the Genomics Research and Innovation NetworkPurpose: Clinicians and researchers must contextualize a patient's genetic variants against population-based references with detailed phenotyping. We sought to establish globally scalable technology, policy, and procedures for sharing biosamples and associated genomic and phenotypic data on broadly consented cohorts, across sites of care.Methods: Three of the nation's leading children's hospitals launched the Genomic Research and Innovation Network (GRIN), with federated information technology infrastructure, harmonized biobanking protocols, and material transfer agreements. Pilot studies in epilepsy and short stature were completed to design and test the collaboration model. Results:Harmonized, broadly consented institutional review board (IRB) protocols were approved and used for biobank enrollment, creating ever-expanding, compatible biobanks. An open source federated query infrastructure was established over genotype-phenotype databases at the three hospitals. Investigators securely access the GRIN platform for prep to research queries, receiving aggregate counts of patients with particular phenotypes or genotypes in each biobank. With proper approvals, de-identified data is exported to a shared analytic workspace. Investigators at all sites enthusiastically collaborated on the pilot studies, resulting in multiple publications. Investigators have also begun to successfully utilize the infrastructure for grant applications. Conclusions:The GRIN collaboration establishes the technology, policy, and procedures for a scalable genomic research network.Genetics in Medicine (2020) 22:371-380; https://doi.

Residual clinical samples represent a very appealing source of biomaterial for translational and clinical research. We describe the implementation of an opt-in biobank, with consent being obtained at the time of registration and the decision stored in our electronic health record, Epic. Information on that decision, along with laboratory data, is transferred to an application that signals to biobank staff whether a given sample can be kept for research. Investigators can search for samples using our i2b2 data warehouse. Patient participation has been overwhelmingly positive and much higher than anticipated. Over 86% of patients provided consent and almost 83% requested to be notified of any incidental research findings. In 6 months, we obtained decisions from over 18 000 patients and processed 8000 blood samples for storage in our research biobank. However, commercial electronic health records like Epic lack key functionality required by a registrar-based consent process, although workarounds exist.

Accumulation of amyloid-β peptides is a dominant feature in the pathogenesis of Alzheimer's disease; however, it is not clear how individual amyloid-β species accumulate and affect other neuropathological and clinical features in the disease. Thus, we compared the accumulation of N-terminally truncated amyloid-β and full-length amyloid-β, depending on disease stage as well as brain area, and determined how these amyloid-β species respectively correlate with clinicopathological features of Alzheimer's disease. To this end, the amounts of amyloid-β species and other proteins related to amyloid-β metabolism or Alzheimer's disease were quantified by enzyme-linked immunosorbent assays (ELISA) or theoretically calculated in 12 brain regions, including neocortical, limbic and subcortical areas from Alzheimer's disease cases (n = 19), neurologically normal elderly without amyloid-β accumulation (normal ageing, n = 13), and neurologically normal elderly with cortical amyloid-β accumulation (pathological ageing, n = 15). We observed that N-terminally truncated amyloid-β42 and full-length amyloid-β42 accumulations distributed differently across disease stages and brain areas, while N-terminally truncated amyloid-β40 and full-length amyloid-β40 accumulation showed an almost identical distribution pattern. Cortical N-terminally truncated amyloid-β42 accumulation was increased in Alzheimer's disease compared to pathological ageing, whereas cortical full-length amyloid-β42 accumulation was comparable between Alzheimer's disease and pathological ageing. Moreover, N-terminally truncated amyloid-β42 were more likely to accumulate more in specific brain areas, especially some limbic areas, while full-length amyloid-β42 tended to accumulate more in several neocortical areas, including frontal cortices. Immunoprecipitation followed by mass spectrometry analysis showed that several N-terminally truncated amyloid-β42 species, represented by pyroglutamylated amyloid-β11-42, were enriched in these areas, consistent with ELISA results. N-terminally truncated amyloid-β42 accumulation showed significant regional association with BACE1 and neprilysin, but not PSD95 that regionally associated with full-length amyloid-β42 accumulation. Interestingly, accumulations of tau and to a greater extent apolipoprotein E (apoE, encoded by APOE) were more strongly correlated with N-terminally truncated amyloid-β42 accumulation than those of other amyloid-β species across brain areas and disease stages. Consistently, immunohistochemical staining and in vitro binding assays showed that apoE co-localized and bound more strongly with pyroglutamylated amyloid-β11-x fibrils than full-length amyloid-β fibrils. Retrospective review of clinical records showed that accumulation of N-terminally truncated amyloid-β42 in cortical areas was associated with disease onset, duration and cognitive scores. Collectively, N-terminally truncated amyloid-β42 species have spatiotemporal accumulation patterns distinct from full-length amyloid-β42, likely due to different mechani...

