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Introduction: Research driven by real-world clinical data is increasingly vital to enabling learning health systems, but integrating such data from across disparate health systems is challenging. As part of the NCATS National COVID Cohort Collaborative (N3C), the N3C Data Enclave was established as a centralized repository of deidentified and harmonized COVID-19 patient data from institutions across the US. However, making this data most useful for research requires linking it with information such as mortality data, images, and viral variants. The objective of this project was to establish privacy-preserving record linkage (PPRL) methods to ensure that patient-level EHR data remains secure and private when governance-approved linkages with other datasets occur. Methods: Separate agreements and approval processes govern N3C data contribution and data access. The Linkage Honest Broker (LHB), an independent neutral party (the Regenstrief Institute), ensures data linkages are robust and secure by adding an extra layer of separation between protected health information and clinical data. The LHB's PPRL methods (including algorithms, processes, and governance) match patient records using "deidentified tokens," which are hashed combinations of identifier fields that define a match across data repositories without using patients' clear-text identifiers. Results: These methods enable three linkage functions: Deduplication, Linking Multiple Datasets, and Cohort Discovery. To date, two external repositories have been cross-linked. As of March 1, 2023, 43 sites have signed the LHB Agreement; 35 sites have sent tokens generated for 9 528 998 patients. In this initial cohort, the LHB identified 135 037 matches and 68 596 duplicates. Conclusion: This large-scale linkage study using deidentified datasets of varying characteristics established secure methods for protecting the privacy of N3C patient data when linked for research purposes. This technology has potential for use with registries for other diseases and conditions.
RESUMEN
OBJECTIVE: Chronic traumatic encephalopathy (CTE) is a neurodegenerative tauopathy associated with repeated subconcussive and concussive head injury. Clinical features include cognitive, behavioral, mood, and motor impairments. Definitive diagnosis is only possible at postmortem. Here, the utility of neuroimaging in the diagnosis of CTE is evaluated by systematically reviewing recent evidence for changes in neuroimaging biomarkers in suspected cases of CTE compared with controls. DATA SOURCES: Providing an update on a previous systematic review of articles published until December 2014, we searched for articles published between December 2014 and July 2016. We searched PubMed for studies assessing neuroimaging changes in symptomatic suspected cases of CTE with a history of repeated subconcussive or concussive head injury or participation in contact sports involving direct impact to the head. Exclusion criteria were case studies, review articles, and articles focusing on repetitive head trauma from military service, head banging, epilepsy, physical abuse, or animal models. MAIN RESULTS: Seven articles met the review criteria, almost all of which studied professional athletes. The range of modalities were categorized into structural magnetic resonance imaging (MRI), diffusion MRI, and radionuclide studies. Biomarkers which differed significantly between suspected CTE and controls were Evans index (P = 0.05), cavum septum pellucidum (CSP) rate (P < 0.0006), length (P < 0.03) and ratio of CSP length to septum length (P < 0.03), regional differences in axial diffusivity (P < 0.05) and free/intracellular water fractions (P < 0.005), single-photon emission computed tomography perfusion abnormalities (P < 0.01), positron emission tomography (PET) signals from tau-binding, glucose-binding, and GABA receptor-binding radionuclides (P < 0.0001, P < 0.005, and P < 0.005, respectively). Important limitations include low specificity in identification of suspected cases of CTE across studies, the need for postmortem validation, and a lack of generalizability to nonprofessional athletes. CONCLUSIONS: The most promising biomarker is tau-binding radionuclide PET signal because it is most specific to the underlying neuropathology and differentiated CTE from both controls and patients with Alzheimer disease (P < 0.0001). Multimodal imaging will improve specificity further. Future research should minimize variability in identification of suspected cases of CTE using published clinical criteria.
