Diabetic retinopathy (DR), which is a common complication of diabetic and the main cause of blindness, brings not only a heavy economic burden to society, but also seriously threatens to the patients’ quality of life. Clinical researches on the therapies of DR are active at present, but how to perform a good clinical research with scientific design should be considered with high priority. The randomized controlled trial (RCT) is considered to be the gold standard for evidence-based medicine, but RCT is not always perfect. Limitations still exist in certain circumstance and the conclusions from RCTs also need to be interpreted by an objective point of view before clinical practice. Real world study (RWS) bridges the gap between RCT and clinical practice, in which the data can be easily collected without much cost, and results might be obtained within a short period. However, RWS is also faced with the challenge of not having standardized data and being susceptible to confounding bias. The standardized single disease database for DR and propensity score matching method can provide a wide range of data sources and avoid of bias for RWS in DR.
ObjectiveWe constructed a real-world evidence evaluation system to provide reference for obtaining high-quality evidence in evidence-based medicine.MethodsThrough the investigation and analysis of the key factors influencing the real-world research evidence, combined with domestic and foreign literature and evaluation tools, we preliminarily constructed the indicators of the real-world evidence evaluation system, then consulted experts in related fields by the Delphi method, modified and determined the final evaluation indicators. ResultsThe indicators of the final real-world evidence evaluation system included 40 items. The recovery efficiencies of the two rounds of expert consultation were 88.2% and 100%; The expert coordination coefficients were 0.174 (P<0.001) and 0.189 (P<0.001). After the second round of consultation, the mean of Likert scale in the range of 3.73~4.93, and the coefficient of variation varied in the range of 0.05~0.21. ConclusionThe real-world evidence evaluation system constructed in this study has certain reliability and scientificity, which can provide a basis and help for the transformation of real-world research into high-quality evidence.
Focusing on research quality is a crucial aspect of modern evidence-based medical practice, providing substantial evidence to underpin clinical decision-making. The increase in real-world studies in recent years has presented challenges, with varying quality stemming from issues such as data integrity and researchers’ expertise levels. Although systematic reviews and meta-analyses are essential references for clinical decisions, their reliability is contingent upon the quality of the primary studies. Making clinical decisions based on inadequate research poses inherent risks. With the lack of a specialized tool for evaluating the quality of real-world studies within systematic reviews and meta-analyses, the Gebrye team has introduced a new assessment tool - QATSM-RWS. Comprising 5 modules and 14 items, this tool aims to improve real-world research evaluation. This article aims to elaborate on the tool’s development process and content, using this tool to evaluate a published real-world study as an example and providing valuable guidance for domestic researchers utilizing this innovative tool.
ObjectiveTo evaluate changes in operational effectiveness after the implementation of ambulatory surgical management in pars plana vitrectomy (PPV). MethodsA retrospective clinical study. 17 528 surgeries in 10 895 eyes of 10 895 patients who underwent minimally invasive PPV on an ambulatory and/or inpatient basis at Tianjin Medical University Eye Hospital from August 2015 to June 2023 were included in this study. Among them, 5 346 eyes in 5 346 cases were male; 5 549 eyes in 5 549 cases were female. The age ranged from 0 to 95 years, with the mean age of (57.74±13.15) years. 6 381 surgeries in 3 615 eyes from August 2015 to December 2018 (the initial period of day surgery) were used as the control group; 11 147 surgeries in 7 280 eyes from January 2019 to June 2023 (the expanded period of day surgery) were used as the observation group. According to the management mode of ambulatory surgery, the observation group was subdivided into the decentralized management group (January 2019 to December 2020) and the centralized management group (January 2021 to June 2023), with 2 905 and 4 375 eyes and 4 646 and 6 501 surgeries, respectively. Changes in the percentage of day surgery, average hospitalization days, and average unplanned reoperation rate were compared. The Mann-Whitney U test was used to compare numerical variables between groups; the chi-square test or Fisher's exact test was used to compare categorical variables. ResultsThe number of cases of daytime PPV performed in the observation group and control group was 7 852 (70.44%, 7 852/11 147) and 24 (0.38%, 24/6 381) cases, respectively, and the average hospitalization days were 1 (1) and 5 (3) d. Compared with the control group, the observation group had a significantly higher percentage of day surgery (χ2=8 051.01) and a considerably lower mean hospitalization day (Z=4 536 844.50), and the differences were statistically significant (P＜0.000 1). The mean hospitalization days in the decentralized and centralized management groups were 2 (3) and 1 (0) d, respectively, and unplanned reoperations were 34 (0.73%, 34/4 646) and 171 (2.63%, 171/6 501) eyes, respectively. Compared with the decentralized management group, average hospitalization days was significantly lower (Z=1 436.94) and unplanned reoperation rate was significantly higher (χ2=54.10) were significantly lower in the centralized management group, both of which were statistically significant (P＜0.000 1). ConclusionPPV ambulatory management model can significantly reduce the average hospitalization day, but also results in higher rates of unplanned reoperations.
