Demonstration of Interoperability Between MIDRC and N3C: A COVID-19 Severity Prediction Use Case

Interoperability between data sources, one of the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management, can enable multi-modality research. The purpose of our study was to investigate the potential for interoperability between an imaging resource, the Medical Imaging and Data Resource Center (MIDRC), and a clinical record resource, the National COVID Cohort Collaborative (N3C). The use case was the prediction of COVID-19 severity, defined as evidence for invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice in the N3C clinical record. Patient-level matching between MIDRC and N3C was identified using Privacy Preserving Record Linking via an honest broker. We identified positive COVID-19 tests and chest radiograph procedures in N3C and used the interval between them to identify images with matching intervals in MIDRC. Of the 236 patients (306 unique images) meeting initial inclusion criteria in MIDRC, 117 patients (and 139 unique images) remained after date interval matching between repositories and exclusion of patients with multiple potential matches. The Charlson Comorbidity Index (CCI) and the minimum mean arterial pressure (MAP) on the day of the chest radiograph were used as clinical indicators. The AUC in the task of predicting severe COVID-19 was evaluated using the computer-extracted imaging index alone (MIDRC), clinical indicators alone (N3C), and both together. Our model combining imaging and clinical indicators (CCI over 2 and MAP below 70) to predict severe COVID had an AUC of 0.73 (95% CI 0.62-0.84), and the models including imaging or clinical indicators alone were 0.67 (95% CI 0.56-0.79) and 0.69 (95% CI 0.59-0.80), respectively. This study highlights the potential for cross-platform data sharing to facilitate future multi-modality research and broader collaborative studies.

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Journal
Journal of Imaging Informatics in Medicine
Published
2025-08-14
DOI
https://doi.org/10.1007/s10278-025-01605-4
Primary Topic
COVID-19 diagnosis using AI
Type
article
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0.00

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article

Demonstration of Interoperability Between MIDRC and N3C: A COVID-19 Severity Prediction Use Case

Rachel Baccile, Weijie Chen, Kenneth Gersing, Heather M. Whitney et al.
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Journal of Imaging Informatics in Medicine
COVID-19 diagnosis using AI
article

Demonstration of Interoperability Between MIDRC and N3C: A COVID-19 Severity Prediction Use Case

Rachel Baccile, Weijie Chen, Kenneth Gersing, Heather M. Whitney, Nicholas P. Gruszauskas, Maryellen L. Giger, Robert L. Grossman, Sandy Napel, Sam Michael, Christopher Meyer, Rui Carlos Sá, Diane S. Lauderdale, Hui Li, Karen Drukker, C Beesley, Seyed Kahaki, Brandy Phalora
article en

Abstract

Interoperability between data sources, one of the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management, can enable multi-modality research. The purpose of our study was to investigate the potential for interoperability between an imaging resource, the Medical Imaging and Data Resource Center (MIDRC), and a clinical record resource, the National COVID Cohort Collaborative (N3C). The use case was the prediction of COVID-19 severity, defined as evidence for invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice in the N3C clinical record. Patient-level matching between MIDRC and N3C was identified using Privacy Preserving Record Linking via an honest broker. We identified positive COVID-19 tests and chest radiograph procedures in N3C and used the interval between them to identify images with matching intervals in MIDRC. Of the 236 patients (306 unique images) meeting initial inclusion criteria in MIDRC, 117 patients (and 139 unique images) remained after date interval matching between repositories and exclusion of patients with multiple potential matches. The Charlson Comorbidity Index (CCI) and the minimum mean arterial pressure (MAP) on the day of the chest radiograph were used as clinical indicators. The AUC in the task of predicting severe COVID-19 was evaluated using the computer-extracted imaging index alone (MIDRC), clinical indicators alone (N3C), and both together. Our model combining imaging and clinical indicators (CCI over 2 and MAP below 70) to predict severe COVID had an AUC of 0.73 (95% CI 0.62-0.84), and the models including imaging or clinical indicators alone were 0.67 (95% CI 0.56-0.79) and 0.69 (95% CI 0.59-0.80), respectively. This study highlights the potential for cross-platform data sharing to facilitate future multi-modality research and broader collaborative studies.

