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1.
ACS Appl Mater Interfaces ; 14(2): 2522-2533, 2022 Jan 19.
Article in English | MEDLINE | ID: covidwho-1606881

ABSTRACT

Electrochemical detection in complex biofluids is a long-standing challenge as electrode biofouling hampers its sensing performance and commercial translation. To overcome this drawback, pyrolyzed paper as porous electrode coupled with the drop casting of an off-the-shelf polysorbate, that is, Tween 20 (T20), is described here by taking advantage of the in situ formation of a hydrophilic nanocoating (2 nm layer of T20). The latter prevents biofouling while providing the capillarity of samples through paper pores, leveraging redox reactions across both only partially fouled and fresh electrodic surfaces with increasing detection areas. The nanometric thickness of this blocking layer is also essential by not significantly impairing the electron-transfer kinetics. These phenomena behave synergistically to enhance the sensibility that further increases over long-term exposures (4 h) in biological fluids. While the state-of-the-art antibiofouling strategies compromise the sensibility, this approach leads to peak currents that are up to 12.5-fold higher than the original currents after 1 h exposure to unprocessed human plasma. Label-free impedimetric immunoassays through modular bioconjugation by directly anchoring spike protein on gold nanoparticles are also allowed, as demonstrated for the COVID-19 screening of patient sera. The scalability and simplicity of the platform combined with its unique ability to operate in biofluids with enhanced sensibility provide the generation of promising biosensing technologies toward real-world applications in point-of-care diagnostics, mass testing, and in-home monitoring of chronic diseases.


Subject(s)
Antibodies, Viral/immunology , Biosensing Techniques/methods , COVID-19 Serological Testing/methods , Diagnostic Tests, Routine/methods , Recombinant Proteins/immunology , Spike Glycoprotein, Coronavirus/immunology , Early Diagnosis , Humans , Sensitivity and Specificity
2.
ACS Appl Mater Interfaces ; 14(2): 2501-2509, 2022 Jan 19.
Article in English | MEDLINE | ID: covidwho-1605760

ABSTRACT

Rapid serology platforms are essential in disease pandemics for a variety of applications, including epidemiological surveillance, contact tracing, vaccination monitoring, and primary diagnosis in resource-limited areas. Laboratory-based enzyme-linked immunosorbent assay (ELISA) platforms are inherently multistep processes that require trained personnel and are of relatively limited throughput. As an alternative, agglutination-based systems have been developed; however, they rely on donor red blood cells and are not yet available for high-throughput screening. Column agglutination tests are a mainstay of pretransfusion blood typing and can be performed at a range of scales, ranging from manual through to fully automated testing. Here, we describe a column agglutination test using colored microbeads coated with recombinant SARS-CoV-2 spike protein that agglutinates when incubated with serum samples collected from patients recently infected with SARS-CoV-2. After confirming specific agglutination, we optimized centrifugal force and time to distinguish samples from uninfected vs SARS-CoV-2-infected individuals and then showed concordant results against ELISA for 22 clinical samples, and also a set of serial bleeds from one donor at days 6-10 postinfection. Our study demonstrates the use of a simple, scalable, and rapid diagnostic platform that can be tailored to detect antibodies raised against SARS-CoV-2 and can be easily integrated with established laboratory frameworks worldwide.


Subject(s)
Agglutination Tests/methods , Antibodies, Viral/immunology , COVID-19 Serological Testing/methods , Diagnostic Tests, Routine/methods , Recombinant Proteins/immunology , Spike Glycoprotein, Coronavirus/immunology , Early Diagnosis , Humans , Sensitivity and Specificity
3.
Br J Radiol ; 95(1129): 20210290, 2022 Jan 01.
Article in English | MEDLINE | ID: covidwho-1603309

ABSTRACT

OBJECTIVE: Early detection of peripheral neuropathy is extremely important as leprosy is one of the treatable causes of peripheral neuropathy. The study was undertaken to assess the role of diffusion tensor imaging (DTI) in ulnar neuropathy in leprosy patients. METHODS: This was a case-control study including 38 patients (72 nerves) and 5 controls (10 nerves) done between January 2017 and June 2019. Skin biopsy proven cases of leprosy, having symptoms of ulnar neuropathy (proven on nerve conduction study) were included. MRI was performed on a 3 T MR system. Mean cross-sectional area, fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values of ulnar nerve at cubital tunnel were calculated. Additional ancillary findings and appearance of base sequences were evaluated. RESULTS: Ulnar nerve showed thickening with altered T2W signal in all the affected nerves, having an average cross-sectional area of 0.26 cm2. Low FA with mean of 0.397 ± 0.19 and high ADC with mean of 1.28 ± 0.427 x 10 -3 mm2/s of ulnar nerve in retrocondylar groove was obtained. In the control group, mean cross-sectional area was 0.71cm2 with mean FA and ADC of 0.53 ± 0.088 and 1.03 ± 0.24 x 10 -3 mm2/s respectively. Statistically no significant difference was seen in diseased and control group. Cut-off to detect neuropathy for FA and ADC is 0.4835 and 1.1020 × 10 -3 mm2/s respectively. CONCLUSION: DTI though is challenging in peripheral nerves, however, is proving to be a powerful complementary tool for assessment of peripheral neuropathy. Our study validates its utility in infective neuropathies. ADVANCES IN KNOWLEDGE: 1. DTI is a potential complementary tool for detection of peripheral neuropathies and can be incorporated in standard MR neurography protocol.2. In leprosy-related ulnar neuropathy, altered signal intensity with thickening or abscess of the nerve is appreciated along with locoregional nodes and secondary denervation changes along with reduction of FA and rise in ADC value.3. Best cut-offs obtained in our study for FA and ADC are 0.4835 and 1.1020 × 10 -3 mm2/s respectively.


