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

Overview

Journal JCO Clin Cancer Inform

Specialty Medical Informatics

Date 2024 May 30

PMID 38815188

Authors

Brian Furner

Alex Cheng

Ami V Desai

Daniel J Benedetti

Debra L Friedman

Kirk D Wyatt

Michael Watkins

Samuel L Volchenboum

Susan L Cohn

Affiliations

Soon will be listed here.

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.

References

Harris P, Taylor R, Thielke R, Payne J, Gonzalez N, Conde J . Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2008; 42(2):377-81. PMC: 2700030. DOI: 10.1016/j.jbi.2008.08.010. View

Twist C, Schmidt M, Naranjo A, London W, Tenney S, Marachelian A . Maintaining Outstanding Outcomes Using Response- and Biology-Based Therapy for Intermediate-Risk Neuroblastoma: A Report From the Children's Oncology Group Study ANBL0531. J Clin Oncol. 2019; 37(34):3243-3255. PMC: 6881103. DOI: 10.1200/JCO.19.00919. View

Strother D, London W, Schmidt M, Brodeur G, Shimada H, Thorner P . Outcome after surgery alone or with restricted use of chemotherapy for patients with low-risk neuroblastoma: results of Children's Oncology Group study P9641. J Clin Oncol. 2012; 30(15):1842-8. PMC: 3383182. DOI: 10.1200/JCO.2011.37.9990. View

Charlton M, Kahl A, McDowell B, Miller R, Komatsoulis G, Koskimaki J . Cancer Registry Data Linkage of Electronic Health Record Data From ASCO's CancerLinQ: Evaluation of Advantages, Limitations, and Lessons Learned. JCO Clin Cancer Inform. 2022; 6:e2100149. PMC: 9088237. DOI: 10.1200/CCI.21.00149. View

Khozin S, Miksad R, Adami J, Boyd M, Brown N, Gossai A . Real-world progression, treatment, and survival outcomes during rapid adoption of immunotherapy for advanced non-small cell lung cancer. Cancer. 2019; 125(22):4019-4032. PMC: 6899461. DOI: 10.1002/cncr.32383. View

Mandl K, Gottlieb D, Mandel J, Ignatov V, Sayeed R, Grieve G . Push Button Population Health: The SMART/HL7 FHIR Bulk Data Access Application Programming Interface. NPJ Digit Med. 2020; 3(1):151. PMC: 7678833. DOI: 10.1038/s41746-020-00358-4. View

Zong N, Wen A, Stone D, Sharma D, Wang C, Yu Y . Developing an FHIR-Based Computational Pipeline for Automatic Population of Case Report Forms for Colorectal Cancer Clinical Trials Using Electronic Health Records. JCO Clin Cancer Inform. 2020; 4:201-209. PMC: 7113084. DOI: 10.1200/CCI.19.00116. View

Park J, Kreissman S, London W, Naranjo A, Cohn S, Hogarty M . Effect of Tandem Autologous Stem Cell Transplant vs Single Transplant on Event-Free Survival in Patients With High-Risk Neuroblastoma: A Randomized Clinical Trial. JAMA. 2019; 322(8):746-755. PMC: 6714031. DOI: 10.1001/jama.2019.11642. View

Odisho A, Bridge M, Webb M, Ameli N, Eapen R, Stauf F . Automating the Capture of Structured Pathology Data for Prostate Cancer Clinical Care and Research. JCO Clin Cancer Inform. 2019; 3:1-8. PMC: 6874052. DOI: 10.1200/CCI.18.00084. View

10.

Irwin M, Naranjo A, Zhang F, Cohn S, London W, Gastier-Foster J . Revised Neuroblastoma Risk Classification System: A Report From the Children's Oncology Group. J Clin Oncol. 2021; 39(29):3229-3241. PMC: 8500606. DOI: 10.1200/JCO.21.00278. View

11.

Miller T, Li Y, Getz K, Dudley J, Burrows E, Pennington J . Using electronic medical record data to report laboratory adverse events. Br J Haematol. 2017; 177(2):283-286. PMC: 5384859. DOI: 10.1111/bjh.14538. View

12.

Flores-Toro J, Jagu S, Armstrong G, Arons D, Aune G, Chanock S . The Childhood Cancer Data Initiative: Using the Power of Data to Learn From and Improve Outcomes for Every Child and Young Adult With Pediatric Cancer. J Clin Oncol. 2023; 41(24):4045-4053. PMC: 10461939. DOI: 10.1200/JCO.22.02208. View

13.

Bagatell R, Beck-Popovic M, London W, Zhang Y, Pearson A, Matthay K . Significance of MYCN amplification in international neuroblastoma staging system stage 1 and 2 neuroblastoma: a report from the International Neuroblastoma Risk Group database. J Clin Oncol. 2008; 27(3):365-70. PMC: 2651034. DOI: 10.1200/JCO.2008.17.9184. View

14.

Miller T, Getz K, Li Y, Demissei B, Adamson P, Alonzo T . Rates of laboratory adverse events by course in paediatric leukaemia ascertained with automated electronic health record extraction: a retrospective cohort study from the Children's Oncology Group. Lancet Haematol. 2022; 9(9):e678-e688. PMC: 9444944. DOI: 10.1016/S2352-3026(22)00168-5. View

15.

Lee J, Kibbe W, Grossman R . Data Harmonization for a Molecularly Driven Health System. Cell. 2018; 174(5):1045-1048. DOI: 10.1016/j.cell.2018.08.012. View

16.

Jensen M, Ferretti V, Grossman R, Staudt L . The NCI Genomic Data Commons as an engine for precision medicine. Blood. 2017; 130(4):453-459. PMC: 5533202. DOI: 10.1182/blood-2017-03-735654. View

17.

Dubois S, London W, Zhang Y, Matthay K, Monclair T, Ambros P . Lung metastases in neuroblastoma at initial diagnosis: A report from the International Neuroblastoma Risk Group (INRG) project. Pediatr Blood Cancer. 2008; 51(5):589-92. PMC: 2746936. DOI: 10.1002/pbc.21684. View

18.

Cheng A, Duda S, Taylor R, Delacqua F, Lewis A, Bosler T . REDCap on FHIR: Clinical Data Interoperability Services. J Biomed Inform. 2021; 121:103871. PMC: 9217161. DOI: 10.1016/j.jbi.2021.103871. View

19.

Cao L, Huang Y, Wu C, Getz K, Miller T, Ruiz J . Leveraging machine learning to identify acute myeloid leukemia patients and their chemotherapy regimens in an administrative database. Pediatr Blood Cancer. 2023; 70(5):e30260. PMC: 10402395. DOI: 10.1002/pbc.30260. View

20.

Morgenstern D, London W, Stephens D, Volchenboum S, Hero B, Di Cataldo A . Metastatic neuroblastoma confined to distant lymph nodes (stage 4N) predicts outcome in patients with stage 4 disease: A study from the International Neuroblastoma Risk Group Database. J Clin Oncol. 2014; 32(12):1228-35. PMC: 4876342. DOI: 10.1200/JCO.2013.53.6342. View