Spatial landscape of malignant pleural and peritoneal mesothelioma tumor immune microenvironment.

Immunotherapies have demonstrated limited clinical efficacy in malignant mesothelioma treatment. We conducted multiplex immunofluorescence (mIF) analyses on tissue microarrays (n=3) from malignant peritoneal (MPeM, n=25) and pleural (MPM, n=88) mesothelioma patients. Our study aimed to elucidate spatial distributions of key immune cell populations and their association with LAG3, BAP1, NF2, and MTAP, with MTAP serving as a CDKN2A/B surrogate marker. Additionally, we examined the relationship between the spatial distribution of major immune cell types with MM patient prognosis and clinical characteristics. We observed a higher degree of interaction between immune cells and tumor cells in MPM compared to MPeM. Notably, within MPM tumors, we detected a significantly increased interaction between tumor cells and CD8+ T cells in tumors with low BAP1 expression compared to those with high BAP1 expression. To support the broader research community, we have developed The Human Spatial Atlas of Malignant Mesothelioma (https://mesotheliomaspatialatlas.streamlit.app/), containing mIF and hematoxylin and eosin (H&E) images.

Post-acute and Chronic Kidney Function Outcomes of COVID-19 in Children and Adolescents: An EHR Cohort Study from the RECOVER Initiative.

We investigated the risks of post-acute and chronic adverse kidney outcomes of SARS-CoV-2 infection in the pediatric population via a retrospective cohort study using data from the RECOVER program. We included 1,864,637 children and adolescents under 21 from 19 children's hospitals and health institutions in the US with at least six months of follow-up time between March 2020 and May 2023. We divided the patients into three strata: patients with pre-existing chronic kidney disease (CKD), patients with acute kidney injury (AKI) during the acute phase (within 28 days) of SARS-CoV-2 infection, and patients without pre-existing CKD or AKI. We defined a set of adverse kidney outcomes for each stratum and examined the outcomes within the post-acute and chronic phases after SARS-CoV-2 infection. In each stratum, compared with the non-infected group, patients with COVID-19 had a higher risk of adverse kidney outcomes. For patients without pre-existing CKD, there were increased risks of CKD stage 2+ (HR 1.20; 95% CI: 1.13-1.28) and CKD stage 3+ (HR 1.35; 95% CI: 1.15-1.59) during the post-acute phase (28 days to 365 days) after SARS-CoV-2 infection. Within the post-acute phase of SARS-CoV-2 infection, children and adolescents with pre-existing CKD and those who experienced AKI were at increased risk of progression to a composite outcome defined by at least 50% decline in estimated glomerular filtration rate (eGFR), eGFR <15 mL/min/1.73m2, End Stage Kidney Disease diagnosis, dialysis, or transplant.

Pediatric Gastrointestinal Outcomes During the Post-Acute Phase of COVID-19: Findings from RECOVER Initiative from 29 Hospitals in the US.

Importance: The profile of gastrointestinal (GI) outcomes that may affect children in post-acute and chronic phases of COVID-19 remains unclear. Objective: To investigate the risks of GI symptoms and disorders during the post-acute phase (28 days to 179 days after SARS-CoV-2 infection) and the chronic phase (180 days to 729 days after SARS-CoV-2 infection) in the pediatric population. Design: We used a retrospective cohort design from March 2020 to Sept 2023. Setting: twenty-nine healthcare institutions. Participants: A total of 413,455 patients aged not above 18 with SARS-CoV-2 infection and 1,163,478 patients without SARS-CoV-2 infection. Exposures: Documented SARS-CoV-2 infection, including positive polymerase chain reaction (PCR), serology, or antigen tests for SARS-CoV-2, or diagnoses of COVID-19 and COVID-related conditions. Main Outcomes and Measures: Prespecified GI symptoms and disorders during two intervals: post-acute phase and chronic phase following the documented SARS-CoV-2 infection. The adjusted risk ratio (aRR) was determined using a stratified Poisson regression model, with strata computed based on the propensity score. Results: Our cohort comprised 1,576,933 patients, with females representing 48.0% of the sample. The analysis revealed that children with SARS-CoV-2 infection had an increased risk of developing at least one GI symptom or disorder in both the post-acute (8.64% vs. 6.85%; aRR 1.25, 95% CI 1.24-1.27) and chronic phases (12.60% vs. 9.47%; aRR 1.28, 95% CI 1.26-1.30) compared to uninfected peers. Specifically, the risk of abdominal pain was higher in COVID-19 positive patients during the post-acute phase (2.54% vs. 2.06%; aRR 1.14, 95% CI 1.11-1.17) and chronic phase (4.57% vs. 3.40%; aRR 1.24, 95% CI 1.22-1.27). Conclusions and Relevance: In the post-acute phase or chronic phase of COVID-19, the risk of GI symptoms and disorders was increased for COVID-positive patients in the pediatric population.

