Eligibility Criteria Linking for Integrated Patient Selection and Emulation (ECLIPSE)

About This Grant

ABSTRACT Clinical trials are essential to evidence generation, yet strict and complex eligibility rules often exclude large segments of real-world patients, limiting generalizability, delaying enrollment, and increasing costs. Target-trial emulations (TTEs) using real-world data offer a complementary approach for evidence generation, allowing investigators to test generalizability of clinical trials, assess how many patients may be eligible for hypothetical trials, and inform the design of future trials. However, TTEs are constrained by a core informatics challenge: most eligibility criteria are not end-to-end computable in structured and unstructured electronic health records (EHRs). Critical eligibility criteria, such as recurrence/progression, performance status, biomarker qualifiers, and substance-use patterns, are often embedded in free-text or are inconsistently encoded, undermining feasibility assessments and reproducible emulations. This project will develop and validate ECLIPSE (Eligibility Criteria Linking for Integrated Patient Selection and Emulation), a standards-aligned framework that compiles full protocol criteria into executable, auditable artifacts applied to structured and unstructured EHR data. Aim 1 will translate protocol eligibility text into computable phenotypes and query packages anchored to standard vocabularies; and validate criterion-level correctness and cohort concordance against expert references and prescreening logs. Aim 2 will build modular LLM pipelines for extracting eligibility criteria typically found in clinical notes (e.g., progression, performance status, biomarker qualifiers, substance use disorder indicators) with schema-constrained decoding and quantified uncertainty to enable auditable integration. Aim 3 will implement ECLIPSE across clinical trials in 3 conditions – kidney cancer, heart failure, and opioid use disorders – at three health systems in order to evaluate overlap with traditionally curated and enrolled cohorts, stability of effect estimates in TTEs, and operational gains in trial feasibility assessments. We will apply post-prediction inference (methods that adjust extracted variables for remaining extraction errors prior to analysis) so that feasibility counts and causal estimates reflect corrected inputs. The study leverages retrospective cohorts (~160,000 patients) across an academic network and a safety-net hospital, with pre-specified quantitative targets (e.g., ≥0.80 criterion-level agreement; ≤10% relative difference in effect estimates; ≥20% reduction in feasibility assessment time). Deliverables include open, versioned phenotype definitions and value sets, an implementation guide for OMOP and FHIR artifacts, validated extraction modules, and reproducible packages to execute artifacts where data reside. By making eligibility criteria computable, auditable, and portable across OMOP and FHIR, ECLIPSE will share interoperable methods, accelerate study feasibility assessment and emulation, and improve representativeness of trial populations, resulting in reusable, high-quality biomedical data resources for the research community.