ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials

• Published in: BMC medical research methodology Vol. 22; no. 1; pp. 141 - 14
• Main Authors: Rafee, Ahmed, Riepenhausen, Sarah, Neuhaus, Philipp, Meidt, Alexandra, Dugas, Martin, Varghese, Julian
• Format: Journal Article
• Language: English
• Published: London BioMed Central 14.05.2022; BMC
• Subjects: Annotations; Automation; Clinical trials; Codes; Data collection; Data models; Datasets; Electronic health records; Eligibility screening; Health Sciences; Laboratories; LOINC; Medical informatics; Medical research; Medicine; Medicine & Public Health; Semantic analysis; Semantics; Statistical Theory and Methods; Statistics for Life Sciences; Terminology; Theory of Medicine/Bioethics; UMLS; UMLS; LOINC; Data models; Eligibility screening; Medical informatics
• ISSN: 1471-2288; 1471-2288
• DOI: 10.1186/s12874-022-01611-y

Background Screening for eligible patients continues to pose a great challenge for many clinical trials. This has led to a rapidly growing interest in standardizing computable representations of eligibility criteria (EC) in order to develop tools that leverage data from electronic health record (EHR) systems. Although laboratory procedures (LP) represent a common entity of EC that is readily available and retrievable from EHR systems, there is a lack of interoperable data models for this entity of EC. A public, specialized data model that utilizes international, widely-adopted terminology for LP, e.g. Logical Observation Identifiers Names and Codes (LOINC®), is much needed to support automated screening tools. Objective The aim of this study is to establish a core dataset for LP most frequently requested to recruit patients for clinical trials using LOINC terminology. Employing such a core dataset could enhance the interface between study feasibility platforms and EHR systems and significantly improve automatic patient recruitment. Methods We used a semi-automated approach to analyze 10,516 screening forms from the Medical Data Models (MDM) portal’s data repository that are pre-annotated with Unified Medical Language System (UMLS). An automated semantic analysis based on concept frequency is followed by an extensive manual expert review performed by physicians to analyze complex recruitment-relevant concepts not amenable to automatic approach. Results Based on analysis of 138,225 EC from 10,516 screening forms, 55 laboratory procedures represented 77.87% of all UMLS laboratory concept occurrences identified in the selected EC forms. We identified 26,413 unique UMLS concepts from 118 UMLS semantic types and covered the vast majority of Medical Subject Headings (MeSH) disease domains. Conclusions Only a small set of common LP covers the majority of laboratory concepts in screening EC forms which supports the feasibility of establishing a focused core dataset for LP. We present ELaPro, a novel, LOINC-mapped, core dataset for the most frequent 55 LP requested in screening for clinical trials. ELaPro is available in multiple machine-readable data formats like CSV, ODM and HL7 FHIR. The extensive manual curation of this large number of free-text EC as well as the combining of UMLS and LOINC terminologies distinguishes this specialized dataset from previous relevant datasets in the literature.