A network analysis of anxiety and depression symptoms among Chinese nurses in the late stage of the COVID-19 pandemic.

Background: Nurses are at high risk for depression and anxiety symptoms after the outbreak of the COVID-19 pandemic. We aimed to assess the network structure of anxiety and depression symptoms among Chinese nurses in the late stage of this pandemic. Method: A total of 6,183 nurses were recruited across China from Oct 2020 to Apr 2021 through snowball sampling. We used Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder scale-7 (GAD-7) to assess depression and anxiety, respectively. We used the Ising model to estimate the network. The index "expected influence" and "bridge expected influence" were applied to determine the central symptoms and bridge symptoms of the anxiety-depression network. We tested the stability and accuracy of the network via the case-dropping procedure and non-parametric bootstrapping procedure. Result: The network had excellent stability and accuracy. Central symptoms included "restlessness", "trouble relaxing", "sad mood", and "uncontrollable worry". "Restlessness", "nervous", and "suicidal thoughts" served as bridge symptoms. Conclusion: Restlessness emerged as the strongest central and bridge symptom in the anxiety-depression network of nurses. Intervention on depression and anxiety symptoms in nurses should prioritize this symptom.

Tri-light Warning System for Hospitalized COVID-19 Patients]{A Tri-light Warning System for Hospitalized COVID-19 Patients: Credibility-based Risk Stratification under Data Shift (preprint)

Early warning of the novel coronavirus pneumonia (COVID-19) during the evolving pandemic waves is crucial for the timely treatment of patients and optimization of medical resource allocation. However, prior AI-based models often lack the reliability and performance validation under data distribution drifts, and are therefore problematic to be reliably utilized in real-world clinical practice. To address this challenge, we developed a tri-light warning system based on conformal prediction for rapidly stratification of COVID-19 inpatients. This system can automatically extract radiomic features from CT images and integrate clinical record information to output a prediction probability, as well as a credibility of each prediction. This system classifies patients in the general ward into red label (high risk) indicating a possible admission to ICU care, yellow label (uncertain risk) indicating closer monitoring, and green label (low risk) indicating a stable condition. The subsequent health policies can be further designed based on this system according to the specific needs of different hospitals. Extensive experiment from a multi-center cohort (n= 8,721) shows that our method is applicable to both the original strain and the variant strains of COVID-19. Given the rapid mutation rate of COVID-19, the proposed system demonstrates its potential to identify epidemiological risks early to improve patient stratification performance under data shift.

Mental health conditions and academic burnout among medical and non-medical undergraduates during the mitigation of COVID-19 pandemic in China.

The outbreak of the novel coronavirus disease 2019 (COVID-19) has posed a great impact on people's mental health, especially for undergraduate students. This study aimed to compare the mental health conditions and academic burnout between medical and non-medical undergraduates in China when the COVID-19 pandemic is mitigating. A cross-sectional online survey was conducted among 4,972 undergraduates between October 2020 and April 2021, when the pandemic was basically under control. The survey included basic demographics information and standardized scales to evaluate depression, anxiety, perceived stress, daytime sleepiness, alcohol abuse/dependence, quality of life, fatigue, and academic burnout. Compared with medical undergraduates, non-medical undergraduates had higher rates of moderate to severe depression symptoms (29.1% vs. 17.9%, P < 0.001), moderate to severe anxiety symptoms (19.7% vs. 8.9%, P < 0.001), alcohol abuse/dependence (16.3% vs.10.3%, P < 0.001), excessive daytime sleepiness (47.4% vs. 43.4%, P = 0.018), high perceived stress (34.7% vs. 22.2%, P < 0.001), high level of fatigue (51.8% vs. 42.2%, P < 0.001), low QOL (35.8% vs. 21.4%, P < 0.001), and higher academic burnout score (59.4 vs. 57.5, P < 0.001). Being non-medical undergraduates, depression, alcohol abuse/dependence, excessive daytime sleepiness, and high perceived stress were positively associated with academic burnout, while high QOL was negatively associated with the burnout (all P < 0.001). Excessive daytime sleepiness was the strongest predictor for academic burnout.

