Book reviews

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45645/1/11199_2004_Article_BF00287975.pd

Privacy-protecting, reliable response data discovery using COVID-19 patient observations

OBJECTIVE To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online. MATERIALS AND METHODS We developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data. RESULTS Our public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems. DISCUSSION AND CONCLUSIONS We present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems

pSCANNER: patient-centered Scalable National Network for Effectiveness Research.

This article describes the patient-centered Scalable National Network for Effectiveness Research (pSCANNER), which is part of the recently formed PCORnet, a national network composed of learning healthcare systems and patient-powered research networks funded by the Patient Centered Outcomes Research Institute (PCORI). It is designed to be a stakeholder-governed federated network that uses a distributed architecture to integrate data from three existing networks covering over 21 million patients in all 50 states: (1) VA Informatics and Computing Infrastructure (VINCI), with data from Veteran Health Administration's 151 inpatient and 909 ambulatory care and community-based outpatient clinics; (2) the University of California Research exchange (UC-ReX) network, with data from UC Davis, Irvine, Los Angeles, San Francisco, and San Diego; and (3) SCANNER, a consortium of UCSD, Tennessee VA, and three federally qualified health systems in the Los Angeles area supplemented with claims and health information exchange data, led by the University of Southern California. Initial use cases will focus on three conditions: (1) congestive heart failure; (2) Kawasaki disease; (3) obesity. Stakeholders, such as patients, clinicians, and health service researchers, will be engaged to prioritize research questions to be answered through the network. We will use a privacy-preserving distributed computation model with synchronous and asynchronous modes. The distributed system will be based on a common data model that allows the construction and evaluation of distributed multivariate models for a variety of statistical analyses

pSCANNER: patient-centered Scalable National Network for Effectiveness Research.

This article describes the patient-centered Scalable National Network for Effectiveness Research (pSCANNER), which is part of the recently formed PCORnet, a national network composed of learning healthcare systems and patient-powered research networks funded by the Patient Centered Outcomes Research Institute (PCORI). It is designed to be a stakeholder-governed federated network that uses a distributed architecture to integrate data from three existing networks covering over 21 million patients in all 50 states: (1) VA Informatics and Computing Infrastructure (VINCI), with data from Veteran Health Administration's 151 inpatient and 909 ambulatory care and community-based outpatient clinics; (2) the University of California Research exchange (UC-ReX) network, with data from UC Davis, Irvine, Los Angeles, San Francisco, and San Diego; and (3) SCANNER, a consortium of UCSD, Tennessee VA, and three federally qualified health systems in the Los Angeles area supplemented with claims and health information exchange data, led by the University of Southern California. Initial use cases will focus on three conditions: (1) congestive heart failure; (2) Kawasaki disease; (3) obesity. Stakeholders, such as patients, clinicians, and health service researchers, will be engaged to prioritize research questions to be answered through the network. We will use a privacy-preserving distributed computation model with synchronous and asynchronous modes. The distributed system will be based on a common data model that allows the construction and evaluation of distributed multivariate models for a variety of statistical analyses

Interobserver Variability in the Assessment of CT Imaging Features of Traumatic Brain Injury

The goal of our study was to determine the interobserver variability between observers with different backgrounds and experience when interpreting computed tomography (CT) imaging features of traumatic brain injury (TBI). We retrospectively identified a consecutive series of 50 adult patients admitted at our institution with a suspicion of TBI, and displaying a Glasgow Coma Scale score ≤12. Noncontrast CT (NCT) studies were anonymized and sent to five reviewers with different backgrounds and levels of experience, who independently reviewed each NCT scan. Each reviewer assessed multiple CT imaging features of TBI and assigned every NCT scan a Marshall and a Rotterdam grading score. The interobserver agreement and coefficient of variation were calculated for individual CT imaging features of TBI as well as for the two scores. Our results indicated that the imaging review by both neuroradiologists and neurosurgeons were consistent with each other. The kappa coefficient of agreement for all CT characteristics showed no significant difference in interpretation between the neurosurgeons and neuroradiologists. The average Bland and Altman coefficients of variation for the Marshall and Rotterdam classification systems were 12.7% and 21.9%, respectively, which indicates acceptable agreement among all five reviewers. In conclusion, there is good interobserver reproducibility between neuroradiologists and neurosurgeons in the interpretation of CT imaging features of TBI and calculation of Marshall and Rotterdam scores

EFFECTS OF 23.4% SODIUM CHLORIDE SOLUTION IN REDUCING INTRACRANIAL PRESSURE IN PATIENTS WITH TRAUMATIC BRAIN INJURY: A PRELIMINARY STUDY

OBJECTIVE: Mannitol is the standard of care for patients with increased intracranial pressure (ICP), but multiple administrations of mannitol risk renal toxicity and fluid accumulation in the brain parenchyma with consequent worsening of cerebral edema. This preliminary study assessed the safety and efficacy of small-volume injections of 23.4% sodium chloride solution for the treatment of intracranial hypertension in patients with traumatic brain injury who became tolerant to mannitol. METHODS: We retrospectively reviewed the charts of 13 adult patients with traumatic brain injury who received mannitol and 23.4% sodium chloride independently for the treatment of intracranial hypertension at San Francisco General Hospital between January and October 2003. Charts were reviewed to determine ICP, cerebral perfusion pressure, mean arterial pressure, serum sodium values, and serum osmolarity before and after treatment with 23.4% sodium chloride and mannitol. Complications were noted. RESULTS: The mean reductions in ICP after treatment were significant for both mannitol (PϽ0.001) and hypertonic saline (PϽ0.001); there were no significant differences between reductions in ICP when comparing the two agents (P ϭ 0.174). The ICP reduction observed for hypertonic saline was durable, and its mean duration of effect (96 min) was significantly longer than that of mannitol treatment (59 min) (P ϭ 0.016). No complications were associated with treatment with hypertonic saline. CONCLUSION: This study suggests that 23.4% hypertonic saline is a safe and effective treatment for elevated ICP in patients after traumatic brain injury. These results warrant a rigorous evaluation of its efficacy as compared to mannitol in a prospective randomized controlled trial

A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research.

BackgroundCentralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner.ObjectiveThe objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies.Materials and methodsBased on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network.ResultsThe authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws.Discussion and conclusionFederated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks