A direct access to the local UMLS database (2021AA) as well as the Metadata Repository (MDR) , the main database of the MDM portal, was granted by the Institute of Medical Informatics of the University of Muenster for the purpose of this analysis. 10516 eligibility screening forms were identified and included in this analysis. An R-based tool was developed and used to directly access and filter EC forms of MDM portal database and connect them to their UMLS-annotated concepts, which is the “raw data of the automated semantic analysis. tool first filters the ID’s of active forms (latest, most up-to-date annotated version) labeled as “Eligibility Determination” and then specifically filters screening EC forms and extracts all UMLS-annotated concepts from these forms. A list of names and DOI’s of all included EC forms on MDM portal is found in Appendix 1.
Semantic Form Annotation
Typically, an EC form consists of 2 item groups; Inclusion Criteria and Exclusion Criteria. Each item group consists of items; each item represents a complete element (criterion) of inclusion or exclusion criteria. All medical concepts of each item (criterion) are coded (annotated) using UMLS codes to standardize the representation of free-text EC. The annotating process is performed by a medical expert and reviewed by a physician experienced in UMLS. The detailed process and workflow of the coding process have been thoroughly described in previous works [68-70].
Automated Semantic Analysis in R
The automated part is based on an R-based tool to facilitate extraction and analysis of UMLS codes and their semantic types from pre-annotated screening forms in MDR (n=10516) and the UMLS database. We performed an automated semantic analysis 10516 eligibility screening forms available on the MDM portal as of August 2021. After a thorough study of the structure of the MDR, it was possible to automatically filter EC forms using a unique ID assigned to all forms of eligibility determination in the MDR. Using names and subheadings of forms, we were able to specifically filter the IDs of screening EC forms and eliminate other unwanted types of EC (e.g. follow-up, randomization or continuation criteria). Once the IDs of screening EC forms were collected, an automated retrieval of all UMLS codes used to annotate medical concepts in these forms was performed. The next step was to automatically measure the frequency of occurrences (n) of these codes and to sort them according to frequency in a descending order.
In order to define the UMLS codes, we used a UMLS table for concept names and sources (MRCONSO) . A subset of MRCONSO that includes the single Preferred Term  of each code was created, this aims to identify the single preferred definition of UMLS concepts. Furthermore, the semantic type of each code was automatically identified utilizing the UMLS table for semantic types (MRSTY) . Figure 1 illustrates the process of automated data collection and analysis in both MDM and UMLS databases.
In order to refine results and extract UMLS codes related to laboratory concepts, we needed to define reference semantic types that represent all laboratory concepts in UMLS metathesaurus. Based on prestudy communication with a senior scientist from the National Library of Medicine (NLM) as well as the definition of semantic types, two UMLS semantic Types, “Laboratory Procedure” and “Laboratory or Test Result”, were considered the two reference semantic types for laboratory tests in the UMLS metathesaurus.
Based on these 2 semantic types, results were divided into 2 groups; Group A was assigned the name “EC Laboratory Codes” and includes concepts (codes) from the 2 reference laboratory semantic types mentioned above, while group B was named “Non-Laboratory EC Codes” and includes codes from all other UMLS semantic types. Group B is necessary to ensure that relevant laboratory concepts, which are not linked to the aforementioned semantic types, are still considered for expert review (e.g. concepts like “Leukocytosis” or “Hemoglobin Increased” and many other concepts of semantic type “Finding”). Absolute frequency (n) was automatically counted for all codes in both groups, concepts were then sorted by absolute frequency in a descending pattern from the most frequent (highest n) to the least frequent (lowest n). Figure 1 is a schematic representation of the semi-automated method of this analysis. A list of unique UMLS concepts of group A and B sorted by frequency is found in Appendix 2A and 2B, respectively. A list of all original EC Questions for all codes in group A and B is found in Appendix 3.