A switching dynamic model based on phased COVID-19 data in Chongqing and its evaluation.

OBJECTIVES: Although COVID-19 has been controlled in China, the risk of invasion of imported cases remains. We aimed to characterize the impact of the number of imported cases and the implementation of first-level emergency response (FLER) policy. METHODS: A SCQIHR switching model was constructed and verified by the complete phased data of COVID-19 in Chongqing in 2020. Then it was used to investigate the impact of the number of imported cases and the timing of FLER. Lastly, it was evaluated by three actual scenarios in Chongqing in 2021. RESULTS: The proposed model can fit the multidimensional time series well. After the implementation of FLER, the mean effective reproduction number, contact rate and misdetection rate were decreased significantly, but the quarantine rate for close contacts and isolation rate for non-hospitalized infectious cases were increased significantly. The peaks of quarantined close contacts and hospitalized infectious cases increased linearly with the increase of the number of imported cases and the lag of FLER time, which was verified by three actual scenarios in Chongqing in 2021. CONCLUSIONS: These findings can provide guidance for local public health policy-making and allocation of medical resources, reduce the impact of COVID-19 on the local population.

Prognostic Value of Coronary CT Angiography-Derived Fractional Flow Reserve in Non-obstructive Coronary Artery Disease: A Prospective Multicenter Observational Study

Coronary artery disease (CAD) is a major contributor to morbidity and mortality worldwide. Myocardial ischemia may occur in patients with normal or non-obstructive CAD on invasive coronary angiography (ICA). The comprehensive evaluation of coronary CT angiography (CCTA) integrated with fractional flow reserve derived from CCTA (CT-FFR) to CAD may be essential to improve the outcomes of patients with non-obstructive CAD. China CT-FFR Study-2 (ChiCTR2000031410) is a large-scale prospective, observational study in 29 medical centers in China. The primary purpose is to uncover the relationship between the CCTA findings (including CT-FFR) and the outcome of patients with non-obstructive CAD. At least 10,000 patients with non-obstructive CAD but without previous revascularization will be enrolled. A 5-year follow-up will be performed. The primary endpoint is the occurrence of major adverse cardiovascular events (MACE), including all-cause mortality, non-fatal myocardial infarct, unplanned revascularization, and hospitalization for unstable angina. Clinical characteristics, laboratory and imaging examination results will be collected to analyze their prognostic value.

A Comparison of COVID-19 Stigma and AIDS Stigma During the COVID-19 Pandemic: A Cross-Sectional Study in China.

Objective: To understand the current situation of stigmatizing attitudes toward Coronavirus Disease 2019 (COVID-19) in China and compare it with acquired immunodeficiency syndrome (AIDS). Methods: Convenient sampling and vignette-based methods were used to recruit participants on WeChat. A demographic form and adopted stigma scale were used to collect participants' demographic information and stigmatizing attitudes toward COVID-19 and AIDS. Results: A total of 13,994 questionnaires were included in this study. A high portion of participants tend to avoid contact with individuals affected with COVID-19 (74.3%) or AIDS (59.0%), as well as their family members (70.4% for COVID-19 and 47.9% for AIDS). About half of the participants agreed that affected persons could not only cause problems to their own family but also have adverse effects on others (59.6% and 55.6% for COVID-19, 56.9 and 47.0% for AIDS). The agreements with statements about perceived stigma were similar but slightly higher than those about personal stigma in both COVID-19 and AIDS. Participants' agreements with all statements regarding personal and perceived stigma attitudes between COVID-19 and AIDS were all statistically significant (p < 0.001). Participants obtained COVID-19-related information mainly from social media (91.3%) and newspaper or television (77.1%) during the epidemic, and 61.0% of them thought information from newspapers or television was the most reliable. Conclusion: Several similarities and differences of people's attitude toward COVID-19 and AIDS were found. Avoidance, blame, and secondary discrimination to diagnosed persons and their surrounding persons were the main representations of COVID-19-related stigma. Stigma of COVID-19 had less moral link but more public panic. Experience from HIV-related stigma reduction and prevention can be applied to reduce COVID-19-related stigma.