Inflammatory and neurodegenerative serum protein biomarkers increase sensitivity to detect clinical and radiographic disease activity in multiple sclerosis.

The multifaceted nature of multiple sclerosis requires quantitative biomarkers that can provide insights related to diverse physiological pathways. To this end, proteomic analysis of deeply-phenotyped serum samples, biological pathway modeling, and network analysis were performed to elucidate inflammatory and neurodegenerative processes, identifying sensitive biomarkers of multiple sclerosis disease activity. Here, we evaluated the concentrations of > 1400 serum proteins in 630 samples from three multiple sclerosis cohorts for association with clinical and radiographic new disease activity. Twenty proteins were associated with increased clinical and radiographic multiple sclerosis disease activity for inclusion in a custom assay panel. Serum neurofilament light chain showed the strongest univariate correlation with gadolinium lesion activity, clinical relapse status, and annualized relapse rate. Multivariate modeling outperformed univariate for all endpoints. A comprehensive biomarker panel including the twenty proteins identified in this study could serve to characterize disease activity for a patient with multiple sclerosis.

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions.

Background: Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality. Methods: We searched PubMed using keywords "SDOH" and "EHR", underwent title/abstract and full-text screening. Included records were analyzed under five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions. Results: We identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes. Discussion: Despite progress identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfill the potential of SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

Limitations of Noninvasive Tests-Based Population-Level Risk Stratification Strategy for Nonalcoholic Fatty Liver Disease.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) are highly prevalent but underdiagnosed. AIMS: We used an electronic health record data network to test a population-level risk stratification strategy using noninvasive tests (NITs) of liver fibrosis. METHODS: Data were obtained from PCORnet® sites in the East, Midwest, Southwest, and Southeast United States from patients aged [Formula: see text] 18 with or without ICD-10-CM diagnosis codes for NAFLD, NASH, and NASH-cirrhosis between 9/1/2017 and 8/31/2020. Average and standard deviations (SD) for Fibrosis-4 index (FIB-4), NAFLD fibrosis score (NFS), and Hepatic Steatosis Index (HSI) were estimated by site for each patient cohort. Sample-wide estimates were calculated as weighted averages across study sites. RESULTS: Of 11,875,959 patients, 0.8% and 0.1% were coded with NAFLD and NASH, respectively. NAFLD diagnosis rates in White, Black, and Hispanic patients were 0.93%, 0.50%, and 1.25%, respectively, and for NASH 0.19%, 0.04%, and 0.16%, respectively. Among undiagnosed patients, insufficient EHR data for estimating NITs ranged from 68% (FIB-4) to 76% (NFS). Predicted prevalence of NAFLD by HSI was 60%, with estimated prevalence of advanced fibrosis of 13% by NFS and 7% by FIB-4. Approximately, 15% and 23% of patients were classified in the intermediate range by FIB-4 and NFS, respectively. Among NAFLD-cirrhosis patients, a third had FIB-4 scores in the low or intermediate range. CONCLUSIONS: We identified several potential barriers to a population-level NIT-based screening strategy. HSI-based NAFLD screening appears unrealistic. Further research is needed to define merits of NFS- versus FIB-4-based strategies, which may identify different high-risk groups.

Avenues for Strengthening PCORnet's Capacity to Advance Patient-Centered Economic Outcomes in Patient-Centered Outcomes Research (PCOR).