Subject(s)
Diffusion Tensor Imaging , Leprosy/complications , Peripheral Nervous System Diseases/diagnostic imaging , Ulnar Nerve/diagnostic imaging , Adult , Case-Control Studies , Early Diagnosis , Female , Humans , Male , Neuroimaging , Peripheral Nervous System Diseases/etiology
4.
Med Sci Monit ; 27: e935379, 2021 Dec 30.
Article in English | MEDLINE | ID: covidwho-1593238

ABSTRACT

BACKGROUND This retrospective study aimed to investigate outcomes and hospitalization rates in patients with a confirmed diagnosis of early COVID-19 treated at home with prescribed and non-prescribed treatments. MATERIAL AND METHODS The medical records of a cohort of 158 Italian patients with early COVID-19 treated at home were analyzed. Treatments consisted of indomethacin, low-dose aspirin, omeprazole, and a flavonoid-based food supplement, plus azithromycin, low-molecular-weight heparin, and betamethasone as needed. The association of treatment timeliness and of clinical variables with the duration of symptoms and with the risk of hospitalization was evaluated by logistic regression. RESULTS Patients were divided into 2 groups: group 1 (n=85) was treated at the earliest possible time (<72 h from onset of symptoms), and group 2 (n=73) was treated >72 h after the onset of symptoms. Clinical severity at the beginning of treatment was similar in the 2 groups. In group 1, symptom duration was shorter than in group 2 (median 6.0 days vs 13.0 days, P<0.001) and no hospitalizations occurred, compared with 19.18% hospitalizations in group 2. One patient in group 1 developed chest X-ray alterations and 2 patients experienced an increase in D-dimer levels, compared with 30 and 22 patients, respectively, in group 2. The main factor determining the duration of symptoms and the risk of hospitalization was the delay in starting therapy (P<0.001). CONCLUSIONS This real-world study of patients in the community showed that early diagnosis and early supportive patient management reduced the severity of COVID-19 and reduced the rate of hospitalization.


Subject(s)
COVID-19/diagnosis , COVID-19/drug therapy , Hospitalization/statistics & numerical data , Time-to-Treatment/statistics & numerical data , Aged , Aged, 80 and over , Aspirin/therapeutic use , Betamethasone/therapeutic use , Cohort Studies , Dietary Supplements , Early Diagnosis , Female , Flavonoids/therapeutic use , Follow-Up Studies , Heparin, Low-Molecular-Weight/therapeutic use , Humans , Indomethacin/therapeutic use , Italy , Male , Middle Aged , Omeprazole/therapeutic use , Patient Acuity , Retrospective Studies , Risk Assessment , SARS-CoV-2 , Time , Treatment Outcome
5.
Semin Respir Crit Care Med ; 42(6): 747-758, 2021 12.
Article in English | MEDLINE | ID: covidwho-1585686

ABSTRACT

Respiratory tract infection is one of the most common diseases in human worldwide. Many viruses are implicated in these infections, including emerging viruses, such as the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Identification of the causative viral pathogens of respiratory tract infections is important to select a correct management of patients, choose an appropriate treatment, and avoid unnecessary antibiotics use. Different diagnostic approaches present variable performance in terms of accuracy, sensitivity, specificity, and time-to-result, that have to be acknowledged to be able to choose the right diagnostic test at the right time, in the right patient. This review describes currently available rapid diagnostic strategies and syndromic approaches for the detection of viruses commonly responsible for respiratory diseases.


Subject(s)
Early Diagnosis , Respiratory Tract Infections/diagnosis , Respiratory Tract Infections/virology , COVID-19/diagnosis , COVID-19/virology , Humans , SARS-CoV-2/isolation & purification , Sensitivity and Specificity , Time Factors
6.
J Infect Dev Ctries ; 15(11): 1625-1629, 2021 11 30.
Article in English | MEDLINE | ID: covidwho-1572705

ABSTRACT

INTRODUCTION: This paper aims to measure the performance of early detection methods, which are usually used for infectious diseases. METHODOLOGY: By using real data of confirmed Coronavirus cases from the Kingdom of Saudi Arabia and Italy, the moving epidemic method (MEM) and the moving average cumulative sums (Mov. Avg Cusum) methods are used in our simulation study. RESULTS: Our results suggested that the CUSUM method outperforms the MEM in detecting the start of the Coronavirus outbreak.


