Objective. To study the prevalence, nature and determinants of aggression among inpatients with acquired brain injury. Background. Patients with acquired brain injury often have difficulty in controlling their aggressive impulses. Design. A prospective observational study design. Methods. By means of the Staff Observation Aggression Scale-Revised, the prevalence, nature and severity of aggressive behaviour of inpatients with acquired brain injury was assessed on a neuropsychiatric treatment ward with 45 beds. Additional data on patient-related variables were gathered from the patients’ files. Results. In total, 388 aggressive incidents were recorded over 17 weeks. Of a total of 57 patients included, 24 (42%) patients had engaged in aggressive behaviour on one or more occasions. A relatively small proportion of patients (n = 8; 14%) was found to be responsible for the majority of incidents (n = 332; 86%). The vast majority of aggression incidents (n = 270; 70%) were directly preceded by interactions between patients and nursing staff. In line with this, most incidents occurred at times of high contact intensity. Aggressive behaviour was associated with male gender, length of stay at the ward, legal status and hypoxia as the cause of brain injury. Conclusion. Aggression was found to be highly prevalent among inpatients with acquired brain injury. The results suggest that for the prevention of aggression on the ward, it may be highly effective to develop individually tailored interventions for the subgroup with serious aggression problems. Relevance to clinical practice. Insight into the frequency, nature and determinants of aggressive behaviour in inpatients with acquired brain injury provides nurses with tools for the prevention and treatment of aggressive behaviour.
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a b s t r a c t: Objective: To study the impact of target volume changes in brain metastases during fractionated stereotactic radiosurgery (fSRS) and identify patients that benefit from MRI guidance. Material and methods: For 15 patients (18 lesions) receiving fSRS only (fSRSonly) and 19 patients (20 lesions) receiving fSRS postoperatively (fSRSpostop), a treatment planning MRI (MR0) and repeated MRI during treatment (MR1) were acquired. The impact of target volume changes on the target coverage was analyzed by evaluating the planned dose distribution (based on MR0) on the planning target volume (PTV) during treatment as defined on MR1. The predictive value of target volume changes before treatment (using the diagnostic MRI (MRD)) was studied to identify patients that experienced the largest changes during treatment. Results: Target volume changes during fSRS did result in large declines of the PTV dose coverage up to 34.8% (median = 3.2%) for fSRSonly patients. For fSRSpostop the variation and declines were smaller (median PTV dose coverage change = 0.5% (4.5% to 1.9%)). Target volumes changes did also impact the minimum dose in the PTV (fSRSonly; 2.7 Gy (16.5 to 2.3 Gy), fSRSpostop; 0.4 Gy (4.2 to 2.5 Gy)). Changes in target volume before treatment (i.e. seen between the MRD and MR0) predicted which patients experienced the largest dose coverage declines during treatment. Conclusion: Target volume changes in brain metastases during fSRS can result in worsening of the target dose coverage. Patients benefiting the most from a repeated MRI during treatment could be identified before treatment.
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Objectives: Participation is considerably restricted in children and adolescents with acquired brain injury (ABI) as compared to their healthy peers. This systematic review aims to identify which factors are associated with participation in children and adolescents with ABI. Methods: A systematic search in Medline and various other electronic databases from January 2001–November 2014 was performed. All clinical studies describing determinants of participation at least 1 year after the diagnosis of ABI by means of one or more pre-defined instruments in patients up to 18 years of age were included. Extracted data included study characteristics, patient characteristics, participation outcome and determinants of participation (categorized into: health conditions (including characteristics of ABI), body functions and structures, activities, personal factors and environmental factors). The methodological quality of the studies was evaluated based on three quality aspects (selection, information and statistical analysis bias) and scored as low, moderate or high. Results: Eight studies using an explicit participation outcome measure were selected after review, including a total of 1863 patients, with a follow-up ranging from 1 up to 288 months. Three studies included patients with a traumatic or a non-traumatic brain injury (TBI or NTBI) and five studies with only TBI patients. Factors consistently found to be associated with more participation restrictions were: greater severity of ABI, impaired motor, cognitive, behavioural and/or sensory functioning, limited accessibility of the physical environmentand worse family functioning. Fewer participation problems were associated with a supportive/nurturing parenting style, higher household income, acceptance and support in the community and availability of special programmes. The overall methodological quality of the included studies was high in two and moderate in six studies. Conclusion: This systematic review shows that only a few, moderate quality, studies on the determinants of participation after paediatric ABI using recommended explicit measurement instruments are available. Various components of the ICF model: health condition, body functions and structures and environmental factors were consistently found to be associated with participation. More methodologically sound studies, using the recommended explicit outcome measures, a standardized set of potential determinants and longterm follow-up are suggested to increase the knowledge on participation in children and youth with ABI.
