现实世界研究生态圈的构建与发展

现实世界证据（real-world evidence，RWE）由现实世界数据（real-world data，RWD）产生，经现实世界研究（real-world study，RWS）转化而来。科学的RWE可为决策提供参考依据，其产生有赖于系统性地构建和发展良性生态圈。本文梳理了RWD、RWS在产生RWE中的角色及转化机制，试图探讨构建现实世界研究良性生态系统的必要性及构成要素，并简要展望生态圈的发展趋势、机遇及挑战。     

不同领域现实世界临床研究证据转化的应用策略

参考已发布的政策法规指导意见，通过文献检索现实世界研究相关的专家共识指南，总结国内外现实世界临床研究不同领域的研究现况，归纳不同领域现实世界临床研究证据转化的观点，结合典型案例，为现实世界数据转化为现实世界证据的应用提供策略支持。     

基于现实世界研究中临床随访数据的两种动态预测建模方法的实证研究

目的： 针对现实世界研究（real-world study，RWS）中常见的具有纵向测量属性的动态观察指标，探讨界标法和联合建模法2种动态预测方法的应用价值。方法： 基于358例某重症肺炎患者预后数据，分别采用界标法和联合建模法，基于R软件，对于第5天、第10天、第15天尚处于观察期的某重症肺炎患者，预测其未来的死亡风险。结果： 2种方法均能在各时间点预测个体未来发生结局事件的概率。第5天、第10天和第15天，利用界标法进行动态预测的AUC分别为81.64%、85.89%和82.15%；而联合建模法的AUC分别为81.11%、85.07%和72.09%。结论： 在针对动态历史数据的现实世界研究中，可采用动态预测模型分析法，从而获得更为丰富的信息。     

获得现实世界证据的因果推断统计学思考

医学研究中，时常观察到相关关系（association），但因果推断（causal inference）才是临床研究的最终目标。因果关系的判定标准包含关联的时序性、强度、可重复性、特异性、一致性、剂量反应关系、生物合理性以及实验证据8个方面。为了获得因果关系，临床研究设计与分析中蕴含了众多因果推断元素。本文解析混杂因素的存在对因果关系的影响，并针对随机分组、分析数据集及亚组分析3个重要问题，探讨其中的因果推断元素。医学工作者应当充分认识到临床研究中的因果要素，从而正确认识研究所能提供的证据等级，并在实际工作中产生高等级的医学证据。     

基于现实冲击理论的心理课程在新入职护士培训中的应用

目的 分析基于现实冲击理论的心理课程在新入职护士培训中的应用效果。方法 采用整群抽样方法，选取新入职于上海交通大学医学院附属第九人民医院的护士作为研究对象。根据护士入职时间分组，将2018年7月入职的护士设为对照组（n=71），将2019年7月入职的护士设为观察组（n=64）。对照组参照《新入职护士培训大纲（试行）》进行培训，观察组在其基础上增加基于现实冲击理论的心理培训。培训1年后，比较两组新入职护士的职业认同感水平、护士工作压力及心理弹性情况。结果 干预后，观察组新护士职业认同高于对照组（P<0.05），心理弹性优于对照组（P<0.05），工作压力小于对照组（P<0.05）。结论 基于现实冲击理论的心理培训能帮助新入职护士增加职业认同感、缓解工作压力、增强心理弹性，值得临床推广。     

2013-2015年我国丙型病毒性肝炎病例报告数据质量核查分析

目的 了解医疗机构丙型病毒性肝炎（丙肝）病例报告存在的问题，提高数据质量。方法 2013-2015年，选择丙肝病例报告数较多的县（区）及以上级别的医院，对其当年1季度丙肝相关病例数据质量进行核查。结果 2013-2015年丙肝病毒抗体阳性报告率分别为44.3%、55.5%和58.3%，核酸阳性报告率分别为46.8%、52.1%和65.6%；诊断分类正确率分别为25.4%、44.6%和44.0%，急慢性分类正确率分别19.2%、38.1%和53.9%。丙肝病例报告率和分类正确率均处于较低水平，但逐年提高。结论 目前我国医疗机构丙肝病例报告质量普遍存在报告率低和分类正确率低的问题，建议完善修订现行《丙肝诊断标准》、加强培训和督导，提高工作质量。     

增强现实技术与医药高等职业教育

增强现实技术作为新兴的技术在诸多领域有所应用,且逐渐受到关注。论文关注了增强现实（AR）技术在医药高等职业教育领域的应用前景,从的两者的契合度,AR技术可能的应用方向,存在的瓶颈和问题三个方面展望了AR技术给医药高等职业教育带来的机遇和问题。AR技术必将给医药高等职业教育发展与深化改革带来技术红利和技术保障。医药高等职业院校需积极转变思路、整合资源、培养开发人才,真正使得AR技术走进课堂。     