Journal of Imaging Informatics in Medicine
National Institutes of Health (US), Regenstrief Institute (US), Chicago Department of Public Health (US), Office of Science (US), University of Chicago (US), University of Chicago Medical Center (US), National Institute of Biomedical Imaging and Bioengineering (US), Office of the Director (US), National Center for Advancing Translational Sciences (US), Stanford University (US)
Bill and Melinda Gates Foundation, Yale University, University of Southern California, Brown University, Children's Hospital of Philadelphia, University of Nebraska Medical Center, Vanderbilt University, University of Miami, University of Pennsylvania, Ohio State University, Emory University, Tufts Medical Center, NYU Langone Medical Center, University of Wisconsin-Madison, Northwestern University, George Washington University, Wake Forest University, Cincinnati Children's Hospital Medical Center, Children's Hospital Colorado, Vanderbilt Institute for Clinical and Translational Research, University of Chicago, University of Minnesota, Washington University in St. Louis, University of Michigan, Harvard Catalyst, Loyola University Chicago, University of Utah, University of Washington, University of Texas Medical Branch, Tulane University, Johns Hopkins University, University of Pittsburgh, University of Oklahoma, Institute for Translational Medicine and Therapeutics, Institute of Clinical and Translational Sciences, Georgetown University, University of Rochester, University of Cincinnati, Case Western Reserve University, Dartmouth College, Pennsylvania State University, Carilion Clinic, Rush University, University of South Carolina, Aurora Health Care, Virginia Commonwealth University, Southern California Clinical and Translational Science Institute, West Virginia University, Montana State University, Rutgers, The State University of New Jersey, MedStar Health Research Institute, Georgetown-Howard Universities Center for Clinical and Translational Science, University of Texas Health Science Center at Houston, NorthShore University HealthSystem, Vanderbilt University Medical Center, Institute of Translational Health Sciences, Georgia Clinical and Translational Science Alliance, Penn State Clinical and Translational Science Institute, Children's National Hospital, Louisiana Clinical and Translational Science Center, Ochsner Health, West Virginia Clinical and Translational Science Institute, Stony Brook University, National Institutes of Health, University of Illinois at Urbana-Champaign, Institute for Clinical and Translational Research, University of Wisconsin, Madison, University of California, Los Angeles, Weill Cornell Medical College, University of California, Davis, Oregon Clinical and Translational Research Institute, University of North Carolina at Chapel Hill, University of California, San Diego, University of Oklahoma Health Sciences Center, University of California, San Francisco, Michigan Institute for Clinical and Health Research, University of Texas Health Science Center at San Antonio, University of California, Irvine, Leonard M. Miller School of Medicine, Advanced Research Projects Agency, Center for Clinical and Translational Research, School of Medicine, Indiana University, University of Colorado Denver, Center for Clinical and Translational Science, University of Massachusetts, Center for Clinical and Translational Science, Mayo Clinic, Center for Clinical and Translational Sciences, University of Texas Health Science Center at Houston, Irving Medical Center, Columbia University, Translational Research Institute, University of Arkansas for Medical Sciences, South Carolina Clinical and Translational Research Institute, Medical University of South Carolina, Clinical and Translational Science Institute, University of Florida, Clinical and Translational Science Institute, Boston University, Frontiers Clinical and Translational Science Institute, University of Kansas, Clinical and Translational Science Center, University of New Mexico, National Institute of Biomedical Imaging and Bioengineering, National Center for Advancing Translational Sciences, Institute for Integration of Medicine and Science, Colorado Clinical and Translational Sciences Institute, Yale Center for Clinical Investigation, Yale School of Medicine, Institute for Clinical and Translational Science, University of California, Irvine, Wake Forest Clinical and Translational Science Institute, Wake Forest School of Medicine, Accelerated Innovation Research Initiative Turning Top Science and Ideas into High-Impact Values
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