Assessing psychosocial vulnerability and care needs of pretransplant patients by means of the INTERMED.

OBJECTIVE: We investigated whether the INTERMED, a generic instrument for assessing biopsychosocial case complexity and direct care, identifies organ transplant patients at risk of unfavourable post-transplant development by comparing it to the Transplant Evaluation Rating Scale (TERS), the established measure for pretransplant psychosocial evaluation. METHOD: One hundred nineteen kidney, liver, and heart transplant candidates were evaluated using the INTERMED, TERS, SF-36, EuroQol, Montgomery-Åsberg Depression Rating Scale (MADRS), and Hospital Anxiety & Depression Scale (HADS). RESULTS: We found significant relationships between the INTERMED and the TERS scores. The INTERMED highly correlated with the HADS,MADRS, and mental and physical health scores of the SF-36 Health Survey. CONCLUSIONS: The results demonstrate the validity and usefulness of the INTERMED instrument for pretransplant evaluation. Furthermore, our findings demonstrate the different qualities of INTERMED and TERS in clinical practice. The advantages of the psychiatric focus of the TERS and the biopsychosocial perspective of the INTERMED are discussed in the context of current literature on integrated care.

Risk of falls and major bleeds in patients on oral anticoagulation therapy.

BACKGROUND: The risk of falls is the most commonly cited reason for not providing oral anticoagulation, although the risk of bleeding associated with falls on oral anticoagulants is still debated. We aimed to evaluate whether patients on oral anticoagulation with high falls risk have an increased risk of major bleeding. METHODS: We prospectively studied consecutive adult medical patients who were discharged on oral anticoagulants. The outcome was the time to a first major bleed within a 12-month follow-up period adjusted for age, sex, alcohol abuse, number of drugs, concomitant treatment with antiplatelet agents, and history of stroke or transient ischemic attack. RESULTS: Among the 515 enrolled patients, 35 patients had a first major bleed during follow-up (incidence rate: 7.5 per 100 patient-years). Overall, 308 patients (59.8%) were at high risk of falls, and these patients had a nonsignificantly higher crude incidence rate of major bleeding than patients at low risk of falls (8.0 vs 6.8 per 100 patient-years, P=.64). In multivariate analysis, a high falls risk was not statistically significantly associated with the risk of a major bleed (hazard ratio 1.09; 95% confidence interval, 0.54-2.21). Overall, only 3 major bleeds occurred directly after a fall (incidence rate: 0.6 per 100 patient-years). CONCLUSIONS: In this prospective cohort, patients on oral anticoagulants at high risk of falls did not have a significantly increased risk of major bleeds. These findings suggest that being at risk of falls is not a valid reason to avoid oral anticoagulants in medical patients.

How useful is consolidation of public sector entities' financial statements ?

Whether or not to consolidate financial statements is dealt with in IPSAS#6. This standard is by and large based on IAS#27. It deals with the criterion according to which an entity's financial statements should be considered and which consolidation technique should be used. However, it remains silent when it comes to exposing the reason why a public sector entity should consolidate its financial statements. The literature is almost as silent as IPSAS on this issue. Which means that there is a lack of both theoretical and empirical knowledge on this subject. This paper explores the usefulness of the consolidation of financial statements (CFS) for different categories of users. It aims at investigating for which purposes consolidation is most useful and whether enlarging the scope of the consolidate group serves these purposes. Five purposes are considered: information, decision- making, accountability, risk-assessment, statistics improvement. The paper also aims at investigating if some categories of users consider CFS more useful than others. The issue is essentially empirical. Therefore it is examined in light of the results of an in-person interviews. We surveyed 25members of parliament, officials, creditors, and consultants of the Swiss central government. The results show that consolidating FS is considered especially important and useful for risk- assessment, information and accountability and to a somewhat lesser extent for decision-making and statistics improvement. Extending the scope of CFS may improve the situation when it comes to statistics but it would only marginally make CFS more relevant for decision making. Consultants and, to a lesser extent, members of the finance ministry are those respondents who deem the scope enlargement to be the most useful.

E-learning initiatives in forensic interpretation: report on experiences from current projects and outlook

This paper reports on the purpose, design, methodology and target audience of E-learning courses in forensic interpretation offered by the authors since 2010, including practical experiences made throughout the implementation period of this project. This initiative was motivated by the fact that reporting results of forensic examinations in a logically correct and scientifically rigorous way is a daily challenge for any forensic practitioner. Indeed, interpretation of raw data and communication of findings in both written and oral statements are topics where knowledge and applied skills are needed. Although most forensic scientists hold educational records in traditional sciences, only few actually followed full courses that focussed on interpretation issues. Such courses should include foundational principles and methodology - including elements of forensic statistics - for the evaluation of forensic data in a way that is tailored to meet the needs of the criminal justice system. In order to help bridge this gap, the authors' initiative seeks to offer educational opportunities that allow practitioners to acquire knowledge and competence in the current approaches to the evaluation and interpretation of forensic findings. These cover, among other aspects, probabilistic reasoning (including Bayesian networks and other methods of forensic statistics, tools and software), case pre-assessment, skills in the oral and written communication of uncertainty, and the development of independence and self-confidence to solve practical inference problems. E-learning was chosen as a general format because it helps to form a trans-institutional online-community of practitioners from varying forensic disciplines and workfield experience such as reporting officers, (chief) scientists, forensic coordinators, but also lawyers who all can interact directly from their personal workplaces without consideration of distances, travel expenses or time schedules. In the authors' experience, the proposed learning initiative supports participants in developing their expertise and skills in forensic interpretation, but also offers an opportunity for the associated institutions and the forensic community to reinforce the development of a harmonized view with regard to interpretation across forensic disciplines, laboratories and judicial systems.

Impact of medical practice guidelines on the assessment of patients with acute coronary syndrome without persistent ST segment elevation.

OBJECTIVE: To assess the impact of introducing clinical practice guidelines on acute coronary syndrome without persistent ST segment elevation (ACS) on patient initial assessment. DESIGN: Prospective before-after evaluation over a 3-month period. SETTING: The emergency ward of a tertiary teaching hospital. PATIENTS: All consecutive patients with ACS evaluated in the emergency ward over the two 3-month periods. INTERVENTION: Implementation of the practice guidelines, and the addition of a cardiology consultant to the emergency team. MAIN OUTCOME MEASURES: Diagnosis, electrocardiogram interpretation, and risk stratification after the initial evaluation. RESULTS: The clinical characteristics of the 328 and 364 patients evaluated in the emergency ward for suspicion of ACS before and after guideline implementation were similar. Significantly more patients were classified as suffering from atypical chest pain (39.6% versus 47.0%; P = 0.006) after guideline implementation. Guidelines availability was associated with significantly more formal diagnoses (79.9% versus 92.9%; P < 0.0001) and risk stratification (53.7% versus 65.4%, P < 0.0001) at the end of initial assessment. CONCLUSION: Guidelines implementation, along with availability of a cardiology consultant in the emergency room had a positive impact on initial assessment of patients evaluated for suspicion of ACS. It led to increased confidence in diagnosis and stratification by risk, which are the first steps in initiating effective treatment for this common condition.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

The Learning Affect Monitor (LAM) - A computer-based system integrating quantitative and qualitative assessment of affective states in daily life.

The Learning Affect Monitor (LAM) is a new computer-based assessment system integrating basic dimensional evaluation and discrete description of affective states in daily life, based on an autonomous adapting system. Subjects evaluate their affective states according to a tridimensional space (valence and activation circumplex as well as global intensity) and then qualify it using up to 30 adjective descriptors chosen from a list. The system gradually adapts to the user, enabling the affect descriptors it presents to be increasingly relevant. An initial study with 51 subjects, using a 1 week time-sampling with 8 to 10 randomized signals per day, produced n = 2,813 records with good reliability measures (e.g., response rate of 88.8%, mean split-half reliability of .86), user acceptance, and usability. Multilevel analyses show circadian and hebdomadal patterns, and significant individual and situational variance components of the basic dimension evaluations. Validity analyses indicate sound assignment of qualitative affect descriptors in the bidimensional semantic space according to the circumplex model of basic affect dimensions. The LAM assessment module can be implemented on different platforms (palm, desk, mobile phone) and provides very rapid and meaningful data collection, preserving complex and interindividually comparable information in the domain of emotion and well-being.

Foot worn inertial sensors for gait assessment and rehabilitation based on motorized shoes.

Fall prevention in elderly subjects is often based on training and rehabilitation programs that include mostly traditional balance and strength exercises. By applying such conventional interventions to improve gait performance and decrease fall risk, some important factors are neglected such as the dynamics of the gait and the motor learning processes. The EU project "Self Mobility Improvement in the eLderly by counteractING falls" (SMILING project) aimed to improve age-related gait and balance performance by using unpredicted external perturbations during walking through motorized shoes that change insole inclination at each stance. This paper describes the shoe-worn inertial module and the gait analysis method needed to control in real-time the shoe insole inclination during training, as well as gait spatio-temporal parameters obtained during long distance walking before and after the 8-week training program that assessed the efficacy of training with these motorized shoes.

