Strategies for secondary use of real-world clinical and administrative data for outcome ascertainment in pragmatic clinical trials

Background: Pragmatic trials are gaining popularity as a cost-effective way to examine treatment effectiveness and generate timely comparative evidence. Incorporating supplementary real-world data is recommended for robust outcome monitoring. However, detailed operational guidelines are needed to inform effective use and integration of heterogeneous databases. Objective: Lessons learned from the Veterans Affairs (VA) Diuretic Comparison Project (DCP) are reviewed, providing adaptable recommendations to capture clinical outcomes from real-world data. Methods: Non-cancer deaths and major cardiovascular (CV) outcomes were determined using VA, Medicare, and National Death Index (NDI) data. Multiple ascertainment strategies were applied, including claims-based algorithms, natural language processing, and systematic chart review. Results: During a mean follow-up of 2.4 (SD = 1.4) years, 907 CV events were identified within the VA healthcare system. Slight delays (∼1 year) were expected in obtaining Medicare data. An additional 298 patients were found having a CV event outside of the VA in 2016 - 2021, increasing the CV event rate from 3.5 % to 5.7 % (770 of 13,523 randomized). NDI data required ∼2 years waiting period. Such inclusion did not increase the number of deaths identified (all 894 deaths were captured by VA data) but enhanced the accuracy in determining cause of death. Conclusion: Our experience supports the recommendation of integrating multiple data sources to improve clinical outcome ascertainment. While this approach is promising, hierarchical data aggregation is required when facing different acquisition timelines, information availability/completeness, coding practice, and system configurations. It may not be feasible to implement comparable applications and solutions to studies conducted under different constraints and practice. The recommendations provide guidance and possible action plans for researchers who are interested in applying cross-source data to ascertain all study outcomes.