Real world data and data science in medical research: present and future

Real-world data on efficacy and safety of 1256 consecutive ultrasound-guided percutaneous carpal tunnel release

PURPOSE

Carpal tunnel syndrome (CTS) is a prevalent condition requiring effective and safe treatment options. This study evaluated the efficacy and safety of ultrasound-guided carpal tunnel release (UCTR) in routine clinical practice. Furthermore, we assessed UCTR outcomes in terms of symptom resolution, patient satisfaction, and complication rates using real-world data extracted via a natural language processing model.

METHODS

A retrospective analysis was performed on 926 patients who underwent 1256 UCTR procedures between November 2018 and May 2024. Primary outcomes included the resolution of paresthesia and pain, and secondary outcomes involved strength restoration and patient satisfaction. Complication rates were assessed to evaluate safety. Data accuracy and precision were validated through standardized methods.

RESULTS

Post-UCTR, significant symptom improvement was observed, with paresthesia rates decreasing from 99% (620/626) to 8.5% (79/926) and pain rates from 96% (654/926) to 9% (83/926). Strength restoration was noted, and 95.8% (628/926) of patients reported positive results with the procedure. Safety was demonstrated by a low complication rate of 0.7% (10/1256). Validation demonstrated data accuracy of 96.3% and precision of 100%.

CONCLUSION

UCTR is a highly effective and safe intervention for CTS that delivers substantial symptom relief, high patient satisfaction, and minimal risk. These findings support the adoption of UCTR in clinical practice. Further prospective studies are recommended to confirm these results and investigate long-term outcomes.

Decades in the Making: The Evolution of Digital Health Research Infrastructure Through Synthetic Data, Common Data Models, and Federated Learning

Traditionally, medical research is based on randomized controlled trials (RCTs) for interventions such as drugs and operative procedures. However, increasingly, there is a need for health research to evolve. RCTs are expensive to run, are generally formulated with a single research question in mind, and analyze a limited dataset for a restricted period. Progressively, health decision makers are focusing on real-world data (RWD) to deliver large-scale longitudinal insights that are actionable. RWD are collected as part of routine care in real time using digital health infrastructure. For example, understanding the effectiveness of an intervention could be enhanced by combining evidence from RCTs with RWD, providing insights into long-term outcomes in real-life situations. Clinicians and researchers struggle in the digital era to harness RWD for digital health research in an efficient and ethically and morally appropriate manner. This struggle encompasses challenges such as ensuring data quality, integrating diverse sources, establishing governance policies, ensuring regulatory compliance, developing analytical capabilities, and translating insights into actionable strategies. The same way that drug trials require infrastructure to support their conduct, digital health also necessitates new and disruptive research data infrastructure. Novel methods such as common data models, federated learning, and synthetic data generation are emerging to enhance the utility of research using RWD, which are often siloed across health systems. A continued focus on data privacy and ethical compliance remains. The past 25 years have seen a notable shift from an emphasis on RCTs as the only source of practice-guiding clinical evidence to the inclusion of modern-day methods harnessing RWD. This paper describes the evolution of synthetic data, common data models, and federated learning supported by strong cross-sector collaboration to support digital health research. Lessons learned are offered as a model for other jurisdictions with similar RWD infrastructure requirements.

An Assessment of Semaglutide Safety Based on Real World Data: From Popularity to Spontaneous Reporting in EudraVigilance Database

Some glucagon-like peptide-1 receptor agonists (GLP-1 RAs), first used in the treatment of type 2 diabetes mellitus (T2DM), have been approved for the treatment of obesity in patients with or without T2DM (liraglutide-LIR, semaglutide-SEM, and tirzepatide-TIR). Social media had an important influence on the off-label use of GLP-1 RAs for obesity, especially for SEM. We analyzed the Google queries related to SEM to assess people's interest in this drug. We also investigated the occurrence of adverse drug reactions (ADRs) by searching the EudraVigilance database (EV) for Individual Case Safety Reports (ICSRs) that reported SEM as the suspected drug and performed a descriptive and a disproportionality analysis. The data obtained for SEM were compared to other GLP-1 RAs. SEM had the highest proportions of searches on Google associated with the term "weight loss" and presented the lowest number of severe ADRs, but it also had the highest number of ICSRs reported in EV. Even though no unexpected safety issues have been reported for it until now, SEM has a hi3gh tendency for overdose reports. The most frequent off-label use was reported for SEM and TIR. In order to lower the risks of ADRs, the off-label use should be reduced and carefully monitored.

Statistical and Scientific Considerations Concerning the Interpretation, Replicability, and Transportability of Research Findings

To advance scientific understanding of disease processes and related intervention effects, study results should be free from bias and replicable. More broadly, investigators seek results that are transportable, that is, applicable to a perceived study population as well as in other environments and populations. We review fundamental statistical issues that arise in the analysis of observational data from disease cohorts and other sources and discuss how these issues affect the transportability and replicability of research results. Much of the literature focuses on estimating average exposure or intervention effects at the population level, but we argue for more nuanced analyses of conditional effects that reflect the complexity of disease processes.

