Current Time Point Research Articles

CHALLENGING BEHAVIORS ON INPATIENT MEDICAL UNITS: INTEGRATED, NON-PHARMACOLOGICAL APPROACH FOR PATIENTS WITH DEMENTIA

Introduction Inpatient medical care is a critical hospital function, but is costly and associated with risks to both patients and staff. Patients often demonstrate behavioral problems that interfere with care, prolong length of hospitalization, increase utilization of resources, delay discharge, and even lead to staff injury. To address these challenges, the San Francisco VA Health Care System (SFVAHCS) developed the Behavioral Education and Support Team (BEST). This team is comprised of psychiatry, psychology, nursing, and occupational therapy staff. BEST provides support, education, and interventions to patients who are engaging in treatment interfering behaviors. Goals include reducing risk to patients and staff, improving treatment adherence, reducing utilization of resources, shortening length of stay, and facilitating discharge. BEST staff also collaborate with inpatient staff to develop and consistently implement behavioral treatment plans. Since implementation in October 2015 to March 2018, BEST has served 488 veteran patients. Data demonstrate that BEST is associated with a 40% reduction in staff injuries in acute medicine settings, a 42% reduction in emergency psychiatric codes, and a 25% reduction in formal inpatient setting complaints. In addition, it is estimated that BEST reduces inpatient costs due to improved time to discharge and reduced staffing turnover and work loss due to injury. This presentation will provide an overview of the BEST team function and outcomes. Given the critical nature and high costs of inpatient medical care, the SFVAHCS determined that investment in a dedicated team to address these problems would be justified by reducing utilization of resources, length of hospitalization, overall cost, and adverse patient and staff outcomes. Methods The BEST team was initially implemented as a behavioral consultation team for inpatient medical patients in October, 2015, and was comprised of 5 members: • A RN team coordinator • A part-time psychiatrist (.2 FTE) • A psychologist • An RN team member • An Occupational Therapist The data from the BEST consultations between October 2015 and March 2018 were reviewed, and analyzed. Analysis of data included identification of total number of consultations, average age of patients, marital status, discharge status, themes for admission, days in hospital, presence of psychiatric disorder, dementia, serious mental illness, staff injury frequency, frequency of emergent psychiatric code calls, frequency of formal inpatient complaints. Results Since initiation in October, 2015, the BEST program has served 488 patients. Complex geriatric patient accounted for 78.7%. As of July, 2017, the majority of veterans served had a psychiatric disorder or need, including depression (50%), dementia (26%), delirium (21%), serious mental illness (18%), and suicidal ideation (10%). Adherence to treatment was a target problem in 56% of those referred. Specific Outcomes: • 40% reduction in staff injuries among acute medicine staff • 42% reduction in emergency psychiatric code calls • 25% reduction in formal inpatient complaints. In addition, a conservative estimate suggests that improving compliance with treatment and reducing length of stay by 1 day per non-compliance referral, extrapolated to the current time point, would generate a savings of approximately $835,000. In addition, inpatient nursing staff report a subjective increase in morale and perception of competence for coping with behavioral disruptions. • Consult Summary (October 2015 to March 2018) • 488 Consults • Average Age 69 (youngest 29 to oldest 101 • 466 Males and 22 Females • Marital Status – 156 Never Married; 96 Married; 18 Separated; 181 Divorced; 33 Widowed; 4 Unknown • Discharge Status – 11 Against Medical Advice; 10 Died; 4 Homeless; 87 Skilled Nursing Facilities; 219 Home/Board & Care/Shelters; 31 Mental Health Rehabilitation; 83 Long-Term Care; 35 Unknown; 8 Acute Hospital • Themes for Admission – 55 Altered Mental Status; 43 Failure to Thrive; 28 Placement Issues; 15 Falls; 68 Pain; 39 Shortness of Breath • Days in the Hospital: • 13,827 Total days in the hospital • 7,218 Days in the hospital awaiting placement after being cleared by medical team • $28 Million – cost awaiting placement Conclusions Since implementation of the BEST team at the SFVA Medical Center, data reveal overall reduction in utilization of resources, length of hospitalizations, overall costs, and adverse patient and staff outcomes. Specifically: 1. A dedicated behavioral team can significantly improve the experience of inpatient nursing staff, reducing injuries, improving morale, reducing staff turnover and burnout. 2. A behavioral intervention team can significantly reduce psychiatric codes and patient complaints, while improving clinical outcomes and patient satisfaction. 3. Reductions in staff injuries and improvements in treatment adherence can recoup costs for team staffing. 4. Reducing risk factors for disruptive behaviors reduces overall costs to the system and overutilization of resources. This research was funded by Funded through the San Francisco VA

