COINSTAC: A Privacy Enabled Model and Prototype for Leveraging and Processing Decentralized Brain Imaging Data

The field of neuroimaging has embraced the need for sharing and collaboration. Data sharing mandates from public funding agencies and major journal publishers have spurred the development of data repositories and neuroinformatics consortia. However, efficient and effective data sharing still faces several hurdles. For example, open data sharing is on the rise but is not suitable for sensitive data that are not easily shared, such as genetics. Current approaches can be cumbersome (such as negotiating multiple data sharing agreements). There are also significant data transfer, organization and computational challenges. Centralized repositories only partially address the issues. We propose a dynamic, decentralized platform for large scale analyses called the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). The COINSTAC solution can include data missing from central repositories, allows pooling of both open and ``closed'' repositories by developing privacy-preserving versions of widely-used algorithms, and incorporates the tools within an easy-to-use platform enabling distributed computation. We present an initial prototype system which we demonstrate on two multi-site data sets, without aggregating the data. In addition, by iterating across sites, the COINSTAC model enables meta-analytic solutions to converge to ``pooled-data'' solutions (i.e. as if the entire data were in hand). More advanced approaches such as feature generation, matrix factorization models, and preprocessing can be incorporated into such a model. In sum, COINSTAC enables access to the many currently unavailable data sets, a user friendly privacy enabled interface for decentralized analysis, and a powerful solution that complements existing data sharing solutions

MRN-Code/coinstac: v2.6.0 Alpha

COINSTAC alpha release, with bug fixes and continued stability enhancements

Inpatient antibiotic utilization in the Veterans' Health Administration during the coronavirus disease 2019 (COVID-19) pandemic.

Antibiotic prescribing practices across the Veterans' Health Administration (VA) experienced significant shifts during the coronavirus disease 2019 (COVID-19) pandemic. From 2015 to 2019, antibiotic use between January and May decreased from 638 to 602 days of therapy (DOT) per 1,000 days present (DP), while the corresponding months in 2020 saw antibiotic utilization rise to 628 DOT per 1,000 DP

MRN-Code/coinstac v4.7.1

Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computatio

COINS Data Exchange: An open platform for compiling, curating, and disseminating neuroimaging data

AbstractNeuroimaging data collection is inherently expensive. Maximizing the return on investment in neuroimaging studies requires that neuroimaging data be re-used whenever possible. In an effort to further scientific knowledge, the COINS Data Exchange (DX) (http://coins.mrn.org/dx) aims to make data sharing seamless and commonplace. DX takes a three-pronged approach towards improving the overall state of data sharing within the neuroscience community. The first prong is compiling data into one location that has been collected from all over the world in many different formats. The second prong is curating the data so that it can be stored in one consistent format and so that data QA/QC measures can be assured. The third prong is disseminating the data so that it is easy to consume and straightforward to interpret. This paper explains the concepts behind each prong and describes some challenges and successes that the Data Exchange has experienced

Evaluation of antibiotic escalation in response to nurse-driven inpatient sepsis screen.

ObjectiveTo determine the frequency and predictors of antibiotic escalation in response to the inpatient sepsis screen at our institution.DesignRetrospective cohort study.SettingTwo affiliated academic medical centers in Los Angeles, California.PatientsHospitalized patients aged 18 years and older who had their first positive sepsis screen between January 1, 2019, and December 31, 2019, on acute-care wards.MethodsWe described the rate and etiology of antibiotic escalation, and we conducted multivariable regression analyses of predictors of antibiotic escalation.ResultsOf the 576 cases with a positive sepsis screen, antibiotic escalation occurred in 131 cases (22.7%). New infection was the most documented etiology of escalation, with 76 cases (13.2%), followed by known pre-existing infection, with 26 cases (4.5%). Antibiotics were continued past 3 days in 17 cases (3.0%) in which new or existing infection was not apparent. Abnormal temperature (adjusted odds ratio [aOR], 3.00; 95% confidence interval [CI], 1.91-4.70) and abnormal lactate (aOR, 2.04; 95% CI, 1.28-3.27) were significant predictors of antibiotic escalation. The patient already being on antibiotics (aOR, 0.54; 95% CI, 0.34-0.89) and the positive screen occurred during a nursing shift change (aOR, 0.36; 95% CI, 0.22-0.57) were negative predictors. Pneumonia was the most documented new infection, but only 19 (50%) of 38 pneumonia cases met full clinical diagnostic criteria.ConclusionsInpatient sepsis screening led to a new infectious diagnosis in 13.2% of all positive sepsis screens, and the risk of prolonged antibiotic exposure without a clear infectious source was low. Pneumonia diagnostics and lactate testing are potential targets for future stewardship efforts