High-field (3T) magnetic resonance defecography with functional assessment of the evacuation phase : a pictorial essay : P10

Purpose: Dynamic high-field magnetic resonance (MR) defecography including the evacuation phase is a promising tool for the assessment of functional pelvic disorders, nowadays seen with increasing frequency in elderly women in particular. Learning objectives: 1. To describe the adequate technique of dynamic high-field MRI (3T) in assessing pelvic floor disorders. 2. To provide an overview of the most common pathologies occurring during the evacuation phase, especially in comparison with results of conventional defecography. Methods and materials: After description of the ideal technical parameters of MR defecography performed in supine position after gel rectal filling with a 3 Tesla unit and including the evacuation phase we stress the importance of using a standardized evaluation system for the exact assessment of pelvic floor pathophysiology. Results: The typical pelvic floor disorders occurring before and/or during the evacuation phase, such as sphincter insufficiency, vaginal vault and/or uterine prolapse, cystourethrocele, peritoneo-/ entero-/ sigmoïdocele or rectal prolapse, are demonstrated. The difference between the terms "pelvic floor descent" and "pelvic floor relaxation" are pictorially outlined. MR results are compared with these of conventional defecography. Conclusion: Exact knowledge about the correct technique including the evacuation phase and the use of a standardized evaluation system in assessing pelvic floor disorders by dynamic high-field MRI is mandatory for accurate and reproducible diagnosis.

Rehabilitation needs assessment in persons with spinal cord injury following the 2010 earthquake in Haiti: a pilot study using an ICF-based tool.

OBJECTIVE: The aim of this pilot study was to describe problems in functioning and associated rehabilitation needs in persons with spinal cord injury after the 2010 earthquake in Haiti by applying a newly developed tool based on the International Classification of Functioning, Disability and Health (ICF). DESIGN: Pilot study. SUBJECTS: Eighteen persons with spinal cord injury (11 women, 7 men) participated in the needs assessment. Eleven patients had complete lesions (American Spinal Injury Association Impairment Scale; AIS A), one patient had tetraplegia. METHODS: Data collection included information from the International Spinal Cord Injury Core Data Set and a newly developed needs assessment tool based on ICF Core Sets. This tool assesses the level of functioning, the corresponding rehabilitation need, and required health professional. Data were summarized using descriptive statistics. RESULTS: In body functions and body structures, patients showed typical problems following spinal cord injury. Nearly all patients showed limitations and restrictions in their activities and participation related to mobility, self-care and aspects of social integration. Several environmental factors presented barriers to these limitations and restrictions. However, the availability of products and social support were identified as facilitators. Rehabilitation needs were identified in nearly all aspects of functioning. To address these needs, a multidisciplinary approach would be needed. CONCLUSION: This ICF-based needs assessment provided useful information for rehabilitation planning in the context of natural disaster. Future studies are required to test and, if necessary, adapt the assessment.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

Imaging Properties of MS Lesions Correlate with Attention Deficits in Early Multiple Sclerosis