PCORnet, the National Patient-Centered Clinical Research Network, provides the ability to conduct prospective and observational pragmatic research by leveraging standardized, curated electronic health records data together with patient and stakeholder engagement. PCORnet is funded by the Patient-Centered Outcomes Research Institute (PCORI) and is composed of 8 Clinical Research Networks that incorporate at total of 79 health system "sites." As the network developed, linkage to commercial health plans, federal insurance claims, disease registries, and other data resources demonstrated the value in extending the networks infrastructure to provide a more complete representation of patient's health and lived experiences. Initially, PCORnet studies avoided direct economic comparative effectiveness as a topic. However, PCORI's authorizing law was amended in 2019 to allow studies to incorporate patient-centered economic outcomes in primary research aims. With PCORI's expanded scope and PCORnet's phase 3 beginning in January 2022, there are opportunities to strengthen the network's ability to support economic patient-centered outcomes research. This commentary will discuss approaches that have been incorporated to date by the network and point to opportunities for the network to incorporate economic variables for analysis, informed by patient and stakeholder perspectives. Topics addressed include: (1) data linkage infrastructure; (2) commercial health plan partnerships; (3) Medicare and Medicaid linkage; (4) health system billing-based benchmarking; (5) area-level measures; (6) individual-level measures; (7) pharmacy benefits and retail pharmacy data; and (8) the importance of transparency and engagement while addressing the biases inherent in linking real-world data sources.

Inflammatory and neurodegenerative serum protein biomarkers increase sensitivity to detect disease activity in multiple sclerosis.

Background/Objectives: Serum proteomic analysis of deeply-phenotyped samples, biological pathway modeling and network analysis were performed to elucidate the inflammatory and neurodegenerative processes of multiple sclerosis (MS) and identify sensitive biomarkers of MS disease activity (DA). Methods: Over 1100 serum proteins were evaluated in >600 samples from three MS cohorts to identify biomarkers of clinical and radiographic (gadolinium-enhancing lesions) new MS DA. Protein levels were analyzed and associated with presence of gadolinium-enhancing lesions, clinical relapse status (CRS), and annualized relapse rate (ARR) to create a custom assay panel. Results: Twenty proteins were associated with increased clinical and radiographic MS DA. Serum neurofilament light chain (NfL) showed the strongest univariate correlation with radiographic and clinical DA measures. Multivariate modeling significantly outperformed univariate NfL to predict gadolinium lesion activity, CRS and ARR. Discussion: These findings provide insight regarding correlations between inflammatory and neurodegenerative biomarkers and clinical and radiographic MS DA. Funding: Octave Bioscience, Inc (Menlo Park, CA).

ReDWINE: A clinical datamart with text analytical capabilities to facilitate rehabilitation research.

Rehabilitation research focuses on determining the components of a treatment intervention, the mechanism of how these components lead to recovery and rehabilitation, and ultimately the optimal intervention strategies to maximize patients' physical, psychologic, and social functioning. Traditional randomized clinical trials that study and establish new interventions face challenges, such as high cost and time commitment. Observational studies that use existing clinical data to observe the effect of an intervention have shown several advantages over RCTs. Electronic Health Records (EHRs) have become an increasingly important resource for conducting observational studies. To support these studies, we developed a clinical research datamart, called ReDWINE (Rehabilitation Datamart With Informatics iNfrastructure for rEsearch), that transforms the rehabilitation-related EHR data collected from the UPMC health care system to the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to facilitate rehabilitation research. The standardized EHR data stored in ReDWINE will further reduce the time and effort required by investigators to pool, harmonize, clean, and analyze data from multiple sources, leading to more robust and comprehensive research findings. ReDWINE also includes deployment of data visualization and data analytics tools to facilitate cohort definition and clinical data analysis. These include among others the Open Health Natural Language Processing (OHNLP) toolkit, a high-throughput NLP pipeline, to provide text analytical capabilities at scale in ReDWINE. Using this comprehensive representation of patient data in ReDWINE for rehabilitation research will facilitate real-world evidence for health interventions and outcomes.

REDCap and the National Mesothelioma Virtual Bank-a scalable and sustainable model for rare disease biorepositories.