Coronary artery calcification and risk of mortality and adverse outcomes in patients with COVID-19: a Chinese multicenter retrospective cohort study.

Background: Coronary artery calcification (CAC) is an independent risk factor of major adverse cardiovascular events; however, the impact of CAC on in-hospital death and adverse clinical outcomes in patients with coronavirus disease 2019 (COVID-19) remains unclear. Objective: To explore the association between CAC and in-hospital mortality and adverse events in patients with COVID-19. Methods: This multicenter retrospective cohort study enrolled 2067 laboratory-confirmed COVID-19 patients with definitive clinical outcomes (death or discharge) admitted from 22 tertiary hospitals in China between January 3, 2020 and April 2, 2020. Demographic, clinical, laboratory results, chest CT findings, and CAC on admission were collected. The primary outcome was in-hospital death and the secondary outcome was composed of in-hospital death, admission to intensive care unit (ICU), and requiring mechanical ventilation. Multivariable Cox regression analysis and Kaplan-Meier plots were used to explore the association between CAC and in-hospital death and adverse clinical outcomes. Results: The mean age was 50 years (SD,16) and 1097 (53.1%) were male. A total of 177 patients showed high CAC level, and compared with patients with low CAC, these patients were older (mean age: 49 vs. 69 years, P < 0.001) and more likely to be male (52.0% vs. 65.0%, P = 0.001). Comorbidities, including cardiovascular disease (CVD) ([33.3%, 59/177] vs. [4.7%, 89/1890], P < 0.001), presented more often among patients with high CAC, compared with patients with low CAC. As for laboratory results, patients with high CAC had higher rates of increased D-dimer, LDH, as well as CK-MB (all P < 0.05). The mean CT severity score in high CAC group was also higher than low CAC group (12.6 vs. 11.1, P = 0.005). In multivariable Cox regression model, patients with high CAC were at a higher risk of in-hospital death (hazard ratio [HR], 1.731; 95% CI 1.010-2.971, P = 0.046) and adverse clinical outcomes (HR, 1.611; 95% CL 1.087-2.387, P = 0.018). Conclusion: High CAC is a risk factor associated with in-hospital death and adverse clinical outcomes in patients with confirmed COVID-19, which highlights the importance of calcium load testing for hospitalized COVID-19 patients and calls for attention to patients with high CAC. Supplementary Information: The online version contains supplementary material available at 10.1007/s42058-021-00072-4.

CT-based Rapid Triage of COVID-19 Patients: Risk Prediction and Progression Estimation of ICU Admission, Mechanical Ventilation, and Death of Hospitalized Patients (preprint)

The wave of COVID-19 continues to overwhelm the medical resources, especially the stressed intensive care unit (ICU) capacity and the shortage of mechanical ventilation (MV). Here we performed CT-based analysis combined with electronic health records and clinical laboratory results on Cohort 1 (n = 1662 from 17 hospitals) with prognostic estimation for the rapid stratification of PCR confirmed COVID-19 patients. These models, validated on Cohort 2 (n = 700) and Cohort 3 (n = 662) constructed from 9 external hospitals, achieved satisfying performance for predicting ICU, MV and death of COVID-19 patients (AUROC 0.916, 0.919 and 0.853), even on events happened two days later after admission (AUROC 0.919, 0.943 and 0.856). Both clinical and image features showed complementary roles in events prediction and provided accurate estimates to the time of progression (p

Current Status of Etiology, Epidemiology, Clinical Manifestations and Imagings for COVID-19.

