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2.
World Journal of Clinical Cases ; 10(20):6784-6793, 2022.
Article in English | EMBASE | ID: covidwho-1928899

ABSTRACT

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In some patients, COVID-19 is complicated with myocarditis. Early detection of myocardial injury and timely intervention can significantly improve the clinical outcomes of COVID-19 patients. Although endomyocardial biopsy (EMB) is currently recognized as the ‘gold standard’ for the diagnosis of myocarditis, there are large sampling errors, many complications and a lack of unified diagnostic criteria. In addition, the clinical methods of treating acute and chronic COVID-19-related myocarditis are different. Cardiac magnetic resonance (CMR) can evaluate the morphology of the heart, left and right ventricular functions, myocardial perfusion, capillary leakage and myocardial interstitial fibrosis to provide a noninvasive and radiation-free diagnostic basis for the clinical detection, efficacy and risk assessment, and followup observation of COVID-19-related myocarditis. However, for the diagnosis of COVID-19-related myocarditis, the Lake Louise Consensus Criteria may not be fully applicable. COVID-19-related myocarditis is different from myocarditis related to other viral infections in terms of signal intensity and lesion location as assessed by CMR, which is used to visualize myocardial damage, locate lesions and quantify pathological changes based on various sequences. Therefore, the standardized application of CMR to timely and accurately evaluate heart injury in COVID-19-related myocarditis and develop rational treatment strategies could be quite effective in improving the prognosis of patients and preventing potential late-onset effects in convalescent patients with COVID-19.

3.
11th International Conference on Image Processing Theory, Tools and Applications, IPTA 2022 ; 2022.
Article in English | Scopus | ID: covidwho-1922716

ABSTRACT

Intimate contact recognition has gained more attention in academia field in recent years due to the outbreak of Covid-19. However, state of the art solutions suffer from either inefficient accuracy or high cost. In this paper, we propose a novel method for COVID-19 intimate contact recognition in public spaces through video camera networks (CCTV). This method leverages distance detection and re-Identification algorithms, so pedestrians in close contact are re-identified, their identity information is obtained and stored in a database to realize contact tracing. We compare different social distance detection algorithms and the Faster-RCNN model outperforms other al-ternatives in terms of running speed. We also evaluate our Re-Identification model on two types of indicators in the PETS2009 dataset: mAP reaches 85.1%;rank-1, rank-5, and rank-10 reach 97.8%, 98.9%, and 98.9%, respectively. Experimental results demonstrate that our solution can be effectively applied in public places to realize fast and accurate automatic contact tracing. © 2022 IEEE.

4.
3rd International Conference on Design, Operation and Evaluation of Mobile Communications, MOBILE 2022 Held as Part of the 24th HCI International Conference, HCII 2022 ; 13337 LNCS:35-48, 2022.
Article in English | Scopus | ID: covidwho-1919596

ABSTRACT

Under the circumstance of continuous variation of COVID-19 virus, verified the temporariness of the vaccines made by various countries. One cannot expect permanent protection by accepting only one dose of vaccine. In order to prepare and respond to the pandemic, many countries are applying different strategies to increase vaccination rates. The WHO appeals to the world to take the vaccine booster shot for community immunity. Relevant authorities then have to provide and spread visual health messages on the booster shot to keep the public informed. This study examine how unofficial organizations can guide and persuade people to adopt relevant health actions more effectively (such as continuous vaccination) by introducing emoji with different emotional valences in different message framing. An online experiment adopted a 2 (emoji: positive versus negative) × 2 (message framing: gain framing versus loss framing) design to investigate the effects of contrary emoji on people’s self-efficacy to continuously take the booster shot. In total of 240 university students were recruited to participate in this study. Within two types of message framing, the experiment simulated 4 pieces of health messages on the COVID-19 booster shot released by an unofficial organization, together with emoji of two emotional valences. The results showed that health messages with negative emoji result in stronger self-efficacy to user. Moreover, there is an interaction effect between emoji and message framing on self-efficacy. This study is intended to provide meaningful insights for health communicators, visual designers and health practitioners concerned. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

5.
American Journal of Respiratory and Critical Care Medicine ; 205:2, 2022.
Article in English | English Web of Science | ID: covidwho-1880007
6.
PubMed; 2022.
Preprint in English | PubMed | ID: ppcovidwho-338449