Subject(s)
COVID-19/diagnosis , Diagnostic Tests, Routine , Early Diagnosis , SARS-CoV-2 , Benchmarking , COVID-19/epidemiology , Databases, Factual , Disease Outbreaks/prevention & control , Humans , Italy/epidemiology , Saudi Arabia/epidemiology
7.
Dis Markers ; 2021: 6304189, 2021.
Article in English | MEDLINE | ID: covidwho-1553755

ABSTRACT

Background: Early identification of patients with severe coronavirus disease (COVID-19) at an increased risk of progression may promote more individualized treatment schemes and optimize the use of medical resources. This study is aimed at investigating the utility of the C-reactive protein to albumin (CRP/Alb) ratio for early risk stratification of patients. Methods: We retrospectively reviewed 557 patients with COVID-19 with confirmed outcomes (discharged or deceased) admitted to the West Court of Union Hospital, Wuhan, China, between January 29, 2020 and April 8, 2020. Patients with severe COVID-19 (n = 465) were divided into stable (n = 409) and progressive (n = 56) groups according to whether they progressed to critical illness or death during hospitalization. To predict disease progression, the CRP/Alb ratio was evaluated on admission. Results: The levels of new biomarkers, including neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, CRP/Alb ratio, and systemic immune-inflammation index, were higher in patients with progressive disease than in those with stable disease. Correlation analysis showed that the CRP/Alb ratio had the strongest positive correlation with the sequential organ failure assessment score and length of hospital stay in survivors. Multivariate logistic regression analysis showed that percutaneous oxygen saturation (SpO2), D-dimer levels, and the CRP/Alb ratio were risk factors for disease progression. To predict clinical progression, the areas under the receiver operating characteristic curves of Alb, CRP, CRP/Alb ratio, SpO2, and D-dimer were 0.769, 0.838, 0.866, 0.107, and 0.748, respectively. Moreover, patients with a high CRP/Alb ratio (≥1.843) had a markedly higher rate of clinical deterioration (log - rank p < 0.001). A higher CRP/Alb ratio (≥1.843) was also closely associated with higher rates of hospital mortality, ICU admission, invasive mechanical ventilation, and a longer hospital stay. Conclusion: The CRP/Alb ratio can predict the risk of progression to critical disease or death early, providing a promising prognostic biomarker for risk stratification and clinical management of patients with severe COVID-19.


Subject(s)
C-Reactive Protein/metabolism , COVID-19/diagnosis , Coronary Disease/diagnosis , Hypertension/diagnosis , Pulmonary Disease, Chronic Obstructive/diagnosis , SARS-CoV-2/pathogenicity , Serum Albumin, Human/metabolism , Aged , Area Under Curve , Biomarkers/blood , Blood Platelets/pathology , Blood Platelets/virology , COVID-19/epidemiology , COVID-19/mortality , COVID-19/virology , China/epidemiology , Comorbidity , Coronary Disease/epidemiology , Coronary Disease/mortality , Coronary Disease/virology , Disease Progression , Early Diagnosis , Female , Fibrin Fibrinogen Degradation Products/metabolism , Humans , Hypertension/epidemiology , Hypertension/mortality , Hypertension/virology , Length of Stay/statistics & numerical data , Lymphocytes/pathology , Lymphocytes/virology , Male , Middle Aged , Neutrophils/pathology , Neutrophils/virology , Prognosis , Pulmonary Disease, Chronic Obstructive/epidemiology , Pulmonary Disease, Chronic Obstructive/mortality , Pulmonary Disease, Chronic Obstructive/virology , ROC Curve , Retrospective Studies , SARS-CoV-2/growth & development , Severity of Illness Index , Survival Analysis
8.
J Med Virol ; 93(12): 6544-6550, 2021 12.
Article in English | MEDLINE | ID: covidwho-1544302

ABSTRACT

We developed a rapid and simple magnetic chemiluminescence enzyme immunoassay on the Real Express-6 analyzer, which could simultaneously detect immunoglobulin G and immunoglobulin M antibodies against SARS-CoV-2 virus in human blood within 18 min, and which could be used to detect clinical studies to verify its clinical efficacy. We selected blood samples from 185 COVID-19 patients confirmed by polymerase chain reaction and 271 negative patients to determine the clinical detection sensitivity, specificity, stability, and precision of this method. Meanwhile, we also surveyed the dynamic variance of viral antibodies during SARS-CoV-2 infection. This rapid immunoassay test has huge potential benefits for rapid screening of SARS-CoV-2 infection and may help clinical drug and vaccine development.


