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1.
Infect Control Hosp Epidemiol ; : 1-7, 2020 Aug 03.
Article in English | MEDLINE | ID: covidwho-693548

ABSTRACT

The implementation effect of the 24-hour Supervise-Correct-Improve (SCI) supervision model was investigated in COVID-19 isolation ward in putting on and taking off process of personal protective equipment. As shown in results, the error rate of taking off process was significantly reduced (P < 0.001) by applying the 24h "SCI" mode. Staffs over 40 years old and workers were more likely to make mistakes. Through uninterrupted supervision and protection, application of this mode is proved to be effective.

2.
Frontiers in medicine ; 7:373-373, 2020.
Article | WHO COVID | ID: covidwho-689146

ABSTRACT

Background: With the adoption of powerful preventive and therapeutic measures, a large number of patients with COVID-19 have recovered and been discharged from hospitals in Wuhan, China Prevention of epidemic rebound is a top priority of current works However, information regarding post-discharge quarantine and surveillance of recovered patients with COVID-19 is scarce Methods: This study followed up 337 patients with COVID-19 in a Wuhan East-West Lake Fangcang shelter hospital during the post-discharge quarantine Demographic, clinical characteristics, comorbidities, and chest computed tomography (CT) image, mental state, medication status, and nucleic acid test data were summarized and analyzed Results: 21/337 (6 2%) patients were SARS-CoV-2 nucleic acid re-positive, and 4 /337(1 2%) patients were suspected positive The median day interval between the discharge to nucleic acid re-positivity was 7 5 days (IQR, 6-13), ranging from 6 to 13 days Cough/expectoration are the most common symptoms, followed by chest congestion/dyspnea during the 2 weeks post-discharge quarantine Risk factors of nucleic acid re-positivity including the number of lobes infiltration (odds ratio[OR], 1 14;95% CI, 1 09-1 19), distribution (OR, 0 16;95% CI, 0 13-0 19), CT imaging feature of patchy shadowing accompanying with consolidation (OR, 9 36;95% CI, 7 84-11 17), respiratory symptoms of cough accompanying with expectoration (OR, 1 39;95% CI, 1 28-1 52), and chest congestion accompanying by dyspnea (OR, 1 42;95% CI, 1 28-1 57) Conclusion: The 2 weeks post-discharge quarantine may be an effective measure to prevent the outbreak from rebounding from the recovered patients The second week is a critical period during post-discharge quarantine Special attention should be paid to cough, expectoration, chest congestion, and dyspnea in recovered COVID-19 patients A few recovered patients may prolong the quarantine based on clinical symptoms and signs and nucleic acid results in the 2 weeks of medical observation

3.
J Affect Disord ; 275: 145-148, 2020 10 01.
Article in English | MEDLINE | ID: covidwho-627108

ABSTRACT

INTRODUCTION: High risk of mental health problems is associated with Coronavirus Disease 2019 (COVID-19). This study explored the prevalence of depressive symptoms (depression hereafter) and its relationship with quality of life (QOL) in clinically stable patients with COVID-19. METHODS: This was an online survey conducted in COVID-19 patients across five designated isolation hospitals for COVID-19 in Hubei province, China. Depression and QOL were assessed with standardized instruments. RESULTS: A total of 770 participants were included. The prevalence of depression was 43.1% (95%CI: 39.6%-46.6%). Binary logistic regression analysis found that having a family member infected with COVID-19 (OR=1.51, P = 0.01), suffering from severe COVID-19 infection (OR=1.67, P = 0.03), male gender (OR=0.53, P<0.01), and frequent social media use to obtain COVID-19 related information (OR=0.65, P<0.01) were independently associated with depression. Patients with depression had lower QOL than those without. CONCLUSION: Depression is highly prevalent in clinically stable patients with COVID-19. Regular screening and appropriate treatment of depression are urgently warranted for this population.

