage or mechanical ventilation. METHODS: Patients with a proven SARS-CoV-2 infection with complete resuscitate orders treated in nine hospitals between 27 February and 30 July 2020 needing hospital admission were included. Primary outcome was the performance of EWS in identifying patients needing ICU admission within 24 hours after ED presentation. RESULTS: In total, 1501 patients were included. Median age was 71 (range 19-99) years and 60.3% were male. Of all patients, 86.9% were admitted to the general ward and 13.1% to the ICU within 24 hours after ED admission. ICU patients had lower peripheral oxygen saturation (86.7% vs 93.7, p≤0.001) and had a higher body mass index (29.2 vs 27.9 p=0.043) compared with non-ICU patients. National Early Warning Score 2 (NEWS2) ≥ 6 and q-COVID Score were superior to all other studied clinical risk scores in predicting ICU admission with a fair area under the receiver operating characteristics curve of 0.740 (95% CI 0.696 to 0.783) and 0.760 (95% CI 0.712 to 0.800), respectively. NEWS2 ≥6 and q-COVID Score ≥3 discriminated patients admitted to the ICU with a sensitivity of 78.1% and 75.9%, and specificity of 56.3% and 61.8%, respectively. CONCLUSION: In this multicentre study, the best performing models to predict ICU admittance were the NEWS2 and the Quick COVID-19 Severity Index Score, with fair diagnostic performance. However, due to the moderate performance, these models cannot be clinically used to adequately predict the need for ICU admission within 24 hours in patients with SARS-CoV-2 infection presenting at the ED.


Subject(s)
COVID-19/diagnosis , Critical Illness , Early Warning Score , Adult , Aged , Aged, 80 and over , COVID-19/classification , Female , Humans , Intensive Care Units , Male , Middle Aged , Patient Admission , Predictive Value of Tests , ROC Curve , Triage
8.
Drug Discov Ther ; 15(4): 171-179, 2021 Sep 22.
Article in English | MEDLINE | ID: covidwho-1449126

ABSTRACT

In the face of the ongoing pandemic, the primary care physicians in India are dealing not only with an increased number of patients but are also facing difficulties in the management of complex critically ill patients. To guide the management plans of primary care physicians, several guidelines have been published by the central and state health bodies. In such a situation, an updated and unifying state, national and international guidelines based on critical analysis and appraisal of evolving data is the need of the hour. In this review, we critically analysed the current existing guidelines that have been formulated within India in light of recent evidence.


Subject(s)
COVID-19/drug therapy , COVID-19/classification , COVID-19/mortality , Clinical Trials as Topic , Disease Management , Humans , India , Practice Guidelines as Topic , Severity of Illness Index , Survival Analysis , Treatment Outcome
9.
Front Immunol ; 12: 723585, 2021.
Article in English | MEDLINE | ID: covidwho-1399140

ABSTRACT

Objectives: Our objective was to determine the antibody and cytokine profiles in different COVID-19 patients. Methods: COVID-19 patients with different clinical classifications were enrolled in this study. The level of IgG antibodies, IgA, IgM, IgE, and IgG subclasses targeting N and S proteins were tested using ELISA. Neutralizing antibody titers were determined by using a toxin neutralization assay (TNA) with live SARS-CoV-2. The concentrations of 8 cytokines, including IL-2, IL-4, IL-6, IL-10, CCL2, CXCL10, IFN-γ, and TNF-α, were measured using the Protein Sample Ella-Simple ELISA system. The differences in antibodies and cytokines between severe and moderate patients were compared by t-tests or Mann-Whitney tests. Results: A total of 79 COVID-19 patients, including 49 moderate patients and 30 severe patients, were enrolled. Compared with those in moderate patients, neutralizing antibody and IgG-S antibody titers in severe patients were significantly higher. The concentration of IgG-N antibody was significantly higher than that of IgG-S antibody in COVID-19 patients. There was a significant difference in the distribution of IgG subclass antibodies between moderate patients and severe patients. The positive ratio of anti-S protein IgG3 is significantly more than anti-N protein IgG3, while the anti-S protein IgG4 positive rate is significantly less than the anti-N protein IgG4 positive rate. IL-2 was lower in COVID-19 patients than in healthy individuals, while IL-4, IL-6, CCL2, IFN-γ, and TNF-α were higher in COVID-19 patients than in healthy individuals. IL-6 was significantly higher in severe patients than in moderate patients. The antibody level of anti-S protein was positively correlated with the titer of neutralizing antibody, but there was no relationship between cytokines and neutralizing antibody. Conclusions: Our findings show the severe COVID-19 patients' antibody levels were stronger than those of moderate patients, and a cytokine storm is associated with COVID-19 severity. There was a difference in immunoglobulin type between anti-S protein antibodies and anti-N protein antibodies in COVID-19 patients. And clarified the value of the profile in critical prevention.


