SHRINE: Enabling Nationally Scalable Multi-Site Disease Studies

Results of medical research studies are often contradictory or cannot be reproduced. One reason is that there may not be enough patient subjects available for observation for a long enough time period. Another reason is that patient populations may vary considerably with respect to geographic and demographic boundaries thus limiting how broadly the results apply. Even when similar patient populations are pooled together from multiple locations, differences in medical treatment and record systems can limit which outcome measures can be commonly analyzed. In total, these differences in medical research settings can lead to differing conclusions or can even prevent some studies from starting. We thus sought to create a patient research system that could aggregate as many patient observations as possible from a large number of hospitals in a uniform way. We call this system the ‘Shared Health Research Information Network’, with the following properties: (1) reuse electronic health data from everyday clinical care for research purposes, (2) respect patient privacy and hospital autonomy, (3) aggregate patient populations across many hospitals to achieve statistically significant sample sizes that can be validated independently of a single research setting, (4) harmonize the observation facts recorded at each institution such that queries can be made across many hospitals in parallel, (5) scale to regional and national collaborations. The purpose of this report is to provide open source software for multi-site clinical studies and to report on early uses of this application. At this time SHRINE implementations have been used for multi-site studies of autism co-morbidity, juvenile idiopathic arthritis, peripartum cardiomyopathy, colorectal cancer, diabetes, and others. The wide range of study objectives and growing adoption suggest that SHRINE may be applicable beyond the research uses and participating hospitals named in this report

R|S Atlas: Identifying Existing Cohort Study Data Resources to Accelerate Epidemiological Research on the Influence of Religion and Spirituality on Human Health

OBJECTIVE: Many studies have documented significant associations between religion and spirituality (R/S) and health, but relatively few prospective analyses exist that can support causal inferences. To date, there has been no systematic analysis of R/S survey items collected in US cohort studies. We conducted a systematic content analysis of all surveys ever fielded in 20 diverse US cohort studies funded by the National Institutes of Health (NIH) to identify all R/S-related items collected from each cohort\u27s baseline survey through 2014. DESIGN: An R|S Ontology was developed from our systematic content analysis to categorise all R/S survey items identified into key conceptual categories. A systematic literature review was completed for each R/S item to identify any cohort publications involving these items through 2018. RESULTS: Our content analysis identified 319 R/S survey items, reflecting 213 unique R/S constructs and 50 R|S Ontology categories. 193 of the 319 extant R/S survey items had been analysed in at least one published paper. Using these data, we created the R|S Atlas (https://atlas.mgh.harvard.edu/), a publicly available, online relational database that allows investigators to identify R/S survey items that have been collected by US cohorts, and to further refine searches by other key data available in cohorts that may be necessary for a given study (eg, race/ethnicity, availability of DNA or geocoded data). CONCLUSIONS: R|S Atlas not only allows researchers to identify available sources of R/S data in cohort studies but will also assist in identifying novel research questions that have yet to be explored within the context of US cohort studies

Constructing Bipartite graphs to map concept systems.

<p><b><i>Left</i></b><i>: Medications</i> are mapped between Children's Hospital Boston (blue) and the RxNorm standard (green) if they share a drug ingredient. The hospital concept code for Acetaminophen is mapped to the RxNorm concept code for Acetaminophen. Codeine also has one mapping. ‘Acetaminophen with Codeine’ has a mapping to RxNorm for each of its ingredients. Patients recorded with the local concept ‘Acetaminophen with Codeine’ will match standard queries using any of the mapped RxNorm drug ingredients. <b><i>Right</i></b><i>: Lab Test concepts</i> are mapped between Children's Hospital Boston (blue) and the LOINC standard (green). Bicarbonate and Blood Urea Nitrogen are each mapped once. Other lab tests require a one-to-many mapping, for example, there are at least four different metabolic tests for sodium (Na+) levels recorded in the Children's Hospital Boston clinical systems.</p

Hospital Data Mapping Scenario.

<p><i>First</i>, existing clinical data are extracted into a locally controlled database for research. <i>Second</i>, each local code is mapped to one or more standard concept codes, and vice versa. <i>Third</i>, related medical concepts are grouped using standard hierarchies curated by medical experts. The bipartite graphs produced by this process enable bidirectional translation between concept systems. <i>Fourth</i>, adapt the incoming query to use the local concept codes.</p

Quadratic growth in the number of edges in a communication network.

<p>Each edge incurs administrative overhead to maintain a list of peer locations and trust relationships. Fully meshed peer-to-peer (P2P) topologies have N*(N-1)/2 edges shown in red. Edge growth of hub-spoke topologies are shown with an average hub size of 3 (size of the first deployments of east and west coast networks). A simple hub-spoke topology requires one additional link per hub, shown in green. A fault tolerant topology requires two additional links per hub, shown in purple. With 60 peers, the number of p2p edges is administratively infeasible with 1,770 firewall rules and trust relationships.</p

Investigator's perspective of the SHRINE Webclient.

<p><i>Group 1</i> defines searches for patients with Acute Lymphoid Leukemia (ALL). <i>Group 2</i> refines the search result to only those patients having one of the medications listed. The medications shown are all chemotherapeutic agents administered during intensive phase. <i>Group 3</i> further refines the result to require a lab test administered during diagnosis. Lab test values can be set directly or flagged as ‘abnormally high/low’. In the Query Status window, patient counts are displayed with a Gaussian blur to provide additional privacy safeguards of small patient populations. Results are shown for each hospital and the aggregated patient set size.</p

Percentage of Diagnosis and Medication concepts mapped for SHRINE queries at participating Harvard affiliated teaching hospitals.

<p><b><i>Left</i></b>: Percentage of ICD9-CM diagnoses concepts mapped to at least one diagnosis concept at the hospital. <b><i>Right</i></b>: Percentage of RxNorm medication concepts mapped to at least one patient medication concept at the hospital.</p

Query Expansion in the Core Ontology.

<p><i>Selected Example</i>: ‘Cardiovascular medications’ is selected and the child contents are shown. At runtime, the query is expanded to include every concept in the cardiovascular medication group, recursively.</p

SHRINE Core Ontology.

<p><i>Left column</i>: categories supported in the core ontology include diagnoses, medications, lab tests, and demographics. <i>Middle column</i>: coding system used for each category. The demographics category uses multiple coding systems to handle the relevant sub-categories such as gender and language. <i>Right column</i>: hierarchy used to group medically related concepts. Standard hierarchies were adopted where possible, which was the case for diagnoses and medications.</p

Development of a Coronavirus Disease 2019 (COVID-19) Application Ontology for the Accrual to Clinical Trials (ACT) network

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that are critical to COVID-19 research. The ontology contains over 50 000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for severe acute respiratory syndrome coronavirus 2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of 9 academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network