Concerns related to the interactions between COVID-19 vaccination and cancer/cancer treatment were barriers to complete primary vaccination series among Chinese cancer patients: A multicentre cross-sectional survey.

COVID-19 vaccination is effective for cancer patients without safety concerns. However, COVID-19 vaccination hesitancy is common among cancer patients. This study investigated factors affecting primary COVID-19 vaccination series completion rate among cancer patients in China. A multicentre cross-sectional study was conducted in four Chinese cities in different geographic regions between May and June 2022. A total of 893 cancer inpatients provided written informed consent and completed the study. Logistic regression models were fitted. Among the participants, 58.8% completed the primary COVID-19 vaccination series. After adjusting for background characteristics, concerns about interactions between COVID-19 vaccination and cancers/cancer treatment (adjusted odds ratios [AOR]: 0.97, 95%CI: 0.94, 0.99) were associated with lower completion of primary vaccination series. In addition, perceived higher risk of COVID-19 infection comparing to people without cancers (AOR: 0.46, 95%CI: 0.24, 0.88), perceived a high chance of having severe consequences of COVID-19 infection (AOR: 0.68, 95%CI: 0.51, 0.91) were also associated with lower completion rate. Being suggested by significant others (AOR: 1.32, 95%CI: 1.23, 1.41) and perceived higher self-efficacy to receive COVID-19 vaccination (AOR: 1.48, 95%CI: 1.31, 1.67) were positively associated with the dependent variable. Completion rate of primary COVID-19 vaccination series was low among Chinese cancer patients. Given the large population size and their vulnerability, this group urgently needs to increase COVID-19 vaccination coverage. Removing concerns about interactions between COVID-19 vaccination and cancers, using fear appeal approach, involving significant others, and facilitating patients to make a plan to receive COVID-19 vaccination might be useful strategies.

Inflammasomes: a rising star on the horizon of COVID-19 pathophysiology.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a contagious respiratory virus that is the cause of the coronavirus disease 2019 (COVID-19) pandemic which has posed a serious threat to public health. COVID-19 is characterized by a wide spectrum of clinical manifestations, ranging from asymptomatic infection to mild cold-like symptoms, severe pneumonia or even death. Inflammasomes are supramolecular signaling platforms that assemble in response to danger or microbial signals. Upon activation, inflammasomes mediate innate immune defense by favoring the release of proinflammatory cytokines and triggering pyroptotic cell death. Nevertheless, abnormalities in inflammasome functioning can result in a variety of human diseases such as autoimmune disorders and cancer. A growing body of evidence has showed that SARS-CoV-2 infection can induce inflammasome assembly. Dysregulated inflammasome activation and consequent cytokine burst have been associated with COVID-19 severity, alluding to the implication of inflammasomes in COVID-19 pathophysiology. Accordingly, an improved understanding of inflammasome-mediated inflammatory cascades in COVID-19 is essential to uncover the immunological mechanisms of COVID-19 pathology and identify effective therapeutic approaches for this devastating disease. In this review, we summarize the most recent findings on the interplay between SARS-CoV-2 and inflammasomes and the contribution of activated inflammasomes to COVID-19 progression. We dissect the mechanisms involving the inflammasome machinery in COVID-19 immunopathogenesis. In addition, we provide an overview of inflammasome-targeted therapies or antagonists that have potential clinical utility in COVID-19 treatment.

An Online Survey on the Relationship Between Positive Coping and Post-Traumatic Stress Symptoms (PTSS) of Medical Students in China During the COVID-19 Pandemic: The Mediating Role of Social Support.