ABSTRACT

Physical interactions between viral and host proteins are responsible for almost all aspects of the viral life cycle and the host's immune response. Studying viral-host protein-protein interactions is thus crucial for identifying strategies for treatment and prevention of viral infection. Here, we use high-throughput yeast two-hybrid and affinity purification followed by mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of both binary and co-complex interactions. We report a total of 739 high-confidence interactions, showing the highest overlap of interaction partners among published datasets as well as the highest overlap with genes differentially expressed in samples (such as upper airway and bronchial epithelial cells) from patients with SARS-CoV-2 infection. Showcasing the utility of our network, we describe a novel interaction between the viral accessory protein ORF3a and the host zinc finger transcription factor ZNF579 to illustrate a SARS-CoV-2 factor mediating a direct impact on host transcription. Leveraging our interactome, we performed network-based drug screens for over 2,900 FDA-approved/investigational drugs and obtained a curated list of 23 drugs that had significant network proximities to SARS-CoV-2 host factors, one of which, carvedilol, showed promising antiviral properties. We performed electronic health record-based validation using two independent large-scale, longitudinal COVID-19 patient databases and found that carvedilol usage was associated with a significantly lowered probability (17%-20%, P < 0.001) of obtaining a SARS-CoV-2 positive test after adjusting various confounding factors. Carvedilol additionally showed anti-viral activity against SARS-CoV-2 in a human lung epithelial cell line [half maximal effective concentration (EC 50 ) value of 4.1 microM], suggesting a mechanism for its beneficial effect in COVID-19. Our study demonstrates the value of large-scale network systems biology approaches for extracting biological insight from complex biological processes.

7.
Embase; 2022.
Preprint in English | EMBASE | ID: ppcovidwho-338077

ABSTRACT

Purpose: In young adults (18 to 49 years old), investigation of the acute respiratory distress syndrome (ARDS) after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been limited. We evaluated the risk factors and outcomes of ARDS following infection with SARS-CoV-2 in a young adult population. Methods: A retrospective cohort study was conducted between January 1st, 2020 and February 28th, 2021 using patient-level electronic health records (EHR), across 241 United States hospitals and 43 European hospitals participating in the Consortium for Clinical Characterization of COVID-19 by EHR (4CE). To identify the risk factors associated with ARDS, we compared young patients with and without ARDS through a federated analysis. We further compared the outcomes between young and old patients with ARDS. Results: Among the 75,377 hospitalized patients with positive SARS-CoV-2 PCR, 1001 young adults presented with ARDS (7.8% of young hospitalized adults). Their mortality rate at 90 days was 16.2% and they presented with a similar complication rate for infection than older adults with ARDS. Peptic ulcer disease, paralysis, obesity, congestive heart failure, valvular disease, diabetes, chronic pulmonary disease and liver disease were associated with a higher risk of ARDS. We described a high prevalence of obesity (53%), hypertension (38%- although not significantly associated with ARDS), and diabetes (32%). Conclusion: Trough an innovative method, a large international cohort study of young adults developing ARDS after SARS-CoV-2 infection has been gather. It demonstrated the poor outcomes of this population and associated risk factor.

8.
Embase; 2022.
Preprint in English | EMBASE | ID: ppcovidwho-338013

ABSTRACT

Background: Admissions are generally classified as COVID-19 hospitalizations if the patient has a positive SARS-CoV-2 polymerase chain reaction (PCR) test. However, because 35% of SARS-CoV-2 infections are asymptomatic, patients admitted for unrelated indications with an incidentally positive test could be misclassified as a COVID-19 hospitalization. EHR-based studies have been unable to distinguish between a hospitalization specifically for COVID-19 versus an incidental SARS-CoV-2 hospitalization. Although the need to improve classification of COVID-19 disease vs. incidental SARS-CoV-2 is well understood, the magnitude of the problems has only been characterized in small, single-center studies. Furthermore, there have been no peer-reviewed studies evaluating methods for improving classification. Objective: The aims of this study were to: first, quantify the frequency of incidental hospitalizations over the first fifteen months of the pandemic in multiple hospital systems in the United States;and second, to apply electronic phenotyping techniques to automatically improve COVID-19 hospitalization classification. Methods: From a retrospective EHR-based cohort in four US healthcare systems in Massachusetts, Pennsylvania, and Illinois, a random sample of 1,123 SARS-CoV-2 PCR-positive patients hospitalized between 3/2020–8/2021 was manually chart-reviewed and classified as admitted-with-COVID-19 (incidental) vs. specifically admitted for COVID-19 (for-COVID-19). EHR-based phenotyping was used to find feature sets to filter out incidental admissions. Results: EHR-based phenotyped feature sets filtered out incidental admissions, which occurred in an average of 26% of hospitalizations (although this varied widely over time, from 0%-75%). The top site-specific feature sets had 79-99% specificity with 62-75% sensitivity, while the best performing across-site feature set had 71-94% specificity with 69-81% sensitivity. Conclusions: A large proportion of SARS-CoV-2 PCR-positive admissions were incidental. Straightforward EHR-based phenotypes differentiated admissions, which is important to assure accurate public health reporting and research.