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Background: Traumatic brain injury (TBI) is in the developed countries the most common cause of death and disability in childhood. Aim: The purpose of this study is to estimate the incidence of TBI for children and young people in an urbanised region of the Netherlands and to describe relevant characteristics of this group. Methods: Patients, aged 1 month - 24 years who presented with traumatic brain injury at the Erasmus University Hospital (including the Sophia Children's Hospital) in 2007 and 2008 were included in a retrospective study. Data were collected by means of diagnosis codes and search terms for TBI in patient records. The incidence of TBI in the different referral areas of the hospital for standard, specialised and intensive patient care was estimated. Results: 472 patients met the inclusion criteria. The severity of the Injury was classified as mild in 342 patients, moderate in 50 patients and severe in 80 patients. The total incidence of traumatic brain injury in the referral area of the Erasmus University Hospital was estimated at 113.9 young people per 100.000. The incidence for mild traumatic brain injury was estimated at 104.4 young people, for moderate 6.1 and for severe 3.4 young people per 100.000. Conclusion: The ratio for mild, moderate and severe traumatic brain injury in children and young people was 33.7e1.8e1.In the mild TBI group almost 17% of the patients reported sequelae. The finding that 42% of them had a normal brain CT scan at admission underwrites the necessity of careful follow up of children and young people with mild TBI.
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In this chapter we discuss the implications of our research in the wider context of current models of brain function, endeavoring to understand the consequences of score-dependence and improvisation in terms of the ‘predicting brain’, the dual-stream model of perception and action, the procedural-declarative model of learning and memory, ideomotor learning and sensorimotor mapping, and the implicit acquisition of hierarchical music syntax.
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Purpose: To study the association between fatigue and participation and QoL after acquired brain injury (ABI) in adolescents and young adults (AYAs). Materials & Methods: Cross-sectional study with AYAs aged 14–25 years, diagnosed with ABI. The PedsQL™ Multidimensional Fatigue Scale, Child & Adolescent Scale of Participation, and PedsQL™4.0 Generic Core Scales were administered. Results: Sixty-four AYAs participated in the study, 47 with traumatic brain injury (TBI). Median age at admission was 17.6 yrs, 0.8 yrs since injury. High levels of fatigue (median 44.4 (IQR 34.7, 59.7)), limited participation (median 82.5 (IQR 68.8, 92.3)), and diminished QoL (median 63.0 (IQR 47.8, 78.3)) were reported. More fatigue was significantly associated with more participation restrictions (β 0.64, 95%CI 0.44, 0.85) and diminished QoL (β 0.87, 95%CI 0.72, 1.02). Conclusions: AYAs with ABI reported high levels of fatigue, limited participation and diminished quality of life with a significant association between fatigue and both participation and QoL. Targeting fatigue in rehabilitation treatment could potentially improve participation and QoL.
