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      Incidence, co-occurrence, and evolution of long-COVID features: A 6-month retrospective cohort study of 273,618 survivors of COVID-19

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          Abstract

          Background

          Long-COVID refers to a variety of symptoms affecting different organs reported by people following Coronavirus Disease 2019 (COVID-19) infection. To date, there have been no robust estimates of the incidence and co-occurrence of long-COVID features, their relationship to age, sex, or severity of infection, and the extent to which they are specific to COVID-19. The aim of this study is to address these issues.

          Methods and findings

          We conducted a retrospective cohort study based on linked electronic health records (EHRs) data from 81 million patients including 273,618 COVID-19 survivors. The incidence and co-occurrence within 6 months and in the 3 to 6 months after COVID-19 diagnosis were calculated for 9 core features of long-COVID (breathing difficulties/breathlessness, fatigue/malaise, chest/throat pain, headache, abdominal symptoms, myalgia, other pain, cognitive symptoms, and anxiety/depression). Their co-occurrence network was also analyzed. Comparison with a propensity score–matched cohort of patients diagnosed with influenza during the same time period was achieved using Kaplan–Meier analysis and the Cox proportional hazard model. The incidence of atopic dermatitis was used as a negative control.

          Among COVID-19 survivors (mean [SD] age: 46.3 [19.8], 55.6% female), 57.00% had one or more long-COVID feature recorded during the whole 6-month period (i.e., including the acute phase), and 36.55% between 3 and 6 months. The incidence of each feature was: abnormal breathing (18.71% in the 1- to 180-day period; 7.94% in the 90- to180-day period), fatigue/malaise (12.82%; 5.87%), chest/throat pain (12.60%; 5.71%), headache (8.67%; 4.63%), other pain (11.60%; 7.19%), abdominal symptoms (15.58%; 8.29%), myalgia (3.24%; 1.54%), cognitive symptoms (7.88%; 3.95%), and anxiety/depression (22.82%; 15.49%). All 9 features were more frequently reported after COVID-19 than after influenza (with an overall excess incidence of 16.60% and hazard ratios between 1.44 and 2.04, all p < 0.001), co-occurred more commonly, and formed a more interconnected network. Significant differences in incidence and co-occurrence were associated with sex, age, and illness severity. Besides the limitations inherent to EHR data, limitations of this study include that (i) the findings do not generalize to patients who have had COVID-19 but were not diagnosed, nor to patients who do not seek or receive medical attention when experiencing symptoms of long-COVID; (ii) the findings say nothing about the persistence of the clinical features; and (iii) the difference between cohorts might be affected by one cohort seeking or receiving more medical attention for their symptoms.

          Conclusions

          Long-COVID clinical features occurred and co-occurred frequently and showed some specificity to COVID-19, though they were also observed after influenza. Different long-COVID clinical profiles were observed based on demographics and illness severity.

          Abstract

          Maxime Taquet and colleagues investigate the incidence, co-occurrence and evolution of long-COVID features in more than a quarter of a million people.

          Author summary

          Why was this study done?
          • Long-COVID has been described in recent studies. But we do not know the risk of developing features of this condition and how it is affected by factors such as age, sex, or severity of infection.

          • We do not know if the risk of having features of long-COVID is more likely after Coronavirus Disease 2019 (COVID-19) than after influenza.

          • We do not know about the extent to which different features of long-COVID co-occur.

          What did the researchers do and find?
          • This research used data from electronic health records of 273,618 patients diagnosed with COVID-19 and estimated the risk of having long-COVID features in the 6 months after a diagnosis of COVID-19. It compared the risk of long-COVID features in different groups within the population and also compared the risk to that after influenza.

          • The research found that over 1 in 3 patients had one or more features of long-COVID recorded between 3 and 6 months after a diagnosis of COVID-19. This was significantly higher than after influenza.

          • For 2 in 5 of the patients who had long-COVID features in the 3- to 6-month period, they had no record of any such feature in the previous 3 months.