Manual Expert Review of Laboratory Concepts
A laborious manual review was necessary to identify and analyze complex concepts that indirectly imply a LP but do not have a laboratory semantic type, thus not amenable to the above mentioned automated semantic analysis. The manual analysis was performed by 2 medical professionals (AR, JV) using Microsoft Excel. If a concept was ambiguous or in doubt it was discussed with 2 additional physicians experienced in UMLS (MD, SR) to decide whether a concept is relevant to a LP or not. We used terms like primary laboratory concept (PLC) and secondary laboratory concept (SLC) to deal with classic issues of UMLS like redundancy (similar, but not identical concepts)) and semantic complexity to help determine the actual representation (nTotal) of laboratory concepts. We provide examples for both in the following two sections.
Primary Laboratory Concept (PLC): refers to the UMLS concept that represents the preferred definition of each laboratory test in the master file. The decision of choosing the UMLS code representing each PLC was made by agreement of 4 physicians. By definition, a PLC must belong to semantic type "Laboratory Procedure" and, if applicable, be as general as possible to accommodate the different standards of the test among different clinical institutes. A PLC for a certain laboratory test is preferably, however not necessarily, the most frequent code among all codes representing that concept. For example, the concept “Creatinine Measurement in Blood” (n = 2) is considered the PLC for creatinine measurement despite having clearly less occurrence frequency than other more specific concepts like “Creatinine Measurement in Serum” (n = 1492) and “Creatinine Measurement in Plasma” (n = 142), since the former is more general and represents other possible variants of the test that might be used in different clinical research institutes.
Secondary Laboratory Concept (SLC): refers to UMLS concepts relevant to a PLC, i.e. it directly or indirectly refers to or implies the same laboratory test component. SLCs include concepts from laboratory semantic types (group A), that are synonymous to a PLC (sibling) as in the previous example of Creatinine, or more typically include concepts from semantic type “Finding”, which usually implies that a test is necessary to evaluate this finding, e.g. “Platelet Count Normal” or “Increased Number of Platelets” imply the need to perform the test, and are therefore secondary to the PLC “Platelet Count Measurement”. SLCs also include certain pathologic conditions that imply the need for a test, e.g. “Hyperkalemia” was considered an SLC to “Blood Potassium Measurement”, “Leukocytosis” is secondary to “White Blood Cell Count Procedure” and “Anemia” is secondary to “Hemoglobin Measurement”, etc. In some rare instances, concepts that referred to a simple relation between two measurable laboratory tests were also considered an SLC if the PLC was part of the ratio, e.g. the concept “Alanine Aminotransferase (ALT) to Aspartate Aminotransferase (AST) Ratio Measurement” was counted with both “ALT Measurement” and with “AST Measurement”.
The Manual Curation (Expert Review): the most common concepts in the laboratory group A (PLCs) were identified based on the frequency of individual occurrence (n), then both A and B groups were searched to find all relevant concepts (SLCs) that directly or indirectly imply the same LP as each of the PLCs. The PLC and its SLCs are then grouped together in a master file to represent one LP (see Figure 2). This process was repeated for each LP identified in group A. Therefore, the results of the manual analysis (master file) include multiple groups of codes, each group represents one LP and is composed of one PLC and multiple SLCs. For each LP, a total count of frequency (nTotal) was calculated by adding all concept occurrences (n) of single codes in the group representing the LP. A “Rank” was assigned to each LP based on its nTotal. The most frequent PLC (highest nTotal) was given rank number 1, second most frequent was given rank number 2 and so on. Unspecific concepts in group A (e.g. “Assay” or “Laboratory Results”) were excluded since they did not refer to any specific test component. Figure 2 shows an example of a manually analyzed LP. A diagram illustrating the manual process is in Appendix 4.
Mapping to LOINC®
The mapping process was based on matching the PLC to a LOINC “COMPONENT”, which is the part of LOINC that specifies what is being measured, evaluated or observed. For most LP, a primary and one or more secondary LOINC codes were assigned. The decision of choosing primary and secondary LOINC concepts was based mainly on a well-recognized core dataset created by the Medical Informatics Initiative (MI-I) [72,74] that includes primary and secondary LOINC concepts for the top 300 most common laboratory tests based on data from 5 different university hospitals in Germany. If no proper matching component was found in the MII core dataset for any of our results, the LOINC database V2.7 was directly used to manually assign a primary, and if applicable a secondary LOINC concept(s). The final step was using the UMLS database to create a core dataset with full LOINC details (component, property, system, etc.) using the LOINC codes mapped to our results and an R-based tool.