Subject(s)
Antibodies, Viral/blood , COVID-19/immunology , Cytokines/blood , SARS-CoV-2/immunology , Adult , Aged , Aged, 80 and over , Antibodies, Neutralizing/blood , COVID-19/classification , Coronavirus Nucleocapsid Proteins/immunology , Enzyme-Linked Immunosorbent Assay , Female , Humans , Immunoglobulin A/blood , Immunoglobulin E/blood , Immunoglobulin G/blood , Immunoglobulin M/blood , Male , Middle Aged , Severity of Illness Index , Spike Glycoprotein, Coronavirus/immunology
10.
Emerg Microbes Infect ; 10(1): 1751-1759, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1393119

ABSTRACT

The effectiveness of inactivated SARS-CoV-2 vaccines against the Delta variant, which has been associated with greater transmissibility and virulence, remains unclear. We conducted a test-negative case-control study to explore the vaccine effectiveness (VE) in real-world settings. We recruited participants aged 18-59 years who consisted of SARS-CoV-2 test-positive cases (n = 74) and test-negative controls (n = 292) during the outbreak of the Delta variant in May 2021 in Guangzhou city, China. Vaccination status was compared to estimate The VE of SARS-CoV-2 inactivated vaccines. A single dose of inactivated SARS-CoV-2 vaccine yielded the VE of only 13.8%. After adjusting for age and sex, the overall VE for two-dose vaccination was 59.0% (95% confidence interval: 16.0% to 81.6%) against coronavirus disease 2019 (COVID-19) and 70.2% (95% confidence interval: 29.6-89.3%) against moderate COVID-19 and 100% against severe COVID-19 which might be overestimated due to the small sample size. The VE of two-dose vaccination against COVID-19 reached 72.5% among participants aged 40-59 years, and was higher in females than in males against COVID-19 and moderate diseases. While single dose vaccination was not sufficiently protective, the two-dose dosing scheme of the inactivated vaccines was effective against the Delta variant infection in real-world settings, with the estimated efficacy exceeding the World Health Organization minimal threshold of 50%.


Subject(s)
COVID-19 Vaccines/standards , COVID-19/prevention & control , SARS-CoV-2/genetics , Adolescent , Adult , Age Distribution , COVID-19/classification , COVID-19 Vaccines/administration & dosage , Case-Control Studies , China , Disease Outbreaks , Female , Genetic Variation , Humans , Male , Middle Aged , Vaccines, Inactivated/administration & dosage , Vaccines, Inactivated/standards , Young Adult
11.
Math Med Biol ; 38(3): 396-416, 2021 08 15.
Article in English | MEDLINE | ID: covidwho-1356687

ABSTRACT

Formulating accurate and robust classification strategies is a key challenge of developing diagnostic and antibody tests. Methods that do not explicitly account for disease prevalence and uncertainty therein can lead to significant classification errors. We present a novel method that leverages optimal decision theory to address this problem. As a preliminary step, we develop an analysis that uses an assumed prevalence and conditional probability models of diagnostic measurement outcomes to define optimal (in the sense of minimizing rates of false positives and false negatives) classification domains. Critically, we demonstrate how this strategy can be generalized to a setting in which the prevalence is unknown by either (i) defining a third class of hold-out samples that require further testing or (ii) using an adaptive algorithm to estimate prevalence prior to defining classification domains. We also provide examples for a recently published SARS-CoV-2 serology test and discuss how measurement uncertainty (e.g. associated with instrumentation) can be incorporated into the analysis. We find that our new strategy decreases classification error by up to a decade relative to more traditional methods based on confidence intervals. Moreover, it establishes a theoretical foundation for generalizing techniques such as receiver operating characteristics by connecting them to the broader field of optimization.