轻度认知功能障碍患者大脑fMRI网络的小世界特性

目的 应用静息态fMRI数据检验轻度认知功能障碍（MCI）患者大脑功能网络是否具有小世界特性。方法 采集13例MCI患者（MCI组）和17名正常老年人（NC组）的大脑静息态BOLD-fMRI数据。采用SPM 5软件对图像进行预处理,将大脑分割为90个区域并计算90个区域间的相关系数。以矩阵稀疏度（Sparsity）为阈值,将相关矩阵转换为网络。计算网络的聚类系数（C）和平均路径长度（L）,若满足γ＝C/Crand＞1且λ＝ L/Lrand ≈1（rand代表相应随机网络）,为该大脑功能网络具有小世界特性。采用双样本t检验比较MCI组与NC组大脑功能网络小世界参数的差异。结果 在0.1～0.4阈值范围内,MCI组和NC组均符合γ＞1且λ≈ 1。MCI组γ和δ均大于NC组,且在0.10≤Sparsity≤0.18时差异具有统计学意义（P＜0.05）;MCI组λ在各阈值处均小于NC组,在Sparsity＝0.18、0.28和0.32处差异具有统计学意义（P＜0.05）。结论 MCI患者大脑fMRI网络具有小世界特性,但与正常老年人相比其小世界特性增强。     

世界无烟日

世卫组织每年于5月31日开展世界无烟日庆祝活动,以强调烟草使用对健康的危害,并倡导实施有效政策减少烟草消费。烟草使用是全球第二大死因（仅次于高血压）,目前在世界范围内造成十分之一的成人死亡。     

想象-现实暴露疗法在慢性心力衰竭合并高血压患者运动恐惧中的应用

目的：探讨想象-现实暴露疗法在慢性心力衰竭合并高血压患者运动恐惧中的应用效果。方法：便利抽样法选取2020年2月至2022年1月心内科住院的慢性心力衰竭合并高血压的运动恐惧患者126例。按随机数字表法分为观察组和对照组各63例。对照组给予常规护理干预，观察组在对照组基础上进行想象-现实暴露疗法干预，干预12周。干预前后比较两组中文版心脏病运动恐惧量表（TSK-Heart-C）、运动自我效能量表（SEE）、心理弹性量表（CD-RISC）评分。结果：研究期间，观察组脱落2例，对照组脱落2例，最终两组各61例完成研究。干预后，观察组运动回避、感知危险、自我功能失调、运动恐惧、TSK-Heart-C总分均低于对照组，差异有统计学意义（P<0.05）；观察组SEE评分、CD-RISC评分高于对照组，差异有统计学意义（P<0.05）。结论：想象-现实暴露疗法能够提高慢性心力衰竭合并高血压患者的运动自我效能与心理弹性水平，降低运动恐惧。     

Use of Real-world Data for New Drug Applications and Line Extensions

PurposeFor this article, the authors compiled, summarized, and analyzed data from 27 cases in which real-world data (RWD) were applied in regulatory approval. The aims were to provide an overview of RWD, based on classifications per therapeutic area, age group, drivers of acceptability, utility, data sources, and timelines, and to present insights on how it has been applied in regulatory decision making to date.MethodsClarivate Analytics was commissioned to collect data from cases in which RWD was used for new drug applications and line extensions submitted to the European Medicines Agency (EMA), the US Food and Drug Administration (FDA), Health Canada, and Japan's Pharmaceuticals and Medical Devices Agency. The query resulted in 27 cases in which regulatory approval was associated with RWD. The data were then categorized and elaborated with supporting information gathered from public databases and company websites.FindingsThere were 17 identified cases in which RWD were used for new drug applications, and 10 for line extensions, between the years 1998 and 2019. Approvals were spread across regulatory bodies: the EMA alone (6 cases), the FDA alone (4 cases), or jointly between the EMA and FDA or other regulatory bodies. The applications were also distributed across age groups and therapeutic areas but were mostly applied in oncology and metabolism. The new drug applications of all 17 products were approved, with drugs from new drug applications initially marketed as orphan drugs. In most cases, RWD were used either as primary data, when noncomparative data were available to demonstrate tolerability and efficacy, or as supportive data when validating findings. Common sources of RWD have been health or medical records (16 cases) and registries (8 cases). Review timelines in which RWD were applied were than 1 year for new drug applications and between 3 and 10 months for line extensions.ImplicationsThe analysis of this study was limited in that the data were gathered from the commissioned query and may therefore have been nonexhaustive. Nonetheless, we recognize that the use of RWD has been gaining attention across the community and is expected to expand as a result of the various initiatives and efforts carried out in the sector. While the current application of RWD has been limited to specific cases, there is a potential to further explore and develop its application. Further refinements in the analytical processes, methodologies, and techniques would need to be established to achieve similar effects observed in randomized controlled trials     

