A Living Recommendation System For Evidence-based Decision-making On Coronavirus Disease 2019

Objective: Evidence-based health decision-making utilizes the best evidence to develop recommendations based on weighing the pros and cons of different options.Since the outbreak of coronavirus disease 2019(COVID-19),the rate of evidence publication and update has been accelerating.The traditional model of translating evidence into practice has failed to meet the needs of clinical evidence-based decisionmaking.To fill the gap in Chinese COVID-19 evidence-based decision support platform,and to combine the successful experience of combining Chinese and Western medicine in the prevention and treatment of COVID-19 in China,we systematically research and develop the "Living China Recommendations System for the Diagnosis and Control of COVID-19"(hereinafter referred to as the Recommendation System).The Recommendations System aims to collect,review,produce,present,and update the best living evidence and recommendations,optimize transparency throughout the evidenceto-recommendation process,and improve the accessibility of the best evidence.The Recommendations System is a platform that could provide evidence-based decisionmaking support for stakeholders such as COVID-19 patients,healthcare professionals,and managers,and demonstrate evidence-based health decision-making for future public health emergencies.Methods:(1)Research and design of the Recommendation System:(1)The expectations of the target users,such as the public,healthcare professionals and managers,in terms of problem-solving and functional implementation of the Recommendation System were summarized and the principles of development and functional orientation were defined.(2)Chosen the appropriate programming language,database structure,operating system and framework structure,taking full account of security,stability,and economic factors.(3)We assessed the functionality of the Recommendation System,the experience of the staff,and the technology available to determine the feasibility of the development,which was carried out with the assistance of software development engineers.(2)First implementation of the Recommendation System:(1)Development of the first version of the Recommendation System: Based on the best published evidence and high-quality guidelines,the first draft of recommendations were developed around the whole process of prevention,diagnosis,treatment,and rehabilitation of COVID-19.Multidisciplinary experts were invited on several occasions to discuss the completeness,scientificity,and adaptability of the content.On this basis,the final draft was formulated and used as a source of evidence and recommendation in the first version of the Recommendation System.(2)Expert acceptance of the Recommendation System:Patient representatives and experts from different fields were invited to meeting to assess whether the system met the expected objectives in terms of content and functional orientation.We designed an evaluation form for experts to vote "satisfied","basically satisfied" or "not satisfied".The Recommendation System was accepted if the number of "satisfied" votes was ≥ 70%;if less than 70%,the framework and content of the system should be improved based on the feedback.A further meeting would be held until the system met the acceptance conditions.(3)Demonstration research on the living Recommendation System:(1)Formation of the living update mechanism: Through literature review and core working group discussions,the living update process included initial,maintenance,and retirement phases,and each phase was defined.(2)Demonstration of living evidence synthesis:Based on the first version of the Recommendation System,updates of the included evidence were monitored;we produced systematic reviews(SR)and meta-analysis(MA)on the efficacy and safety of Traditional Chinese Medicine(TCM)for COVID-19;the evidence synthesis module of the Recommendation System was updated in realtime.Initial phase: for the newly produced SRMA,we systematically searched four Chinese and five English databases from December 2019 to March 2022,identified randomized controlled trials(RCTs)and non-randomized studies of interventions(NRSIs),extracted information,assessed risk of bias,performed evidence synthesis and used the Grades of Recommendations Assessment,Development and Evaluation(GRADE)method to assess the certainty of evidence.Maintenance phase: for highquality evidence already included in the Recommendation System,its updates were regularly monitored and entered into the system;for newly produced SRMAs,new evidence was regularly monitored and results were assessed and updated.(3)Demonstration of living recommendations: In the context of the rapid,living evidencebased practice points of drug therapy for COVID-19,updated recommendations based on the first version of the recommendation system as a first phase.Maintenance phase:The evidence-based practice points working group(including steering group,secretariat group,and evidence appraisal group)was established to identify clinical issues of concern.Recommendation decision forms were developed based on the best evidence,taking into account factors such as certainty of evidence,decision thresholds,cost,and accessibility of interventions.The secretariat group drafted the first version of the recommendations and the expert steering group revised and improved to produce the final version.The decision to update or withdraw the recommendation was based on the results of the evidence update.Results:(1)(1)Design of the Recommendation System: The principles of practicality,efficiency,scalability,network security,progressiveness,economy,and integration mode were followed to design the Recommendation System.(2)The core modules(living evidence synthesis and recommendation)were presented in a multilevel and diversified way.Evidence synthesis modules presented results in the categories of diagnosis,prevention,treatment,and rehabilitation.The recommendation module was divided into an evidence map,an evidence list,and a mind map.