Use of Real-World Evidence in Regulatory Decisions for Traditional Chinese Medicine: Current Status and Future Directions

Traditional Chinese medicine (TCM) is a valuable resource unique to China with a long history of human use and clinical practice, which can be analyzed to generate real-world evidence (RWE). The Chinese government has been actively promoting regulatory reform that is in line with the characteristics of TCM, optimizing the clinical evidence system for TCM, and exploring the important role of RWE in supporting the development of new drugs and regulatory decision-making for TCM. This article aims to provide a comprehensive review of the use of RWE in regulatory decisions for TCM. Based on the characteristics of TCM, this study focuses on the application scenarios, challenges, and opportunities of RWE in TCM. And some suggestions are put forward to promote the wider application of RWE in TCM development and supervision.

Association of Bypass Surgery and Mortality in Moyamoya Disease

BACKGROUND

Patients with moyamoya disease (MMD) have a high risk of stroke or death. We investigated whether extracranial to intracranial bypass surgery can reduce mortality by preventing strokes in patients with MMD.

METHODS AND RESULTS

This nationwide retrospective cohort study encompassed patients with MMD registered under the Rare Intractable Diseases program via the Relieved Co-Payment Policy between 2006 and 2019, using the Korean National Health Insurance Service database. Following a 4-year washout period, landmark analyses were employed to assess mortality and stroke occurrence between the bypass surgery group and the nonsurgical control group at specific time points postindex date (1 month and 3, 6, 12, and 36 months). The study included 18 480 patients with MMD (mean age, 40.7 years; male to female ratio, 1:1.86) with a median follow-up of 5.6 years (interquartile range, 2.5-9.3; mean, 6.1 years [SD, 4.0 years]). During 111 775 person-years of follow-up, 265 patients in the bypass surgery group and 1144 patients in the nonsurgical control group died (incidence mortality rate of 618.1 events versus 1660.3 events, respectively, per 105 person-years). The overall adjusted hazard ratio (HR) revealed significantly lower all-cause mortality in the bypass surgery group from the 36-month landmark time point, for any stroke mortality from 3- and 6-month landmark time points, and for hemorrhagic stroke mortality from the 6-month landmark time point. Furthermore, the overall adjusted HRs for hemorrhagic stroke occurrence were beneficially maintained from all 5 landmark time points in the bypass surgery group.

CONCLUSIONS

Bypass surgery in patients with MMD was associated with a lower risk of all-cause and hemorrhagic stroke mortality and hemorrhagic stroke occurrence compared with nonsurgical control.

Overcoming personal information protection challenges involving real-world data to support public health efforts in China

In the information age, real-world data-based evidence can help extrapolate and supplement data from randomized controlled trials, which can benefit clinical trials and drug development and improve public health decision-making. However, the legitimate use of real-world data in China is limited due to concerns over patient confidentiality. The use of personal information is a core element of data governance in public health. In China's public health data governance, practical problems exist, such as balancing personal information protection and public value conflict. In 2021, China adopted the Personal Information Protection Law (PIPL) to provide a consistent legal framework for protecting personal information, including sensitive medical health data. Despite the PIPL offering critical legal safeguards for processing health data, further clarification is needed regarding specific issues, including the meaning of "separate consent," cross-border data transfer requirements, and exceptions for scientific research. A shift in the law and regulatory framework is necessary to advance public health research further and realize the potential benefits of combining real-world evidence and digital health while respecting privacy in the technological and demographic change era.

Strategies to Address Current Challenges in Real-World Evidence Generation in Japan

The generation of real-world evidence (RWE), which describes patient characteristics or treatment patterns using real-world data (RWD), is rapidly growing more popular as a tool for decision-making in Japan. The aim of this review was to summarize challenges to RWE generation in Japan related to pharmacoepidemiology, and to propose strategies to address some of these challenges. We first focused on data-related issues, including the lack of transparency of RWD sources, linkage across different care settings, definitions of clinical outcomes, and the overall assessment framework of RWD when used for research purposes. Next the study reviewed methodology-related challenges. As lack of design transparency impairs study reproducibility, transparent reporting of study design is critical for stakeholders. For this review, we considered different sources of biases and time-varying confounding, along with potential study design and methodological solutions. Additionally, the implementation of robust assessment of definition uncertainty, misclassification, and unmeasured confounders would enhance RWE credibility in light of RWD source-related limitations, and is being strongly considered by task forces in Japan. Overall, the development of guidance for best practices on data source selection, design transparency, and analytical methods to address different sources of biases and robustness in the process of RWE generation will enhance credibility for stakeholders and local decision-makers.