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      Long-term consequences of COVID-19: research needs

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          Abstract

          Weeks and months after the onset of acute COVID-19, people continue to suffer. Paul Garner, a professor of epidemiology at Liverpool School of Tropical Medicine, UK, wrote on the 95th day after the onset of symptoms that “I am unable to be out of bed for more than three hours at a stretch, my arms and legs are permanently fizzing as if injected with Szechuan peppercorns, I have ringing in the ears, intermittent brain fog, palpitations, and dramatic mood swings.” 1 Other people also describe similar complaints.2, 3 78 of 100 patients in an observational cohort study who had recovered from COVID-19 had abnormal findings on cardiovascular MRI (median of 71 days after diagnosis) and 36 of those reported dyspnoea and unusual fatigue. 4 We are seeing patients in clinics dedicated to COVID-19 convalescents, and for some of these patients the return to their former health trajectory is slow and painful. These patients are not only those recovering from the severe form of the acute disease (ie, post intensive care syndrome), but also those who had mild and moderate disease. A summary of the most common complaints, based on our clinical impressions, is shown in the appendix (p 1). Rare long-term sequelae can result after other viral infections—eg, infectious mononucleosis, measles, and hepatitis B. Long-term sequelae of COVID-19 are unknown (as are many aspects of the acute disease). Long-term consequences were observed in survivors of severe acute respiratory syndrome (SARS)5, 6 but it is unknown whether lessons from SARS are applicable to COVID-19. Other concerns are rising: does acute COVID-19 cause diabetes? 7 Or other metabolic disorders? Will patients develop interstitial lung disease? We are still in the first months of the pandemic and we do not know what to tell our patients when they are asking about the course and prognosis of their ongoing complaints. The number of people affected by COVID-19 is unprecedented. We owe good answers on the long-term consequences of the disease to our patients and health-care providers. The obvious answer is in research. In the appendix (p 2) we have compiled a list of questions we think should be answered. This list is based on the authors' views and experience rather than on the literature, which is scant. For efficient research and for research that our patients (and we) can trust, some common problems in the description and research of acute COVID-19 should be avoided. The main problem is fragmentation. For example, Wynants and colleagues 8 described 47 models for predicting COVID-19 infection and 16 prognostic models for COVID-19 patients. Most of these models had a high risk of bias and most of them did not have external validation. Additionally, randomised controlled trials on interventions to treat the acute disease were stopped before enlisting the planned sample size. Although much effort was invested in these studies, we have learned little. Fragmentation also happens by discipline,6, 7 and the follow-up (for clinical and research purposes) should be multinational, multidisciplinary, comprehensive, and homogenous. Careful recording of symptoms and patient examination should allow understanding of which part of the sequelae is common to all severe infections, which symptoms might be explained by the anxiety caused by a new disease and by the isolation, 9 and which symptoms are secondary to a complicated form of COVID-19 (eg, pulmonary involvement during the acute disease). If indeed COVID-19 is causing long-term sequelae then are the mechanisms underlying the long-term consequences immunological? Or caused by new or relapsing inflammation, ongoing infection, or side-effects of immunomodulatory treatment? Such data can serve to point at candidate management strategies to be tested in trials. Support for research is needed on the trajectory of people recovering from COVID-19. To avoid the problems we have witnessed in the research of the acute phase of the disease, a clear definition of patient inclusion criteria, a common protocol, and uniform definitions of outcomes and ways to measure them are required. Additionally, data should be collected in real time and computational tools are needed to be able to do this (appendix p 3). The participation of an international and interdisciplinary group of researchers is essential. Multisite and multinational projects are needed because a description from one group or one site cannot discern between universal features and features of the local health system or the local population. By comparing data from different sites and countries we can learn which characteristics of the disease are universal and which are local. In addition to improving the care of our patients with long-term consequences of COVID-19, we hope to be able to join such necessary research efforts soon.