OBJECTIVE: Rare disease research requires data sharing networks to power translational studies. We describe novel use of Research Electronic Data Capture (REDCap), a web application for managing clinical data, by the National Mesothelioma Virtual Bank, a federated biospecimen, and data sharing network. MATERIALS AND METHODS: National Mesothelioma Virtual Bank (NMVB) uses REDCap to integrate honest broker activities, enabling biospecimen and associated clinical data provisioning to investigators. A Web Portal Query tool was developed to source and visualize REDCap data in interactive, faceted search, enabling cohort discovery by public users. An AWS Lambda function behind an API calculates the counts visually presented, while protecting record level data. The user-friendly interface, quick responsiveness, automatic generation from REDCap, and flexibility to new data, was engineered to sustain the NMVB research community. RESULTS: NMVB implementations enabled a network of 8 research institutions with over 2000 mesothelioma cases, including clinical annotations and biospecimens, and public users' cohort discovery and summary statistics. NMVB usage and impact is demonstrated by high website visits (>150 unique queries per month), resource use requests (>50 letter of interests), and citations (>900) to papers published using NMVB resources. DISCUSSION: NMVB's REDCap implementation and query tool is a framework for implementing federated and integrated rare disease biobanks and registries. Advantages of this framework include being low-cost, modular, scalable, and efficient. Future advances to NVMB's implementations will include incorporation of -omics data and development of downstream analysis tools to advance mesothelioma and rare disease research. CONCLUSION: NVMB presents a framework for integrating biobanks and patient registries to enable translational research for rare diseases.

Clinical validation of a multi-protein, serum-based assay for disease activity assessments in multiple sclerosis.

An 18-protein multiple sclerosis (MS) disease activity (DA) test was validated based on associations between algorithm scores and clinical/radiographic assessments (N = 614 serum samples; Train [n = 426; algorithm development] and Test [n = 188; evaluation] subsets). The multi-protein model was trained based on presence/absence of gadolinium-positive (Gd+) lesions and was also strongly associated with new/enlarging T2 lesions, and active versus stable disease (composite of radiographic and clinical evidence of DA) with improved performance (p < 0.05) compared to the neurofilament light single protein model. The odds of having ≥1 Gd+ lesions with a moderate/high DA score were 4.49 times that of a low DA score, and the odds of having ≥2 Gd+ lesions with a high DA score were 20.99 times that of a low/moderate DA score. The MSDA Test was clinically validated with improved performance compared to the top-performing single-protein model and can serve as a quantitative tool to enhance the care of MS patients.

Non-asbestiform elongate mineral particles and mesothelioma risk: Human and experimental evidence.

The presentations in this session of the Monticello II conference were aimed at summarizing what is known about asbestiform and non-asbestiform elongate mineral particles (EMPs) and mesothelioma risks based on evidence from experimental and epidemiology studies. Dr. Case discussed case reports of mesothelioma over the last several decades. Dr. Taioli indicated that the epidemiology evidence concerning non-asbestiform EMPs is weak or lacking, and that progress would be limited unless mesothelioma registries are established. One exception discussed is that of taconite miners, who are exposed to grunerite. Drs. Mandel and Odo noted that studies of taconite miners in Minnesota have revealed an excess rate of mesothelioma, but the role of non-asbestiform EMPs in this excess incidence of mesothelioma is unclear. Dr. Becich discussed the National Mesothelioma Virtual Bank (NMVB), a virtual mesothelioma patient registry that includes mesothelioma patients' lifetime work histories, exposure histories, biospecimens, proteogenomic information, and imaging data that can be used in epidemiology research on mesothelioma. Dr. Bernstein indicated that there is a strong consensus that long, highly durable respirable asbestiform EMPs have the potential to cause mesothelioma, but there is continued debate concerning the biodurability required, and the dimensions (both length and diameter), the shape, and the dose associated with mesothelioma risk. Finally, Dr. Nel discussed how experimental studies of High Aspect Ratio Engineered Nanomaterials have clarified dimensional and durability features that impact disease risk, the impact of inflammation and oxidative stress on the epigenetic regulation of tumor suppressor genes, and the generation of immune suppressive effects in the mesothelioma tumor microenvironment. The session ended with a discussion of future research needs.

Industry, occupation, and exposure history of mesothelioma patients in the U.S. National Mesothelioma Virtual Bank, 2006-2022.