BACKGROUND The COVID-19 pandemic has caused varying degrees of psychological stress among medical students. This research explored the post-traumatic stress symptoms (PTSS) of medical students in China and their relationship with positive coping and social support. MATERIAL AND METHODS In the form of cross-sectional online survey, 2280 medical students locked down at home were selected by random cluster method to investigate social support, coping style, and PTSS using the Social Support Rating Scale (SSRS), Simplified Coping Style Questionnaire (SCSQ), and Post-traumatic Stress Disorder (PTSD) Checklist-Civilian Version (PCL-C), respectively. RESULTS This research found that the PTSS detection rate in medical students was 10.42% during the COVID-19 pandemic. The PTSS scores of females were significantly higher than that of the males. However, the PTSS detection rate in females (9.71%) was not significantly different from that in males (11.24%). Compared with those of the non-PTSS group, the total score and its all-factor score of social support, the total score of coping style and the positive coping score of the PTSS group were much lower, while the negative coping score of the PTSS group was much higher (P<0.01). Positive coping was positively correlated with social support, while positive coping and social support were negatively correlated with PTSS. The total effect of positive coping on PTSS was -0.310 (P<0.001), the direct effect was -0.128 (P<0.01), and the indirect effect was -0.182 (P<0.001). Social support played a mediating role between positive coping and PTSS, with the mediating effect accounting for 58.81% of the total effect. CONCLUSIONS Social support plays a mediating role between positive coping and post-traumatic stress symptoms. Objective support and positive coping are the 2 main protective factors of PTSS.

Longitudinal changes in mental health among medical students in China during the COVID-19 epidemic: depression, anxiety and stress at 1-year follow -up.

This study aimed to evaluate the influence of COVID-19 on the mental health of Chinese medical students at 1-year of follow-up. From 2 February 2020 to 23 February 2021, we conducted three waves of research online (T1 = during outbreak, T2 = controlling period, T3 = 1 year after outbreak). The survey collected demographic data and several self reporting questionnaires to measure the depressive, anxiety and stress symptoms. A total of 4002 participants complete the whole research phases. The study major, grade level and gender were the main factors related to psychological distress caused by the COVID-19 crisis. Importantly, medical knowledge has a protective effect on medical students' psychological distress during the COVID-19 period.

Causal associations between type 1 diabetes and COVID-19 infection and prognosis: a two-sample Mendelian randomization study.

INTRODUCTION: It has been suggested that type 1 diabetes was associated with increased COVID-19 morbidity and mortality. However, their causal relationship is still unclear. Herein, we performed a two-sample Mendelian randomization (MR) to investigate the causal effect of type 1 diabetes on COVID-19 infection and prognosis. RESEARCH DESIGN AND METHODS: The summary statistics of type 1 diabetes were obtained from two published genome-wide association studies of European population, one as a discovery sample including 15 573 cases and 158 408 controls, and the other data as a replication sample consisting of 5913 cases and 8828 controls. We first performed a two-sample MR analysis to evaluate the causal effect of type 1 diabetes on COVID-19 infection and prognosis. Then, reverse MR analysis was conducted to determine whether reverse causality exists. RESULTS: MR analysis results showed that the genetically predicted type 1 diabetes was associated with higher risk of severe COVID-19 (OR=1.073, 95% CI: 1.034 to 1.114, pFDR=1.15×10-3) and COVID-19 death (OR=1.075, 95% CI: 1.033 to 1.119, pFDR=1.15×10-3). Analysis of replication dataset showed similar results, namely a positive association between type 1 diabetes and severe COVID-19 (OR=1.055, 95% CI: 1.029 to 1.081, pFDR=1.59×10-4), and a positively correlated association with COVID-19 death (OR=1.053, 95% CI: 1.026 to 1.081, pFDR=3.50×10-4). No causal association was observed between type 1 diabetes and COVID-19 positive, hospitalized COVID-19, the time to the end of COVID-19 symptoms in the colchicine treatment group and placebo treatment group. Reverse MR analysis showed no reverse causality. CONCLUSIONS: Type 1 diabetes had a causal effect on severe COVID-19 and death after COVID-19 infection. Further mechanistic studies are needed to explore the relationship between type 1 diabetes and COVID-19 infection and prognosis.

Social anxiety and depression symptoms in Chinese left-behind children after the lifting of COVID-19 lockdown: A network analysis.