On the convergence of spectral deferred correction methods

In this work we analyze the convergence properties of the Spectral Deferred Correction (SDC) method originally proposed by Dutt et al. [BIT, 40 (2000), pp. 241--266]. The framework for this high-order ordinary differential equation (ODE) solver is typically described wherein a low-order approximation (such as forward or backward Euler) is lifted to higher order accuracy by applying the same low-order method to an error equation and then adding in the resulting defect to correct the solution. Our focus is not on solving the error equation to increase the order of accuracy, but on rewriting the solver as an iterative Picard integral equation solver. In doing so, our chief finding is that it is not the low-order solver that picks up the order of accuracy with each correction, but it is the underlying quadrature rule of the right hand side function that is solely responsible for picking up additional orders of accuracy. Our proofs point to a total of three sources of errors that SDC methods carry: the error at the current time point, the error from the previous iterate, and the numerical integration error that comes from the total number of quadrature nodes used for integration. The second of these two sources of errors is what separates SDC methods from Picard integral equation methods; our findings indicate that as long as difference between the current and previous iterate always gets multiplied by at least a constant multiple of the time step size, then high-order accuracy can be found even if the underlying "solver" is inconsistent the underlying ODE. From this vantage, we solidify the prospects of extending spectral deferred correction methods to a larger class of solvers to which we present some examples.

Hippocampal Segmentation From Longitudinal Infant Brain MR Images via Classification-Guided Boundary Regression

Hippocampal segmentation from infant brain MR images is indispensable for studying early brain development. However, most of the hippocampal segmentation methods were developed for population-based adult brain images, which are not suitable for longitudinal infant brain images acquired in the first year of life due to the low image contrast and variable development patterns of the hippocampal structure. To address these challenges, we propose a classification-guided boundary regression method to first detect hippocampal boundaries in the longitudinal infant brain images and then use those detected boundaries to guide the deformable model for final segmentation. Specifically, we first employ a classification-guided regression forest to predict the 3D displacements from individual image voxels to the potential hippocampal boundaries. These predicted displacements then determine the boundary maps by a voting strategy. Second, we iteratively enhance the voted hippocampal boundary map by incorporating the spatial context information given the tentative boundary estimation of the current time point. Besides, the longitudinal context information from all time points of the temporal sequence of the same subject (i.e., given their tentative segmentation results) is also utilized to facilitate accurate segmentation. Finally, a deformable model is applied to the enhanced voted boundary maps for achieving the longitudinal hippocampal segmentation. The experiments on infant brain MR images acquired from 2-week-old to 1-year-old show promising hippocampal segmentation results, indicating the applicability of our method in early brain development study.

August 2018 Letter to the Editor-in-Chief.

Letter to the Editor-in-Chief of JOSPT as follows: "Promoting Roles and Services Within Physical Therapy Not Supported by Evidence" with Authors' Response J Orthop Sports Phys Ther 2018;48(8):669-671. doi:10.2519/jospt.2018.0204.

Mapping ecosystem service value in Germany

ABSTRACTCurrently, a national TEEB study is being conducted in Germany. However, it lacks an overall estimation of ecosystem service value (ESV) at the national level. In this paper, we estimate the ESV in Germany based on the national land cover data and the unit value transfer method and examine the relationships between ESV and gross domestic product (GDP). The results indicate that the total ESV amounts to 248,895 million US$/yr, accounting for 7.91% of the sum of ESV and GDP. Cropland with 111,704 million US$/yr makes the largest contribution to the total ESV, and the second largest state, Lower Saxony, has the greatest ESV of 60,346 million US$/yr. In addition, a spatial pattern that distinguishes four regions in terms of the relationship between ESV and GDP at the district level is identified. This study illustrates the overall status of ecosystem services at the current point of time in Germany, raises public awareness about the magnitude of these services relative to other services captured in GDP, and implies suggestions for future research and ecological policy.

