Additional file 4 of ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials

Additional file 4. Appendix 4: A schematic illustration of the manual part of the analysis

Additional file 7 of ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials

Additional file 7. Appendix 7: A complete list of all UMLS primary and secondary laboratory concepts used in the manual analysis to produce the dataset of top 55 most common Laboratory Procedures. This includes 703 unique UMLS concepts, among which 311 concepts belong to Group A (Concepts of laboratory semantic types). Concepts are sorted in 55 Ranks according to the nTotal of primary and secondary concepts of each Laboratory Procedure

Additional file 3 of ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials

Additional file 3. Appendix 3: A complete detailed list of all occurrences of all UMLS concepts identified within EC forms. This database includes details like the question text, names and IDs of all item groups and items as well as UMLS preferred definition of every single UMLS concept occurrence identified within EC forms

Additional file 8 of ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials

Additional file 8. Appendix 8A: The final dataset, ELaPro, in CSV format after complete mapping to LOINC. B: The final dataset, ELaPro, reported as ODM file (CDASH standard). 8C. The final dataset, ELaPro, in in FHIR format (HL7 standard)

Additional file 1 of ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials

Additional file 1. Appendix 1: A complete list of names and URIs s of all included EC forms from MDM Portal

Additional file 5 of ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials

Additional file 5. Appendix 5: A table of MeSH categories in eligibility criteria forms sorted according to absolute frequencies (n)

Additional file 6 of ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials

Additional file 6. Appendix 6: A Table of UMLS semantic types in eligibility criteria forms sorted by absolute frequencies (n)

Additional file 2 of ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials

Additional file 2. Appendix 2A. A list of unique UMLS concepts of group A sorted by absolute frequency. B: A list of unique UMLS concepts of group B sorted by absolute frequency

Completeness page of the web-application.

Calculated completeness of the study data, i.e., if all items have been completed for each subject. The hierarchical structure of the metadata is displayed similar to the analysis page. Colored bars indicate the completeness of each metadata element.</p

ODM Data Analysis—A tool for the automatic validation, monitoring and generation of generic descriptive statistics of patient data

<div><p>Introduction</p><p>A required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data.</p><p>Methods</p><p>The system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application’s performance and functionality.</p><p>Results</p><p>The system is implemented as an open source web application (available at <a href="https://odmanalysis.uni-muenster.de" target="_blank">https://odmanalysis.uni-muenster.de</a>) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects.</p><p>Discussion</p><p>Medical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.</p></div