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      Methodological challenges of analysing COVID-19 data during the pandemic

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      BMC Medical Research Methodology

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          Abstract

          Editorial On March 11, 2020, the World Health Organization (WHO) declared that COVID-19 can be characterized as a pandemic [1]. The disease is caused by the novel coronavirus SARS-CoV-2, which rapidly overwhelmed the entire world. The virus was first described in China in December 2019, in early January it was already characterized, and already on January 30, 2020, the outbreak was declared a Public Health Emergency of International Concern, which later evolved into a pandemic [1]. Devastating and unpredictable spread of COVID-19 throughout the world has caused unprecedented global lockdowns and immense burden for healthcare systems. The WHO called for immediate research actions including “immediately assess available data to learn what standard of care approaches are the most effective” and “evaluate as fast as possible the effect of adjunctive and supportive therapies” [1]. This pandemic is now an enormous challenge for researchers, clinicians, health-care workers, epidemiologists and decision-makers. BMC Medical Research Methodology would like to contribute to this global endeavour by setting up a collection of articles called “Methodologies for COVID-19 research and data analysis”. As Guest Editors of the Collection, we would like to offer our views regarding methodological challenges where researchers can help. Statistical challenges of analysing COVID-19 data Statistical models will play a major role in “fighting panic with information” [2] to avoid or at least minimize the risk of bias which is a common threat in clinical and epidemiological studies. In this article, we describe the most striking challenges for statisticians and data analysts who want to provide support in this pandemic with their expertise. Getting proper clinical data of active and closed COVID-19 cases After the outbreak in Wuhan, China (available as open access epidemiological data [3]), clinical data can be prospectively collected in a cohort study design. Merging and cleaning of data from large multi-centre hospitals is crucial and requires sophisticated data management. Artificial intelligence and deep learning algorithm might be suitable to tackle this challenge. Data security, patients consent, ethics statements are essential in non-pandemic situation but they are bureaucratic barriers to get rapid access to clinical data. Pandemic situations require specific handling of these issues and should be discussed on national level. We have to distinguish between active (still hospitalized) and closed (discharged or dead) COVID-19 cases. Case report forms (CRF) for patients with suspected or confirmed COVID-19 are needed to collect and store their data in a standardised way. There are two main initiatives which created protocols for the investigators, the ‘International Severe Acute Respiratory and emerging Infection Consortium (ISARIC)’ (isaric.tghn.org) and the ‘Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS)’ (leoss.net). In these two initiatives, it is planned that only closed COVID-19 cases are stored. Understanding the complexity of clinical endpoints Endpoints in patients with severe pneumonia are challenging [4]. For COVID-19 patients, the most relevant clinical endpoints are the admission to intensive care, invasive ventilation and survival. Less relevant endpoints include the need of supportive oxygen. The analysis of these endpoints requires complex models which handles the time-dependent dynamic of the data. Understanding common statistical pitfalls in clinical epidemiology Clinical data are highly time-dependent and require advanced statistical methods to avoid common pitfalls such as selection, length, immortal-time and competing risk bias [5–8]. Developing appropriate analysis strategies In the same way as data should be collected in a standardised way, data should also be analysed in a standardised way. Statisticians are encouraged to develop suitable analytical strategies to analyse data which were collected from standardised protocols (such as ISARIC and LEOSS). Communicating statistical effects and distinguishing them from artefacts Communicating statistics, especially in hectic times during a pandemic, is very challenging. Statisticians are encouraged to support this with clear and transparent statements. Learning from similar studies about SARS, MERS and influenza A(H1N1pdm09) As in other outbreaks such as SARS in 2002–2003, clinicians are confronted with new diseases for which there is limited knowledge of effective treatment options [9]. Since there is no targeted agent for COVID-19 in such an early outbreak phase, repurposing of available anti-viral drugs and corticosteroids is discussed [9–16], based on case series [17–23]. Until promising targeted randomized controlled trials exist, it is expected that large observational clinical studies will be performed to evaluate potential treatment effects as it was done, for instance, for SARS, MERS and influenza A(H1N1pdm09) on hospital mortality [24–27]. Observational studies cannot replace randomized controlled trials due to their limited ability to draw causal conclusions. However, they can be used to stimulate further research on the effectiveness of potential treatment options. Updating reporting guidelines for observational studies during a pandemic In pandemic situation, rapid and valid information flow and reporting is crucial. Long-lasting