9.
Frontiers in Environmental Science ; 10, 2022.
Article in English | Scopus | ID: covidwho-1875407

ABSTRACT

The COVID-19 pandemic has led to a burgeoning demand for active travel (walking or cycling), which is a healthy, pollution-free, and affordable daily transportation mode. Park green space (PGS), as an open natural landscape, have become a popular destination for active travel trips in metropolitan areas. Pedestrians and cyclists are often at high crash risk when exposed to complicated traffic environments in urban areas. Therefore, this study aims to propose a safety assessment framework for evaluating active travel traffic safety (ATTS) near PGS from the perspective of urban planning and exploring the effect of the point-of-interest (POI) aggregation phenomenon on ATTS. First, links between ATTS and the environment variables were investigated and integrated into the framework using the catastrophe model. Second, the relationship between the POI density and ATTS was investigated using three spatial regression models. Results in the Wuhan Metropolitan Area as a case study have shown that (1) the population density, road density, nighttime brightness, and vegetation situation near PGS have pronounced effects on ATTS;(2) pedestrians near PGS enjoy safer road facilities than cyclists. Active travel traffic near PGS requires more attention than non-park neighborhoods;(3) among four park categories, using active travel to access theme parks is the safest;and (4) SEM has the best fit for POI cluster research. Increases in leisure facility density and residence density may lead to deterioration and improvement in ATTS safety levels near PGSs, respectively. The safety framework can be applied in other regions because the selected environment indicators are common and accessible. The findings offer appropriate traffic planning strategies to improve the safety of active travel users when accessing PGS. Copyright © 2022 Luo, Liu, Xing, Wang and Rao.

10.
Journal of Electronic Imaging ; 31(2), 2022.
Article in English | Scopus | ID: covidwho-1846312

ABSTRACT

Recent research on facial expression recognition (FER) in the wild shows challenges still remain. Different from laboratory-controlled expression in the past, images in the wild contain more uncertainties, such as different forms of face information occlusion, ambiguous facial images, noisy labels, and so on. Among them, real-world facial occlusion is the most general and crucial challenge for FER. In addition, because of the COVID-19 disease epidemic, people have to wear masks in public, which brings new challenges to FER tasks. Due to the recent success of the Transformer on numerous computer vision tasks, we propose a Collaborative Attention Transformer (CAT) network that first uses Cross-Shaped Window Transformer as the backbone for the FER task. Meanwhile, two attention modules are collaborated. Channel-Spatial Attention Module is designed to increase the attention of the network to global features. Moreover, Window Attention Gate is used to enhance the ability of the model to focus on local details. The proposed method is evaluated on two public in-The-wild facial expression datasets, RAF-DB and FERPlus, and the results demonstrate that our CAT performs superior to the state-of-The-Art methods. © 2022 SPIE and IST.