Subject(s)
Antibodies, Viral/blood , COVID-19 Serological Testing/methods , COVID-19/diagnosis , Immunoglobulin G/blood , Immunoglobulin M/blood , SARS-CoV-2/immunology , Adolescent , Adult , Aged , Aged, 80 and over , Cross Reactions/immunology , Early Diagnosis , Female , Humans , Immunoassay/methods , Luminescent Measurements , Male , Mass Screening/methods , Middle Aged , Polymerase Chain Reaction , Sensitivity and Specificity , Young Adult
9.
Blood Coagul Fibrinolysis ; 32(8): 544-549, 2021 Dec 01.
Article in English | MEDLINE | ID: covidwho-1526211

ABSTRACT

Standard biomarkers have been widely used for COVID-19 diagnosis and prognosis. We hypothesize that thrombogenicity metrics measured by thromboelastography will provide better diagnostic and prognostic utility versus standard biomarkers in COVID-19 positive patients. In this observational prospective study, we included 119 hospitalized COVID-19 positive patients and 15 COVID-19 negative patients. On admission, we measured standard biomarkers and thrombogenicity using a novel thromboelastography assay (TEG-6s). In-hospital all-cause death and thrombotic occurrences (thromboembolism, myocardial infarction and stroke) were recorded. Most COVID-19 patients were African--Americans (68%). COVID-19 patients versus COVID-19 negative patients had higher platelet-fibrin clot strength (P-FCS), fibrin clot strength (FCS) and functional fibrinogen level (FLEV) (P ≤ 0.003 for all). The presence of high TEG-6 s metrics better discriminated COVID-19 positive from negative patients. COVID-19 positive patients with sequential organ failure assessment (SOFA) score at least 3 had higher P-FCS, FCS and FLEV than patients with scores less than 3 (P ≤ 0.001 for all comparisons). By multivariate analysis, the in-hospital composite endpoint occurrence of death and thrombotic events was independently associated with SOFA score more than 3 [odds ratio (OR) = 2.9, P = 0.03], diabetes (OR = 3.3, P = 0.02) and FCS > 40 mm (OR = 3.4, P = 0.02). This largest observational study suggested the early diagnostic and prognostic utility of thromboelastography to identify COVID-19 and should be considered hypothesis generating. Our results also support the recent FDA guidance regarding the importance of measurement of whole blood viscoelastic properties in COVID-19 patients. Our findings are consistent with the observation of higher hospitalization rates and poorer outcomes for African--Americans with COVID-19.


Subject(s)
COVID-19/blood , SARS-CoV-2 , Thrombophilia/diagnosis , Adult , African Americans/statistics & numerical data , Aged , Aged, 80 and over , Biomarkers , COVID-19/complications , COVID-19/epidemiology , COVID-19 Testing , Cardiovascular Diseases/epidemiology , Comorbidity , Diabetes Mellitus/epidemiology , Early Diagnosis , Female , Fibrin/analysis , Fibrin Clot Lysis Time , Fibrinogen/analysis , Hospitalization , Humans , Hyperlipidemias/epidemiology , L-Lactate Dehydrogenase/blood , Male , Middle Aged , Obesity/epidemiology , Organ Dysfunction Scores , Prognosis , Prospective Studies , Thrombelastography , Thrombophilia/blood , Thrombophilia/drug therapy , Thrombophilia/etiology , Treatment Outcome , /statistics & numerical data
10.
PLoS One ; 16(9): e0256630, 2021.
Article in English | MEDLINE | ID: covidwho-1518353

ABSTRACT

Pneumonia is a respiratory infection caused by bacteria or viruses; it affects many individuals, especially in developing and underdeveloped nations, where high levels of pollution, unhygienic living conditions, and overcrowding are relatively common, together with inadequate medical infrastructure. Pneumonia causes pleural effusion, a condition in which fluids fill the lung, causing respiratory difficulty. Early diagnosis of pneumonia is crucial to ensure curative treatment and increase survival rates. Chest X-ray imaging is the most frequently used method for diagnosing pneumonia. However, the examination of chest X-rays is a challenging task and is prone to subjective variability. In this study, we developed a computer-aided diagnosis system for automatic pneumonia detection using chest X-ray images. We employed deep transfer learning to handle the scarcity of available data and designed an ensemble of three convolutional neural network models: GoogLeNet, ResNet-18, and DenseNet-121. A weighted average ensemble technique was adopted, wherein the weights assigned to the base learners were determined using a novel approach. The scores of four standard evaluation metrics, precision, recall, f1-score, and the area under the curve, are fused to form the weight vector, which in studies in the literature was frequently set experimentally, a method that is prone to error. The proposed approach was evaluated on two publicly available pneumonia X-ray datasets, provided by Kermany et al. and the Radiological Society of North America (RSNA), respectively, using a five-fold cross-validation scheme. The proposed method achieved accuracy rates of 98.81% and 86.85% and sensitivity rates of 98.80% and 87.02% on the Kermany and RSNA datasets, respectively. The results were superior to those of state-of-the-art methods and our method performed better than the widely used ensemble techniques. Statistical analyses on the datasets using McNemar's and ANOVA tests showed the robustness of the approach. The codes for the proposed work are available at https://github.com/Rohit-Kundu/Ensemble-Pneumonia-Detection.