Parameter Learning and Change Detection Using a Particle Filter With Accelerated Adaptation

This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where the subsequent data in online sequential filtering does not match the model posterior filtered based on data up to a current point in time. The examples considered encompass parameter regime shifts and stochastic volatility. The filter adapts to regime shifts extremely rapidly and delivers a clear heuristic for distinguishing between regime shifts and stochastic volatility, even though the model dynamics assumed by the filter exhibit neither of those features.

Intonation patterns of questions in Malaysian English

ABSTRACTThis article examines the intonation patterns used by Malaysian English (MalE) speakers when asking ‘wh-’ questions and ‘yes/no’ questions. Analysis on recordings of Malay, Chinese and Indian speakers revealed similar patterns in the intonation patterns of MalE wh- questions and yes/no questions regardless of the ethnicity of the speaker: wh- questions and yes/no questions tended to start with a level tone; yes/no questions ended with rises, while wh- questions ended with either falls or rises. These patterns cannot merely be attributed to the influence of the speakers’ first languages nor were they learner errors. However, whether these features are accepted as norms of MalE pronunciation will, to a certain extent, be dependent on the perception of MalE among Malaysians, which at the current point in time is generally inconsistent.

Efficient hbox {SMC}^2 schemes for stochastic kinetic models

Fitting stochastic kinetic models represented by Markov jump processes within the Bayesian paradigm is complicated by the intractability of the observed-data likelihood. There has therefore been considerable attention given to the design of pseudo-marginal Markov chain Monte Carlo algorithms for such models. However, these methods are typically computationally intensive, often require careful tuning and must be restarted from scratch upon receipt of new observations. Sequential Monte Carlo (SMC) methods on the other hand aim to efficiently reuse posterior samples at each time point. Despite their appeal, applying SMC schemes in scenarios with both dynamic states and static parameters is made difficult by the problem of particle degeneracy. A principled approach for overcoming this problem is to move each parameter particle through a Metropolis-Hastings kernel that leaves the target invariant. This rejuvenation step is key to a recently proposed hbox {SMC}^2 algorithm, which can be seen as the pseudo-marginal analogue of an idealised scheme known as iterated batch importance sampling. Computing the parameter weights in hbox {SMC}^2 requires running a particle filter over dynamic states to unbiasedly estimate the intractable observed-data likelihood up to the current time point. In this paper, we propose to use an auxiliary particle filter inside the hbox {SMC}^2 scheme. Our method uses two recently proposed constructs for sampling conditioned jump processes, and we find that the resulting inference schemes typically require fewer state particles than when using a simple bootstrap filter. Using two applications, we compare the performance of the proposed approach with various competing methods, including two global MCMC schemes.

Relationships with the Past: How Australian Television Dramas Talk about Indigenous History

Relationships with the Past: How Australian Television Dramas Talk about Indigenous History

Sorption of metal cations on natural sorbents: Current state, problems and prospects

Data obtained in recent years that characterize the sorption capacity of natural sorbents with respect to the cations of a number of metals from nonexchangeable solutions and working fluids are generalized. We develop a technique for estimating the slip of pollutants with solutions in which the concentration of toxic cations does not exceed regulatory requirements. A method for calculating the kinetic curves of sorption is considered, which makes it possible to estimate the concentration of the pollutant coming out of the adsorber and the specific dynamic capacity of the sorbent to any current point of time from the beginning of the process.