Coronavirus disease 2019 (COVID-19) is a transmissible respiratory disease that was initially reported in Wuhan, China in December 2019. With the alarming levels of COVID-19 spread worldwide, the World Health Organization characterized COVID-19 as a pandemic. Over the past several months, chest CT has played a vital role in early identification, disease severity assessment, and dynamic disease course monitoring of COVID-19. The published data has enriched our knowledge on the etiology, epidemiology, clinical manifestations, and pathologic findings of COVID-19. Additionally, as the imaging spectrum of the disease continues to be defined, extrapulmonary infections or other complications will require further attention. This review aims to provide an updated framework and essential knowledge with which radiologists can better understand COVID-19.

A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images.

OBJECTIVES: To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents. METHODS: A deep learning algorithm consisted of lesion detection, segmentation, and location was trained and validated in 14,435 participants with chest CT images and definite pathogen diagnosis. The algorithm was tested in a non-overlapping dataset of 96 confirmed COVID-19 patients in three hospitals across China during the outbreak. Quantitative detection performance of the model was compared with three radiological residents with two experienced radiologists' reading reports as reference standard by assessing the accuracy, sensitivity, specificity, and F1 score. RESULTS: Of 96 patients, 88 had pneumonia lesions on CT images and 8 had no abnormities on CT images. For per-patient basis, the algorithm showed superior sensitivity of 1.00 (95% confidence interval (CI) 0.95, 1.00) and F1 score of 0.97 in detecting lesions from CT images of COVID-19 pneumonia patients. While for per-lung lobe basis, the algorithm achieved a sensitivity of 0.96 (95% CI 0.94, 0.98) and a slightly inferior F1 score of 0.86. The median volume of lesions calculated by algorithm was 40.10 cm3. An average running speed of 20.3 s ± 5.8 per case demonstrated the algorithm was much faster than the residents in assessing CT images (all p < 0.017). The deep learning algorithm can also assist radiologists make quicker diagnosis (all p < 0.0001) with superior diagnostic performance. CONCLUSIONS: The algorithm showed excellent performance in detecting COVID-19 pneumonia on chest CT images compared with resident radiologists. KEY POINTS: • The higher sensitivity of deep learning model in detecting COVID-19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis. • The deep learning model improves diagnosis efficiency by shortening processing time. • The deep learning model can automatically calculate the volume of the lesions and whole lung.

Coronavirus Disease 2019 (COVID-19): Chinese Radiologists Are Acting.

The ongoing outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly throughout China and other countries, and has been characterized as a pandemic. With the strict prevention and control measures implemented by the Chinese government, the spread of the epidemic in China has come under preliminary control by the end of February, 2020, whereas the situation of some countries outside China is not optimistic and raises great public concern. In fighting COVID-19, radiologic examinations played a critical role in the early diagnosis of COVID-19, and could also help assess disease course and severity. There is an urgent need to share useful experience and effective measures internationally. This article outlines the collaborative actions and efforts by the Chinese radiology field and the situation of front-line health care workers in radiology departments to present the world with experiences and examples of Chinese radiology during the COVID-19 outbreak.

Infection Control and Management Strategy for COVID-19 in the Radiology Department: Focusing on Experiences from China.

Coronavirus disease 2019 (COVID-19) is a new infectious disease rapidly spreading around the world, raising global public health concerns. Radiological examinations play a crucial role in the early diagnosis and follow-up of COVID-19. Cross infection among patients and radiographers can occur in radiology departments due to the close and frequent contact of radiographers with confirmed or potentially infected patients in a relatively confined room during radiological workflow. This article outlines our experience in the emergency management procedure and infection control of the radiology department during the COVID-19 outbreak.

Risk factors for adverse clinical outcomes with COVID-19 in China: a multicenter, retrospective, observational study.

