Consistency of recommendations and methodological quality of guidelines for the diagnosis and treatment of COVID‐19

Abstract

Objective Since the beginning of the COVID‐19 epidemic, a large number of guidelines on diagnosis and treatment of COVID‐19 have been developed, but the quality of those guidelines and the consistency of recommendations are unclear. The objective of this study is to evaluate the quality of the diagnosis and treatment guidelines on COVID‐19 and analyze the consistency of the recommendations of these guidelines. Methods We searched for guidelines on diagnosis and/or treatment of COVID‐19 through PubMed, CBM, CNKI, and WanFang Data, from January 1, 2020 to August 31, 2020. In addition, we also searched official websites of the US CDC, European CDC and WHO, and some guideline collection databases. We included diagnosis and/or treatment guidelines for COVID‐19, including rapid advice guidelines and interim guidelines. Two trained researchers independently extracted data and four trained researchers evaluated the quality of the guidelines using the AGREE II instruments. We extracted information on the basic characteristics of the guidelines, guideline development process, and the recommendations. We described the consistency of the direction of recommendations for treatment and diagnosis of COVID‐19 across the included guidelines. Results A total of 37 guidelines were included. Most included guidelines were assessed as low quality, with only one of the six domains of AGREE II (clarity of presentation) having a mean score above 50%. The mean scores of three domains (stakeholder involvement, the rigor of development and applicability) were all below 30%. The recommendations on diagnosis and treatment were to some extent consistent between the included guidelines. Computed tomography (CT), X‐rays, lung ultrasound, RT‐PCR, and routine blood tests were the most commonly recommended methods for COVID‐19 diagnosis. Thirty guidelines were on the treatment of COVID‐19. The recommended forms of treatment included supportive care, antiviral therapy, glucocorticoid therapy, antibiotics, immunoglobulin, extracorporeal membrane oxygenation (ECMO), convalescent plasma, and psychotherapy. Conclusions The methodological quality of currently available diagnosis and treatment guidelines for COVID‐19 is low. The diagnosis and treatment recommendations between the included guidelines are highly consistent. The main diagnostic methods for COVID‐19 are RT‐PCR and CT, with ultrasound as a potential diagnostic tool. As there is no effective treatment against COVID‐19 yet, supportive therapy is at the moment the most important treatment option.

The rapid, massive growth of COVID-19 authors in the scientific literature

“…The quality of the published work was not assessed in our analysis, given the broad scope and huge diversity of the included papers. Nevertheless, many surveys of the quality of COVID-19 publications already exist [15,[25][26][27][28][29][30][31][32][33][34][35][36][37]. Although existing surveys of the quality of COVID-19 research do not cover all subfields of investigation and quality is often difficult to measure precisely, the consistent finding of the high prevalence of low-quality studies across very different types of study designs suggests that a large portion ( perhaps even the large majority) of the immense and rapidly growing COVID-19 literature may be of low quality.…”

Quality of and Recommendations for Relevant Clinical Practice Guidelines for COVID-19 Management: A Systematic Review and Critical Appraisal

“…A large number of CB-CPGs and EB-CPGs in accordance with experience of frontline health professionals, such as experts in infectious disease, medical imaging, and clinical immunology, have put forward valuable suggestions to guide clinical practice. Although the methodological quality of EB-CPGs is higher than CB-CPGs in general, they all have deficiencies in the following aspects, including obtaining the views and preferences of the target population, considering benefits and risks when formulating recommendations, introducing a detailed update plan, and providing implementation strategy for the recommendations or methods for managing potential conflicts of interest, similar to Dagens', Luo', and Zhao' studies ( 3 , 4 , 6 ). In view of the above topics, there are some examples of good practice, for example, conducting interviews and group surveys to collect information on treatment evidence from frontline experts fighting the disease ( 34 ); inviting patients recovering from COVID-19 to get involved in the guideline development panel ( 45 ); critically assessing new studies where these supersede previous outdated recommendations ( 14 ); providing available recommendation summaries in user-friendly and multilayered formats for clinicians and patients through the MAGIC app ( 55 ) or the provision of consultation decision aids to facilitate shared decision-making ( 45 ); and using the GRADEpro guideline development tool online software to conduct evidence-based CPGs ( 56 ).…”

“…Previous reviews have also concentrated on methodological quality and recommendations for COVID-19 guidelines, but these have covered a narrow range of topics ( 3 – 6 ). The methods and reporting quality of practice guidelines for five different viruses causing public health emergencies of international concern, including the severe acute respiratory syndrome coronavirus 2, tended to be low, particularly in stakeholder involvement and applicability.…”

The rapid, massive growth of COVID-19 authors in the scientific literature

“…Nevertheless, many surveys of the quality of COVID-19 publications already exist. [25][26][27][28][29][30][31][32][33][34][35][36][37][38] Although existing surveys of the quality of COVID-19 research do not cover all subfields of investigation and quality is often difficult to measure precisely, the consistent finding of high prevalence of low quality studies across very different types of study designs suggests that a large portion (perhaps even the large majority) of the immense and rapidly growing COVID-19 literature may be of low quality. Moreover, massive productivity has been described in the pre-COVID era, as affecting researchers across many fields 39 and may be a particular feature for COVID-19 research.…”