reporting guidelines might do more harm than good. Specific reporting guidelines are needed for pandemic settings. Statistical support for randomized trial The first randomized trial about Lopinavir–Ritonavir for Covid-19 patients has already been published and showed no promising effect [28]. Statistical expertise is needed to understand potential effects on the complexity of clinical endpoints. Other methodological challenges in research on COVID-19 Beyond challenges related to data analysis, there are many other methodological challenges related to research on SARS-CoV-2 and COVID-19. Searching for relevant information sources We are witnessing tremendous growth of articles published on this topic, already counting in thousands. For methodologists and researchers in the field of evidence synthesis, the challenge will be searching for the relevant information sources. Creating specialized, publicly accessible collection of studies with original studies about COVID-19 can surely help in this. For example, WHO has set up a collection of articles about COVID-19, compiled in a publicly available database. On March 30, 2020 this database had already included 3294 articles. Source of those articles is described by WHO as [quote]: “We update the database daily from searches of bibliographic databases, hand searches of the table of contents of relevant journals, and the addition of other relevant scientific articles that come to our attention” [29]. However, by 6 April 2020 it was not publicly reported which databases and journals are searched for this purpose. The WHO web site offers several crude search filters available, for searching these articles. The WHO also offers filtering for “Newest updates”, but it is not clear how new are the newest updates, i.e. there is no search by date. The articles in the database can be downloaded, but cursory look at those articles indicates that the majority of them do not have original data; instead it appears that the majority are news, commentaries and opinions. Thus, it would be useful to separate articles in this database that actually report original data. At the time when this article went to publication, multiple other collections of evidence on COVID-19 were being announced and set up, indicating that multiple teams globally are creating the same or similar evidence collections, leading to needless waste of human resources. Synthesizing evidence rapidly In a world where each day brings hundreds of new articles on a hot topic, conducting evidence synthesis will be particularly challenging. Systematic reviews are considered by many as the highest-level of evidence in the hierarchy of evidence in medicine, but their production often takes years [30, 31]. However, multiple systematic reviews about COVID-19 have already been published. It remains to be seen what is the quality of those rapidly produced systematic reviews. Producing evidence syntheses on a short time scale usually requires cutting corners with methodology, and for this reason, rapid reviews have evolved. Rapid reviews are conducted with a condensed timeline, sacrificing certain aspects of systematic review methodology for speed [32]. Pilot study has shown, for example, that rapid research needs appraisal can be conducted within 5 days in the case of an infectious disease outbreak [33]. However, it has also been shown that transparency and inadequate reporting are the major limitations of rapid reviews [34]. Ensuring adequate quality of published research Journal editors are currently under pressure to publish relevant articles on COVID-19 quickly, which has been described as “rather maddening”. It has been argued that this could also be advantageous in a long run, as it can help journals to become more efficient in future. However, haste is likely to be detrimental to the quality of publications. Speed is not necessarily a friend of good science. Articles may be assembled too quickly, publishing processes may be hastened, and quality of peer-review may not be adequate. Anecdotal reports indicate that highly specialized experts in the field may be swamped with requests for peer-review that they are unable to accommodate, which may lead to inviting less specialized peer-reviewers, to the detriment of manuscript quality check. We will need to wait to find out how many corrections and retractions there will be for journals published hastily on the topic of COVID-19, and whether methodological and reporting quality of those articles will be lower compared to the articles on other topics. In the times of emergency, researchers should still pay attention to transparency and adequate reporting of their research, to ensure its reproducibility. Data sharing To enable analysis of data gathered during COVID-19 pandemic, principles of open science and raw data sharing will be of utmost importance. Global norms have been proposed [35] for data sharing during global health emergencies, and it remains to be seen whether researchers will be more likely to share their raw data publicly in articles covering COVID-19. In conclusion, there are many methodological challenges related to producing, gathering, analysing, reporting and publishing data in condensed timelines required during a pandemic. We certainly did not mention all of them, but we hope that researchers willing to contribute to research methodology related to COVID-19 will help us address those other issues as well. It is customarily said that each crisis is also an opportunity, and therefore we hope that the BMC Medical Research Methodology will have an opportunity to publish research articles that will help the humanity win the battle against SARS-CoV-2.