Subject(s)
COVID-19/diagnosis , Early Diagnosis , Pneumonia/diagnosis , Thorax/diagnostic imaging , COVID-19/diagnostic imaging , COVID-19/virology , Deep Learning , Humans , Lung/diagnostic imaging , Lung/pathology , Neural Networks, Computer , North America , Pneumonia/diagnostic imaging , SARS-CoV-2/isolation & purification , SARS-CoV-2/pathogenicity , Thorax/pathology , X-Rays
12.
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
13.
J Vasc Surg Venous Lymphat Disord ; 9(3): 605-614.e2, 2021 05.
Article in English | MEDLINE | ID: covidwho-1510080

ABSTRACT

OBJECTIVE: Early reports suggest that patients with novel coronavirus disease-2019 (COVID-19) infection carry a significant risk of altered coagulation with an increased risk for venous thromboembolic events. This report investigates the relationship of significant COVID-19 infection and deep venous thrombosis (DVT) as reflected in the patient clinical and laboratory characteristics. METHODS: We reviewed the demographics, clinical presentation, laboratory and radiologic evaluations, results of venous duplex imaging and mortality of COVID-19-positive patients (18-89 years) admitted to the Indiana University Academic Health Center. Using oxygen saturation, radiologic findings, and need for advanced respiratory therapies, patients were classified into mild, moderate, or severe categories of COVID-19 infection. A descriptive analysis was performed using univariate and bivariate Fisher's exact and Wilcoxon rank-sum tests to examine the distribution of patient characteristics and compare the DVT outcomes. A multivariable logistic regression model was used to estimate the adjusted odds ratio of experiencing DVT and a receiver operating curve analysis to identify the optimal cutoff for d-dimer to predict DVT in this COVID-19 cohort. Time to the diagnosis of DVT from admission was analyzed using log-rank test and Kaplan-Meier plots. RESULTS: Our study included 71 unique COVID-19-positive patients (mean age, 61 years) categorized as having 3% mild, 14% moderate, and 83% severe infection and evaluated with 107 venous duplex studies. DVT was identified in 47.8% of patients (37% of examinations) at an average of 5.9 days after admission. Patients with DVT were predominantly male (67%; P = .032) with proximal venous involvement (29% upper and 39% in the lower extremities with 55% of the latter demonstrating bilateral involvement). Patients with DVT had a significantly higher mean d-dimer of 5447 ± 7032 ng/mL (P = .0101), and alkaline phosphatase of 110 IU/L (P = .0095) than those without DVT. On multivariable analysis, elevated d-dimer (P = .038) and alkaline phosphatase (P = .021) were associated with risk for DVT, whereas age, sex, elevated C-reactive protein, and ferritin levels were not. A receiver operating curve analysis suggests an optimal d-dimer value of 2450 ng/mL cutoff with 70% sensitivity, 59.5% specificity, and 61% positive predictive value, and 68.8% negative predictive value. CONCLUSIONS: This study suggests that males with severe COVID-19 infection requiring hospitalization are at highest risk for developing DVT. Elevated d-dimers and alkaline phosphatase along with our multivariable model can alert the clinician to the increased risk of DVT requiring early evaluation and aggressive treatment.


Subject(s)
Alkaline Phosphatase/blood , COVID-19 , Extremities , Fibrin Fibrinogen Degradation Products/analysis , Risk Assessment/methods , Ultrasonography, Doppler, Duplex , Venous Thrombosis , Anticoagulants/administration & dosage , Biomarkers/blood , Blood Coagulation , COVID-19/blood , COVID-19/complications , COVID-19/mortality , COVID-19/therapy , Early Diagnosis , Extremities/blood supply , Extremities/diagnostic imaging , Female , Humans , Indiana/epidemiology , Male , Middle Aged , Predictive Value of Tests , Retrospective Studies , SARS-CoV-2/isolation & purification , Time-to-Treatment/statistics & numerical data , Ultrasonography, Doppler, Duplex/methods , Ultrasonography, Doppler, Duplex/statistics & numerical data , Venous Thrombosis/diagnosis , Venous Thrombosis/drug therapy , Venous Thrombosis/etiology , Venous Thrombosis/prevention & control
14.
Ann Intern Med ; 174(8): 1081-1089, 2021 08.
Article in English | MEDLINE | ID: covidwho-1497803

ABSTRACT

BACKGROUND: Evidence to understand effective strategies for surveillance and early detection of SARS-CoV-2 is limited. OBJECTIVE: To describe the results of a rigorous, large-scale COVID-19 testing and monitoring program. DESIGN: The U.S. National Football League (NFL) and the NFL Players Association (NFLPA) instituted a large-scale COVID-19 monitoring program involving daily testing using 2 reverse transcription polymerase chain reaction (RT-PCR) platforms (Roche cobas and Thermo Fisher QuantStudio), a transcription-mediated amplification platform (Hologic Panther), and an antigen point-of-care (aPOC) test (Quidel Sofia). SETTING: 32 NFL clubs in 24 states during the 2020 NFL season. PARTICIPANTS: NFL players and staff. MEASUREMENTS: SARS-CoV-2 test results were described in the context of medically adjudicated status. Cycle threshold (Ct) values are reported when available. RESULTS: A total of 632 370 tests administered across 11 668 persons identified 270 (2.4%) COVID-19 cases from 1 August to 14 November 2020. Positive predictive values ranged from 73.0% to 82.0% across the RT-PCR platforms. High Ct values (33 to 37) often indicated early infection. For the first positive result, the median Ct value was 32.77 (interquartile range, 30.02 to 34.72) and 22% of Ct values were above 35. Among adjudicated COVID-19 cases tested with aPOC, 42.3% had a negative result. Positive concordance between aPOC test result and adjudicated case status increased as viral load increased. LIMITATIONS: Platforms varied by laboratory, and test variability may reflect procedural differences. CONCLUSION: Routine RT-PCR testing allowed early detection of infection. Cycle threshold values provided a useful guidepost for understanding results, with high values often indicating early infection. Antigen POC testing was unable to reliably rule out COVID-19 early in infection. PRIMARY FUNDING SOURCE: The NFL and the NFLPA.