11.
Hong Kong Journal of Paediatrics ; 27(2):118-125, 2022.
Article in English | Scopus | ID: covidwho-1843202

ABSTRACT

Since the first report of COVID-19 in Wuhan, China, the disease has rapidly spread to many countries worldwide. The initial reports showed that the incidence rate in adults was higher, while children and adolescents had fewer cases of infection. However, the number of COVID-19 cases has gradually increased in children and adolescents. Therefore, this study aimed to assess the percentage of children and/or adolescents of the total patients diagnosed with COVID-19. PubMed, Embase, Web of Science and the Cochrane Library were searched to find relevant studies. All statistical analyses were conducted using StataMP 14 software. A total of 12 studies met the inclusion criteria. The final results showed that the percentage of children and/or adolescents of all COVID-19 cases was 0.06 [95% confidence interval (CI), 0.04-0.07], which meant an average of 6 cases in children per 10,000 COVID-19 cases. The percentage of children and/or adolescents with COVID-19 was 0.03 (95% CI, 0.01-0.05), 0.09 (95% CI, 0.08-0.09), 0.09 (95% CI, 0.03-0.16) and 0.04 (95% CI, 0.00-0.10) in Asia, South America, North America and Europe, respectively. The present study showed a low percentage of COVID-19 cases of children and/or adolescents, but not without infection risk. Therefore, we should pay attention to the cases of children and/or adolescents during the COVID-19 period and raise our vigilance. © 2022, Medcom Limited. All rights reserved.

12.
Cochrane Database of Systematic Reviews ; 2022(3), 2022.
Article in English | EMBASE | ID: covidwho-1813443

ABSTRACT

Objectives: This is a protocol for a Cochrane Review (intervention). The objectives are as follows:. To assess the effectiveness and safety of internet-based cognitive behavioural therapy for preventing postnatal depression.

13.
40th IEEE International Performance, Computing, and Communications Conference (IPCCC) ; 2021.
Article in English | Web of Science | ID: covidwho-1806937

ABSTRACT

Based on the air pollution data in China from January 1,2014 to December 31,2020, the characteristics of extreme value and period of air quality in different regions on different time scales were studied by using wavelet analysis. Wavelet coherence analysis was used to evaluate the relationship between air quality and meteorological factors in the period of COVID-19. We found that the spatial characteristics of air quality changed significantly in summer. Generally, air pollution is more severe in spring and winter. During the lockdown period, the overall air quality in the study area improved significantly. In general, except for 03, the concentration of all other pollutants has dropped considerably. The improvement in air quality is a direct result of emission reductions due to the implementation of the COVID-19 blockade, which is unsustainable in the long term. Eventually, a prediction model attention_CNN_LSTM based on deep learning method is proposed in this paper. The experimental results show that the attention proposed in this study the model has a good prediction effect in the long-term prediction of air quality, but the attention mechanism's impact is lower. After shortening the prediction period, the attention_CNN_LSTM model has good prediction performance on most data sets, with average MAPE = 2.67% and RMSE = 2.29.

14.
PubMed; 2021.
Preprint in English | PubMed | ID: ppcovidwho-333657

ABSTRACT

Understanding protective mechanisms of antibody recognition can inform vaccine and therapeutic strategies against SARS-CoV-2. We discovered a new antibody, 910-30, that targets the SARS-CoV-2 ACE2 receptor binding site as a member of a public antibody response encoded by IGHV3-53/IGHV3-66 genes. We performed sequence and structural analyses to explore how antibody features correlate with SARS-CoV-2 neutralization. Cryo-EM structures of 910-30 bound to the SARS-CoV-2 spike trimer revealed its binding interactions and ability to disassemble spike. Despite heavy chain sequence similarity, biophysical analyses of IGHV3-53/3-66 antibodies highlighted the importance of native heavy:light pairings for ACE2 binding competition and for SARS-CoV-2 neutralization. We defined paired heavy:light sequence signatures and determined antibody precursor prevalence to be ~1 in 44,000 human B cells, consistent with public antibody identification in several convalescent COVID-19 patients. These data reveal key structural and functional neutralization features in the IGHV3-53/3-66 public antibody class to accelerate antibody-based medical interventions against SARS-CoV-2. HIGHLIGHTS: A molecular study of IGHV3-53/3-66 public antibody responses reveals critical heavy and light chain features for potent neutralizationCryo-EM analyses detail the structure of a novel public antibody class member, antibody 910-30, in complex with SARS-CoV-2 spike trimerCryo-EM data reveal that 910-30 can both bind assembled trimer and can disassemble the SARS-CoV-2 spikeSequence-structure-function signatures defined for IGHV3-53/3-66 class antibodies including both heavy and light chainsIGHV3-53/3-66 class precursors have a prevalence of 1:44,000 B cells in healthy human antibody repertoires.

