Clinical features and outcomes of discharged coronavirus disease 2019 patients: a prospective cohort study

Wang, X; Xu, Hua; Jiang, Hua; Wang, L; Lu, Caiyi; Xiang, Wei; Liu, J; Xu, Shuyun

doi:10.1093/qjmed/hcaa178

Cited by 129 publications

(190 citation statements)

References 13 publications

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“…Although the study of readmitted patients with COVID-19 is a very relevant problem, to date very little attention has been devoted to its different clinical aspects. [5][6][7] Knowledge of the characteristics of patients who are most at risk of being readmitted to the hospital in the following weeks could help to better decide when to discharge and to select patients who need closer clinical follow-up after discharge.…”

Section: Introductionmentioning

confidence: 99%

<p>Hospital Readmissions of Discharged Patients with COVID-19</p>

Parra¹,

Cantero²,

Morrás³

et al. 2020

IJGM

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Objective: To analyse the rate of occurrence and the clinical variables associated with readmission of patients who had previously been discharged after admission for COVID-19. Setting: University hospital in Madrid (Spain). Participants: Sixty-one patients (74% male) who presented COVID-19 were readmitted during the 3 weeks after discharge from hospital. Interventions: Nested case-control study paired (1:1 ratio) by age, sex and period of admission. Outcome Measures: Rate of readmission rate of patients discharged after suffering COVID-19 and identification of the clinical variables associated with it. Results: Out of 1368 patients who were discharged during the study period, 61 patients (4.4%) were readmitted. Immunocompromised patients (N=10.2%) were at increased risk for readmission (p=0.04). There was also a trend towards a higher probability of readmission in hypertensive patients (p=0.07). Cases had had a shorter hospital stay and a higher prevalence of fever during the 48 hours prior to discharge. There were no significant differences in oxygen levels measured at admission and discharge by pulse oximetry intra-subject or between the groups. Neutrophil-to-lymphocyte ratio at hospital admission tended to be higher in cases than in controls (p=0.06). Neither glucocorticoids nor anticoagulants prescribed at hospital discharge were associated with a lower readmission rate. Patients who were readmitted due to a thrombotic event (8 patients, 13.1%) presented a higher level of D-dimer at discharge of initial admission. Conclusion: The rate of readmission after discharge from hospital for COVID-19 was low. Immunocompromised patients and those presenting with fever during the 48 hours prior to discharge were at greater risk of readmission to hospital.

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Section: Introductionmentioning

confidence: 99%

<p>Hospital Readmissions of Discharged Patients with COVID-19</p>

Parra¹,

Cantero²,

Morrás³

et al. 2020

IJGM

View full text Add to dashboard Cite

show abstract

“…According to a previous study on high-risk readmitted patients, intensive management and a period of self-isolation after discharge seem to be effective measures to prevent readmission [6]. However, there have been only a few studies on readmitted patients, so little is known about the clinical effects of re-hospitalization on the patients' prognosis or risk factors related to readmission [7].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Risk Factors on Readmission Cases of COVID-19 in the Republic of Korea: Using Nationwide Health Claims Data

Jeon

Seon

Park

et al. 2020

IJERPH

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In South Korea, 4.5% patients of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were readmitted to hospitals after discharge. However, there is insufficient research on risk factors for readmission and management of patients after discharge is poor. In this study, 7590 confirmed coronavirus disease (COVID-19) patients were defined as a target for analysis using nationwide medical claims data. The demographic characteristics, underlying diseases, and the use of medical resources were used to examine the association with readmission through the chi-square test and then logistic regression analysis was performed to analyze factors affecting readmission. Of the 7590 subjects analyzed, 328 patients were readmitted. The readmission rates of men, older age and patients with medical benefits showed a high risk of readmission. The Charlson Comorbidity Index score was also related to COVID-19 readmission. Concerning requiring medical attention, there was a higher risk of readmission for the patients with chest radiographs, computed tomography scans taken and lopinavir/ritonavir at the time of their first admission. Considering the risk factors presented in this study, classifying patients with a high risk of readmission and managing patients before and after discharge based on priority can make patient management and medical resource utilization more efficient. This study also indicates the importance of lifestyle management after discharge.

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“…There is less information about the full course of the disease, including pre and post hospitalization, with recovery spanning months. 17 Also, it is plausible that the course of the disease for people who never see a doctor might be different, and there are few if any resources to learn of their experiences. Details about impacts and experiences with COVID-19 in daily life pre and post hospitalization might not be communicated to clinicians or captured in publications.…”

Section: Introductionmentioning

confidence: 99%

Social Listening as a Rapid Approach to Collecting and Analyzing COVID-19 Symptoms and Disease Natural Histories Reported by Large Numbers of Individuals

Picone¹,

Inoue²,

DeFelice³

et al. 2020

Population Health Management

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Given the severe and rapid impact of COVID-19, the pace of information sharing has been accelerated. However, traditional methods of disseminating and digesting medical information can be time-consuming and cumbersome. In a pilot study, the authors used social listening to quickly extract information from social media channels to explore what people with COVID-19 are talking about regarding symptoms and disease progression. The goal was to determine whether, by amplifying patient voices, new information could be identified that might have been missed through other sources. Two data sets from social media groups of people with or presumed to have COVID-19 were analyzed: a Facebook group poll, and conversation data from a Reddit group including detailed disease natural history-like posts. Content analysis and a customized analytics engine that incorporates machine learning and natural language processing were used to quickly identify symptoms mentioned. Key findings include more than 20 symptoms in the data sets that were not listed in online lists of symptoms from 4 respected medical information sources. The disease natural history-like posts revealed that people can experience symptoms for many weeks and that some symptoms change over time. This study demonstrates that social media can offer novel insights into patient experiences as a source of real-world data. This inductive research approach can quickly generate descriptive information that can be used to develop hypotheses and new research questions. Also, the method allows rapid assessments of large numbers of social media conversations that could be applied to monitor public health for emerging and rapidly spreading diseases such as COVID-19.

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Clinical features and outcomes of discharged coronavirus disease 2019 patients: a prospective cohort study

Abstract: Summary Background Coronavirus disease 2019 (COVID-19) is a global pandemic but the follow-up data of discharged patients was barely described. Aim To investigate clinical outcomes, distribution of quarantine locations and the infection status of the contacts of C… Show more

Cited by 129 publications

References 13 publications

<p>Hospital Readmissions of Discharged Patients with COVID-19</p>

<p>Hospital Readmissions of Discharged Patients with COVID-19</p>

Analysis of Risk Factors on Readmission Cases of COVID-19 in the Republic of Korea: Using Nationwide Health Claims Data

Social Listening as a Rapid Approach to Collecting and Analyzing COVID-19 Symptoms and Disease Natural Histories Reported by Large Numbers of Individuals

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