BACKGROUND: Malignant mesothelioma is associated with environmental and occupational exposure to certain mineral fibers, especially asbestos. This study aims to examine work histories of mesothelioma patients and their survival time. METHOD: Using the NIOSH Industry and Occupation Computerized Coding System, we mapped occupations and industries recorded for 748 of 1444 patients in the U.S. National Mesothelioma Virtual Bank (NMVB) during the period 2006-2022. Descriptive and survival analyses were conducted. RESULTS: Among the 1023 industries recorded for those having mesothelioma, the most frequent cases were found for those in manufacturing (n = 225, 22.0%), construction (138, 13.5%), and education services (66, 6.5%); among the 924 occupation records, the most frequent cases were found for those in construction and extraction (174, 18.8%), production (145, 15.7%), and management (84, 9.1%). Males (583) or persons aged >40 years (658) at the time of diagnosis tended to have worked in industries traditionally associated with mesothelioma (e.g., construction), while females (163) or persons aged 20-40 years (27) tended to have worked in industries not traditionally associated with mesothelioma (e.g., health care). Asbestos, unknown substances, and chemical solvents were the most frequently reported exposure, with females most often reporting an unknown substance. A multi-variable Cox Hazard Regression analysis showed that significant prognostic factors associated with decreased survival in mesothelioma cases are sex (male) and work experience in utility-related industry, while factor associated with increased survival are epithelial or epithelioid histological type, prior history of surgery and immunotherapy, and industry experience in accommodation and food services. CONCLUSION: The NMVB has the potential of serving as a sentinel surveillance mechanism for identifying industries and occupations not traditionally associated with mesothelioma. Results indicate the importance of considering all potential sources of asbestos exposures including occupational, environmental, and extra-occupational exposures when evaluating mesothelioma patients and advising family members.

Analytical validation of a multi-protein, serum-based assay for disease activity assessments in multiple sclerosis.

PURPOSE: To characterize and analytically validate the MSDA Test, a multi-protein, serum-based biomarker assay developed using Olink® PEA methodology. EXPERIMENTAL DESIGN: Two lots of the MSDA Test panel were manufactured and subjected to a comprehensive analytical characterization and validation protocol to detect biomarkers present in the serum of patients with multiple sclerosis (MS). Biomarker concentrations were incorporated into a final algorithm used for calculating four Disease Pathway scores (Immunomodulation, Neuroinflammation, Myelin Biology, and Neuroaxonal Integrity) and an overall Disease Activity score. RESULTS: Analytical characterization demonstrated that the multi-protein panel satisfied the criteria necessary for a fit-for-purpose validation considering the assay's intended clinical use. This panel met acceptability criteria for 18 biomarkers included in the final algorithm out of 21 biomarkers evaluated. VCAN was omitted based on factors outside of analytical validation; COL4A1 and GH were excluded based on imprecision and diurnal variability, respectively. Performance of the four Disease Pathway and overall Disease Activity scores met the established acceptability criteria. CONCLUSIONS AND CLINICAL RELEVANCE: Analytical validation of this multi-protein, serum-based assay is the first step in establishing its potential utility as a quantitative, minimally invasive, and scalable biomarker panel to enhance the standard of care for patients with MS.

Isolated BAP1 Genomic Alteration in Malignant Pleural Mesothelioma Predicts Distinct Immunogenicity with Implications for Immunotherapeutic Response.

Malignant pleural mesothelioma (MPM), an aggressive cancer of the mesothelial cells lining the pleural cavity, lacks effective treatments. Multiple somatic mutations and copy number losses in tumor suppressor genes (TSGs) BAP1, CDKN2A/B, and NF2 are frequently associated with MPM. The impact of single versus multiple genomic alterations of TSG on MPM biology, the immune tumor microenvironment, clinical outcomes, and treatment responses are unknown. Tumors with genomic alterations in BAP1 alone were associated with a longer overall patient survival rate compared to tumors with CDKN2A/B and/or NF2 alterations with or without BAP1 and formed a distinct immunogenic subtype with altered transcription factor and pathway activity patterns. CDKN2A/B genomic alterations consistently contributed to an adverse clinical outcome. Since the genomic alterations of only BAP1 was associated with the PD-1 therapy response signature and higher LAG3 and VISTA gene expression, it might be a candidate marker for immune checkpoint blockade therapy. Our results on the impact of TSG genotypes on MPM and the correlations between TSG alterations and molecular pathways provide a foundation for developing individualized MPM therapies.