BACKGROUND: Returning to social life after the lifting of COVID-19 lockdown may increase risk of social anxiety, which is highly co-morbid with depression. However, few studies have reported the association between them. AIMS: To explore the complex relationship between social anxiety and depression symptoms in left-behind children after the lifting of the COVID-19 lockdown. METHODS: A cross-sectional survey was conducted 6 months after the lockdown removal. A total of 3,107 left-behind children completed the survey with a mean age of 13.33 and a response rate of 87.77%. Depression and social anxiety severity were assessed by the DSM-5 Patient Health Questionnaire for Adolescents and the DSM-5 Social Anxiety Disorder Questionnaire, respectively. The symptom-level association between the two disorders was examined using network analysis. RESULTS: After the lifting of COVID-19 lockdown, the prevalence of depression and social anxiety in left-behind children was 19.57% and 12.36%, respectively, with a co-morbidity rate of 8.98%. Network analysis showed that "Social tension" and "Social avoidance" had the greatest expected influence; "Humiliation" and "Motor" were bridge symptom nodes in the network. The directed acyclic graph indicated that "Social fright" was at the upstream of all symptoms. CONCLUSION: Attention should be paid to social anxiety symptoms in left-behind children after the lifting of COVID-19 lockdown. Prevention and intervention measures should be taken promptly to reduce the comorbidity of social anxiety and depression symptoms in the left-behind children after the lifting of lockdown.

Chinese university students showed less disordered eating during the COVID-19 campus lockdown.

OBJECTIVE: The rapid spread of the Omicron variant of COVID-19 in China had resulted in campus lockdown in many universities since February 2022, profoundly affecting students' daily lives. Campus lockdown conditions differ considerably from home quarantine, so that the eating patterns of university students may be different. Thus, the current study aimed to: (1) investigate university students' eating patterns during campus lockdown; (2) identify factors associated with their disordered eating. METHOD: An online survey about recent life changes, disordered eating, stress, depression, and anxiety was carried out from April 8th to May 16th, 2022. A total of 2541 responses from 29 provinces/cities of China were received. RESULTS: 2213 participants were included in the main analysis, and other 86 participants were analyzed separately as a subgroup due to their diagnosis of eating disorder. Participants who were undergoing campus lockdown (the lockdown group) showed less disordered eating than those who had never been in campus lockdown (the never-lockdown group), as well as those who had experienced campus lockdown before (the once-lockdown group). However, they perceived more stress and felt more depressed. Being female, higher BMI, gaining weight, increasing exercise, spending more time on social media, higher level of depression and anxiety were all related to disordered eating in the lockdown group. CONCLUSIONS: Disordered eating among Chinese university students was less prevalent during campus lockdown due to the strict and regular diet. However, there is a potential risk of "revenge eating" after campus lockdown ends. Thus, there should be further tracking and related prevention. LEVEL OF EVIDENCE: IV, uncontrolled trials without any interventions.

Engineering highly efficient NIR-II FRET platform for Background-Free homogeneous detection of SARS-CoV-2 neutralizing antibodies in whole blood.

Förster or fluorescence resonance energy transfer (FRET) enables to probe biomolecular interactions, thus playing a vital role in bioassays. However, conventional FRET platforms suffer from limited sensitivity due to the low FRET efficiency and poor anti-interference of existing FRET pairs. Here we report a NIR-II (1000-1700 nm) FRET platform with extremely high FRET efficiency and exceptional anti-interference capability. This NIR-II FRET platform is established based on a pair of lanthanides downshifting nanoparticles (DSNPs) by employing Nd3+ doped DSNPs as an energy donor and Yb3+ doped DSNPs as an energy acceptor. The maximum FRET efficiency of this well-engineered NIR-II FRET platform reaches up to 92.2%, which is much higher than most commonly used ones. Owing to the all-NIR advantage (λex = 808 nm, λem = 1064 nm), this highly efficient NIR-II FRET platform exhibits extraordinary anti-interference in whole blood, and thus enabling background-free homogeneous detection of SARS-CoV-2 neutralizing antibodies in clinical whole blood sample with high sensitivity (limit of detection = 0.5 µg/mL) and specificity. This work opens up new opportunities for realizing highly sensitive detection of various biomarkers in biological samples with severe background interference.

The relationship between gut microbiota and COVID-19 progression: new insights into immunopathogenesis and treatment.

