Expectations of nursing home residents and their family members during the COVID-19 epidemic: A qualitative study (preprint)

Background COVID-19 has spread worldwide. Older people are at the greatest risk of contracting and dying from the virus. Nursing homes are densely populated places for older adults who are generally vulnerable and at high-risk. Although Chinese nursing homes have been trying to protect their residents, the needs and expectations of the residents and their families have been ignored. The aim of this study was to explore and better understand the expectations of nursing home residents and their family members during the COVID-19 epidemic in China.Methods Data was collected via face-to-face semi-structured interviews with nursing home residents and focus group online interviews with family members between June 2021 and February 2022. Data analysis followed inductive content analysis.Results 16 residents and 24 family members were interviewed. Four themes with 11 sub-themes were identified from the descriptions of participants. Their expectations were mainly focus on prevention and control measures for COVID-19, medical capacity of nursing homes, health education and expectations for some aged care policies.Conclusions In the face of concerns about the impact of COVID-19 on nursing homes, we sought to bring firsthand perspectives to the forefront by interviewing residents and their family members about their expectations to address this issue. Our findings provide important areas on which should be focused and may improve the sense of gain, happiness, and security of nursing home residents during the COVID-19 epidemic.

Establishment of a Diagnostic Model to Distinguish Coronavirus Disease 2019 From Influenza a Based on Laboratory Findings (preprint)

BackgroundCoronavirus disease 2019 (COVID-19) and Influenza A are common disease caused by viral infection. The clinical symptoms and transmission routes of the two diseases are similar. This study established a model of laboratory findings to distinguish COVID-19 from influenza A perfectly. MethodsIn this study, 56 COVID-19 patients and 54 influenza A patients were included. Laboratory findings, epidemiological characteristics and demographic data were obtained from electronic medical record databases. Elastic network models, followed by a stepwise logistic regression model were implemented to identify indicators capable of discriminating COVID-19 and influenza A. ResultsA monogram is diagramed to show the resulting discriminative model. The majority of hematological and biochemical parameters in COVID-19 patients were significantly different from those in influenza A patients. In the final model, albumin/globulin, total bilirubin and erythrocyte specific volume were selected as predictors. This model has been demonstrated to have a satisfactory predictive performance to discriminate between COVID-19 and influenza A (AUC=0.844) using an external validation set. ConclusionThe establishment of a diagnostic model on laboratory findings is of great significance for the identification of COVID-19 and influenza A.

Establishment of a diagnostic model to distinguish coronavirus disease 2019 from influenza A based on laboratory findings (preprint)

Background: Coronavirus disease 2019 (COVID-19) and Influenza A are common disease caused by viral infection. The clinical symptoms and transmission routes of the two diseases are similar. However, there are no relevant studies on laboratory diagnostic models to discriminate COVID-19 and influenza A. This study aims at establishing a signature of laboratory findings to tell patients with COVID-19 apart from those with influenza A perfectly. Materials: In this study, 56 COVID-19 patients and 54 influenza A patients were included. Laboratory findings, epidemiological characteristics and demographic data were obtained from electronic medical record databases. Elastic network models, followed by a stepwise logistic regression model were implemented to identify indicators capable of discriminating COVID-19 and influenza A. A nomogram is diagramed to show the resulting discriminative model. Results: The majority of hematological and biochemical parameters in COVID-19 patients were significantly different from those in influenza A patients. In the final model, albumin/globulin (A/G), total bilirubin (TBIL) and erythrocyte specific volume (HCT) were selected as predictors. Using an external dataset, the model was validated to perform well. Conclusion: A diagnostic model of laboratory findings was established, in which A/G, TBIL and HCT were included as highly relevant indicators for the segmentation of COVID-19 and influenza A, providing a complimentary means for the precise diagnosis of these two diseases.

Longitudinal analysis of laboratory findings during the process of recovery for patients with COVID-19 (preprint)

Objective To explore longitudinal change patterns of key laboratory tests in patients with COVID-19, and to identify independent prognostic factors by examining the associations between laboratory findings and outcomes of patients. Methods The multicenter study prospectively included 59 patients with COVID-19 treated at Jilin province from January 21, 2020 to May 5, 2020. Laboratory tests were included haematological, biochemical, and immunological tests. Results Laboratory findings, the characteristics of epidemiological and demographic data were extracted from electronic medical records. Eosinopenia was shown in 52.6% cases at onset, and the average value of eosinophil continued to significantly increase thereafter. Lymphopenia was found in 40.4% cases at onset, and the average value of lymphocyte was slowly elevated after day 5. Thrombocytopenia was shown in 12.3% cases at onset, and the average value of mean platelet volume was decreased sharply after day 7. The values of aspartate aminotransferase, lactate dehydrogenase, creatine kinase, creatinine kinase-muscle/brain activity, and cardiac troponin I, serum cardiac markers, were beyond the upper limit of RI from 6.1% to 30.6% at onset. The abnormity of liver function tests, kidney function tests, electrolytes was 2.0%~59.2%, 2.0%~4.1%, 6.0%~30.0%, respectively. Eosinophil, platelet and carbondioxide combining power were selected as the prognostic factors. Conclusions The haematological, biochemical, and immunological tests were found significant abnormity at onset and longitudinal change patterns in the patients with COVID-19. Age, Eosinophil, PLT and CO2 may used to predict the recovery probability. Risk stratification and management could be improved for the patients with COVID-19 according to temporal trajectories of laboratory tests.