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          Most cited references 19

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          Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study

          Summary Background In December, 2019, a pneumonia associated with the 2019 novel coronavirus (2019-nCoV) emerged in Wuhan, China. We aimed to further clarify the epidemiological and clinical characteristics of 2019-nCoV pneumonia. Methods In this retrospective, single-centre study, we included all confirmed cases of 2019-nCoV in Wuhan Jinyintan Hospital from Jan 1 to Jan 20, 2020. Cases were confirmed by real-time RT-PCR and were analysed for epidemiological, demographic, clinical, and radiological features and laboratory data. Outcomes were followed up until Jan 25, 2020. Findings Of the 99 patients with 2019-nCoV pneumonia, 49 (49%) had a history of exposure to the Huanan seafood market. The average age of the patients was 55·5 years (SD 13·1), including 67 men and 32 women. 2019-nCoV was detected in all patients by real-time RT-PCR. 50 (51%) patients had chronic diseases. Patients had clinical manifestations of fever (82 [83%] patients), cough (81 [82%] patients), shortness of breath (31 [31%] patients), muscle ache (11 [11%] patients), confusion (nine [9%] patients), headache (eight [8%] patients), sore throat (five [5%] patients), rhinorrhoea (four [4%] patients), chest pain (two [2%] patients), diarrhoea (two [2%] patients), and nausea and vomiting (one [1%] patient). According to imaging examination, 74 (75%) patients showed bilateral pneumonia, 14 (14%) patients showed multiple mottling and ground-glass opacity, and one (1%) patient had pneumothorax. 17 (17%) patients developed acute respiratory distress syndrome and, among them, 11 (11%) patients worsened in a short period of time and died of multiple organ failure. Interpretation The 2019-nCoV infection was of clustering onset, is more likely to affect older males with comorbidities, and can result in severe and even fatal respiratory diseases such as acute respiratory distress syndrome. In general, characteristics of patients who died were in line with the MuLBSTA score, an early warning model for predicting mortality in viral pneumonia. Further investigation is needed to explore the applicability of the MuLBSTA score in predicting the risk of mortality in 2019-nCoV infection. Funding National Key R&D Program of China.
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            Clinical evidence does not support corticosteroid treatment for 2019-nCoV lung injury

