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Context: To identify VA and non-VA Emergency Department (ED) and hospital utilization by veterans with spinal cord injury and disorders (SCI/D) in California.Design: Retrospective cohort study.Setting: VA and Office of Statewide Health Planning and Development (OSHPD) in California.Participants: Total 300 veterans admitted to the study VA SCI/D Center for initial rehabilitations from 01/01/1999 through 08/17/2014.Interventions: N/A.Outcome Measures: Individual-level ED visits and hospitalizations during the first-year post-rehabilitation.Results: Among 145 veterans for whom ED visit data available, 168 ED visits were identified: 94 (55.2%) at non-VA EDs and 74 (44.8%) at the VA ED, with a mean of 1.16 (±2.21) ED visit/person. Seventy-seven (53.1%) veterans did not visit any ED. Of 68 (46.9%) veterans with ≥ one ED visit, 20 (29.4%) visited the VA ED only, 34 (50.0%) visited non-VA EDs only, and 14 (20.6%) visited both VA and non-VA EDs. Among 212 Veterans for whom hospitalization data were available, 247 hospitalizations were identified: 82 (33.2%) non-VA hospitalizations and 165 (66.8%) VA hospitalization with a mean of 1.17 (±1.62) hospitalizations/person. One hundred-seven (50.5%) veterans had no hospitalizations. Of 105 veterans with ≥ one hospitalization, 58 (55.2%) were hospitalized at the study VA hospital, 15 (14.3%) at a non-VA hospital, and 32 (30.5%) at both VA and non-VA hospitals.Conclusion: Non-VA ED and hospital usage among veterans with SCI/D occurred frequently. The acquisition of non-VA healthcare data managed by state agencies is vital to accurately and comprehensively evaluate needs and utilization rates among veteran populations.

AimsReadmission rates for patients with heart failure have consistently remained high over the past two decades. As more electronic data, computing power, and newer statistical techniques become available, data-driven care could be achieved by creating predictive models for adverse outcomes such as readmissions. We therefore aimed to review models for predicting risk of readmission for patients admitted for heart failure. We also aimed to analyze and possibly group the predictors used across the models.MethodsMajor electronic databases were searched to identify studies that examined correlation between readmission for heart failure and risk factors using multivariate models. We rigorously followed the review process using PRISMA methodology and other established criteria for quality assessment of the studies.ResultsWe did a detailed review of 334 papers and found 25 multivariate predictive models built using data from either health system or trials. A majority of models was built using multiple logistic regression followed by Cox proportional hazards regression. Some newer studies ventured into non-parametric and machine learning methods. Overall predictive accuracy with C-statistics ranged from 0.59 to 0.84. We examined significant predictors across the studies using clinical, administrative, and psychosocial groups.ConclusionsComplex disease management and correspondingly increasing costs for heart failure are driving innovations in building risk prediction models for readmission. Large volumes of diverse electronic data and new statistical methods have improved the predictive power of the models over the past two decades. More work is needed for calibration, external validation, and deployment of such models for clinical use.

In electrical field flow fractionation (EFFF or EIFFF), an electric potential is applied across a narrow gap filled with a weak electrolyte fluid. Charge buildup at the two poles (electrodes) and the formation of an electric double layer shields the channel, making the effective field in the bulk fluid very weak. Recent computational research suggests that pulsed field protocols, however, should improve retention and may enhance separation in EFFF through systematic disruptions of the double layer resulting in a stronger effective field in the bulk fluid. Improved retention has already been demonstrated experimentally. Accurate modeling and subsequent device optimization and design, however, depends, in part, on formulating a suitable model for the capacitative response of the channel and double layer at the electrode surfaces. Early models do not correctly describe experimentally observed current-time response and are not physically meaningful even when accurate mathematical fits of the data are realized. A new model and conceptual framework based on electrical resistance and capacitance variations of the double layer is suggested here. Physical interpretations of the electrical response have been developed and compared to published experimental data sets.