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      Extracting Electronic Health Record Neuroblastoma Treatment Data With High Fidelity Using the REDCap Clinical Data Interoperability Services Module

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          Abstract

          PURPOSE

          Although the International Neuroblastoma Risk Group Data Commons (INRGdc) has enabled seminal large cohort studies, the research is limited by the lack of real-world, electronic health record (EHR) treatment data. To address this limitation, we evaluated the feasibility of extracting treatment data directly from EHRs using the REDCap Clinical Data Interoperability Services (CDIS) module for future submission to the INRGdc.

          METHODS

          Patients enrolled on the Children's Oncology Group neuroblastoma biology study ANBL00B1 (ClinicalTrials.gov identifier: NCT00904241) who received care at the University of Chicago (UChicago) or the Vanderbilt University Medical Center (VUMC) after the go-live dates for the Fast Healthcare Interoperability Resources (FHIR)–compliant EHRs were identified. Antineoplastic drug orders were extracted using the CDIS module. To validate the CDIS output, antineoplastic agents extracted through FHIR were compared with those queried through EHR relational databases (UChicago's Clinical Research Data Warehouse and VUMC's Epic Clarity database) and manual chart review.

          RESULTS

          The analytic cohort consisted of 41 patients at UChicago and 32 VUMC patients. Antineoplastic drug orders were identified in the extracted EHR records of 39 (95.1%) UChicago patients and 26 (81.3%) VUMC patients. Manual chart review confirmed that patients with missing (n = 8) or discontinued (n = 1) orders in the CDIS output did not receive antineoplastic agents during the timeframe of the study. More than 99% of the antineoplastic drug orders in the EHR relational databases were identified in the corresponding CDIS output.

          CONCLUSION

          Our results demonstrate the feasibility of extracting EHR treatment data with high fidelity using HL7-FHIR via REDCap CDIS for future submission to the INRGdc.

          Abstract

          A path to unlocking high-quality EHR treatment data for neuroblastoma research using REDCap CDIS.

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          Most cited references35

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          Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

          Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
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            The REDCap consortium: Building an international community of software platform partners

            The Research Electronic Data Capture (REDCap) data management platform was developed in 2004 to address an institutional need at Vanderbilt University, then shared with a limited number of adopting sites beginning in 2006. Given bi-directional benefit in early sharing experiments, we created a broader consortium sharing and support model for any academic, non-profit, or government partner wishing to adopt the software. Our sharing framework and consortium-based support model have evolved over time along with the size of the consortium (currently more than 3200 REDCap partners across 128 countries). While the "REDCap Consortium" model represents only one example of how to build and disseminate a software platform, lessons learned from our approach may assist other research institutions seeking to build and disseminate innovative technologies.
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              Advances in Risk Classification and Treatment Strategies for Neuroblastoma.

              Risk-based treatment approaches for neuroblastoma have been ongoing for decades. However, the criteria used to define risk in various institutional and cooperative groups were disparate, limiting the ability to compare clinical trial results. To mitigate this problem and enhance collaborative research, homogenous pretreatment patient cohorts have been defined by the International Neuroblastoma Risk Group classification system. During the past 30 years, increasingly intensive, multimodality approaches have been developed to treat patients who are classified as high risk, whereas patients with low- or intermediate-risk neuroblastoma have received reduced therapy. This treatment approach has resulted in improved outcome, although survival for high-risk patients remains poor, emphasizing the need for more effective treatments. Increased knowledge regarding the biology and genetic basis of neuroblastoma has led to the discovery of druggable targets and promising, new therapeutic approaches. Collaborative efforts of institutions and international cooperative groups have led to advances in our understanding of neuroblastoma biology, refinements in risk classification, and stratified treatment strategies, resulting in improved outcome. International collaboration will be even more critical when evaluating therapies designed to treat small cohorts of patients with rare actionable mutations.
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                Author and article information

                Journal
                JCO Clin Cancer Inform
                JCO Clin Cancer Inform
                cci
                CCI
                JCO Clinical Cancer Informatics
                Wolters Kluwer Health
                2473-4276
                2024
                30 May 2024
                30 May 2024
                : 8
                : e2400009
                Affiliations
                [ 1 ]Department of Pediatrics, Section of Hematology/Oncology, The University of Chicago, Chicago, IL
                [ 2 ]Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
                [ 3 ]Department of Pediatrics, Division of Hematology/Oncology, Vanderbilt University Medical Center, Nashville, TN
                [ 4 ]Department of Pediatric Hematology/Oncology, Roger Maris Cancer Center, Sanford Health, Fargo, ND
                Author notes
                Brian Furner, MS; e-mail: bfurner@ 123456bsd.uchicago.edu .
                Author information
                https://orcid.org/0000-0001-7074-7247
                https://orcid.org/0000-0002-1787-691X
                https://orcid.org/0000-0001-9658-3156
                https://orcid.org/0000-0003-1524-3518
                https://orcid.org/0000-0001-5733-5088
                https://orcid.org/0000-0002-3856-2055
                https://orcid.org/0000-0003-0613-1127
                https://orcid.org/0000-0001-9863-851X
                https://orcid.org/0000-0001-5749-7650
                Article
                CCI.24.00009 00053
                10.1200/CCI.24.00009
                11371086
                38815188
                3d088c2c-fd7f-4c1f-b5d7-0f5b8c0fde09
                © 2024 by American Society of Clinical Oncology

                Creative Commons Attribution Non-Commercial No Derivatives 4.0 License: https://creativecommons.org/licenses/by-nc-nd/4.0/

                History
                : 12 January 2024
                : 20 March 2024
                : 12 April 2024
                Page count
                Figures: 3, Tables: 2, Equations: 0, References: 40, Pages: 10
                Categories
                ORIGINAL REPORTS
                Electronic Health Records
                Custom metadata
                TRUE

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