The coronavirus disease 2019 (COVID-19) has been evaluated as a pandemic by the World Health Organization (WHO). Although several vaccines with an efficacy of more than 50% have been developed, in terms of treatment, remdesivir remains the antiviral drug approved by the U.S. Food and Drug Administration (FDA) for the treatment of COVID-19, while none of the other treatments has been recommended by FDA due to insufficient clinical data. A number of clinical trials have been registered to study therapeutic drugs or vaccines for COVID-19. To promote the collection, tabulation, analysis of COVID-19 clinical research data, improve the efficiency of clinical research, and facilitate the integration, sharing and secondary analysis of multiple similar research data, CDISC has developed a therapeutic area user guide for COVID-19 and resources for public health researchers. The resources included two documents, one is a SDTM annotated CRF based on ISARIC and WHO core COVID-19 case report form, and the other is SDTM and CDASH mapping spreadsheet. Moreover, CDISC has developed the guidance for ongoing studies disrupted by COVID-19 pandemic and interim ADaM guidance for ongoing studies disrupted by COVID-19 pandemic to help disrupted clinical trials to collect, store, and analyze relevant data. This paper introduced the structure and content of the guide and its related standards, with a view to promoting its application in COVID-19 clinical trials and in ongoing studies disrupted by COVID-19 pandemic.
Dynamic medication management can help to improve efficacy and save consumption in such trials when an investigational medication is expensive or in short supply or has a fairly short shelf-life in a long-lasting clinical trial, or, if protocol changes during study period. We presented the standard operating procedure of dynamic medication management and drug labeling through an instance of double-blind randomized controlled trial of Chinese Medicine for hepatopathy. Compared to conventional drug labeling, dynamic medication management and drug labeling is more flexibility to deal with problems during the study period, such as protocol update, medication waste, and outdated medication and so on. Appropriate method and reasonable procedure can ensure the precise implementation for drug labeling and medication management of double-blinded randomized controlled trials.
ObjectiveTo investigate the current situation of domestic registry studies, and to provide basis for future research.MethodsWorld Health Organization (WHO) registration platform, ClinicalTrials.gov registration platform and other registration platforms were searched to collect the registered registration studies in Mainland China from inception to July 31st, 2018. Two researchers collected and collated data, analyzed by descriptive statistical methods, and then python-igraph package in Python 3.5 was used to draw the network diagram.ResultsA total of 247 studies were retrieved, mainly for disease registry and pharmaceutical product registry. Cohort and case-series were the principally study design. The research focused on chronic diseases such as cardiovascular and cerebrovascular diseases, and research sites were based on the economically developed area, for example, Beijing, Shanghai and Guangdong. The network diagram of study design and disease system shows that cohort research design is widely used in the nervous system and circulatory system; the network diagram of the research sites and disease system found that Beijing mainly studied circulatory and nervous system diseases, Shanghai mainly studied circulatory system diseases, and Guangdong relatively studied more on nervous system and urinary system diseases.ConclusionThe increasing number of registry studies in China reflects the importance of long-term outcome assessment of diseases.
ObjectiveTo systematically analyze the distribution of research evidence on diabetes in current clinical practice guidelines of Chinese medicine (CM). MethodsThe PubMed, Embase, Cochrane Library, CBM, WanFang Data, CNKI, VIP databases and related guideline websites were electronically searched to collect clinical practice guidelines for CM in diabetes published before December 2023. We systematically reviewed the distribution of evidence in these guidelines. ResultsThe 27 CM guidelines on diabetes mainly covered syndrome differentiation and treatment, specific disease-specific drugs, diet, external therapies, and traditional exercises. The included guidelines used three different levels of evidence grading, with most of the evidence falling into the low-to-moderate level (67.3%). However, guidelines on diabetes-related osteoporosis, diabetic cardiomyopathy, prediabetes, and diabetic peripheral neuropathy had a relatively low proportion of high-level evidence, accounting for only 7.2%, 7.6%, 13.2%, and 13.3% respectively. Only guidelines on diabetic nephropathy provided evidence on the toxicity of Chinese herbal medicine, while other guidelines did not cover this aspect. Acupuncture, Tai Chi, Baduanjin, and other characteristics therapies had varying levels of evidence for different types of complications. Low-level evidence mainly focused on syndrome differentiation and treatment, symptom-based treatment, sign-based treatment, indicator-based treatment, Chinese patent medicine, specific disease-specific formulas, etc., for diabetes and related complications. ConclusionCurrently, topics supported by low/no evidence, new themes, inconsistent content between guidelines, evidence sources for overlapping targets, classical formulas, toxicity of Chinese herbal medicine, and characteristic CM therapies can provide directions for future research on CM in diabetes. We advocate addressing important issues related to diabetes specifically, to improve research value, eliminate unnecessary duplication of studies and resource waste, and promote the healthy development of CM research in the field of diabetes.