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed a global health crisis. Increasing evidence underlines the key role of competent immune responses in resisting SARS-CoV-2 infection and manifests the disastrous consequence of host immune dysregulation. Elucidating the mechanisms responsible for deregulated host immunity in COVID-19 may provide a theoretical basis for further research on new treatment modalities. Gut microbiota comprises trillions of microorganisms colonizing the human gastrointestinal tract and has a vital role in immune homeostasis and the gut-lung crosstalk. Particularly, SARS-CoV-2 infection can lead to the disruption of gut microbiota equilibrium, a condition called gut dysbiosis. Due to its regulatory effect on host immunity, gut microbiota has recently received considerable attention in the field of SARS-CoV-2 immunopathology. Imbalanced gut microbiota can fuel COVID-19 progression through production of bioactive metabolites, intestinal metabolism, enhancement of the cytokine storm, exaggeration of inflammation, regulation of adaptive immunity and other aspects. In this review, we provide an overview of the alterations in gut microbiota in COVID-19 patients, and their effects on individuals' susceptibility to viral infection and COVID-19 progression. Moreover, we summarize currently available data on the critical role of the bidirectional regulation between intestinal microbes and host immunity in SARS-CoV-2-induced pathology, and highlight the immunomodulatory mechanisms of gut microbiota contributing to COVID-19 pathogenesis. In addition, we discuss the therapeutic benefits and future perspectives of microbiota-targeted interventions including faecal microbiota transplantation (FMT), bacteriotherapy and traditional Chinese medicine (TCM) in COVID-19 treatment.

Interactive COVID-19 Mobility Impact and Social Distancing Analysis Platform

The research team has utilized privacy-protected mobile device location data, integrated with COVID-19 case data and census population data, to produce a COVID-19 impact analysis platform that can inform users about the effects of COVID-19 spread and government orders on mobility and social distancing. The platform is being updated daily, to continuously inform decision-makers about the impacts of COVID-19 on their communities, using an interactive analytical tool. The research team has processed anonymized mobile device location data to identify trips and produced a set of variables, including social distancing index, percentage of people staying at home, visits to work and non-work locations, out-of-town trips, and trip distance. The results are aggregated to county and state levels to protect privacy, and scaled to the entire population of each county and state. The research team is making their data and findings, which are updated daily and go back to January 1, 2020, for benchmarking, available to the public to help public officials make informed decisions. This paper presents a summary of the platform and describes the methodology used to process data and produce the platform metrics.

The Infection of Healthcare Workers and the Reinfection of Patients by Omicron Variant - Jiangsu Province, China, December 2022 to January 2023.

What is already known about this topic?: Healthcare workers (HCWs) and previously infected patients (PIPs) may experience a wave of epidemic following the modification of the country's coronavirus disease (COVID)-zero policy in China. What is added by this report?: As of early January 2023, the initial wave of the COVID-19 pandemic among HCWs had effectively subsided, with no statistically significant differences observed in infection rates compared to those of their co-occupants. The proportion of reinfections among PIPs was relatively low, particularly in those with recent infections. What are the implications for public health practice?: Medical and health services have resumed normal operations. For patients who have recently experienced severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, appropriate relaxation of policies may be considered.

A case report of encephalitis induced by SARS-CoV-2 confirmed by etiology: first case in Qingdao, China.

The neurological manifestations of SARS-CoV-2-infected patients are receiving increasing attention with the global spread of SARS-CoV-2. Here, we report the first case of SARS-CoV-2-induced encephalitis in Qingdao, China. We detected SARS-CoV-2 in nasopharyngeal swabs and cerebrospinal fluid from this 68-year-old female patient.

Blood unconjugated bilirubin and tacrolimus are negative predictors of specific cellular immunity in kidney transplant recipients after SAR-CoV-2 inactivated vaccination.

The immunogenicity of SARS-CoV-2 vaccines is poor in kidney transplant recipients (KTRs). The factors related to poor immunogenicity to vaccination in KTRs are not well defined. Here, observational study demonstrated no severe adverse effects were observed in KTRs and healthy participants (HPs) after first or second dose of SARS-CoV-2 inactivated vaccine. Different from HPs with excellent immunity against SARS-CoV-2, IgG antibodies against S1 subunit of spike protein, receptor-binding domain, and nucleocapsid protein were not effectively induced in a majority of KTRs after the second dose of inactivated vaccine. Specific T cell immunity response was detectable in 40% KTRs after the second dose of inactivated vaccine. KTRs who developed specific T cell immunity were more likely to be female, and have lower levels of total bilirubin, unconjugated bilirubin, and blood tacrolimus concentrations. Multivariate logistic regression analysis found that blood unconjugated bilirubin and tacrolimus concentration were significantly negatively associated with SARS-CoV-2 specific T cell immunity response in KTRs. Altogether, these data suggest compared to humoral immunity, SARS-CoV-2 specific T cell immunity response are more likely to be induced in KTRs after administration of inactivated vaccine. Reduction of unconjugated bilirubin and tacrolimus concentration might benefit specific cellular immunity response in KTRs following vaccination.