Subject(s)
COVID-19 Nucleic Acid Testing , COVID-19 Serological Testing , COVID-19/diagnosis , Football , Occupational Health , COVID-19/epidemiology , Early Diagnosis , Humans , Incidence , SARS-CoV-2 , United States/epidemiology
15.
Indian J Pathol Microbiol ; 64(4): 735-740, 2021.
Article in English | MEDLINE | ID: covidwho-1485280

ABSTRACT

Background: COVID-19 is a pandemic viral disease that has affected the Indian population very badly with more than 8.46 million cases and > 0.125 million deaths. Aim: Primary objective of the study is to establish the role of hematological, coagulation and inflammatory biomarkers in early identification of clinically severe covid-19 cases. Materials and Methods: This study was conducted from July 2020 to August 2020 at a dedicated COVID-19 referral hospital in central India. Only RT-PCR confirmed COVID-19 positive 300 cases admitted in the hospital were included in this study. Based on the clinical assessment, patients were categorised as mild, moderate, and severe groups as per ICMR guidelines. Blood samples of all cases were tested for haematological, coagulation and inflammatory biomarkers and mean values were compared among the three groups of patients. Results: 46% patients belonged to >60 years of age group. Hematological parameters like total leukocyte count, absolute neutrophil count, Neutrophil: Lymphocyte ratio, Platelet: Lymphocyte ratio significantly increased with lymphocytopenia (P=0.001). Coagulation profile(D-dimer and PT) and inflammatory biomarkers like CRP, LDH, ferritin, procalcitonin and NT- Pro BNP, all were significantly increased with severity of patients(p=0.001). ROC plotted for all the parameters between severe v/s non-severe cases showed that CRP, LDH and D-dimer had a good discriminative precision with AUC >0.8. Conclusion: We suggest that biochemical markers like CRP, LDH and D-dimer can be used as a screening tool to differentiate severe patients from non-severe patients of Covid-19 disease in order to identify severe disease at early stage for optimal utilization of resources & reducing further morbidity & mortality.


Subject(s)
Biomarkers/blood , Blood Coagulation/physiology , COVID-19/physiopathology , Early Diagnosis , Inflammation/blood , Inflammation/physiopathology , Severity of Illness Index , Adult , Aged , Aged, 80 and over , Female , Humans , India , Male , Middle Aged , Predictive Value of Tests , Prognosis , SARS-CoV-2
16.
Comput Math Methods Med ; 2021: 6919483, 2021.
Article in English | MEDLINE | ID: covidwho-1484105

ABSTRACT

In March 2020, the World Health Organization announced the COVID-19 pandemic, its dangers, and its rapid spread throughout the world. In March 2021, the second wave of the pandemic began with a new strain of COVID-19, which was more dangerous for some countries, including India, recording 400,000 new cases daily and more than 4,000 deaths per day. This pandemic has overloaded the medical sector, especially radiology. Deep-learning techniques have been used to reduce the burden on hospitals and assist physicians for accurate diagnoses. In our study, two models of deep learning, ResNet-50 and AlexNet, were introduced to diagnose X-ray datasets collected from many sources. Each network diagnosed a multiclass (four classes) and a two-class dataset. The images were processed to remove noise, and a data augmentation technique was applied to the minority classes to create a balance between the classes. The features extracted by convolutional neural network (CNN) models were combined with traditional Gray-level Cooccurrence Matrix (GLCM) and Local Binary Pattern (LBP) algorithms in a 1-D vector of each image, which produced more representative features for each disease. Network parameters were tuned for optimum performance. The ResNet-50 network reached accuracy, sensitivity, specificity, and Area Under the Curve (AUC) of 95%, 94.5%, 98%, and 97.10%, respectively, with the multiclasses (COVID-19, viral pneumonia, lung opacity, and normal), while it reached accuracy, sensitivity, specificity, and AUC of 99%, 98%, 98%, and 97.51%, respectively, with the binary classes (COVID-19 and normal).


Subject(s)
COVID-19/diagnostic imaging , Deep Learning , SARS-CoV-2 , Tomography, X-Ray Computed/methods , Algorithms , Computational Biology , Databases, Factual/statistics & numerical data , Diagnosis, Computer-Assisted/methods , Diagnosis, Computer-Assisted/statistics & numerical data , Early Diagnosis , Humans , Lung/diagnostic imaging , Neural Networks, Computer , Pandemics , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/statistics & numerical data
17.
Sci Rep ; 11(1): 15409, 2021 10 11.
Article in English | MEDLINE | ID: covidwho-1462018

ABSTRACT

Early diagnosis of COVID-19 in suspected patients is essential for contagion control and damage reduction strategies. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in oropharyngeal swab suspension fluid to predict COVID-19 positive samples. The study included samples of 243 patients from two Brazilian States. Samples were transported by using different viral transport mediums (liquid 1 or 2). Clinical COVID-19 diagnosis was performed by the RT-PCR. We built a classification model based on partial least squares (PLS) associated with cosine k-nearest neighbours (KNN). Our analysis led to 84% and 87% sensitivity, 66% and 64% specificity, and 76.9% and 78.4% accuracy for samples of liquids 1 and 2, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective solution for high-throughput screening of suspect patients for COVID-19 in health care centres and emergency departments.