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          Most cited references5

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          Persistent Symptoms in Patients After Acute COVID-19

          This case series describes COVID-19 symptoms persisting a mean of 60 days after onset among Italian patients previously discharged from COVID-19 hospitalization.
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            COVID-19 pandemic and mental health consequences: systematic review of the current evidence

            Highlights • COVID-19 patients displayed high levels of PTSS and increased levels of depression. • Patients with preexisting psychiatric disorders reported worsening of psychiatric symptoms. • Higher levels of psychiatric symptoms were found among health care workers. • A decrease in psychological well-being was observed in the general public. • However, well conducted large-scale studies are highly needed.
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              Is Open Access

              Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal

              Abstract Objective To review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at risk of being admitted to hospital for covid-19 pneumonia. Design Rapid systematic review and critical appraisal. Data sources PubMed and Embase through Ovid, Arxiv, medRxiv, and bioRxiv up to 24 March 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 2696 titles were screened, and 27 studies describing 31 prediction models were included. Three models were identified for predicting hospital admission from pneumonia and other events (as proxy outcomes for covid-19 pneumonia) in the general population; 18 diagnostic models for detecting covid-19 infection (13 were machine learning based on computed tomography scans); and 10 prognostic models for predicting mortality risk, progression to severe disease, or length of hospital stay. Only one study used patient data from outside of China. The most reported predictors of presence of covid-19 in patients with suspected disease included age, body temperature, and signs and symptoms. The most reported predictors of severe prognosis in patients with covid-19 included age, sex, features derived from computed tomography scans, C reactive protein, lactic dehydrogenase, and lymphocyte count. C index estimates ranged from 0.73 to 0.81 in prediction models for the general population (reported for all three models), from 0.81 to more than 0.99 in diagnostic models (reported for 13 of the 18 models), and from 0.85 to 0.98 in prognostic models (reported for six of the 10 models). All studies were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, and high risk of model overfitting. Reporting quality varied substantially between studies. Most reports did not include a description of the study population or intended use of the models, and calibration of predictions was rarely assessed. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that proposed models are poorly reported, at high risk of bias, and their reported performance is probably optimistic. Immediate sharing of well documented individual participant data from covid-19 studies is needed for collaborative efforts to develop more rigorous prediction models and validate existing ones. The predictors identified in included studies could be considered as candidate predictors for new models. Methodological guidance should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, studies should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.
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                Author and article information

                Journal
                Lancet Infect Dis
                Lancet Infect Dis
                The Lancet. Infectious Diseases
                Elsevier Ltd.
                1473-3099
                1474-4457
                1 September 2020
                1 September 2020
                Affiliations
                [a ]Unit of infectious Diseases, Beilinson Hospital, Rabin Medical Center, Petah-Tiqva 49100, Israel
                [b ]Management, Beilinson Hospital, Rabin Medical Center, Petah-Tiqva 49100, Israel
                [c ]Research Authority, Beilinson Hospital, Rabin Medical Center, Petah-Tiqva 49100, Israel
                [d ]Geneva Centre for Emerging Viral Diseases, Geneva University Hospitals, Geneva, Switzerland
                [e ]Laboratory of Virology, Division of Infectious Diseases, Division of Laboratory Medicine, University Hospitals of Geneva and Faculty of Medicine, University of Geneva, Geneva, Switzerland
                [f ]Division of Infectious Diseases, University of Nebraska Medical Center, Omaha, NE, USA
                [g ]Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
                [h ]Department of Respiratory Medicine and Allergy, Karolinska University Hospital, Stockholm, Sweden
                [i ]Karolinska University Hospital, Stockholm, Sweden
                [j ]Divison of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
                [k ]Department of Surgical, Medical, Dental and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy
                [l ]Department of Infectious Diseases, University of Modena and Reggio Emilia, Modena, Italy
                [m ]Department of Infectious Diseases, Hospital Universitari de Bellvitge, Institut Investigacions Biomèdiques de Bellvitge, University of Barcelona, Barcelona, Spain
                [n ]Clinic of Infectious Diseases, IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano Foundation, Milan, Italy
                [o ]Scientific Direction, IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano Foundation, Milan, Italy
                [p ]Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
                [q ]Infectious Diseases Institute, Rambam Health Care Campus and The Ruth and Bruce Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
                [r ]Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
                Article
                S1473-3099(20)30701-5
                10.1016/S1473-3099(20)30701-5
                7462626
                32888409
                c0d7bfc0-93e5-46c4-a278-bcc93374a5a0
                © 2020 Elsevier Ltd. All rights reserved.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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                Infectious disease & Microbiology
                Infectious disease & Microbiology

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