Subject(s)
COVID-19 Serological Testing/statistics & numerical data , COVID-19/diagnosis , SARS-CoV-2 , Algorithms , Antibodies, Viral/blood , COVID-19/classification , COVID-19/epidemiology , COVID-19 Serological Testing/classification , Computational Biology , Data Analysis , Decision Theory , Humans , Immunoglobulin G/blood , Models, Statistical , Pandemics/statistics & numerical data , Prevalence , ROC Curve , Uncertainty
12.
Brief Bioinform ; 22(2): 896-904, 2021 03 22.
Article in English | MEDLINE | ID: covidwho-1343621

ABSTRACT

The novel coronavirus (2019-nCoV) has recently caused a large-scale outbreak of viral pneumonia both in China and worldwide. In this study, we obtained the entire genome sequence of 777 new coronavirus strains as of 29 February 2020 from a public gene bank. Bioinformatics analysis of these strains indicated that the mutation rate of these new coronaviruses is not high at present, similar to the mutation rate of the severe acute respiratory syndrome (SARS) virus. The similarities of 2019-nCoV and SARS virus suggested that the S and ORF6 proteins shared a low similarity, while the E protein shared the higher similarity. The 2019-nCoV sequence has similar potential phosphorylation sites and glycosylation sites on the surface protein and the ORF1ab polyprotein as the SARS virus; however, there are differences in potential modification sites between the Chinese strain and some American strains. At the same time, we proposed two possible recombination sites for 2019-nCoV. Based on the results of the skyline, we speculate that the activity of the gene population of 2019-nCoV may be before the end of 2019. As the scope of the 2019-nCoV infection further expands, it may produce different adaptive evolutions due to different environments. Finally, evolutionary genetic analysis can be a useful resource for studying the spread and virulence of 2019-nCoV, which are essential aspects of preventive and precise medicine.


Subject(s)
COVID-19/classification , Phylogeny , Bayes Theorem , COVID-19/genetics , COVID-19/virology , Evolution, Molecular , Humans , SARS Virus/genetics , SARS Virus/isolation & purification
13.
Biomed Pharmacother ; 142: 112015, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1340558

ABSTRACT

COVID-19, an infectious disease, has emerged as one of the leading causes of death worldwide, making it one of the severe public health issues in recent decades. nCoV, the novel SARS coronavirus that causes COVID-19, has brought together scientists in the quest for possible therapeutic and preventive measures. The development of new drugs to manage COVID-19 effectively is a challenging and time-consuming process, thus encouraging extensive investigation of drug repurposing and repositioning candidates. Several medications, including remdesivir, hydroxychloroquine, chloroquine, lopinavir, favipiravir, ribavirin, ritonavir, interferons, azithromycin, capivasertib and bevacizumab, are currently under clinical trials for COVID-19. In addition, several medicinal plants with considerable antiviral activities are potential therapeutic candidates for COVID-19. Statistical data show that the pandemic is yet to slow down, and authorities are placing their hopes on vaccines. Within a short period, four types of vaccines, namely, whole virus, viral vector, protein subunit, and nucleic acid (RNA/DNA), which can confer protection against COVID-19 in different ways, were already in a clinical trial. SARS-CoV-2 variants spread is associated with antibody escape from the virus Spike epitopes, which has grave concerns for viral re-infection and even compromises the effectiveness of the vaccines. Despite these efforts, COVID-19 treatment is still solely based on clinical management through supportive care. We aim to highlight the recent trends in COVID-19, relevant statistics, and clinical findings, as well as potential therapeutics, including in-line treatment methods, preventive measures, and vaccines to combat the prevalence of COVID-19.