To enhance the quality and transparency of oncology real-world evidence studies, the European Society for Medical Oncology (ESMO) has developed the first specific reporting guidelines for oncology RWE studies in peer-reviewed journals "the ESMO Guidance for Reporting Oncology Real-World Evidence (GROW)". To facilitate readers understanding and application of these reporting standards, this article introduces and interprets the development process and main contents of the ESMO-GROW checklist.
ObjectiveTo analyze the limitations and challenges for the use of real-world data in the decision making of drug reimbursement through literature review and provide standard process and guideline for the real-world study supporting drug reimbursement. MethodsBy summarizing the relevant policies, regulations, and guiding principles of major drug regulatory agencies worldwide, the study analyzed the applicable conditions, framework, and reimbursement mode for using real-world evidence in the decision making of drug reimbursement. ResultsThe study found that the health technology assessment departments of major developed countries and Asian countries have used real -world evidence to evaluate the drug efficacy and safety. The application scope of real-world data for reimbursement decision included describing the treatment process of the disease, assessing economic burden, verifying economic models, and evaluating the efficacy and safety of drugs. Some developed countries including the United Kingdom and the United States had released guidelines or frameworks of the real-world study for reimbursement decision. The process and framework of using real-world data in reimbursement decision could be divided into three models: coverage with evidence development, outcome-based contract, and re-assessment. ConclusionReal-world data has been widely used in the process of health technology assessment. To adapt to the development of the pharmaceutical industry and to meet the needs of clinical patients, it is urgent to standardize the process of collecting real-world data and formulate the scope and process of using real-world data in the reimbursement process.
With the gradual standardization and improvement of the real-world study system, real-world evidence, as a supplement to evidence from classical randomized controlled trials, is increasingly used to evaluate the effectiveness and safety of pharmaceuticals and medical devices. High-quality real-world evidence is not only related to the quality of real-world data, but also depends on the type of study design. Therefore, as one of the important designs for pragmatic clinical trials, the Zelen design has received much attention from investigators in recent years. This paper discussed the implementation processes, subtypes of design, advantages, limitations, statistical concerns, and appropriate application scenarios of the Zelen design, on the basis of published papers, in order to clarify its application value, and to provide references for future research.
As an important source for real-world data, existing health and medical data have gained wide attentions recently. As the first part of the serial technical guidance for real-world data and studies, this report introduced the concepts, features and potential applications of existing medical and health data, proposed recommendations for planning and developing a research database using existing health and medical data, and developed essential indicators for assessing the quality of such research databases. The technical guidance may standardize and improve the development of research database using existing health and medical data in China.
Earthquake emergency medical rescue evidence-based decision-making is a typical case of real-world evidence deriving from real-world data, conducting real-world research, and producing real-world evidence for solving real-world problems. This article focuses on the use of evidence-based science in the real-world through a problem-oriented, evidence-based decision making way, as well as transferring of results to practice and continuing outcome evaluation.
With the boom of information technology and data science, real-world evidence (RWE) which is produced using diverse real-world data (RWD) has become an important source for healthcare practice and policy decisions, such as regulatory and coverage decisions, guideline development, and disease management. The production of high-quality RWE requires not only complete, accurate and usable data, but also scientific and sound study designs and data analyses to enable the questions of interest to be reliably answered. In order to improve the quality of production and use of RWE, China REal world data and studies ALliance (ChinaREAL) has developed the first series of technical guidance for developing real-world data and subsequent studies. The efforts are ongoing which would ultimately inform better healthcare practice and policy decisions.