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Background: Modern modeling techniques may potentially provide more accurate predictions of dichotomous outcomes than classical techniques. Objective: In this study, we aimed to examine the predictive performance of eight modeling techniques to predict mortality by frailty. Methods: We performed a longitudinal study with a 7-year follow-up. The sample consisted of 479 Dutch community-dwelling people, aged 75 years and older. Frailty was assessed with the Tilburg Frailty Indicator (TFI), a self-report questionnaire. This questionnaire consists of eight physical, four psychological, and three social frailty components. The municipality of Roosendaal, a city in the Netherlands, provided the mortality dates. We compared modeling techniques, such as support vector machine (SVM), neural network (NN), random forest, and least absolute shrinkage and selection operator, as well as classical techniques, such as logistic regression, two Bayesian networks, and recursive partitioning (RP). The area under the receiver operating characteristic curve (AUROC) indicated the performance of the models. The models were validated using bootstrapping. Results: We found that the NN model had the best validated performance (AUROC=0.812), followed by the SVM model (AUROC=0.705). The other models had validated AUROC values below 0.700. The RP model had the lowest validated AUROC (0.605). The NN model had the highest optimism (0.156). The predictor variable “difficulty in walking” was important for all models. Conclusions: Because of the high optimism of the NN model, we prefer the SVM model for predicting mortality among community-dwelling older people using the TFI, with the addition of “gender” and “age” variables. External validation is a necessary step before applying the prediction models in a new setting.
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Individuals with autism increasingly enroll in universities, but little is known about predictors for their success. This study developed predictive models for the academic success of autistic bachelor students (N=101) in comparison to students with other health conditions (N=2465) and students with no health conditions (N=25,077). We applied propensity score weighting to balance outcomes. The research showed that autistic students’ academic success was predictable, and these predictions were more accurate than predictions of their peers’ success. For first-year success, study choice issues were the most important predictors (parallel program and application timing). Issues with participation in pre-education (missingness of grades in pre-educational records) and delays at the beginning of autistic students’ studies (reflected in age) were the most influential predictors for the second-year success and delays in the second and final year of their bachelor’s program. In addition, academic performance (average grades) was the strongest predictor for degree completion in 3 years. These insights can enable universities to develop tailored support for autistic students. Using early warning signals from administrative data, institutions can lower dropout risk and increase degree completion for autistic students.
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Background: Advanced statistical modeling techniques may help predict health outcomes. However, it is not the case that these modeling techniques always outperform traditional techniques such as regression techniques. In this study, external validation was carried out for five modeling strategies for the prediction of the disability of community-dwelling older people in the Netherlands. Methods: We analyzed data from five studies consisting of community-dwelling older people in the Netherlands. For the prediction of the total disability score as measured with the Groningen Activity Restriction Scale (GARS), we used fourteen predictors as measured with the Tilburg Frailty Indicator (TFI). Both the TFI and the GARS are self-report questionnaires. For the modeling, five statistical modeling techniques were evaluated: general linear model (GLM), support vector machine (SVM), neural net (NN), recursive partitioning (RP), and random forest (RF). Each model was developed on one of the five data sets and then applied to each of the four remaining data sets. We assessed the performance of the models with calibration characteristics, the correlation coefficient, and the root of the mean squared error. Results: The models GLM, SVM, RP, and RF showed satisfactory performance characteristics when validated on the validation data sets. All models showed poor performance characteristics for the deviating data set both for development and validation due to the deviating baseline characteristics compared to those of the other data sets. Conclusion: The performance of four models (GLM, SVM, RP, RF) on the development data sets was satisfactory. This was also the case for the validation data sets, except when these models were developed on the deviating data set. The NN models showed a much worse performance on the validation data sets than on the development data sets.
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This pilot study explores the possibility of cognitive training software Neurotracker (NT), to have potential beneficial effects for Traumatic Brain Injury patients with Sensory Processing Disorder. Five subjects with TBI and SPD trained for 5 weeks/21 sessions with Neurotracker. Pre-post training cognitive tests (WAIS TMTA, TMTB, LNS) and surveys were conducted to measure possible cognitive differences with no statistical significant results. However, significant improvement in Neurotracker scores were found. =2.73, SD = 0.55) and positive changes associated with attention attention span, divided attention, (multiple) object tracking and motion sickness. LinkedIn: https://www.linkedin.com/in/bernard-de-roosz-28b96b125/
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