Subject(s)
Antiviral Agents , COVID-19 Vaccines , COVID-19/drug therapy , SARS-CoV-2/drug effects , Antiviral Agents/classification , Antiviral Agents/pharmacology , COVID-19/classification , COVID-19/complications , COVID-19/prevention & control , COVID-19 Vaccines/classification , COVID-19 Vaccines/pharmacology , Drug Development/methods , Drug Discovery/methods , Drug Repositioning/methods , Humans
14.
Am J Epidemiol ; 190(8): 1681-1688, 2021 08 01.
Article in English | MEDLINE | ID: covidwho-1337251

ABSTRACT

We evaluated whether randomly sampling and testing a set number of individuals for coronavirus disease 2019 (COVID-19) while adjusting for misclassification error captures the true prevalence. We also quantified the impact of misclassification error bias on publicly reported case data in Maryland. Using a stratified random sampling approach, 50,000 individuals were selected from a simulated Maryland population to estimate the prevalence of COVID-19. We examined the situation when the true prevalence is low (0.07%-2%), medium (2%-5%), and high (6%-10%). Bayesian models informed by published validity estimates were used to account for misclassification error when estimating COVID-19 prevalence. Adjustment for misclassification error captured the true prevalence 100% of the time, irrespective of the true prevalence level. When adjustment for misclassification error was not done, the results highly varied depending on the population's underlying true prevalence and the type of diagnostic test used. Generally, the prevalence estimates without adjustment for misclassification error worsened as the true prevalence level increased. Adjustment for misclassification error for publicly reported Maryland data led to a minimal but not significant increase in the estimated average daily cases. Random sampling and testing of COVID-19 are needed with adjustment for misclassification error to improve COVID-19 prevalence estimates.


Subject(s)
COVID-19 Testing/statistics & numerical data , COVID-19/epidemiology , Decision Support Techniques , Statistics as Topic/methods , Bayes Theorem , COVID-19/classification , Humans , Maryland/epidemiology , Prevalence , SARS-CoV-2 , Selection Bias
15.
Med Sci Monit ; 27: e934171, 2021 Aug 02.
Article in English | MEDLINE | ID: covidwho-1337822

ABSTRACT

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19) commonly presents with pneumonia. However, COVID-19 is now recognized to involve multiple organ systems with varying severity and duration. In July 2021, the findings from a retrospective population study from the National COVID Cohort Collaborative (N3C) Consortium were published that included analysis by machine learning methods of 174,568 adults with SARS-CoV-2 infection from 34 medical centers in the US. The study stratified patients for COVID-19 according to the World Health Organization (WHO) Clinical Progression Scale (CPS). Severe clinical outcomes were identified as the requirement for invasive ventilatory support, or extracorporeal membrane oxygenation (ECMO), and patient mortality. Machine learning analysis showed that the factor most strongly associated with severity of clinical course in patients with COVID-19 was pH. A separate multivariable logistic regression model showed that independent factors associated with more severe clinical outcomes included age, dementia, male gender, liver disease, and obesity. This Editorial aims to present the rationale and findings of the largest population cohort of adult patients with COVID-19 to date and highlights the importance of using large population studies with sophisticated analytical methods, including machine learning.


Subject(s)
COVID-19 , Machine Learning , Adult , COVID-19/classification , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19/mortality , Diagnosis, Computer-Assisted , Female , Humans , Male , Middle Aged , Models, Statistical , Population Health , Risk Factors , SARS-CoV-2 , Severity of Illness Index
16.
AJR Am J Roentgenol ; 217(3): 623-632, 2021 09.
Article in English | MEDLINE | ID: covidwho-1311346

