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Background: Many models are published which predict outcomes in hospitalized COVID-19 patients. The generalizability of many is unknown. We evaluated the performance of selected models from the literature and our own models to predict outcomes in patients at our institution.Methods: We searched the literature for models predicting outcomes in inpatients with COVID-19. We produced models of mortality or criticality (mortality or ICU admission) in a development cohort. We tested external models which provided sufficient information and our models using a test cohort of our most recent patients. The performance of models was compared using the area under the receiver operator curve (AUC).Results: Our literature review yielded 41 papers. Of those, 8 were found to have sufficient documentation and concordance with features available in our cohort to implement in our test cohort. All models were from Chinese patients. One model predicted criticality and seven mortality. Tested against the test cohort, internal models had an AUC of 0.84 (0.74-0.94) for mortality and 0.83 (0.76-0.90) for criticality. The best external model had an AUC of 0.89 (0.82-0.96) using three variables, another an AUC of 0.84 (0.78-0.91) using ten variables. AUC's ranged from 0.68 to 0.89. On average, models tested were unable to produce predictions in 27% of patients due to missing lab data.Conclusion: Despite differences in pandemic timeline, race, and socio-cultural healthcare context some models derived in China performed well. For healthcare organizations considering implementation of an external model, concordance between the features used in the model and features available in their own patients may be important. Analysis of both local and external models should be done to help decide on what prediction method is used to provide clinical decision support to clinicians treating COVID-19 patients as well as what lab tests should be included in order sets. [ABSTRACT FROM AUTHOR]

Objective: Aimed to summarize the characteristics of chest CT imaging in Chinese hospitalized patients with Coronavirus Disease 2019 (COVID-19) to provide reliable evidence for further guiding clinical routine. Methods: PubMed, Embase and Web of Science databases were searched to identify relevant articles involving the features of chest CT imaging in Chinese patients with COVID-19. All data were analyzed utilizing R i386 4.0.0 software. Random-effects models were employed to calculate pooled mean differences. Results: 19 retrospective studies (1332 cases) were included. The results demonstrated that the combined proportion of ground-glass opacities (GGO) was 0.79 (95% CI 0.68, 0.89), consolidation was 0.34 (95% CI 0.23, 0.47); mixed GGO and consolidation was 0.46 (95% CI 0.37; 0.56); air bronchogram sign was 0.41 (95% CI 0.26; 0.55); crazy paving pattern was 0.32 (95% CI 0.17, 0.47); interlobular septal thickening was 0.55 (95% CI 0.42, 0.67); reticulation was 0.30 (95% CI 0.12, 0.48); bronchial wall thickening was 0.24 (95% CI 0.11, 0.40); vascular enlargement was 0.74 (95% CI 0.64, 0.86); subpleural linear opacity was 0.28 (95% CI 0.12, 0.48); intrathoracic lymph node enlargement was 0.03 (95% CI 0.00, 0.07); pleural effusions was 0.03 (95% CI 0.02, 0.06). The distribution in lung: the combined proportion of central was 0.05 (95% CI 0.01, 0.11); peripheral was 0.74 (95% CI 0.62, 0.84); peripheral involving central was 0.38 (95% CI 0.19, 0.75); diffuse was 0.19 (95% CI 0.06, 0.32); unifocal involvement was 0.09 (95% CI 0.05, 0.14); multifocal involvement was 0.57 (95% CI 0.48, 0.68); unilateral was 0.16 (95% CI 0.10, 0.23); bilateral was 0.83 (95% CI 0.78, 0.89); The combined proportion of lobes involved (> 2) was 0.70 (95% CI 0.61, 0.78); lobes involved (≦ 2) was 0.35 (95% CI 0.26, 0.44). Conclusion: GGO, vascular enlargement, interlobular septal thickening more frequently occurred in patients with COVID-19, which distribution features were peripheral, bilateral, involved lobes > 2. Therefore, based on chest CT features of COVID-19 mentioned, it might be a promising means for identifying COVID-19. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to investigate the psychological status of the general population in mainland China during the outbreak of coronavirus disease 2019 (COVID-19), and to explore the factors influencing psychological distress, in order to provide the basis for further psychological intervention programs. Methods: We administered three questionnaires on-line to a convenience sample of the general population from different regions of mainland China from February 1 to February 4, 2020. We used the Mandarin versions of the six-item Kessler psychological distress scale (K6), the Simplified Coping Style Questionnaire (SCSQ), and the Social Support Rating Scale (SSRS). We also collected demographic data and other information related to the COVID-19 outbreak. Multivariate binary logistic regression analysis was used to identify factors influencing psychological distress. Results: Of 1607 respondents, 1588 returned valid questionnaires and were included in the analysis. Nearly one quarter (22.8%) had high levels of psychological distress (K6 score ≥ 13). Individuals with higher psychological distress were more likely to be unmarried, spend more than 6 h per day searching for information about COVID-19, more frequently adopt a passive coping style, and report less social support than those with lower psychological distress. Conclusions: The COVID-19 outbreak in China has a great impact on the mental health status of the general population. Active coping strategies and increased social support are significantly correlated with decreased psychological distress, and may serve as the basis for psychological interventions. [ABSTRACT FROM AUTHOR]