The Withdrawal Assessment Tool–1 (WAT–1): An Assessment Instrument for Monitoring Opioid and Benzodiazepine Withdrawal Symptoms in Pediatric Patients

Objective: To develop and test the validity and reliability of the Withdrawal Assessment Tool–1 for monitoring opioid and benzodiazepine withdrawal symptoms in pediatric patients. Design: Prospective psychometric evaluation. Pediatric critical care nurses assessed eligible at-risk pediatric patients for the presence of 19 withdrawal symptoms and rated the patient’s overall withdrawal intensity using a Numeric Rating Scale where zero indicated no withdrawal and 10 indicated worst possible withdrawal. The 19 symptoms were derived from the Opioid and Benzodiazepine Withdrawal Score, the literature and expert opinion. Setting: Two pediatric intensive care units in university-affiliated academic children’s hospitals. Patients: Eighty-three pediatric patients, median age 35 mos (interquartile range: 7 mos−10 yrs), recovering from acute respiratory failure who were being weaned from more than 5 days of continuous infusion or round-the-clock opioid and benzodiazepine administration. Interventions: Repeated observations during analgesia and sedative weaning. A total of 1040 withdrawal symptom assessments were completed, with a median (interquartile range) of 11 (6–16) per patient over 6.6 (4.8−11) days. Measurements and Main Results: Generalized linear modeling was used to analyze each symptom in relation to withdrawal intensity ratings, adjusted for site, subject, and age group. Symptoms with high redundancy or low levels of association with withdrawal intensity ratings were dropped, resulting in an 11-item (12-point) scale. Concurrent validity was indicated by high sensitivity (0.872) and specificity (0.880) for Withdrawal Assessment Tool–1 \u3e 3 predicting Numeric Rating Scale \u3e 4. Construct validity was supported by significant differences in drug exposure, length of treatment and weaning from sedation, length of mechanical ventilation and intensive care unit stay for patients with Withdrawal Assessment Tool–1 scores \u3e 3 compared with those with lower scores. Conclusions: The Withdrawal Assessment Tool–1 shows excellent preliminary psychometric performance when used to assess clinically important withdrawal symptoms in the pediatric intensive care unit setting. Further psychometric evaluation in diverse at-risk groups is needed

State Behavioral Scale (SBS) A Sedation Assessment Instrument for Infants and Young Children Supported on Mechanical Ventilation

Objective: To develop and test the reliability and validity of the State Behavioral Scale for use in describing sedation/agitation levels in young intubated patients supported on mechanical ventilation. Design: In this prospective, psychometric evaluation, pairs of trained pediatric critical care nurse evaluators simultaneously and independently assessed a convenience sample of pediatric intensive care unit patients along eight state/behavioral dimensions and a numeric rating scale (NRS) of 0 (extremely sedated) to 10 (extremely agitated). The eight dimensions were derived from the sedation/agitation literature and expert opinion and included respiratory drive, response to ventilation, coughing, best response to stimulation, attentiveness to careprovider, tolerance to care, consolability, and movement after consoled, each with 3–5 levels. Setting: An 18-bed pediatric medical–surgical intensive care unit and 26-bed pediatric cardiovascular intensive care unit in a university-affiliated academic children’s hospital. Patients: A total of 91 intubated mechanically ventilated patients 6 wks to 6 yrs of age provided a median of two observations (interquartile range, 1–3) for a total of 198 sets of observations. Excluded were postoperative patients or those receiving neuromuscular blockade. Interventions: Patients were observed for 1 min, and then incremental levels of stimulation were applied until patient response. After 2 mins of consoling, the state behavioral assessment and NRS were completed. Measurements: Weighted kappa and intraclass coefficients were generated to assess interrater reliability of the eight dimension and NRS ratings. Distinct state behavior profiles were empirically identified from the dimension ratings using hierarchical cluster analysis using a squared Euclidean distance measure and between-groups linkage. Construct validity of these profiles was assessed by comparing group mean NRS scores using one-way analysis of variance. Main Results: Weighted kappa scores for all 198 dimension ratings ranged from .44 to .76, indicating moderate to good interrater reliability. The intraclass coefficient of .79 was high for NRS ratings. Cluster analysis revealed five distinct state profiles, with mean NRS ratings of 1.1, 2.5, 4.0, 5.3, and 7.6, all of which differed significantly from each other (F = 75.8, p \u3c .001), supporting the profiles’ construct validity. Conclusions: Based on empirically derived state behavior profiles, we have constructed the State Behavioral Scale to allow systematic description of the sedation–agitation continuum in young pediatric patients supported on mechanical ventilation. Further studies including prospective validation and describing the effect of State Behavioral Scale implementation on clinical outcomes, including the quality of sedation and length of mechanical ventilation, are warranted

An Automated Mobile Game-based Screening Tool for Patients with Alcohol Dependence