ABSTRACT

BACKGROUND. Chest radiographs (CXRs) are typically obtained early in patients admitted with coronavirus disease (COVID-19) and may help guide prognosis and initial management decisions. OBJECTIVE. The purpose of this study was to assess the performance of an admission CXR severity scoring system in predicting hospital outcomes in patients admitted with COVID-19. METHODS. This retrospective study included 240 patients (142 men, 98 women; median age, 65 [range, 50-80] years) admitted to the hospital from March 16 to April 13, 2020, with COVID-19 confirmed by real-time reverse-transcriptase polymerase chain reaction who underwent chest radiography within 24 hours of admission. Three attending chest radiologists and three radiology residents independently scored patients' admission CXRs using a 0- to 24-point composite scale (sum of scores that range from 0 to 3 for extent and severity of disease in upper and lower zones of left and right lungs). Interrater reliability of the score was assessed using the Kendall W coefficient. The mean score was obtained from the six readers' scores for further analyses. Demographic variables, clinical characteristics, and admission laboratory values were collected from electronic medical records. ROC analysis was performed to assess the association between CXR severity and mortality. Additional univariable and multivariable logistic regression models incorporating patient characteristics and laboratory values were tested for associations between CXR severity and clinical outcomes. RESULTS. Interrater reliability of CXR scores ranged from 0.687 to 0.737 for attending radiologists, from 0.653 to 0.762 for residents, and from 0.575 to 0.666 for all readers. A composite CXR score of 10 or higher on admission achieved 53.0% (35/66) sensitivity and 75.3% (131/174) specificity for predicting hospital mortality. Hospital mortality occurred in 44.9% (35/78) of patients with a high-risk admission CXR score (≥ 10) versus 19.1% (31/162) of patients with a low-risk CXR score (< 10) (p < .001). Admission composite CXR score was an independent predictor of death (odds ratio [OR], 1.17; 95% CI, 1.10-1.24; p < .001). composite CXR score was a univariable predictor of intubation (OR, 1.23; 95% CI, 1.12-1.34; p < .001) and continuous renal replacement therapy (CRRT) (OR, 1.15; 95% CI, 1.04-1.27; p = .007) but was not associated with these in multivariable models (p > .05). CONCLUSION. For patients admitted with COVID-19, an admission CXR severity score may help predict hospital mortality, intubation, and CRRT. CLINICAL IMPACT. CXR may assist risk assessment and clinical decision-making early in the course of COVID-19.


Subject(s)
COVID-19/diagnostic imaging , Lung/diagnostic imaging , Radiography, Thoracic , Severity of Illness Index , Aged , Aged, 80 and over , COVID-19/classification , COVID-19/diagnosis , COVID-19 Nucleic Acid Testing , Female , Hospital Mortality , Hospitalization , Humans , Male , Middle Aged , Prognosis , Reproducibility of Results , Retrospective Studies
17.
J Infect Dev Ctries ; 15(6): 766-772, 2021 06 30.
Article in English | MEDLINE | ID: covidwho-1304762

ABSTRACT

INTRODUCTION: COVID-19 is the infection caused by the new coronavirus. Specific treatment for COVID-19 has not been established, yet. It is important to determine the disease severity of the patients at the first admission. Therefore, the exploration of biomarkers is deemed necessary. We aimed to assess the diagnostic and early prognostic value of CRP and LDH levels in possible COVID-19 patients presenting with a severe clinical picture. METHODOLOGY: We evaluated the correlations of relevant routine laboratory test results with disease severity in COVID-19 patients admitted to our infectious diseases clinic. Patients were divided into severe and non-severe disease groups based on clinical findings, oxygen saturation levels in the arterial blood, biochemical test results, and radiological findings. Differences in the findings between the two disease severity groups were examined to determine potential biomarkers. RESULTS: Median age and the CRP and LDH levels in the severe disease group were statistically significantly higher compared to the nonsevere group (p < 0.0001). No other parameters statistically significant differences have been observed between the two groups (P > 0.05). CONCLUSIONS: CRP and LDH levels were positively correlated with lung lesions in early-stage COVID-19, potentially reflecting disease severity. Because LDH and CRP levels can potentially reflect the pulmonary function, they can be potential predictors of COVID-19- related respiratory failure. For avoiding poor prognosis; LDH and CRP should be considered as potential predictors for identifying the need for thoracic CT scans, close monitoring of pulmonary function, and aggressive supportive therapy early in the course of COVID-19.


