Establishment of an International Evidence Sharing Network Through Common Data Model for Cardiovascular Research

Maximizing the Value of Real-World Data and Real-World Evidence to Accelerate Healthcare Transformation in China: Summary of External Advisory Committee Meetings

Use of real-world data (RWD) is gaining wide attention. To bridge the gap between diverse healthcare stakeholders and to leverage the impact of Chinese real-world evidence (RWE) globally, a multi-stakeholder External Advisory Committee (EAC) and EAC meetings were initiated, aiming to elucidate the current and evolving RWD landscape in China, articulate the values of RWE in ensuring Chinese patients' equitable access to affordable medicines and solutions, and identify strategic opportunities and partnerships for expansion of RWE generation in China. Chinese and international experts who are clinicians and academic researchers were selected as EAC members based on their professional background and familiarity with RWD/RWE. Three EAC meetings were held quarterly in 2023. Various topics were presented and discussed for insights and suggestions. Nine experts from China, one from South Korea, and two from Europe were selected as EAC members and attended these meetings. Experts' presentations were summarized by theme, including the RWD landscape and RWE enablement in China, as well as global development of a patient-centric ecosystem. Experts' insights and suggestions on maximizing the RWD/RWE value to accelerate healthcare transformation in China were collected. We concluded that though data access, sharing, and quality are still challenging, RWD is developing to support evidence generation in the medicinal product lifecycle, inform clinical practice, and empower patient management in China. RWD/RWE creates value, accelerates healthcare transformation, and improves patient outcomes. Fostering a patient-centric ecosystem across healthcare stakeholders and maintaining global partnerships and collaboration are essential for unlocking the power of RWD/RWE.

Scalable Infrastructure Supporting Reproducible Nationwide Healthcare Data Analysis toward FAIR Stewardship

Transparent and FAIR disclosure of meta-information about healthcare data and infrastructure is essential but has not been well publicized. In this paper, we provide a transparent disclosure of the process of standardizing a common data model and developing a national data infrastructure using national claims data. We established an Observational Medical Outcome Partnership (OMOP) common data model database for national claims data of the Health Insurance Review and Assessment Service of South Korea. To introduce a data openness policy, we built a distributed data analysis environment and released metadata based on the FAIR principle. A total of 10,098,730,241 claims and 56,579,726 patients' data were converted as OMOP common data model. We also built an analytics environment for distributed research and made the metadata publicly available. Disclosure of this infrastructure to researchers will help to eliminate information inequality and contribute to the generation of high-quality medical evidence.

Impact of Trimetazidine on the Incident Heart Failure After Coronary Artery Revascularization

ABSTRACT

Abnormal myocardial metabolism is a common pathophysiological process underlying ischemic heart disease and heart failure (HF). Trimetazidine is an antianginal agent with a unique mechanism of action that regulates myocardial energy metabolism and might have a beneficial effect in preventing HF in patients undergoing myocardial revascularization. We aimed to evaluate the potential benefit of trimetazidine in preventing incident hospitalization for HF after myocardial revascularization. Using the common data model, we identified patients without prior HF undergoing myocardial revascularization from 8 hospital databases in Korea. To compare clinical outcomes using trimetazidine, database-level hazard ratios (HRs) were estimated using large-scale propensity score matching for each database and pooled using a random-effects model. The primary outcome was incident hospitalization for HF. The secondary outcome of interest was major adverse cardiac events (MACEs). After propensity score matching, 6724 and 11,211 patients were allocated to trimetazidine new-users and nonusers, respectively. There was no significant difference in the incidence of hospitalization for HF between the 2 groups (HR: 1.08, 95% confidence interval [CI], 0.88-1.31; P = 0.46). The risk of MACE also did not differ between the 2 groups (HR: 1.07, 95% CI, 0.98-1.16; P = 0.15). In conclusion, the use of trimetazidine did not reduce the risk of hospitalization for HF or MACE in patients undergoing myocardial revascularization. Therefore, the role of trimetazidine in contemporary clinical practice cannot be expanded beyond its current role as an add-on treatment for symptomatic angina.

How do we build a comprehensive Vein-to-Vein (V2V) database for conduct of observational studies in transfusion medicine? Demonstrated with the Recipient Epidemiology and Donor Evaluation Study-IV-Pediatric V2V database protocol

BACKGROUND

The Recipient Epidemiology and Donor Evaluation Study-IV-Pediatric (REDS-IV-P) is the fourth iteration of the National Heart, Lung, and Blood Institute's REDS program and includes a focus on pediatric populations. The REDS-IV-P Vein-to-Vein (V2V) database encompasses linked information from blood donors, blood components, and patients to facilitate studies in transfusion medicine.

STUDY DESIGN AND METHODS

The V2V database is an Observational Medical Outcomes Partnership Common Data Model database. The study period is April 1, 2019 through December 31, 2023. Data from all donors and donations at participating blood centers, all blood components derived from the donations, and all inpatient visits and selected outpatient visits at participating hospitals are included. The database captures all information within patient data domains not restricting data to a preselected subset of medical records.

RESULTS

The V2V database contains data from 7 blood centers and 22 hospitals. We project the database will have over 2 billion pieces of information from 1.3 million patients with 20.6 million healthcare encounters. The database will include data on approximately 1 million transfused units and 2.3 million donors with approximately 6.8 million donation visits.

CONCLUSION

The REDS-IV-P V2V database is a comprehensive database with data from millions of blood donors, blood components, and patients. A diverse set of data from the encounters are included in the database such that emerging questions can likely be addressed. The Observational Medical Outcomes Partnership Common Data Model is an efficient, flexible, and increasingly used common data model. The final de-identified database will be publicly available.