            The 2019 novel coronavirus (2019-nCoV) outbreak is a major challenge for clinicians. The clinical course of patients remains to be fully characterised, little data are available that describe the disease pathogenesis, and no pharmacological therapies of proven efficacy yet exist. Corticosteroids were widely used during the outbreaks of severe acute respiratory syndrome (SARS)-CoV 1 and Middle East respiratory syndrome (MERS)-CoV, 2 and are being used in patients with 2019-nCoV in addition to other therapeutics. 3 However, current interim guidance from WHO on clinical management of severe acute respiratory infection when novel coronavirus (2019-nCoV) infection is suspected (released Jan 28, 2020) advises against the use of corticosteroids unless indicated for another reason. 4 Understanding the evidence for harm or benefit from corticosteroids in 2019-nCoV is of immediate clinical importance. Here we discuss the clinical outcomes of corticosteroid use in coronavirus and similar outbreaks (table ). Table Summary of clinical evidence to date Outcomes of corticosteroid therapy * Comment MERS-CoV Delayed clearance of viral RNA from respiratory tract 2 Adjusted hazard ratio 0·4 (95% CI 0·2–0·7) SARS-CoV Delayed clearance of viral RNA from blood 5 Significant difference but effect size not quantified SARS-CoV Complication: psychosis 6 Associated with higher cumulative dose, 10 975 mg vs 6780 mg hydrocortisone equivalent SARS-CoV Complication: diabetes 7 33 (35%) of 95 patients treated with corticosteroid developed corticosteroid-induced diabetes SARS-CoV Complication: avascular necrosis in survivors 8 Among 40 patients who survived after corticosteroid treatment, 12 (30%) had avascular necrosis and 30 (75%) had osteoporosis Influenza Increased mortality 9 Risk ratio for mortality 1·75 (95% CI 1·3–2·4) in a meta-analysis of 6548 patients from ten studies RSV No clinical benefit in children10, 11 No effect in largest randomised controlled trial of 600 children, of whom 305 (51%) had been treated with corticosteroids CoV=coronavirus. MERS=Middle East respiratory syndrome. RSV=respiratory syncytial virus. SARS=severe acute respiratory syndrome. * Hydrocortisone, methylprednisolone, dexamethasone, and prednisolone. Acute lung injury and acute respiratory distress syndrome are partly caused by host immune responses. Corticosteroids suppress lung inflammation but also inhibit immune responses and pathogen clearance. In SARS-CoV infection, as with influenza, systemic inflammation is associated with adverse outcomes. 12 In SARS, inflammation persists after viral clearance.13, 14 Pulmonary histology in both SARS and MERS infections reveals inflammation and diffuse alveolar damage, 15 with one report suggesting haemophagocytosis. 16 Theoretically, corticosteroid treatment could have a role to suppress lung inflammation. In a retrospective observational study reporting on 309 adults who were critically ill with MERS, 2 almost half of patients (151 [49%]) were given corticosteroids (median hydrocortisone equivalent dose [ie, methylprednisolone 1:5, dexamethasone 1:25, prednisolone 1:4] of 300 mg/day). Patients who were given corticosteroids were more likely to require mechanical ventilation, vasopressors, and renal replacement therapy. After statistical adjustment for immortal time and indication biases, the authors concluded that administration of corticosteroids was not associated with a difference in 90-day mortality (adjusted odds ratio 0·8, 95% CI 0·5–1·1; p=0·12) but was associated with delayed clearance of viral RNA from respiratory tract secretions (adjusted hazard ratio 0·4, 95% CI 0·2–0·7; p=0·0005). However, these effect estimates have a high risk of error due to the probable presence of unmeasured confounders. In a meta-analysis of corticosteroid use in patients with SARS, only four studies provided conclusive data, all indicating harm. 1 The first was a case-control study of SARS patients with (n=15) and without (n=30) SARS-related psychosis; all were given corticosteroid treatment, but those who developed psychosis were given a higher cumulative dose than those who did not (10 975 mg hydrocortisone equivalent vs 6780 mg; p=0·017). 6 The second was a randomised controlled trial of 16 patients with SARS who were not critically ill; the nine patients who were given hydrocortisone (mean 4·8 days [95% CI 4·1–5·5] since fever onset) had greater viraemia in the second and third weeks after infection than those who were given 0·9% saline control. 5 The remaining two studies reported diabetes and avascular necrosis as complications associated with corticosteroid treatment.7, 8 A 2019 systematic review and meta-analysis 9 identified ten observational studies in influenza, with a total of 6548 patients. The investigators found increased mortality in patients who were given corticosteroids (risk ratio [RR] 1·75, 95% CI 1·3–2·4; p=0·0002). Among other outcomes, length of stay in an intensive care unit was increased (mean difference 2·1, 95% CI 1·2–3·1; p<0·0001), as was the rate of secondary bacterial or fungal infection (RR 2·0, 95% CI 1·0–3·8; p=0·04). Corticosteroids have been investigated for respiratory syncytial virus (RSV) in clinical trials in children, with no conclusive evidence of benefit and are therefore not recommended. 10 An observational study of 50 adults with RSV infection, in which 33 (66%) were given corticosteroids, suggested impaired antibody responses at 28 days in those given corticosteroids. 17 Life-threatening acute respiratory distress syndrome occurs in 2019-nCoV infection. 18 However, generalising evidence from acute respiratory distress syndrome studies to viral lung injury is problematic because these trials typically include a majority of patients with acute respiratory distress syndrome of non-pulmonary or sterile cause. A review of treatments for acute respiratory distress syndrome of any cause, based on six studies with a total of 574 patients, 19 concluded that insufficient evidence exists to recommend corticosteroid treatment. 20 Septic shock has been reported in seven (5%) of 140 patients with 2019-nCoV included in published reports as of Jan 29, 2020.3, 18 Corticosteroids are widely used in septic shock despite uncertainty over their efficacy. Most patients in septic shock trials have bacterial infection, leading to vasoplegic shock and myocardial insufficiency.21, 22 In this group, there is potential that net benefit might be derived from steroid treatment in severe shock.21, 22 However, shock in severe hypoxaemic respiratory failure is often a consequence of increased intrathoracic pressure (during invasive ventilation) impeding cardiac filling, and not vasoplegia. 23 In this context, steroid treatment is unlikely to provide a benefit. No clinical data exist to indicate that net benefit is derived from corticosteroids in the treatment of respiratory infection due to RSV, influenza, SARS-CoV, or MERS-CoV. The available observational data suggest increased mortality and secondary infection rates in influenza, impaired clearance of SARS-CoV and MERS-CoV, and complications of corticosteroid therapy in survivors. If it is present, the effect of steroids on mortality in those with septic shock is small, and is unlikely to be generalisable to shock in the context of severe respiratory failure due to 2019-nCoV. Overall, no unique reason exists to expect that patients with 2019-nCoV infection will benefit from corticosteroids, and they might be more likely to be harmed with such treatment. We conclude that corticosteroid treatment should not be used for the treatment of 2019-nCoV-induced lung injury or shock outside of a clinical trial.
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              Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case series