Errors in Patient Positioning for Bone Mineral Density Assessment by Dual X-Ray Absorptiometry: Effect of Technologist Retraining

Improper positioning is one of the factors that can lead to incorrect bone mineral density (BMD) results. This study aimed to assess the frequencies of erroneous positioning during three periods: before retraining of the technologists (BR), after retraining (AR), and at the current timepoint 8 years after retraining (C). The BMD images of the first 150 consecutive patients who underwent DXA of the lumbar spine and hip during each of the three periods were retrospectively reviewed. Patients were excluded if they had severe scoliosis, rendering proper positioning impossible. Each BMD image was assessed by an International Society of Clinical Densitometry certified clinical densitometrist who was blinded to the date of the initial examination. For the lumbar spine in the BR group, the criteria frequently not met were inclusion of both iliac crests (33.8%), straightness (30.3%), and midline positioning (20.4%); the respective frequencies were significantly reduced to 0.8%−5.6%, 2.1%−3.0%, and 0%−2.8% in the AR and C groups (p < 0.05). For the hip in the BR group, the criteria frequently not met were straightness (52.8%) and internal rotation (21.8%); the respective frequencies were significantly reduced to 0%−4.2% and 8.3%−8.4% in the AR and C groups (p < 0.05). Overall improper positioning in the BR group was 49.3% and 57.3% at the lumbar spine and the hip, respectively; the respective frequencies were reduced to 9.3% and 12.7% in the AR group, and to 2.7% and 7.3% in the C group. The least significant change values for the lumbar spine, femoral neck, and total hip also became smaller after retraining. Retraining the technologists improved patient positioning, as evidenced by the decreased frequencies of erroneous positioning and the improved least significant change values after the retraining.

Early prediction of future hand movements using sEMG data.

We study in this work the feasibility of early prediction of hand movement based on sEMG signals to overcome the time delay issue of the conventional classification. Opposed to the classification task, the objective of early prediction is to predict a hand movement that is going to occur in the future given the information up to the current time point. The ability of early prediction may allow a hand prosthesis control system to compensate for the time delay and, as a result, improve the usability. Experimental results on the Ninapro database show that we can predict up to 300 ms ahead in the future while the prediction accuracy remains very close to that of the standard classification, i.e. it is just marginally lower. Furthermore, historical data prior the current time window is shown to be very important to improve performance, not only for the prediction but also the classification task.

P092 Cortico-spinal and cortico-cortical excitability is influenced by the phase of oscillatory alpha activity at the time of the stimulus

A somewhat surprising feature of the TMS evoked EEG and EMG potentials is their large variability. A number of factors are known to contribute to the variability of TMS responses but one factor about which tantalizingly little is known is the impact of instantaneous brain state at the time of stimulation. Here, we investigate the relationship between the phase of local mu-alpha oscillations at the time of stimulation with the magnitude of cortico-spinally evoked motor EMG potentials and the shape of the cortico-cortically evoked EEG potential. 12 healthy subjects were included the study and during a single session received TMS pulses over left motor cortex at an intensity to evoke EMG potentials at the right hand with 1 mV peak-to-peak amplitude. Phase triggers were generated based on the instantaneous phase of local sensorimotor rhythm using a Hjorth filter in the 5 EEG channels centered on electrode C3. The phase was estimated using a sliding window of data that was filtered, autoregressively forward predicted and hilbert transformed around the current time point in real-time using a custom closed-loop signal processing system based on Mathworks Simulink. The session consisted of a randomized sequence of 600 epochs in total, 300 triggered by the positive peak, and 300 triggered by the negative trough of the oscillations. Half of the trials were randomly not stimulated. Stimulated trials with pre-innervation in the EMG channel during the 500 ms preceding the stimulus were manually excluded. TMS evoked EEG potentials were analyzed by comparing the difference between stimulated and non-stimulated epochs after manually excluding trials with EEG artifacts, in order to subtract the oscillatory background activity. The magnitude of evoked motor potentials as measured by average peak-to-peak EMG potentials significantly differed by ca. 20% on average between the epochs evoked during the positive peak of ongoing alpha activity and the epochs evoked during the negative trough. At the level of the individual analysis, all of the 12 subjects showed a difference consistent with the group data (higher EMG amplitudes when stimulating in the negative trough of mu-alpha activity). The evoked EEG potentials showed a similar effect with the amplitude of the individual peaks higher when stimulating during the trough as compared to when stimulating during the peak.