Interactive COVID-19 Mobility Impact and Social Distancing Analysis Platform.

The research team has utilized privacy-protected mobile device location data, integrated with COVID-19 case data and census population data, to produce a COVID-19 impact analysis platform that can inform users about the effects of COVID-19 spread and government orders on mobility and social distancing. The platform is being updated daily, to continuously inform decision-makers about the impacts of COVID-19 on their communities, using an interactive analytical tool. The research team has processed anonymized mobile device location data to identify trips and produced a set of variables, including social distancing index, percentage of people staying at home, visits to work and non-work locations, out-of-town trips, and trip distance. The results are aggregated to county and state levels to protect privacy, and scaled to the entire population of each county and state. The research team is making their data and findings, which are updated daily and go back to January 1, 2020, for benchmarking, available to the public to help public officials make informed decisions. This paper presents a summary of the platform and describes the methodology used to process data and produce the platform metrics.

AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study.

OBJECTIVES: Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence. METHODS: We included n = 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (n = 991 CT scans; n = 462 COVID-19, and n = 529 CAP) from three centers in China. An independent Chinese and German test dataset of n = 600 CT scans from six centers (COVID-19 / CAP; n = 300 each) was used to test the performance of eight blinded radiologists and the AI algorithm. A subtest dataset (180 CT scans; n = 90 each) was used to evaluate the radiologists' performance without and with AI assistance to quantify changes in diagnostic accuracy, reporting time, and diagnostic confidence. RESULTS: The diagnostic accuracy of the AI algorithm in the Chinese-German test dataset was 76.5%. Without AI assistance, the eight radiologists' diagnostic accuracy was 79.1% and increased with AI assistance to 81.5%, going along with significantly shorter decision times and higher confidence scores. CONCLUSION: This large multicenter study demonstrates that AI assistance in CT-based differentiation of COVID-19 and CAP increases radiological performance with higher accuracy and specificity, faster diagnostic time, and improved diagnostic confidence. KEY POINTS: • AI can help radiologists to get higher diagnostic accuracy, make faster decisions, and improve diagnostic confidence. • The China-German multicenter study demonstrates the advantages of a human-machine interaction using AI in clinical radiology for diagnostic differentiation between COVID-19 and CAP in CT scans.

Do Infrared Thermometers Hold Promise for an Effective Early Warning System for Emerging Respiratory Infectious Diseases?

BACKGROUND: Major respiratory infectious diseases, such as influenza, SARS-CoV, and SARS-CoV-2, have caused historic global pandemics with severe disease and economic burdens. Early warning and timely intervention are key to suppress such outbreaks. OBJECTIVE: We propose a theoretical framework for a community-based early warning (EWS) system that will proactively detect temperature abnormalities in the community based on a collective network of infrared thermometer-enabled smartphone devices. METHODS: We developed a framework for a community-based EWS and demonstrated its operation with a schematic flowchart. We emphasize the potential feasibility of the EWS and potential obstacles. RESULTS: Overall, the framework uses advanced artificial intelligence (AI) technology on cloud computing platforms to identify the probability of an outbreak in a timely manner. It hinges on the detection of geospatial temperature abnormalities in the community based on mass data collection, cloud-based computing and analysis, decision-making, and feedback. The EWS may be feasible for implementation considering its public acceptance, technical practicality, and value for money. However, it is important that the proposed framework work in parallel or in combination with other early warning mechanisms due to a relatively long initial model training process. CONCLUSIONS: The framework, if implemented, may provide an important tool for important decisions for early prevention and control of respiratory diseases for health stakeholders.

A real-time heart rate estimation framework based on a facial video while wearing a mask.