BackgroundParents of children newly diagnosed with cancer are confronted with multiple stressors that place them at risk for significant psychological distress. One strategy that has been shown to help reduce uncertainty is the provision of basic information; however, families of newly diagnosed cancer patients are often bombarded with educational material. Technology has the potential to help families manage their informational needs and move towards normalization.ObjectiveThe aim of this study was to create a mobile app that pulls together data from both the electronic health record (EHR) and vetted external information resources to provide tailored information to parents of newly diagnosed children as one method to reduce the uncertainty around their child’s illness. This app was developed to be used by families in a National Institutes of Health (NIH)-funded randomized controlled trial (RCT) aimed at decreasing uncertainty and the subsequent psychological distress.MethodsA 2-phase qualitative study was conducted to elicit the features and content of the mobile app based on the needs and experience of parents of children newly diagnosed with cancer and their providers. Example functions include the ability to view laboratory results, look up appointments, and to access educational material. Educational material was obtained from databases maintained by the National Cancer Institute (NCI) as well as from groups like the Children’s Oncology Group (COG) and care teams within Cincinnati Children’s Hospital Medical Center (CCHMC). The use of EHR-based Web services was explored to allow data like laboratory results to be retrieved in real-time.ResultsThe ethnographic design process resulted in a framework that divided the content of the mobile app into the following 4 sections: (1) information about the patient’s current treatment and other data from the EHR; (2) educational background material; (3) a calendar to view upcoming appointments at their medical center; and (4) a section where participants in the RCT document the study data. Integration with the NCI databases was straightforward; however, accessing the EHR Web services posed a challenge, though the roadblocks were not technical in nature. The lack of a formal, end-to-end institutional process for requesting Web service access and a mechanism to shepherd the request through all stages of implementation proved to be the biggest barrier.ConclusionsWe successfully deployed a mobile app with a custom user interface that can integrate with the EHR to retrieve laboratory results and appointment information using vendor-provided Web services. Developers should expect to face hurdles when integrating with the EHR, but many of them can be addressed with frequent communication and thorough documentation. Executive sponsorship is also a key factor for success.Trial RegistrationClinicalTrials.gov NCT02505165; https://clinicaltrials.gov/ct2/show/NCT02505165 (Archived by WebCite at http://www.Webcitation.org/6r9ZSUgoT)

Background We offered adolescents personalized choices about the type of genetic results they wanted to learn during a research study and created a workflow to filter and transfer the results to the electronic health record (EHR). Methods We describe adaptations needed to ensure that adolescents' results documented in the EHR and returned to adolescent/parent dyads matched their choices. A web application enabled manual modification of the underlying laboratory report data based on adolescents' choices. The final PDF format of the laboratory reports was not viewable through the EHR patient portal, so an EHR form was created to support the manual entry of discrete results that could be viewed in the portal. Results Enabling adolescents' choices about genetic results was a labor-intensive process. More than 350 hours was required for development of the application and EHR form, as well as over 50 hours of a study professional's time to enter choices into the application and EHR. Adolescents and their parents who learned genetic results through the patient portal indicated that they were satisfied with the method of return and would make their choices again if given the option. Conclusion Although future EHR upgrades are expected to enable patient portal access to PDFs, additional improvements are needed to allow the results to be partitioned and filtered based on patient preferences. Furthermore, separating these results into more discrete components will allow them to be stored separately in the EHR, supporting the use of these data in clinical decision support or artificial intelligence applications.