              Abstract Objective To study the clinical characteristics of patients in Zhejiang province, China, infected with the 2019 severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) responsible for coronavirus disease 2019 (covid-2019). Design Retrospective case series. Setting Seven hospitals in Zhejiang province, China. Participants 62 patients admitted to hospital with laboratory confirmed SARS-Cov-2 infection. Data were collected from 10 January 2020 to 26 January 2020. Main outcome measures Clinical data, collected using a standardised case report form, such as temperature, history of exposure, incubation period. If information was not clear, the working group in Hangzhou contacted the doctor responsible for treating the patient for clarification. Results Of the 62 patients studied (median age 41 years), only one was admitted to an intensive care unit, and no patients died during the study. According to research, none of the infected patients in Zhejiang province were ever exposed to the Huanan seafood market, the original source of the virus; all studied cases were infected by human to human transmission. The most common symptoms at onset of illness were fever in 48 (77%) patients, cough in 50 (81%), expectoration in 35 (56%), headache in 21 (34%), myalgia or fatigue in 32 (52%), diarrhoea in 3 (8%), and haemoptysis in 2 (3%). Only two patients (3%) developed shortness of breath on admission. The median time from exposure to onset of illness was 4 days (interquartile range 3-5 days), and from onset of symptoms to first hospital admission was 2 (1-4) days. Conclusion As of early February 2020, compared with patients initially infected with SARS-Cov-2 in Wuhan, the symptoms of patients in Zhejiang province are relatively mild.
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                Author and article information

                Contributors
                wolke@imbi.uni-freiburg.de
                livia.puljak@unicath.hr
                Journal
                BMC Med Res Methodol
                BMC Med Res Methodol
                BMC Medical Research Methodology
                BioMed Central (London )
                1471-2288
                14 April 2020
                14 April 2020
                2020
                : 20
                Affiliations
                [1 ]GRID grid.5963.9, Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, , University of Freiburg, ; 79104 Freiburg, Germany
                [2 ]GRID grid.440823.9, ISNI 0000 0004 0546 7013, Center for Evidence-Based Medicine and Health Care, , Catholic University of Croatia, ; Ilica 242, 10000 Zagreb, Croatia
                Article
                972
                10.1186/s12874-020-00972-6
                7154065
                50139846-07c4-4c06-b08b-d390bef31254
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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