(3)The system was built using Vue 3.0 as the system framework,Java 1.8 as the programming language,MYSQL 8.2 as the database management system,Community Enterprise Operating System(Cent OS)as the operating system,and Spring Cloud as the distributed system infrastructure.(2)First implementation of the Recommendation System:(1)Development of the first version of the Recommendation System: The living evidence synthesis module was formed based on two BMJ studies,Prophylaxis against covid-19: living systematic review and network meta-analysis and Drug treatments for covid-19: living systematic review and network meta-analysis,showing population included and results(text,tables and graphs).The living recommendations module was based on the A living WHO guideline on drugs for covid-19,The Guideline for Diagnosis and Treatment COVID-19(The 9th Trial Edition),and Integrating Chinese and western medicine for COVID-19: A living evidence-based guideline(version 1),focused on the entire COVID-19 consultation process and initially identified 10 clinical questions containing63 recommendations.The 47 recommendations were developed through 3-4 rounds of face-to-face meetings and discussions between frontline clinical experts and guideline development methodologists.(2)Acceptance of the Recommendation System: Five clinical experts,two evidence-based methodologists,two computer science and information technology experts,and one patient representative were invited to accept the system.100% of the evaluation forms were returned and 80% of the members voted "satisfied".The meeting collected suggestions for optimization as follows: In terms of content,there was lack of systematic studies that review the efficacy of TCM for patients,and changes in the evidence and environment were monitored so that recommendations were updated in a timely manner;interactive features were added;guidance was provided on how to use the system;the process of generating evidence and recommendations was demonstrated.Based on the feedback,the following adjustments were made to the system: a video module "How to use the platform" was added to the main page;references to published studies were added and specific evidence generation processes for unpublished studies were presented;the system update mode was set and update factors were identified.(3)Display of the first version of the Recommendation System: through the form of pictures,the first version of the recorded content was presented visually,showing the main page of the system,the evidence synthesis module,the recommendations module,the backstage management module,and providing operational guidance.(3)Demonstration research on the living Recommendation System:(1)Set living update parameters: parameters included monitoring the frequency of evidence,assessment of new evidence or triggers for a recommendation,and changes in the social environment.During the maintenance phase,existing recommendations were maintained if experts judged that new evidence could not change the recommendation,or updated if the evidence could change the recommendation.The retirement phase was implemented when the following four elements were present: the topic was judged to be no longer a priority,the topic was outdated,the updated evidence no longer changed the recommendation,and no new evidence was generated over a period of time.(2)Evidence synthesis: in the initial phase,SRMA included 35 RCTs and 24 NRSIs for TCM treatment of non-severe(mild and moderate)patients,and 6 RCTs and 15 NRSIs for severe and critical patients.The results showed that TCM could reduce mortality,mechanical ventilation,and rate of conversion to severe and critical cases.There were also advantages in improving the oxygenation index for severe and critical patients,shortening the time to symptom resolution,length of hospital stay and time to viral clearance for all types of patients.During the maintenance phase,3 RCTs were updated in October and December 2022.A third update is planned for April 2023.(3)Living evidence-based practice points production: in the maintenance stage,the working group consisted of a 10-member steering group,a 3-member secretariat group,and an 8-member evidence evaluation group.8 of the experts had senior titles and 6 were male.In response to the new situation of COVID-19 being treated as "category B disease",the "living evidence-based practice points of drug therapy for COVID-19" was used as a model,and 12 recommendations were formulated based on the A living WHO guideline on drugs for covid-19 and the newly produced SRMA for the TCM treatment of COVID-19 patients.12 recommendations were developed.Nine of the recommendations for non-severe patients included 12 TCM and 3 WM,while the 3recommendations for severe/critical patients included 2 TCM and 4 WM.The system would be in a long-term maintenance phase,with the panel regularly assessing whether existing recommendations can be modified and updated based on the results of synthetic evidence updates.Conclusion: This study has build a hierarchical,living updated,efficient and operational " Living China Recommendations System for the Diagnosis and Control of COVID-19".The best published evidence was integrated and the first version of the evidence synthesis and recommendations was rapidly completed.A living update model and setting parameters were developed.The "living evidence-based practice points of drug therapy for COVID-19" was used as a model,the SR for patients with COVID-19 and 12 recommendations were developed,and updated during the maintenance phase to complete the second version of the Recommendation System.The Recommendation System provided the best evidence and recommendations for the prevention and treatment of COVDID-19,as well as a recommendation model and organizational approach for the development of evidence-based decision platforms for other diseases.