Traditional methods for screening and diagnosis of alcohol dependence are typically administered by trained clinicians in medical settings and often rely on interview responses. These self-reports can be unintentionally or deliberately false, and misleading answers can, in turn, lead to inaccurate assessment and diagnosis. In this study, we examine the use of user-game interaction patterns on mobile games to develop an automated diagnostic and screening tool for alcohol-dependent patients. Our approach relies on the capture of interaction patterns during gameplay, while potential patients engage with popular mobile games on smartphones. The captured signals include gameplay performance, touch gestures, and device motion, with the intention of identifying patients with alcohol dependence. We evaluate the classification performance of various supervised learning algorithms on data collected from 40 patients and 40 age-matched healthy adults. The results show that patients with alcohol dependence can be automatically identified accurately using the ensemble of touch, device motion, and gameplay performance features on 3-minute samples (accuracy=0.95, sensitivity=0.95, and specificity=0.95). The present findings provide strong evidence suggesting the potential use of user-game interaction metrics on existing mobile games as discriminant features for developing an implicit measure to identify alcohol dependence conditions. In addition to supporting healthcare professionals in clinical decision-making, the game-based self-screening method could be used as a novel strategy to promote alcohol dependence screening, especially outside of clinical settings

The Evolution of Compact Binary Star Systems

We review the formation and evolution of compact binary stars consisting of white dwarfs (WDs), neutron stars (NSs), and black holes (BHs). Binary NSs and BHs are thought to be the primary astrophysical sources of gravitational waves (GWs) within the frequency band of ground-based detectors, while compact binaries of WDs are important sources of GWs at lower frequencies to be covered by space interferometers (LISA). Major uncertainties in the current understanding of properties of NSs and BHs most relevant to the GW studies are discussed, including the treatment of the natal kicks which compact stellar remnants acquire during the core collapse of massive stars and the common envelope phase of binary evolution. We discuss the coalescence rates of binary NSs and BHs and prospects for their detections, the formation and evolution of binary WDs and their observational manifestations. Special attention is given to AM CVn-stars -- compact binaries in which the Roche lobe is filled by another WD or a low-mass partially degenerate helium-star, as these stars are thought to be the best LISA verification binary GW sources.Comment: 105 pages, 18 figure

Modelling of the effect of ELMs on fuel retention at the bulk W divertor of JET

Effect of ELMs on fuel retention at the bulk W target of JET ITER-Like Wall was studied with multi-scale calculations. Plasma input parameters were taken from ELMy H-mode plasma experiment. The energetic intra-ELM fuel particles get implanted and create near-surface defects up to depths of few tens of nm, which act as the main fuel trapping sites during ELMs. Clustering of implantation-induced vacancies were found to take place. The incoming flux of inter-ELM plasma particles increases the different filling levels of trapped fuel in defects. The temperature increase of the W target during the pulse increases the fuel detrapping rate. The inter-ELM fuel particle flux refills the partially emptied trapping sites and fills new sites. This leads to a competing effect on the retention and release rates of the implanted particles. At high temperatures the main retention appeared in larger vacancy clusters due to increased clustering rate

Impact of fast ions on density peaking in JET: fluid and gyrokinetic modeling

The effect of fast ions on turbulent particle transport, driven by ion temperature gradient (ITG)/ trapped electron mode turbulence, is studied. Two neutral beam injection (NBI) heated JET discharges in different regimes are analyzed at the radial position ρ$_{t}$=0.6, one of them an L-mode and the other one an H-mode discharge. Results obtained from the computationally efficient fluid model EDWM and the gyro-fluid model TGLF are compared to linear and nonlinear gyrokinetic GENE simulations as well as the experimentally obtained density peaking. In these models, the fast ions are treated as a dynamic species with a Maxwellian background distribution. The dependence of the zero particle flux density gradient (peaking factor) on fast ion density, temperature and corresponding gradients, is investigated. The simulations show that the inclusion of a fast ion species has a stabilizing influence on the ITG mode and reduces the peaking of the main ion and electron density profiles in the absence of sources. The models mostly reproduce the experimentally obtained density peaking for the L-mode discharge whereas the H-mode density peaking is significantly underpredicted, indicating the importance of the NBI particle source for the H-mode density profile

Current Research into Applications of Tomography for Fusion Diagnostics

Retrieving spatial distribution of plasma emissivity from line integrated measurements on tokamaks presents a challenging task due to ill-posedness of the tomography problem and limited number of the lines of sight. Modern methods of plasma tomography therefore implement a-priori information as well as constraints, in particular some form of penalisation of complexity. In this contribution, the current tomography methods under development (Tikhonov regularisation, Bayesian methods and neural networks) are briefly explained taking into account their potential for integration into the fusion reactor diagnostics. In particular, current development of the Minimum Fisher Regularisation method is exemplified with respect to real-time reconstruction capability, combination with spectral unfolding and other prospective tasks