Subject(s)
COVID-19 Testing/methods , COVID-19/diagnosis , SARS-CoV-2/isolation & purification , Spectroscopy, Fourier Transform Infrared/methods , Adult , Aged , Brazil/epidemiology , COVID-19/epidemiology , Early Diagnosis , Female , Humans , Least-Squares Analysis , Machine Learning , Male , Middle Aged , Time Factors
18.
West J Emerg Med ; 21(5): 1201-1210, 2020 Aug 24.
Article in English | MEDLINE | ID: covidwho-1456475

ABSTRACT

INTRODUCTION: For early detection of sepsis, automated systems within the electronic health record have evolved to alert emergency department (ED) personnel to the possibility of sepsis, and in some cases link them to suggested care pathways. We conducted a systematic review of automated sepsis-alert detection systems in the ED. METHODS: We searched multiple health literature databases from the earliest available dates to August 2018. Articles were screened based on abstract, again via manuscript, and further narrowed with set inclusion criteria: 1) adult patients in the ED diagnosed with sepsis, severe sepsis, or septic shock; 2) an electronic system that alerts a healthcare provider of sepsis in real or near-real time; and 3) measures of diagnostic accuracy or quality of sepsis alerts. The final, detailed review was guided by QUADAS-2 and GRADE criteria. We tracked all articles using an online tool (Covidence), and the review was registered with PROSPERO registry of reviews. A two-author consensus was reached at the article choice stage and final review stage. Due to the variation in alert criteria and methods of sepsis diagnosis confirmation, the data were not combined for meta-analysis. RESULTS: We screened 693 articles by title and abstract and 20 by full text; we then selected 10 for the study. The articles were published between 2009-2018. Two studies had algorithm-based alert systems, while eight had rule-based alert systems. All systems used different criteria based on systemic inflammatory response syndrome (SIRS) to define sepsis. Sensitivities ranged from 10-100%, specificities from 78-99%, and positive predictive value from 5.8-54%. Negative predictive value was consistently high at 99-100%. Studies showed some evidence for improved process-of-care markers, including improved time to antibiotics. Length of stay improved in two studies. One low quality study showed improved mortality. CONCLUSION: The limited evidence available suggests that sepsis alerts in the ED setting can be set to high sensitivity. No high-quality studies showed a difference in mortality, but evidence exists for improvements in process of care. Significant further work is needed to understand the consequences of alert fatigue and sensitivity set points.


Subject(s)
Decision Support Systems, Clinical/standards , Early Diagnosis , Emergency Service, Hospital/organization & administration , Sepsis/diagnosis , Critical Pathways , Humans , Quality Improvement
19.
Cochrane Database Syst Rev ; 5: CD013212, 2020 05 07.
Article in English | MEDLINE | ID: covidwho-1453527

ABSTRACT

BACKGROUND: Hypertension is a major public health challenge affecting more than one billion people worldwide; it disproportionately affects populations in low- and middle-income countries (LMICs), where health systems are generally weak. The increasing prevalence of hypertension is associated with population growth, ageing, genetic factors, and behavioural risk factors, such as excessive salt and fat consumption, physical inactivity, being overweight and obese, harmful alcohol consumption, and poor management of stress. Over the long term, hypertension leads to risk for cardiovascular events, such as heart disease, stroke, kidney failure, disability, and premature mortality. Cardiovascular events can be preventable when high-risk populations are targeted, for example, through population-wide screening strategies. When available resources are limited, taking a total risk approach whereby several risk factors of hypertension are taken into consideration (e.g. age, gender, lifestyle factors, diabetes, blood cholesterol) can enable more accurate targeting of high-risk groups. Targeting of high-risk groups can help reduce costs in that resources are not spent on the entire population. Early detection in the form of screening for hypertension (and associated risk factors) can help identify high-risk groups, which can result in timely treatment and management of risk factors. Ultimately, early detection can help reduce morbidity and mortality linked to it and can help contain health-related costs, for example, those associated with hospitalisation due to severe illness and poorly managed risk factors and comorbidities. OBJECTIVES: To assess the effectiveness of different screening strategies for hypertension (mass, targeted, or opportunistic) to reduce morbidity and mortality associated with hypertension. SEARCH METHODS: An Information Specialist searched the Cochrane Register of Studies (CRS-Web), the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, Latin American Caribbean Health Sciences Literature (LILACS) Bireme, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) without language, publication year, or publication status restrictions. The searches were conducted from inception until 9 April 2020. SELECTION CRITERIA: Randomised controlled trials (RCTs) and non-RCTs (NRCTs), that is, controlled before and after (CBA), interrupted time series (ITS), and prospective analytic cohort studies of healthy adolescents, adults, and elderly people participating in mass, targeted, or opportunistic screening of hypertension. DATA COLLECTION AND ANALYSIS: Screening of all retrieved studies was done in Covidence. A team of reviewers, in pairs, independently assessed titles and abstracts of identified studies and acquired full texts for studies that were potentially eligible. Studies were deemed to be eligible for full-text screening if two review authors agreed, or if consensus was reached through discussion with a third review author. It was planned that at least two review authors would independently extract data from included studies, assess risk of bias using pre-specified Cochrane criteria, and conduct a meta-analysis of sufficiently similar studies or present a narrative synthesis of the results. MAIN RESULTS: We screened 9335 titles and abstracts. We identified 54 potentially eligible studies for full-text screening. However, no studies met the eligibility criteria. AUTHORS' CONCLUSIONS: There is an implicit assumption that early detection of hypertension through screening can reduce the burden of morbidity and mortality, but this assumption has not been tested in rigorous research studies. High-quality evidence from RCTs or programmatic evidence from NRCTs on the effectiveness and costs or harms of different screening strategies for hypertension (mass, targeted, or opportunistic) to reduce hypertension-related morbidity and mortality is lacking.