Background: The risk factors for adverse events of Coronavirus Disease-19 (COVID-19) have not been well described. We aimed to explore the predictive value of clinical, laboratory and CT imaging characteristics on admission for short-term outcomes of COVID-19 patients. Methods: This multicenter, retrospective, observation study enrolled 703 laboratory-confirmed COVID-19 patients admitted to 16 tertiary hospitals from 8 provinces in China between January 10, 2020 and March 13, 2020. Demographic, clinical, laboratory data, CT imaging findings on admission and clinical outcomes were collected and compared. The primary endpoint was in-hospital death, the secondary endpoints were composite clinical adverse outcomes including in-hospital death, admission to intensive care unit (ICU) and requiring invasive mechanical ventilation support (IMV). Multivariable Cox regression, Kaplan-Meier plots and log-rank test were used to explore risk factors related to in-hospital death and in-hospital adverse outcomes. Results: Of 703 patients, 55 (8%) developed adverse outcomes (including 33 deceased), 648 (92%) discharged without any adverse outcome. Multivariable regression analysis showed risk factors associated with in-hospital death included ≥ 2 comorbidities (hazard ratio [HR], 6.734; 95% CI; 3.239-14.003, p < 0.001), leukocytosis (HR, 9.639; 95% CI, 4.572-20.321, p < 0.001), lymphopenia (HR, 4.579; 95% CI, 1.334-15.715, p = 0.016) and CT severity score > 14 (HR, 2.915; 95% CI, 1.376-6.177, p = 0.005) on admission, while older age (HR, 2.231; 95% CI, 1.124-4.427, p = 0.022), ≥ 2 comorbidities (HR, 4.778; 95% CI; 2.451-9.315, p < 0.001), leukocytosis (HR, 6.349; 95% CI; 3.330-12.108, p < 0.001), lymphopenia (HR, 3.014; 95% CI; 1.356-6.697, p = 0.007) and CT severity score > 14 (HR, 1.946; 95% CI; 1.095-3.459, p = 0.023) were associated with increased odds of composite adverse outcomes. Conclusion: The risk factors of older age, multiple comorbidities, leukocytosis, lymphopenia and higher CT severity score could help clinicians identify patients with potential adverse events.

Psychological morbidities and fatigue in patients with confirmed COVID-19 during disease outbreak: prevalence and associated biopsychosocial risk factors (preprint)

Objective: The coronavirus disease 2019 (COVID-19) - a novel and highly infectious pneumonia - has now spread across China and beyond for over four months. However, its psychological impact on patients is unclear. We aim to examine the prevalence and associated risk factors for psychological morbidities and fatigue in patients with confirmed COVID-19 infection. Methods: Amidst the disease outbreak, 41 out of 105 COVID-19 patients in a local designated hospital in China were successfully assessed using a constellation of psychometric questionnaires to determine their psychological morbidities and fatigue. Several potential biopsychosocial risk factors (including pre-existing disabilities, CT severity score of pneumonia, social support, coping strategies) were assessed through multivariable logistic regression analyses to clarify their association with mental health in patients. Results: 43.9% of 41 patients presented with impaired general mental health, 12.2% had post-traumatic stress disorder (PTSD) symptoms, 26.8% had anxiety and/or depression symptoms, and 53.6% had fatigue. We did not find any association between pneumonia severity and psychological morbidities or fatigue in COVID-19 patients. However, high perceived stigmatization was associated with an increased risk of impaired general mental health and high perceived social support was associated with decreased risk. Besides, negative coping inclination was associated with an increased risk of PTSD symptoms; high perceived social support was associated with a decreased risk of anxiety and/or depression symptoms. Conclusions: Psychological morbidities and chronic fatigue are common among COVID-19 patients. Negative coping inclination and being stigmatized are primary risk factors while perceived social support is the main protective factor.

Coronavirus Disease 2019 (COVID-19): A Perspective from China.

In December 2019, an outbreak of severe acute respiratory syndrome coronavirus 2 infection occurred in Wuhan, Hubei Province, China, and spread across China and beyond. On February 12, 2020, the World Health Organization officially named the disease caused by the novel coronavirus as coronavirus disease 2019 (COVID-19). Because most patients infected with COVID-19 had pneumonia and characteristic CT imaging patterns, radiologic examinations have become vital in early diagnosis and the assessment of disease course. To date, CT findings have been recommended as major evidence for clinical diagnosis of COVID-19 in Hubei, China. This review focuses on the etiology, epidemiology, and clinical symptoms of COVID-19 while highlighting the role of chest CT in prevention and disease control.