BACKGROUND: The imaging photoplethysmography (iPPG) method is a non-invasive, non-contact measurement method that uses a camera to detect physiological indicators. On the other hand, wearing a mask has become essential today when COVID-19 is rampant, which has become a new challenge for heart rate (HR) estimation from facial videos recorded by a camera. OBJECTIVE: The aim is to propose an iPPG-based method that can accurately estimate HR with or without a mask. METHODS: First, the facial regions of interest (ROI) were divided into two sub-ROIs, and the original signal was obtained through spatial averaging with different weights according to the result of judging whether wearing a mask or not, and the CDF, which emphasizes the main component signal, was combined with the improved POS suitable for real-time HR estimation to obtain the noise-removed BVP signal. RESULTS: For self-collected data while wearing a mask, MAE, RMSE, and ACC were 1.09 bpm, 1.44 bpm, and 99.08%, respectively. CONCLUSION: Experimental results show that the proposed framework can estimate HR stably in real-time in both cases of wearing a mask or not. This study expands the application range of HR estimation based on facial videos and has very practical value in real-time HR estimation in daily life.

Handheld NIR-to-NIR Platform for on-site evaluating protective neutralizing antibody against SARS-CoV-2 ancestral strain and Omicron variant after vaccination or infection

Lateral flow assays (LFAs) are promising points-of-care tests, playing a vital role in diseases screening, diagnosis and surveillance. However, development of portable, cheap, and smart LFAs platform for sensitive and accurate quantification of disease biomarkers in complex media is challenging. Here, a cheap handheld device was developed to realize on-site detection of disease biomarkers by Nd3+/Yb3+ co-doped near-infrared (NIR)-to-NIR downconversion nanoparticles (DCNPs) based LFA. Its sensitivity is at least 8-fold higher for detecting NIR light signal from Nd3+/Yb3+ co-doped nanoparticles than conventional expensive InGaAs camera based detection platform. Additionally, we enhance NIR quantum yield of Nd3+/Yb3+ co-doped nanoparticles up to 35.5% via simultaneous high dopant of sensitizer ions Nd3+ and emitter ions Yb3+. Combination of NIR-to-NIR handheld detection device and ultra-bright NIR emitting NaNbF4:Yb60%@NaLuF4 nanoparticle probe allows the detection sensitivity of SARS-CoV-2 ancestral strain and Omicron variants specific neutralizing antibodies LFA up to the level of commercial enzyme linked immunosorbent assay kit. Furthermore, by this robust method, enhanced neutralizing antibodies against SARS-CoV-2 ancestral strain and Omicron variants are observed in healthy participants with Ad5-nCoV booster on top of two doses of inactivated vaccine. This NIR-to-NIR handheld platform provides a promising strategy for on-site evaluating protective humoral immunity after SARS-CoV-2 vaccination or infection.

Using NEWS2 to triage newly admitted patients with COVID-19.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has spread globally and caused a major worldwide health crisis. Patients who are affected more seriously by COVID-19 usually deteriorate rapidly and need further intensive care. AIMS AND OBJECTIVES: We aimed to assess the performance of the National Early Warning Score 2 (NEWS2) as a risk stratification tool to discriminate newly admitted patients with COVID-19 at risk of serious events. DESIGN: We conducted a retrospective single-centre case-control study on 200 unselected patients consecutively admitted in March 2020 in a public general hospital in Wuhan, China. METHODS: The following serious events were considered: mortality, unplanned intensive care unit (ICU) admission, and non-invasive ventilation treatment. Receiver operating characteristic (ROC) analysis and logistic regression analysis were used to quantify the association between outcomes and NEWS2. RESULTS: There were 12 patients (6.0%) who had serious events, where 7 patients (3.5%) experienced unplanned ICU admissions. The area under the ROC curve (AUROC) and cut-off of NEWS2 for the composite outcome were 0.83 and 3, respectively. For patients with NEWS2 ≥ 4, the odds of being at risk for serious events was 16.4 (AUROC = 0.74), while for patients with NEWS2 ≥ 7, the odds of being at risk for serious events was 18.2 (AUROC = 0.71). CONCLUSIONS: NEWS2 has an appropriate ability to triage newly admitted patients with COVID-19 into three levels of risk: low risk (NEWS2 = 0-3), medium risk (NEWS2 = 4-6), and high risk (NEWS2 ≥ 7). RELEVANCE TO CLINICAL PRACTICE: Using NEWS2 may help nurses in early identification of at-risk COVID-19 patients and clinical nursing decision-making. Using NEWS2 to triage new patients with COVID-19 may help nurses provide more appropriate level of care and medical resources allocation for patients safety.