          • The risk of long-COVID features was higher in patients who had more severe COVID-19 illness, and slightly higher among females and young adults. White and non-white patients were equally affected.

          What do these findings mean?
          • Knowing the risk of long-COVID features helps in planning the relevant healthcare service provision.

          • The fact that the risk is higher after COVID-19 than after influenza suggests that their origin might, in part, directly involve infection with SARS-CoV-2 and is not just a general consequence of viral infection. This might help in developing effective treatments against long-COVID.

          • The findings in the subgroups, and the fact that the majority of patients who have features of long-COVID in the 3- to 6-month period already had symptoms in the first 3 months, may help in identifying those at greatest risk.

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          Most cited references33

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          OpenSAFELY: factors associated with COVID-19 death in 17 million patients

          COVID-19 has rapidly impacted on mortality worldwide. 1 There is unprecedented urgency to understand who is most at risk of severe outcomes, requiring new approaches for timely analysis of large datasets. Working on behalf of NHS England we created OpenSAFELY: a secure health analytics platform covering 40% of all patients in England, holding patient data within the existing data centre of a major primary care electronic health records vendor. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19 related deaths. COVID-19 related death was associated with: being male (hazard ratio 1.59, 95%CI 1.53-1.65); older age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared to people with white ethnicity, black and South Asian people were at higher risk even after adjustment for other factors (HR 1.48, 1.29-1.69 and 1.45, 1.32-1.58 respectively). We have quantified a range of clinical risk factors for COVID-19 related death in the largest cohort study conducted by any country to date. OpenSAFELY is rapidly adding further patients’ records; we will update and extend results regularly.
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            Dysregulation of immune response in patients with COVID-19 in Wuhan, China

            Abstract Background In December 2019, coronavirus disease 2019 (COVID-19) emerged in Wuhan and rapidly spread throughout China. Methods Demographic and clinical data of all confirmed cases with COVID-19 on admission at Tongji Hospital from January 10 to February 12, 2020, were collected and analyzed. The data of laboratory examinations, including peripheral lymphocyte subsets, were analyzed and compared between severe and non-severe patients. Results Of the 452 patients with COVID-19 recruited, 286 were diagnosed as severe infection. The median age was 58 years and 235 were male. The most common symptoms were fever, shortness of breath, expectoration, fatigue, dry cough and myalgia. Severe cases tend to have lower lymphocytes counts, higher leukocytes counts and neutrophil-lymphocyte-ratio (NLR), as well as lower percentages of monocytes, eosinophils, and basophils. Most of severe cases demonstrated elevated levels of infection-related biomarkers and inflammatory cytokines. The number of T cells significantly decreased, and more hampered in severe cases. Both helper T cells and suppressor T cells in patients with COVID-19 were below normal levels, and lower level of helper T cells in severe group. The percentage of naïve helper T cells increased and memory helper T cells decreased in severe cases. Patients with COVID-19 also have lower level of regulatory T cells, and more obviously damaged in severe cases. Conclusions The novel coronavirus might mainly act on lymphocytes, especially T lymphocytes. Surveillance of NLR and lymphocyte subsets is helpful in the early screening of critical illness, diagnosis and treatment of COVID-19.
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              The trinity of COVID-19: immunity, inflammation and intervention

              Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the ongoing coronavirus disease 2019 (COVID-19) pandemic. Alongside investigations into the virology of SARS-CoV-2, understanding the fundamental physiological and immunological processes underlying the clinical manifestations of COVID-19 is vital for the identification and rational design of effective therapies. Here, we provide an overview of the pathophysiology of SARS-CoV-2 infection. We describe the interaction of SARS-CoV-2 with the immune system and the subsequent contribution of dysfunctional immune responses to disease progression. From nascent reports describing SARS-CoV-2, we make inferences on the basis of the parallel pathophysiological and immunological features of the other human coronaviruses targeting the lower respiratory tract — severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). Finally, we highlight the implications of these approaches for potential therapeutic interventions that target viral infection and/or immunoregulation.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: SoftwareRole: Writing – review & editing
                Role: Data curationRole: ResourcesRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Med
                PLoS Med
                plos
                PLoS Medicine
                Public Library of Science (San Francisco, CA USA )
                1549-1277
                1549-1676
                28 September 2021
                September 2021
                : 18
                : 9
                : e1003773
                Affiliations
                [1 ] Department of Psychiatry, University of Oxford, United Kingdom
                [2 ] Oxford Health NHS Foundation Trust, Oxford, United Kingdom
                [3 ] TriNetX Inc., Cambridge, Massachusetts, United States of America
                [4 ] Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
                [5 ] Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
                Universitair Medisch Centrum Utrecht, NETHERLANDS
                Author notes

                I have read the journal’s policy and the authors of this manuscript have the following competing interests: SL is an employee of TriNetX Inc. The other authors report no interests to declare.

                Author information
                https://orcid.org/0000-0002-8987-952X
                https://orcid.org/0000-0001-8264-347X
                https://orcid.org/0000-0002-6850-9255
                Article
                PMEDICINE-D-21-02226
                10.1371/journal.pmed.1003773
                8478214
                34582441
                9d926137-6284-4c9a-af6a-a52530f2f743
                © 2021 Taquet et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 19 May 2021
                : 18 August 2021
                Page count
                Figures: 5, Tables: 1, Pages: 22
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000272, National Institute for Health Research;
                Award ID: BRC-1215-20005
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100010269, wellcome trust;
                Award ID: Principal Research Fellowship 206330/Z/17/Z
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000272, National Institute for Health Research;
                Award ID: Academic Clinical Fellowship
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100013373, nihr oxford biomedical research centre;
                Award ID: Oxford Biomedical Research Centre
                Award Recipient :
                MT, PJH, and JRG were supported by the National Institute for Health Research (NIHR) Oxford Health Biomedical Research Centre (BRC-1215-20005). MT is an NIHR Academic Clinical Fellow and an NIHR Oxford Health BRC Senior Research Fellow. MH is supported by a Wellcome Trust Principal Research Fellowship (206330/Z/17/Z) and the NIHR Oxford Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the UK National Health Service, NIHR, or the UK Department of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. NIHR: https://www.nihr.ac.uk Wellcome Trust: https://wellcome.org.
                Categories
                Research Article
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Viral Diseases
                Covid 19
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Viral Diseases
                Influenza
                Medicine and Health Sciences
                Epidemiology
                Medicine and Health Sciences
                Epidemiology
                Medical Risk Factors
                Medicine and Health Sciences
                Clinical Medicine
                Signs and Symptoms
                Pain
                Myalgia
                Medicine and Health Sciences
                Clinical Medicine
                Signs and Symptoms
                Pain
                Abdominal Pain
                Medicine and Health Sciences
                Clinical Medicine
                Signs and Symptoms
                Headaches
                Medicine and Health Sciences
                Health Care
                Health Information Technology
                Electronic Medical Records
                Computer and Information Sciences
                Information Technology
                Health Information Technology
                Electronic Medical Records
                Custom metadata
                The TriNetX system returned the results of these analyses as csv files which were downloaded and archived. Aggregate data as presented in this paper can be freely accessed at https://osf.io/xrvj6. The data we used for this paper were acquired from TriNetX ( https://www.trinetx.com/platform). This study had no special privileges. Inclusion criteria specified in the Methods section would allow other researchers to identify similar cohorts of patients as the authors used for these analyses; however, TriNetX is a live platform with new data being added daily so exact counts will vary. To gain access to the data, a request can be made to TriNetX ( join@ 123456trinetx.com ), but costs may be incurred, and a data sharing agreement would be necessary.
                COVID-19

                Medicine
                Medicine

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