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Background Several reports on epidemiological and clinical features of the 2019 coronavirus disease (COVID-19) have been published. However, mortality and morbidity analyses, important for better understanding the pathogenesis of this disease, are scarce. We examine the clinical and laboratory features of 14 patients who died of COVID-19. Methods The cohort consisted of 11 male and 3 female patients, with 9 patients aged 70 years or above, and nearly all had underlying diseases. Results Fever with bilateral pneumonia was the main manifestation. Most patients had consolidations combined with ground glass opacity (GGO) on chest computed tomography scan. Laboratory tests showed lymphocytopenia in 10 patients, high blood glucose in 11, GGT in 5 of the 14 patients, and high LDH in 5 of 6 patients tested. In addition, this cohort had high level of cytokines such as interleukin-6 in all 8 patients tested. Conclusions The clinical and laboratory parameters in the cohort of fatal cases may be incorporated into future clinical prognosis models and will be of help in understanding the pathogenesis of this disease. [ABSTRACT FROM AUTHOR]

Aim: To investigate the prevalence of insomnia among front‐line nurses fighting against COVID‐19 in Wuhan, China, and analyse its influencing factors. Background: Insomnia is an important factor that can affect the health and work quality of nurses. However, there is a lack of big‐sample studies exploring factors that affect the insomnia of nurses fighting against COVID‐19. Method: This cross‐sectional study using the Ascension Insomnia Scale, Fatigue Scale‐14 and Perceived Stress Scale took place in March 2020. Participants were 1,794 front‐line nurses from four tertiary‐level general hospitals. Results: The prevalence of insomnia among participants was 52.8%. Insomnia was predicted by gender, working experience, chronic diseases, midday nap duration, direct participation in the rescue of patients with COVID‐19, frequency of night shifts, professional psychological assistance during the pandemic, negative experiences (such as family, friends or colleagues being seriously ill or dying due to COVID‐19), the degree of fear of COVID‐19, fatigue and perceived stress. Conclusion: The level of insomnia among participants was higher than the normal level. Interventions based on influencing factors should be implemented to ensure nurses' sleep quality. Implications for Nursing Management: An in‐depth understanding of the influencing factors of insomnia among front‐line nurses can help nurse managers develop solutions to improve front‐line nurses' sleep quality, which will enhance the physical and mental conditions of nurses and promote the quality of care. [ABSTRACT FROM AUTHOR]

Aim: We aim to investigate the prevalence and associated factors for compassion fatigue among nurses in Fangcang Shelter Hospitals in Wuhan. Studies have shown that compassion fatigue was more common among nurses than other health‐care providers, and its predictors were also different. In recent years, most studies have investigated compassion fatigue in emergency and oncology nurses, whereas there is little information on compassion fatigue among nurses from the frontline of Fangcang Shelter Hospitals during the COVID‐19 pandemic. Methods: A descriptive, cross‐sectional design was used in this study. An online survey was conducted among nurses (n = 972) of five Fangcang Shelter Hospitals in Wuhan, Hubei province, China, from 6 March to 10 March 2020. A self‐administered questionnaire including demographic information, work‐related information, General Health Questionnaire, Perceived Stress Scale and Compassion Fatigue Scale was used. Results: The prevalence of compassion fatigue among nurses in Fangcang Shelter Hospitals was moderate, and most cases were mild. There was a significant relationship between compassion fatigue and work‐related factors, mental health and perceived stress among nurses working in Fangcang Shelter Hospitals. Conclusions: Various factors contribute to compassion fatigue, including lower job satisfaction and job adaptability, less praise from patients, more fear of infection and more perceived stress. A good working atmosphere, organizational support and psychological consultation are essential to alleviate nurses' compassion fatigue during the anti‐epidemic period. Summary statement: What is already known about this topic? Nurses are particularly vulnerable to compassion fatigue among health‐care providers, and it causes billions of dollars in productivity losses to economies in different countries every year.Personal factors (professional cognition, health condition, gender etc.), working environment (working hours, working load, working system problems, professional roles etc.), mental health and stress may affect nurses' compassion fatigue. What this paper adds? Both extrinsic and intrinsic factors played a crucial role in nurses in Fangcang Shelter Hospitals who are exposed to adverse events, leading to compassion fatigue. The implications of this paper: This paper reveals a new perspective on the factors influencing compassion fatigue of nurses working in Fangcang Shelter Hospitals.We suggest that compassion fatigue among nurses in Fangcang Shelter Hospitals could be reduced by maintaining a good working atmosphere, providing organizational support, helping nurses keep a healthy mental state and reducing stress levels. [ABSTRACT FROM AUTHOR]

Background: In December 2019, a rare respiratory disease named coronavirus disease 2019 (COVID-19) broke out, leading to great concern around the world. Purpose: To develop and validate a radiomics nomogram for predicting the fatal outcome of COVID-19 pneumonia. Material and Methods: The present study consisted of a training dataset (n = 66) and a validation dataset (n = 30) with COVID-19 from January 2020 to March 2020. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics score (Rad-score) was developed from the training cohort. The radiomics model, clinical model, and integrated model were built to assess the association between radiomics signature/clinical characteristics and the mortality of COVID-19 cases. The radiomics signature combined with the Rad-score and the independent clinical factors and radiomics nomogram were constructed. Results: Seven stable radiomics features associated with the mortality of COVID-19 were finally selected. A radiomics nomogram was based on a combined model consisting of the radiomics signature and the clinical risk factors indicating optimal predictive performance for the fatal outcome of patients with COVID-19 with a C-index of 0.912 (95% confidence interval [CI] 0.867–0.957) in the training dataset and 0.907 (95% CI 0.849–0.966) in the validation dataset. The calibration curves indicated optimal consistency between the prediction and the observation in both training and validation cohorts. Conclusion: The CT-based radiomics nomogram indicated favorable predictive efficacy for the overall survival risk of patients with COVID-19, which could help clinicians intensively follow up high-risk patients and make timely diagnoses. [ABSTRACT FROM AUTHOR]