At present, the network meta-analysis has been rapidly developed and widely used, and it has the characteristic of quantifying and comparing the relative advantages of two or more different interventions for a single health outcome. However, comparison of multiple interventions has increased the complexity of drawing conclusions from network meta-analysis, and ignorance of the certainty of evidence has also led to misleading conclusions. Recently, the GRADE (grading of recommendations assessment, development and evaluation) working group proposed two approaches for obtaining conclusions from a network meta-analysis of interventions, namely, the partially contextualised framework and the minimally contextualised framework. When using partially contextualised framework, authors should establish ranges of magnitudes of effect that represent a trivial to no effect, minimal but important effect, moderate effect, and large effect. The guiding principles of this framework are that interventions should be grouped in categories based on the magnitude of the effect and its benefit or harm; and that when classifying, consider the point estimates, the rankings, and the certainty of the evidence comprehensively to draw conclusions. This article employs a case to describe and explain the principles and four steps of partially contextualised framework to provide guidance for the application of this GRADE approach in the interpretation of results and conclusions drawing from a network meta-analysis.
The primary advantage of network meta-analysis is the capability to quantify and compare different interventions for the same diseases and rank their benefits or harms according to a certain health outcome. The inclusion of a variety of interventions has increased the complexity of the conclusions drawing from a network meta-analysis, and based on the ranking results alone may lead to misleading conclusions. At present, there are no accepted standards for the conclusion drawing from a network meta-analysis. In November 2020, based on the evidence certainty results of network meta-analysis, the GRADE (Grades of Recommendations Assessment, Development and Evaluation) working group proposed two approaches to draw conclusions from a network meta-analysis: the partially contextualised framework and the minimally contextualised framework. This paper aimed to introduce principles and procedures of the minimal contextualised framework through a specific example to provide guidance for the network meta-analysis authors in China to present and interpret the results using minimally contextualised framework.
Network meta-analysis (NMA) is a method that can compare and rank the effects of different interventions, which plays an important role in evidence translation and evidence-based decision-making. In 2014, the GRADE working group first introduced the GRADE method for NMA evidence certainty grading. Since then, its method system has been gradually supplemented and improved. In recent years, the GRADE working group has further improved the methods for evaluating intransitivity and imprecision in NMA, and made recommendations for the presentation and interpretation of NMA results, forming a complete methodological chain of NMA evidence certainty grading and result interpretation consisting of 6 steps. Our team updated the method system of GRADE applied in NMA with specific cases to provide references for relevant researchers
ObjectiveIn order to summarize the best evidence, evaluate the efficacy and safety of interventions for the treatment of COVID-19, and provide practical guidance for medical workers, public health workers, and COVID-19 patients, we formulated the evidence-based practice points. MethodsWe followed the "Evidence-based practice points: methods and processes of development", with comprehensively considering the pros and cons of evidence, quality of evidence, public and patient preferences and values, cost of interventions, acceptability, and feasibility based on systematic reviews. Practice points Finally, 12 practice points were formed for non-severe, severe and critical COVID-19 patients. Non-severe: ① Consider Hanshiyi formula or Gegenqinlian pills for patients with nausea, vomiting and diarrhea; ② Consider Huashibaidu granules (decoration), Jinyinhua oral liquid, Jinhuaqinggan granules, Xuanfeibaidu granules (decoration), Lianhuaqingwen capsules (granules), or Reyanning mixture for patients with sore throat, fever, muscle aches or cough; ③ Consider Qingfeipaidu granules (decoration) for patients with nasal congestion, runny nose, cough, low-grade fever, aversion to wind and cold, and fatigue; ④ Consider Toujiequwen granules for patients with fever, chills, itchy throat, cough, dry mouth and throat, and constipation; ⑤ Consider Reduning injection or Xiyanping injection for patients with high fever, mild aversion to wind and cold, headache and body pain, cough, and yellow phlegm; ⑥ Consider molnupiravir, nirmatrelvir–ritonavir (Paxlovid), remdesivir or VV116 for patients within 5 to 7 days of the onset of symptoms and at high risk for progressing to severe disease. Severe: ① Consider Shenhuang granules or Xuebijing injection for patients with high fever, irritability, and thirst; ② Consider remdesivir used as soon as possible for patients with severe symptoms. Critical severe: Consider corticosteroids, IL-6 receptor inhibitors, and baricitinib for patients 7 days after the onset of symptoms.