The Veterans Affairs Precision Oncology Data Repository, a Clinical, Genomic, and Imaging Research Database.

The Veterans Affairs Precision Oncology Data Repository (VA-PODR) is a large, nationwide repository of de-identified data on patients diagnosed with cancer at the Department of Veterans Affairs (VA). Data include longitudinal clinical data from the VA's nationwide electronic health record system and the VA Central Cancer Registry, targeted tumor sequencing data, and medical imaging data including computed tomography (CT) scans and pathology slides. A subset of the repository is available at the Genomic Data Commons (GDC) and The Cancer Imaging Archive (TCIA), and the full repository is available through the Veterans Precision Oncology Data Commons (VPODC). By releasing this de-identified dataset, we aim to advance Veterans' health care through enabling translational research on the Veteran population by a wide variety of researchers.

An application to support COVID-19 occupational health and patient tracking at a Veterans Affairs medical center.

OBJECTIVE: Reducing risk of coronavirus disease 2019 (COVID-19) infection among healthcare personnel requires a robust occupational health response involving multiple disciplines. We describe a flexible informatics solution to enable such coordination, and we make it available as open-source software. MATERIALS AND METHODS: We developed a stand-alone application that integrates data from several sources, including electronic health record data and data captured outside the electronic health record. RESULTS: The application facilitates workflows from different hospital departments, including Occupational Health and Infection Control, and has been used extensively. As of June 2020, 4629 employees and 7768 patients and have been added for tracking by the application, and the application has been accessed over 46 000 times. DISCUSSION: Data captured by the application provides both a historical and real-time view into the operational impact of COVID-19 within the hospital, enabling aggregate and patient-level reporting to support identification of new cases, contact tracing, outbreak investigations, and employee workforce management. CONCLUSIONS: We have developed an open-source application that facilitates communication and workflow across multiple disciplines to manage hospital employees impacted by the COVID-19 pandemic.

Machine Learning Methods to Predict Lung Cancer Survival Using the Veterans Affairs Research Precision Oncology Data Commons.

We completed a pilot study to guide the development of the VA Research Precision Oncology Data Commons infrastructure as a collaboration platform with the greater research community. Our results using a small subset of patients from the VA's Precision Oncology Program demonstrate the feasibility of our data sharing platform to build predictive models for lung cancer survival using machine learning, as well as highlight the potential of target genome sequencing data.

The association between height and prostate cancer grade in the Early Stage Prostate Cancer Cohort Study.

OBJECTIVE: We examined the relationship between height and prostate cancer grade. METHODS: The Early Stage Prostate Cancer Cohort Study is an observational cohort of 1,037 men diagnosed with early-stage prostate cancer, T(0-3)N(x)M(0). High-grade prostate cancer was defined as a biopsy Gleason score ≥ 7 (4 + 3). Logistic regression models were created to calculate odds ratios (OR) and 95% confidence intervals (CI) for the cross-sectional relationship between height and prostate cancer grade in the overall cohort and subpopulations. RESULTS: We identified 939 participants with a biopsy Gleason score. High-grade prostate cancer was diagnosed in 138 participants. Overall, participants in the highest quartile of height were more than twice as likely to have a Gleason score ≥ 7 (4 + 3) than participants in the lowest quartile of height, OR 2.14 (95% CI 1.11, 4.14), after multivariate adjustment. Participants in the highest quartile of height were more likely to be diagnosed with high-grade prostate cancer than participants in the lowest quartile of height among participants who were black, OR 8.00 (95% CI 1.99, 32.18), and participants who had diabetes mellitus, OR 5.09 (95% CI 1.30, 19.98). CONCLUSIONS: Height is associated with increased risk of high-grade prostate cancer overall and perhaps among certain subpopulations.

The ACE-MAESTRO instrument on SCISAT: description, performance, and preliminary results.

The Measurement of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation (MAESTRO) instrument on the SCISAT satellite is a simple, compact spectrophotometer for the measurement of atmospheric extinction, ozone, nitrogen dioxide, and other trace gases in the stratosphere and upper troposphere as part of the Atmospheric Chemistry Experiment (ACE) mission. We provide an overview of the instrument from requirements to realization, including optical design, prelaunch and on-orbit performance, and a preliminary examination of retrievals of ozone and NO(2).