Rules of engagement: Promoting academic-industry partnership in the era of digital pathology and artificial intelligence.

Academic industry partnership (AIP) represents an important alliance between academic researchers and industry that helps translate technology and complete the innovation cycle within academic health systems. Despite diverging missions and skillsets the culture for academia and industry is changing in response to the current digital era which is spawning greater collaboration between physicians and businesses in this marketplace. In the field of pathology, this is further driven by the fact that traditional funding sources cannot keep pace with the innovation needed in digital pathology and artificial intelligence. This concept article from the Digital Pathology Association (DPA) describes the rules of engagement for pathology innovators in academia and for their corporate partners to help establish best practices in this critical area. Stakeholders include pathologists, basic and translational researchers, university technology transfer and sponsored research offices, as well as industry relations officers. The article discusses the benefits and pitfalls of an AIP, reviews different partnership models, examines the role of pathologists in the innovation cycle, explains various agreements that may need to be signed, covers conflict of interest and intellectual property issues, and offers recommendations for ensuring successful partnerships.

Artificial intelligence in clinical and translational science: Successes, challenges and opportunities.

Artificial intelligence (AI) is transforming many domains, including finance, agriculture, defense, and biomedicine. In this paper, we focus on the role of AI in clinical and translational research (CTR), including preclinical research (T1), clinical research (T2), clinical implementation (T3), and public (or population) health (T4). Given the rapid evolution of AI in CTR, we present three complementary perspectives: (1) scoping literature review, (2) survey, and (3) analysis of federally funded projects. For each CTR phase, we addressed challenges, successes, failures, and opportunities for AI. We surveyed Clinical and Translational Science Award (CTSA) hubs regarding AI projects at their institutions. Nineteen of 63 CTSA hubs (30%) responded to the survey. The most common funding source (48.5%) was the federal government. The most common translational phase was T2 (clinical research, 40.2%). Clinicians were the intended users in 44.6% of projects and researchers in 32.3% of projects. The most common computational approaches were supervised machine learning (38.6%) and deep learning (34.2%). The number of projects steadily increased from 2012 to 2020. Finally, we analyzed 2604 AI projects at CTSA hubs using the National Institutes of Health Research Portfolio Online Reporting Tools (RePORTER) database for 2011-2019. We mapped available abstracts to medical subject headings and found that nervous system (16.3%) and mental disorders (16.2) were the most common topics addressed. From a computational perspective, big data (32.3%) and deep learning (30.0%) were most common. This work represents a snapshot in time of the role of AI in the CTSA program.

An atomic approach to the design and implementation of a research data warehouse.

OBJECTIVE: As a long-standing Clinical and Translational Science Awards (CTSA) Program hub, the University of Pittsburgh and the University of Pittsburgh Medical Center (UPMC) developed and implemented a modern research data warehouse (RDW) to efficiently provision electronic patient data for clinical and translational research. MATERIALS AND METHODS: We designed and implemented an RDW named Neptune to serve the specific needs of our CTSA. Neptune uses an atomic design where data are stored at a high level of granularity as represented in source systems. Neptune contains robust patient identity management tailored for research; integrates patient data from multiple sources, including electronic health records (EHRs), health plans, and research studies; and includes knowledge for mapping to standard terminologies. RESULTS: Neptune contains data for more than 5 million patients longitudinally organized as Health Insurance Portability and Accountability Act (HIPAA) Limited Data with dates and includes structured EHR data, clinical documents, health insurance claims, and research data. Neptune is used as a source for patient data for hundreds of institutional review board-approved research projects by local investigators and for national projects. DISCUSSION: The design of Neptune was heavily influenced by the large size of UPMC, the varied data sources, and the rich partnership between the University and the healthcare system. It includes several unique aspects, including the physical warehouse straddling the University and UPMC networks and management under an HIPAA Business Associates Agreement. CONCLUSION: We describe the design and implementation of an RDW at a large academic healthcare system that uses a distinctive atomic design where data are stored at a high level of granularity.