Data and Fundamental Rights

This chapter provides an overview of the relationship between data and fundamental rights at the current point in time, and directions as to where and how this relationship might continue. At the basis of this relationship are the fundamental rights to privacy and free expression; however with the digital society being more pervasive, other fundamental rights, including freedom from discrimination and labour rights are now implicated by data. The role of private actors is prominent in discussions on data and fundamental rights given their key role in providing data infrastructure and services, in ways which may infringe users’ fundamental rights. In addition, fundamental rights organisations themselves are turning to the collection and use of data to assist with their functions. All of these topics will be explained and discussed before concluding with an outline of some possible future developments for data and fundamental rights, including the implementation of new technologies such as robotics and their impact on fundamental rights and the extent to which existing rights are still appropriate for the current and futures scenarios, or whether new kinds of fundamental rights or new kinds of implementations such as the digital constitutional project need to be recognised.

Time evolving clustering of the low-frequency magnetic field radiation emitted from laptop computers

Abstract The study presents a new time evolving clustering method for tracing the evolution of magnetic field ranges emitted from laptops over time. It is an extension of K-Medians algorithm for which cluster set found at current time point depends not only on the current measured values but also on the cluster sets found at the previous time points. The method is employed on the measured magnetic field radiation of the laptops in the extremely low frequency range at multiple frequencies of TCO standard. Specifically, a new measurement methodology of the magnetic field to which the users of the laptop are exposed in the office environment is proposed. The level of the magnetic field variation in time is measured. Results show that method can be employed to detect the most dangerous magnetic field ranges in laptop areas. Also, it is usable by manufacturers for advancing the safe laptop design or by laptop users for safer and more appropriate use.

Nonparametric Profile Monitoring using Dynamic Probability Control Limits

This article focuses on monitoring nonparametric profile with time‐varying sample sizes and random predictors. Traditional profile monitoring schemes, whose control limits are often determined before the monitoring initiates, are constructed based on perfect knowledge of profile sample sizes and predictors. In practice, however, our foreknowledge about future random sample sizes and predictors is seldom available. An inappropriate assumption or estimation of the sample sizes model and/or predictors distribution function may lead to unexpected performance of traditional control charts. To overcome this problem, we propose a kernel‐based nonparametric profile monitoring scheme which integrates the multivariate exponentially weighted moving average procedure with the probability control limits. The success of the proposed chart lies in the use of dynamic control limits which are determined online, essentially aiming at guaranteeing the conditional probability that the charting statistic exceeds the control limit at present given that there is no alarm before the current time point to meet a pre‐specified false alarm rate. The simulation studies show that the proposed control scheme has good in‐control and out‐of‐control performances under various scenarios of time‐varying sample sizes and random predictors. Copyright © 2016 John Wiley &amp; Sons, Ltd.

A Distribution-Free Multivariate Control Chart

Monitoring multivariate quality variables or data streams remains an important and challenging problem in statistical process control (SPC). Although the multivariate SPC has been extensively studied in the literature, designing distribution-free control schemes are still challenging and yet to be addressed well. This article develops a new nonparametric methodology for monitoring location parameters when only a small reference dataset is available. The key idea is to construct a series of conditionally distribution-free test statistics in the sense that their distributions are free of the underlying distribution given the empirical distribution functions. The conditional probability that the charting statistic exceeds the control limit at present given that there is no alarm before the current time point can be guaranteed to attain a specified false alarm rate. The success of the proposed method lies in the use of data-dependent control limits, which are determined based on the observations online rather than decided before monitoring. Our theoretical and numerical studies show that the proposed control chart is able to deliver satisfactory in-control run-length performance for any distributions with any dimension. It is also very efficient in detecting multivariate process shifts when the process distribution is heavy-tailed or skewed. Supplementary materials for this article are available online.