Subject(s)
Hypertension/diagnosis , Early Diagnosis , Humans , Mass Screening
20.
JAMA Netw Open ; 4(9): e2128534, 2021 09 01.
Article in English | MEDLINE | ID: covidwho-1441922

ABSTRACT

Importance: Currently, there are no presymptomatic screening methods to identify individuals infected with a respiratory virus to prevent disease spread and to predict their trajectory for resource allocation. Objective: To evaluate the feasibility of using noninvasive, wrist-worn wearable biometric monitoring sensors to detect presymptomatic viral infection after exposure and predict infection severity in patients exposed to H1N1 influenza or human rhinovirus. Design, Setting, and Participants: The cohort H1N1 viral challenge study was conducted during 2018; data were collected from September 11, 2017, to May 4, 2018. The cohort rhinovirus challenge study was conducted during 2015; data were collected from September 14 to 21, 2015. A total of 39 adult participants were recruited for the H1N1 challenge study, and 24 adult participants were recruited for the rhinovirus challenge study. Exclusion criteria for both challenges included chronic respiratory illness and high levels of serum antibodies. Participants in the H1N1 challenge study were isolated in a clinic for a minimum of 8 days after inoculation. The rhinovirus challenge took place on a college campus, and participants were not isolated. Exposures: Participants in the H1N1 challenge study were inoculated via intranasal drops of diluted influenza A/California/03/09 (H1N1) virus with a mean count of 106 using the median tissue culture infectious dose (TCID50) assay. Participants in the rhinovirus challenge study were inoculated via intranasal drops of diluted human rhinovirus strain type 16 with a count of 100 using the TCID50 assay. Main Outcomes and Measures: The primary outcome measures included cross-validated performance metrics of random forest models to screen for presymptomatic infection and predict infection severity, including accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). Results: A total of 31 participants with H1N1 (24 men [77.4%]; mean [SD] age, 34.7 [12.3] years) and 18 participants with rhinovirus (11 men [61.1%]; mean [SD] age, 21.7 [3.1] years) were included in the analysis after data preprocessing. Separate H1N1 and rhinovirus detection models, using only data on wearble devices as input, were able to distinguish between infection and noninfection with accuracies of up to 92% for H1N1 (90% precision, 90% sensitivity, 93% specificity, and 90% F1 score, 0.85 [95% CI, 0.70-1.00] AUC) and 88% for rhinovirus (100% precision, 78% sensitivity, 100% specificity, 88% F1 score, and 0.96 [95% CI, 0.85-1.00] AUC). The infection severity prediction model was able to distinguish between mild and moderate infection 24 hours prior to symptom onset with an accuracy of 90% for H1N1 (88% precision, 88% sensitivity, 92% specificity, 88% F1 score, and 0.88 [95% CI, 0.72-1.00] AUC) and 89% for rhinovirus (100% precision, 75% sensitivity, 100% specificity, 86% F1 score, and 0.95 [95% CI, 0.79-1.00] AUC). Conclusions and Relevance: This cohort study suggests that the use of a noninvasive, wrist-worn wearable device to predict an individual's response to viral exposure prior to symptoms is feasible. Harnessing this technology would support early interventions to limit presymptomatic spread of viral respiratory infections, which is timely in the era of COVID-19.


Subject(s)
Biometry/methods , Common Cold/diagnosis , Influenza A Virus, H1N1 Subtype , Influenza, Human/diagnosis , Rhinovirus , Severity of Illness Index , Wearable Electronic Devices , Adult , Area Under Curve , Biological Assay , Biometry/instrumentation , Cohort Studies , Common Cold/virology , Early Diagnosis , Feasibility Studies , Female , Humans , Influenza A Virus, H1N1 Subtype/growth & development , Influenza, Human/virology , Male , Mass Screening , Models, Biological , Rhinovirus/growth & development , Sensitivity and Specificity , Virus Shedding , Young Adult
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