Design and evaluation of an automated pediatric acute lymphoblastic leukemia registry from clinical data warehouses

BACKGROUND: Real-world data (RWD)–based pediatric acute lymphoblastic leukemia (ALL) registries often face scalability and reliability limits owing to manual data entry and inter-institutional heterogeneity. We evaluated the feasibility of building an automated, high-quality registry using electronic medical records (EMR) and clinical data warehouses (CDWs) to quantitatively assess the Pediatric ALL Automated Registry (PeARL) for (1) automatic extractability (ADE) (2), data quality, and (3) multicenter applicability. METHODS: In this study, we used CDWs to identify patients aged < 18 years with ALL (ICD-10 C91.0) who visited Seoul National University Hospital (SNUH) and the Catholic Medical Center (CMC). between 1990 and 2023. An automated extraction pipeline applied standard mapping, multivariate transformations, and rule-based natural language processing (NLP). Key variables were selected using the Medical Information Standards for Hematologic Cancer. Data quality was evaluated based on 228 rules across five dimensions (completeness, validity, accuracy, uniqueness, and consistency) based on DQ4HEALTH; error rates, defined as the proportion of data elements violating these rules, were calculated before and after quality management. RESULTS: Overall, 1,609 patients were included (CMC 946; SNUH 663). ADE for key variables was 89.7% at SNUH and 75.0% at CMC; most automations were single-field transformations (SNUH 61.8%; CMC 84.7%), with multivariate transformations and rule-based NLP addressing complex elements. Initial overall error rates were 1.858% (SNUH) and 0.129% (CMC), decreasing to 0.001% at both institutions after quality processes. Differences in CDW structure required additional preprocessing for laboratory and transplant variables, but harmonization was achieved via standardized table specifications and cross-site review. CONCLUSIONS: PeARL integrated standardized mapping, multivariate transformations, and rule-based NLP to enable large-scale automation of a pediatric ALL registry, achieving a 0.001% overall error rate under DQ4HEALTH-based quality management. This clinically guided, standardized framework enables reproducible implementation and scalable automation of pediatric ALL registry construction, supporting multicenter research and the generation of regulatory-grade real-world evidence. CLINICAL TRIAL NUMBER: Not applicable.