Introduction: Cognitive impairment affects 40-65% of multiple sclerosis (MS) patients, often since early stages of the disease (relapsing remitting MS, RRMS). Frequently affected functions are memory, attention or executive abilities but the most sensitive measure of cognitive deficits in early MS is the information processing speed (Amato, 2008). MRI has been extensively exploited to investigate the substrate of cognitive dysfunction in MS but the underlying physiopathological mechanisms remain unclear. White matter lesion load, whole-brain atrophy and cortical lesions' number play a role but correlations are in some cases modest (Rovaris, 2006; Calabrese, 2009). In this study, we aimed at characterizing and correlating the T1 relaxation times of cortical and sub-cortical lesions with cognitive deficits detected by neuropsychological tests in a group of very early RR MS patients. Methods: Ten female patients with very early RRMS (age: 31.6 ±4.7y; disease duration: 3.8 ±1.9y; EDSS disability score: 1.8 ±0.4) and 10 age- and gender-matched healthy volunteers (mean age: 31.2 ±5.8y) were included in the study. All participants underwent the following neuropsychological tests: Rao's Brief Repeatable Battery of Neuropsychological tests (BRB-N), Stockings of Cambridge, Trail Making Test (TMT, part A and B), Boston Naming Test, Hooper Visual Organization Test and copy of the Rey-Osterrieth Complex Figure. Within 2 weeks from neuropsychological assessment, participants underwent brain MRI at 3T (Magnetom Trio a Tim System, Siemens, Germany) using a 32-channel head coil. The imaging protocol included 3D sequences with 1x1x1.2 mm3 resolution and 256x256x160 matrix, except for axial 2D-FLAIR: -DIR (T2-weighted, suppressing both WM and CSF; Pouwels, 2006) -MPRAGE (T1-weighted; Mugler, 1991) -MP2RAGE (T1-weighted with T1 maps; Marques, 2010) -FLAIR SPACE (only for patient 4-10, T2-weighted; Mugler, 2001) -2D Axial FLAIR (0.9x0.9x2.5 mm3, 256x256x44 matrix). Lesions were identified by one experienced neurologist and radiologist using all contrasts, manually contoured and assigned to regional locations (cortical or sub-cortical). Lesion number, volume and T1 relaxation time were calculated for lesions in each contrast and in a merged mask representing the union of the lesions from all contrasts. T1 relaxation times of lesions were normalized with the mean T1 value in corresponding control regions of the healthy subjects. Statistical analysis was performed using GraphPad InStat software. Cognitive scores were compared between patients and controls with paired t-tests; p values ≤ 0.05 were considered significant. Spearmann correlation tests were performed between the cognitive tests, which differed significantly between patients and controls, and lesions' i) number ii) volume iii) T1 relaxation time iv) disease duration and v) years of study. Results: Cortical and sub-cortical lesions count, T1 values and volume are reported in Table 1 (A and B). All early RRMS patients showed cortical lesions (CLs) and the majority consisted of CLs type I (lesions with a cortical component extending to the sub-cortical tissue). The rest of cortical lesions were characterized as type II (intra-cortical lesions). No type III/IV lesions (large sub-pial lesions) were detected. RRMS patients were slightly less educated (13.5±2.5y vs. 16.3±1.8y of study, p=0.02) than the controls. Signs of cortical dysfunction (i.e. impaired learning, language, visuo-spatial skills or gnosis) were rare in all patients. However, patients showed on average lower scores on measures of visual attention and information processing speed (TMT-part A: p=0.01; TMT-part B: p=0.006; PASAT-included in the BRB-N: p=0.04). The T1 relaxation values of CLs type I negatively correlated with the TMT-part A score (r=0.78, p<0.01). The correlations of TMT-part B score and PASAT score with T1 relaxation time of lesions as well and the correlation between TMT-part A, TMT-part B and PASAT score with lesions' i) number ii) volume iii) disease duration and iv) years of study did not reach significance. In order to preclude possible influences from partial volume effects on the T1 values, the correlation between lesion volume and T1 value of CLs type I was calculated; no correlation was found, suggesting that partial volume effects did not affect the statistics. Conclusions: The present pilot study reports for the first time the presence and the T1 characteristics at 3 T of cortical lesions in very early RRMS (< 6 y disease duration). It also shows that CLS type I represents the most frequent cortical lesion type in this cohort of RRMS patients. In addition, it reveals a negative correlation between the attentional test TMT-part A and the T1 properties of cortical lesions type I. In other words, lower attention deficits are concomitant with longer T1-relaxation time in cortical lesions. In respect to this last finding, it could be speculated that long relaxation time correspond to a certain degree of tissue loss that is enough to stimulate compensatory mechanisms. This hypothesis is in line with previous fMRI studies showing functional compensatory mechanisms to help maintaining normal or sub-normal attention performances in RR MS patients (Penner, 2003).

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

An empirical hierarchical Bayesian unification of occupational exposure assessment methods.

In occupational exposure assessment of airborne contaminants, exposure levels can either be estimated through repeated measurements of the pollutant concentration in air, expert judgment or through exposure models that use information on the conditions of exposure as input. In this report, we propose an empirical hierarchical Bayesian model to unify these approaches. Prior to any measurement, the hygienist conducts an assessment to generate prior distributions of exposure determinants. Monte-Carlo samples from these distributions feed two level-2 models: a physical, two-compartment model, and a non-parametric, neural network model trained with existing exposure data. The outputs of these two models are weighted according to the expert's assessment of their relevance to yield predictive distributions of the long-term geometric mean and geometric standard deviation of the worker's exposure profile (level-1 model). Bayesian inferences are then drawn iteratively from subsequent measurements of worker exposure. Any traditional decision strategy based on a comparison with occupational exposure limits (e.g. mean exposure, exceedance strategies) can then be applied. Data on 82 workers exposed to 18 contaminants in 14 companies were used to validate the model with cross-validation techniques. A user-friendly program running the model is available upon request.