Subject(s)
C-Reactive Protein/analysis , COVID-19/blood , COVID-19/diagnosis , L-Lactate Dehydrogenase/blood , Severity of Illness Index , Adult , Biomarkers/blood , COVID-19/classification , Female , Hospitalization , Humans , Lung/pathology , Lung/virology , Male , Middle Aged , Prognosis , Retrospective Studies , Risk Factors , Turkey
18.
Bull World Health Organ ; 99(1): 62-66, 2021 Jan 01.
Article in English | MEDLINE | ID: covidwho-1304565

ABSTRACT

Problem: The surge in coronavirus disease 2019 (COVID-19) cases overwhelmed the health system in the Republic of Korea. Approach: To help health-care workers prioritize treatment for patients with more severe disease and to decrease the burden on health systems caused by COVID-19, the government established a system to classify disease severity. Health-care staff in city- and provincial-level patient management teams classified the patients into the different categories according to the patients' pulse, systolic blood pressure, respiratory rate, body temperature and level of consciousness. Patients categorized as having moderate, severe and very severe disease were promptly assigned to beds or negative-pressure isolation rooms for hospital treatment, while patients with mild symptoms were monitored in 16 designated facilities across the country. Local setting: The case fatality rate was higher in the city of Daegu and the Gyeongsangbuk-do province (1.6%; 124/7756) than the rest of the country (0.5%; 7/1485). Relevant changes: From 25 February to 26 March 2020, the ratio of negative-pressure isolation rooms per COVID-19 patient was below 0.15 in the city of Daegu and the Gyeongsangbuk-do province. In the rest of the country, this ratio decreased from 5.56 to 0.63 during the same period. Before the classification system was in place, eight (15.7%) out of the 51 deaths occurred at home or during transfer from home to health-care institutions. Lessons learnt: Categorizing patients according to their disease severity should be a prioritized measure to ease the burden on health systems and reduce the case fatality rate.


Subject(s)
COVID-19/classification , COVID-19/epidemiology , Severity of Illness Index , Humans , Pandemics , Patient Isolation , Pneumonia, Viral/epidemiology , Republic of Korea/epidemiology , SARS-CoV-2 , Vital Signs
20.
Viruses ; 13(6)2021 06 03.
Article in English | MEDLINE | ID: covidwho-1259624

ABSTRACT

BACKGROUND: Cytokine storm in COVID-19 is heterogenous. There are at least three subtypes: cytokine release syndrome (CRS), macrophage activation syndrome (MAS), and sepsis. METHODS: A retrospective study comprising 276 patients with SARS-CoV-2 pneumonia. All patients were tested for ferritin, interleukin-6, D-Dimer, fibrinogen, calcitonin, and C-reactive protein. According to the diagnostic criteria, three groups of patients with different subtypes of cytokine storm syndrome were identified: MAS, CRS or sepsis. In the MAS and CRS groups, treatment results were assessed depending on whether or not tocilizumab was used. RESULTS: MAS was diagnosed in 9.1% of the patients examined, CRS in 81.8%, and sepsis in 9.1%. Median serum ferritin in patients with MAS was significantly higher (5894 vs. 984 vs. 957 ng/mL, p < 0.001) than in those with CRS or sepsis. Hypofibrinogenemia and pancytopenia were also observed in MAS patients. In CRS patients, a higher mortality rate was observed among those who received tocilizumab, 21 vs. 10 patients (p = 0.043), RR = 2.1 (95% CI 1.0-4.3). In MAS patients, tocilizumab decreased the mortality, 13 vs. 6 patients (p = 0.013), RR = 0.50 (95% CI 0.25-0.99). CONCLUSIONS: Tocilizumab therapy in patients with COVID-19 and CRS was associated with increased mortality, while in MAS patients, it contributed to reduced mortality.


Subject(s)
Antibodies, Monoclonal, Humanized/therapeutic use , COVID-19/drug therapy , Cytokine Release Syndrome/classification , Cytokine Release Syndrome/drug therapy , Aged , COVID-19/classification , COVID-19/immunology , COVID-19/mortality , Cytokine Release Syndrome/immunology , Cytokine Release Syndrome/mortality , Female , Ferritins/blood , Humans , Macrophage Activation Syndrome/drug therapy , Macrophage Activation Syndrome/mortality , Macrophage Activation Syndrome/virology , Male , Retrospective Studies , Sepsis/drug therapy , Sepsis/virology , Treatment Outcome
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