Evaluation of Maximum a Posteriori Estimation as Data Assimilation Method for Forecasting Infiltration-Inflow Affected Urban Runoff with Radar Rainfall Input

High quality on-line flow forecasts are useful for real-time operation of urban drainage systems and wastewater treatment plants. This requires computationally efficient models, which are continuously updated with observed data to provide good initial conditions for the forecasts. This paper presents a way of updating conceptual rainfall-runoff models using Maximum a Posteriori estimation to determine the most likely parameter constellation at the current point in time. This is done by combining information from prior parameter distributions and the model goodness of fit over a predefined period of time that precedes the forecast. The method is illustrated for an urban catchment, where flow forecasts of 0–4 h are generated by applying a lumped linear reservoir model with three cascading reservoirs. Radar rainfall observations are used as input to the model. The effects of different prior standard deviations and lengths of the auto-calibration period on the resulting flow forecast performance are evaluated. We were able to demonstrate that, if properly tuned, the method leads to a significant increase in forecasting performance compared to a model without continuous auto-calibration. Delayed responses and erratic behaviour in the parameter variations are, however, observed and the choice of prior distributions and length of auto-calibration period is not straightforward.

Promoter hypermethylation of HS3ST2, SEPTIN9 and SLIT2 combined with FGFR3 mutations as a sensitive/specific urinary assay for diagnosis and surveillance in patients with low or high-risk non-muscle-invasive bladder cancer.

BackgroundNon-muscle-invasive bladder cancer (NMIBC) is a high incidence form of bladder cancer (BCa), where genetic and epigenetic alterations occur frequently. We assessed the performance of associating a FGFR3 mutation assay and a DNA methylation analysis to improve bladder cancer detection and to predict disease recurrence of NMIBC patients.MethodsWe used allele specific PCR to determine the FGFR3 mutation status for R248C, S249C, G372C, and Y375C. We preselected 18 candidate genes reported in the literature as being hypermethylated in cancer and measured their methylation levels by quantitative multiplex-methylation specific PCR. We selected HS3ST2, SLIT2 and SEPTIN9 as the most discriminative between control and NMIBC patients and we assayed these markers on urine DNA from a diagnostic study consisting of 167 NMIBC and 105 controls and a follow-up study consisting of 158 NMIBC at diagnosis time’s and 425 at follow-up time. ROC analysis was performed to evaluate the diagnostic accuracy of each assay alone and in combination.ResultsFor Diagnosis: Using a logistic regression analysis with a model consisting of the 3 markers’ methylation values, FGFR3 status, age and known smoker status at the diagnosis time we obtained sensitivity/specificity of 97.6 %/84.8 % and an optimism-corrected AUC of 0.96. With an estimated BCa prevalence of 12.1 % in a hematuria cohort, this corresponds to a negative predictive value (NPV) of 99.6 %. For Follow-up: Using a logistic regression with FGFR3 mutation and the CMI at two time points (beginning of the follow-up and current time point), we got sensitivity/specificity/NPV of 90.3 %/65.1 %/97.0 % and a corrected AUC of 0.84. We also tested a thresholding algorithm with FGFR3 mutation and the two time points as described above, obtaining sensitivity/specificity/NPV values of, respectively, 94.5 %/75.9 %/98.5 % and an AUC of 0.82.ConclusionsWe showed that combined analysis of FGFR3 mutation and DNA methylation markers on urine can be a useful strategy in diagnosis, surveillance and for risk stratification of patients with NMIBC. These results provide the basis for a highly accurate noninvasive test for population screening and allowing to decrease the frequency of cystoscopy, an important feature for both patient quality of life improvement and care cost reduction.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-016-2748-5) contains supplementary material, which is available to authorized users.

주관적·객관적 가중치를 활용한 TOPSIS 기반 단위유역별 물이용 취약성 평가

In the present specification, selected is an indicator considering a situation of each watershed for assessing vulnerability of each small watershed. Indicator data is constructed by using national statistics and reports based on a current point of time and a SWAT model that can be simulated for each watershed. Also, multiple weights are applied by using subjective and objective weights. The vulnerability of each unit watershed by applying a TOPSIS method among multiple criteria decision methods. When assessing water resource vulnerability, a water resource is classified according to water quantity and water quality/aquatic ecology, thereby grasping a part with high vulnerability according to each watershed.