15.
Environmental Science-Nano ; : 11, 2022.
Article in English | Web of Science | ID: covidwho-1778647

ABSTRACT

Hydrogen peroxide (H2O2) solution and its aerosols are common disinfectants, especially for urgent reuse of personal protective equipment during the COVID-19 pandemic. Highly sensitive and selective evaluation of the H2O2 concentration is key to customizing the sufficient disinfection process and avoiding disinfection overuse. Amperometric electrochemical detection is an effective means but poses challenges originated from the precarious state of H2O2. Here, an atomic Co-N-x-C site anchored neuronal-like carbon modified amperometric sensor (denoted as the CoSA-N/C@rGO sensor) is designed, which exhibits a broad detection range (from 250 nM to 50 mM), superior sensitivity (743.3 mu A mM(-1) cm(-2), the best among carbon-based amperometric sensors), strong selectivity (no response to interferents), powerful reliability (only 2.86% decay for one week) and fast response (just 5 s) for residual H2O2 detection. We validated the accuracy and practicability of the CoSA-N/C@rGO sensor in the actual H2O2 disinfection process of personal protective equipment. Further characterization verifies that the electrocatalytic activity and selective reduction of H2O2 is determined by the atomically dispersed Co-N-x-C sites and the high oxygen content of CoSA-N/C@rGO, where the response time and reliability of H2O2 detection is determined by the neuronal-like structure with high nitrogen content. Our findings pave the way for developing a sensor with superior sensitivity, selectivity and stability, rendering promising applications such as medical care and environmental treatment.

16.
Journal of Safety Science and Resilience ; 2(3):146-156, 2021.
Article in English | Scopus | ID: covidwho-1773520

ABSTRACT

The needs of mitigating COVID-19 epidemic prompt policymakers to make public health-related decision under the guidelines of science. Tremendous unstructured COVID-19 publications make it challenging for policymakers to obtain relevant evidence. Knowledge graphs (KGs) can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently. Here, we introduce a novel framework that can extract the COVID-19 public health evidence knowledge graph (CPHE-KG) from papers relating to a modelling study. We screen out a corpus of 3096 COVID-19 modelling study papers by performing a literature assessment process. We define a novel annotation schema to construct the COVID-19 modelling study-related IE dataset (CPHIE). We also propose a novel multi-tasks document-level information extraction model SS-DYGIE++ based on the dataset. Leveraging the model on the new corpus, we construct CPHE-KG containing 60,967 entities and 51,140 relations. Finally, we seek to apply our KG to support evidence querying and evidence mapping visualization. Our SS-DYGIE++(SpanBERT) model has achieved a F1 score of 0.77 and 0.55 respectively in document-level entity recognition and coreference resolution tasks. It has also shown high performance in the relation identification task. With evidence querying, our KG can present the dynamic transmissions of COVID-19 pandemic in different countries and regions. The evidence mapping of our KG can show the impacts of variable non-pharmacological interventions to COVID-19 pandemic. Analysis demonstrates the quality of our KG and shows that it has the potential to support COVID-19 policy making in public health. © 2021

17.
Open Forum Infectious Diseases ; 8(SUPPL 1):S373, 2021.
Article in English | EMBASE | ID: covidwho-1746454

ABSTRACT

Background. Molnupiravir (MOV, MK-4482, EIDD-2801) is an orally administered prodrug of N-hydroxycytidine (NHC, EIDD-1931), a nucleoside with broad antiviral activity against a range of RNA viruses. MOV acts by driving viral error catastrophe following its incorporation by the viral RdRp into the viral genome. Given its mechanism of action, MOV activity should not be affected by substitutions in the spike protein present in SARS-CoV-2 variants of concern which impact efficacy of therapeutic neutralizing antibodies and vaccine induced immunity. We characterized MOV activity against variants by assessing antiviral activity in vitro and virologic response from the Phase 2/3 clinical trials (MOVe-In, MOVe-Out) for treatment of COVID-19. Methods. MOV activity against several SARS-CoV-2 variants, was evaluated in an in vitro infection assay. Antiviral potency of NHC (IC50) was determined in Vero E6 cells infected with virus at MOI ~0.1 by monitoring CPE. Longitudinal SARSCoV-2 RNA viral load measures in participants enrolled in MOVe-In and MOVe-Out were analyzed based on SARS-CoV-2 genotype. Sequences of SARS-CoV-2 from study participants were amplified from nasal swabs by PCR and NGS was performed on samples with viral genome RNA of >22,000 copies/ml amplified by primers covering full length genome with Ion Torrent sequencing to identify clades represented in trial participants. SARS-CoV-2 clades were assigned using clade.nextstrain.org. Results. In vitro, NHC was equally effective against SARS-CoV-2 variants B.1.1.7 (20I), B.1351 (20H), and P1 (20J), compared with the original WA1 (19B) isolate. In clinical trials, no discernable difference was observed in magnitude of viral response measured by change from baseline in RNA titer over time across all clades represented including 20A through 20E and 20G to 20I. No participants at the time of the study presented with 20F, 20J, or 21A. Conclusion. Distribution of clades in participants in MOVe-In and MOVe-Out was representative of those circulating globally at the time of collection (Oct 2020 -Jan 2021). Both in vitro and clinical data suggest that spike protein substitutions do not impact antiviral activity of MOV and suggest its potential use for the treatment of SARS-CoV-2 variants.