在缺乏数据进行 Meta 分析的系统评价中，通常使用替代合成方法，但这些方法却很少被报道，而模糊的方法阐述可能会导致人们质疑系统评价结果的真实性。无 Meta 分析数据合成（SWiM）报告规范是用于指导采用了替代合成方法评估干预措施效应的系统评价进行清晰报告的规范。本文介绍了 SWiM 规范的研制过程及 9 个 SWiM 报告条目及其相应的解释与示例。
Objective To summarize the current studies of the core outcome set of traditional Chinese medicine (COS-TCM) and analyze their possible problems. Methods The CNKI, WanFang Data, VIP, PubMed, Web of Science, Embase, and Cochrane Library databases were electronically searched to collect studies from inception to April 18, 2023. The relevant characteristics of the included studies were extracted, and the development steps, stakeholders, and outcomes of COS-TCM were analyzed. Results A total of 28 COS-TCM studies were included. Two studies, only published systematic reviews without providing more information, were excluded from the analysis. Among the 26 studies for analysis, 25 studies (96.15%) used a mixture of methods to develop COS-TCM. Clinicians (n=25) were the most common participants, followed by methodologists and patients. Fifteen studies (57.69%) reported measures to help patients better participate. Twelve consensus definitions were found in the included studies, of which 14 studies' consensus definitions were divided into three levels:"consensus in", "consensus out", and "no consensus". Among the 14 studies that reported the final COS-TCM results, only 4 studies recommended Chinese medicine characteristic outcomes. For the measurement of outcomes, 14 studies (53.85%) made plans for the selection of tools. Conclusion The current COS-TCM research has made some progress, and the common developing methods are roughly the same as those nationally used. However, there are still some problems, such as inadequate and low-transparency reports, lack of TCM characteristic outcomes, and so on. We suggest that future COS-TCM studies should refer to COS-STAP, COS-STAR, COS-STAD, and other international standards as well as emphasize the advantages of TCM during development and reporting so that it can improve the transparency of developing methods, research quality, and the proportion of TCM characteristics of the final COS.
ObjectiveTo evaluate the quality of protocols and reports on the core outcome set of traditional Chinese medicine (COS-TCM), and to provide some evidence for COS-TCM developers to carry out studies and improve the reporting quality and methodological quality during their studies. MethodsLiterature databases in Chinese and English were searched to collect COS-TCM protocols or study reports from inception to April 18, 2023. The Core Outcome Set-STAndards for Reporting (COS-STAR), Core Outcome Set-STAndards for Development (COS-STAD), and Core Outcome Set-STAndardised Protocol Items (COS-STAP) were used to evaluate their reporting and methodological quality. Additionally, the consistency of studies with both published protocols and results was evaluated. ResultsA total of 14 protocols and 14 reports (involving 23 COS-TCM studies) were included. The evaluation of COS-TCM protocols according to the COS-STAP found that the reporting rates of "Stakeholders" (71.4%) and "Missing data" (42.9%) were relatively low. For the reports of COS-TCM, the evaluation based on the COS-STAD found that the reporting rates of "the population (s) covered by the COS" (35.7%) and "care was taken to avoid ambiguity of language used in the list of outcomes" (28.6%) were relatively low. Based on the COS-STAR, the items with low reporting rates were "Protocol Deviation" (7.1%), "Participants" (21.4%), and "Conflicts of interest" (28.6%). Additionally, the consistency evaluation found that there were inconsistencies between protocols and their results, such as the types of research included in the systematic review, the methods of qualitative research, the way of holding consensus meetings, scoring methods, etc. Moreover, only one study reported protocol deviation and reasons for change. ConclusionCOS-TCM studies need to improve their methodological quality and report transparency. When developing COS-TCM, we should pay attention to the characteristics of TCM while basing on international standards. The quality evaluation guidelines and standards of reporting for COS-TCM study need to be developed in the future.