The Case for Real-world Evidence in the Future of Clinical Research on Chronic Myeloid Leukemia

Purpose

In light of recently published guidelines from the US Food and Drug Administration (FDA) on the communication of real-world data (RWD) and real-world evidence (RWE) to support regulatory decision making, it is important to understand how such data are developed, the limitations of these data, and how to best use RWD to improve patient care. Historically, the use of RWE has been approached with skepticism because of its often-retrospective nature compared with data from conventional randomized controlled trials (RCTs). This review discusses the role and function of RWE and RWD in clinical research. We summarize the types of RWE used in clinical research, outline the challenges and limitations involved with these data, and suggest how these types of analyses can supplement results from clinical trials to foster a more complete understanding of a drug or disease area of interest. In particular, we focus on the role of RWE in investigating chronic myeloid leukemia (CML) and tyrosine kinase inhibitor therapy for CML.

Practical considerations of utilizing propensity score methods in clinical development using real-world and historical data

In recent years, with the rapid increase in the volume and accessibility of Real-World-Data (RWD) and Real-World-Evidence (RWE), we have seen the unprecedented opportunities for their use in drug clinical development and life-cycle management. RWD and RWE have demonstrated the significant potential to improve the design, planning, and execution of clinical development. Furthermore, they can feature in the designs as either a substitute or compliment to traditional clinical trials. However, to utilize RWD and RWE appropriately and wisely, it is critical to apply rigorous statistical methodologies that enable the robustness of results to be characterized and ascertained. Several statistical methodologies including exact matching, propensity score methods, matching-adjusted indirect comparisons and meta-analysis have been proposed for analyzing RWD. Among them, propensity score method is one of the most commonly used methods for non-randomized trials with indirect comparison. Although massive methodologies and examples have been published and discussed since propensity score methods were introduced, systematic review and discussion of how to rigorously use propensity score methods in the practical clinical development is still deficient. This paper introduces commonly used and emerging propensity score methods with detailed discussions of their pros and cons. Three different case studies are presented to illustrate the practical considerations of utilizing propensity score methods in the study design and evaluation using real-world and historical data. Additional considerations including selection of patient populations, endpoints, baseline covariates, propensity score methods, sensitivity analysis and practical implementation flow in clinical development will be discussed.     

Routinely collected data as real-world evidence for physiotherapy practice

Evidence-based practice is the current undisputed predominant paradigm within medicine and allied health care, particularly in physiotherapy. Despite its potential benefits, over the years various points of criticism have been formulated one of which is the overreliance on randomized clinical trials as the highest level of evidence for treatment effectiveness. In the current era, where the availability of large amounts of clinical data gathered during the course of care delivery is rapidly increasing as well as our ability to access, process, link, and analyze these data in fairly efficient ways, alternative sources to supplement rather than replace evidence from RCTs look promising. In this Editorial, we discuss the opportunities and limitations of these routinely collected data in physiotherapy research and provide several examples from the literature. We conclude that the use of routinely collected data in physiotherapy research has the potential to increasingly contribute to real-world evidence, particularly in musculoskeletal primary care physiotherapy, provided that researchers are aware of methodological limitations and adhere to reporting standards.     

Key considerations in the design of real-world studies

Randomized controlled clinical trials (RCTs) are the gold standard for evaluating the safety and efficacy of pharmaceutical drugs, but in many cases their costs, duration, limited generalizability, and ethical or technical feasibility have caused some to look for real-world studies as alternatives. However, real-world studies may be less convincing due to the lack of randomization and blinding. In this article, we discuss some key considerations in the design of real-world studies, which include experimental studies (e.g., hybrid or pragmatic clinical trials and non-randomized single-arm clinical trials with external controls) and non-experimental studies (e.g., cohort studies, cross-sectional studies, and case-control studies). Causal inference plays a critical role in the derivation of robust real-world evidence (RWE) from the analysis of real-world data (RWD). Therefore, we apply the hypothetical strategy, along with the concept of potential outcome, to lay out these key considerations, and we hope these considerations are helpful for the design, conduct, and analysis of real-world studies.