18.
Food Science and Technology (Brazil) ; 42, 2022.
Article in English | Scopus | ID: covidwho-1745259

ABSTRACT

To explore the effect of Multi-Disciplinary Team (MDT) mode in the diagnosis and treatment of Coronavirus Disease 2019 (COVID-19) Pneumonia. A total of 65 patients with suspected COVID-19 pneumonia were included. On February 8, 2020, our hospital officially became a designated hospital for the treatment of COVID-19, and the MDT mode was implemented throughout the diagnosis and treatment for newly admitted patients with suspected COVID-19. The patients were divided into control group and observation group according to whether received MDT mode. Our results showed that the diagnosis time in the observation group was significantly shortened than that in the control group (2.51 days vs. 3.47 days) (p < 0.05). The average daily hospitalization costs in the observation group was significantly decreased in comparison with the control group (¥766.1 vs. ¥1190.4) (p < 0.001). The average daily cost of protective materials in the observation group was significantly reduced in comparison with the control group (¥4226.90 vs. ¥5308.20) (p < 0.001). Compared with the control group, the subjective symptoms of patients in the observation group were significantly improved (p < 0.001). In conclusion, the MDT mode shortens the diagnosis time of, reduces the hospitalization costs, and improves the subjective symptoms of COVID-19. © 2022, Sociedade Brasileira de Ciencia e Tecnologia de Alimentos, SBCTA. All rights reserved.

19.
PubMed; 2021.
Preprint in English | PubMed | ID: ppcovidwho-330358

ABSTRACT

OBJECTIVES: To perform an international comparison of the trajectory of laboratory values among hospitalized patients with COVID-19 who develop severe disease and identify optimal timing of laboratory value collection to predict severity across hospitals and regions. DESIGN: Retrospective cohort study. SETTING: The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), an international multi-site data-sharing collaborative of 342 hospitals in the US and in Europe. PARTICIPANTS: Patients hospitalized with COVID-19, admitted before or after PCR-confirmed result for SARS-CoV-2. Primary and secondary outcome measures: Patients were categorized as ever-severe or never-severe using the validated 4CE severity criteria. Eighteen laboratory tests associated with poor COVID-19-related outcomes were evaluated for predictive accuracy by area under the curve (AUC), compared between the severity categories. Subgroup analysis was performed to validate a subset of laboratory values as predictive of severity against a published algorithm. A subset of laboratory values (CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin) was compared between North American and European sites for severity prediction. RESULTS: Of 36,447 patients with COVID-19, 19,953 (43.7%) were categorized as ever-severe. Most patients (78.7%) were 50 years of age or older and male (60.5%). Longitudinal trajectories of CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin showed association with disease severity. Significant differences of laboratory values at admission were found between the two groups. With the exception of D-dimer, predictive discrimination of laboratory values did not improve after admission. Sub-group analysis using age, D-dimer, CRP, and lymphocyte count as predictive of severity at admission showed similar discrimination to a published algorithm (AUC=0.88 and 0.91, respectively). Both models deteriorated in predictive accuracy as the disease progressed. On average, no difference in severity prediction was found between North American and European sites. CONCLUSIONS: Laboratory test values at admission can be used to predict severity in patients with COVID-19. Prediction models show consistency across international sites highlighting the potential generalizability of these models.

20.
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