4.
J Appl Lab Med ; 2020 Jun 26.
Article in English | MEDLINE | ID: covidwho-615990
5.
Clin Infect Dis ; 71(15): 833-840, 2020 07 28.
Article in English | MEDLINE | ID: covidwho-612035

ABSTRACT

BACKGROUND: Because there is no reliable risk stratification tool for severe coronavirus disease 2019 (COVID-19) patients at admission, we aimed to construct an effective model for early identification of cases at high risk of progression to severe COVID-19. METHODS: In this retrospective multicenter study, 372 hospitalized patients with nonsevere COVID-19 were followed for > 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and those who maintained a nonsevere state were assigned to the severe and nonsevere groups, respectively. Based on baseline data of the 2 groups, we constructed a risk prediction nomogram for severe COVID-19 and evaluated its performance. RESULTS: The training cohort consisted of 189 patients, and the 2 independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.4%) patients developed severe COVID-19. Older age; higher serum lactate dehydrogenase, C-reactive protein, coefficient of variation of red blood cell distribution width, blood urea nitrogen, and direct bilirubin; and lower albumin were associated with severe COVID-19. We generated the nomogram for early identifying severe COVID-19 in the training cohort (area under the curve [AUC], 0.912 [95% confidence interval {CI}, .846-.978]; sensitivity 85.7%, specificity 87.6%) and the validation cohort (AUC, 0.853 [95% CI, .790-.916]; sensitivity 77.5%, specificity 78.4%). The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analyses indicated that nomogram conferred high clinical net benefit. CONCLUSIONS: Our nomogram could help clinicians with early identification of patients who will progress to severe COVID-19, which will enable better centralized management and early treatment of severe disease.

6.
Sleep Medicine ; 2020.
Article | WHO COVID | ID: covidwho-597911

ABSTRACT

Purpose To examine insomnia disorder and its association with sociodemographic factors and poor mental health in 2019 novel coronavirus (COVID-19) inpatients in Wuhan, China Design and Methods: A total of 484 COVID-19 inpatients in Wuhan Tongji Hospital were selected and interviewed with standardized assessment tools Insomnia disorder was measured by the Chinese version of the Insomnia Severity Index (ISI-7), a total score of 8 or more was accepted as the threshold for diagnosing insomnia disorder Results The prevalence of insomnia disorder in the whole sample was 42 8% Binary logistic regression analysis revealed that female gender, younger age, and higher fatigue and anxiety severity were more likely to experience insomnia disorder Conclusion Given the high rate of insomnia disorder status among COVID-19 inpatients in Wuhan, China, and its negative effects, follow-up assessments and appropriate psychological interventions for insomnia disorder are needed in this population

7.
Clin Infect Dis ; 71(15): 833-840, 2020 07 28.
Article in English | MEDLINE | ID: covidwho-66425

ABSTRACT

BACKGROUND: Because there is no reliable risk stratification tool for severe coronavirus disease 2019 (COVID-19) patients at admission, we aimed to construct an effective model for early identification of cases at high risk of progression to severe COVID-19. METHODS: In this retrospective multicenter study, 372 hospitalized patients with nonsevere COVID-19 were followed for > 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and those who maintained a nonsevere state were assigned to the severe and nonsevere groups, respectively. Based on baseline data of the 2 groups, we constructed a risk prediction nomogram for severe COVID-19 and evaluated its performance. RESULTS: The training cohort consisted of 189 patients, and the 2 independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.4%) patients developed severe COVID-19. Older age; higher serum lactate dehydrogenase, C-reactive protein, coefficient of variation of red blood cell distribution width, blood urea nitrogen, and direct bilirubin; and lower albumin were associated with severe COVID-19. We generated the nomogram for early identifying severe COVID-19 in the training cohort (area under the curve [AUC], 0.912 [95% confidence interval {CI}, .846-.978]; sensitivity 85.7%, specificity 87.6%) and the validation cohort (AUC, 0.853 [95% CI, .790-.916]; sensitivity 77.5%, specificity 78.4%). The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analyses indicated that nomogram conferred high clinical net benefit. CONCLUSIONS: Our nomogram could help clinicians with early identification of patients who will progress to severe COVID-19, which will enable better centralized management and early treatment of severe disease.

8.
J Appl Lab Med ; 5(4): 824-828, 2020 07 01.
Article in English | MEDLINE | ID: covidwho-60502
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