Benchmarking therapeutic drug monitoring software: A systematic evaluation of available computer tools

Introduction: Therapeutic drug monitoring (TDM) aims at optimizing treatment by individualizing dosage regimen based on measurement of blood concentrations. Maintaining concentrations within a target range requires pharmacokinetic and clinical capabilities. Bayesian calculation represents a gold standard in TDM approach but requires computing assistance. In the last decades computer programs have been developed to assist clinicians in this assignment. The aim of this benchmarking was to assess and compare computer tools designed to support TDM clinical activities.¦Method: Literature and Internet search was performed to identify software. All programs were tested on common personal computer. Each program was scored against a standardized grid covering pharmacokinetic relevance, user-friendliness, computing aspects, interfacing, and storage. A weighting factor was applied to each criterion of the grid to consider its relative importance. To assess the robustness of the software, six representative clinical vignettes were also processed through all of them.¦Results: 12 software tools were identified, tested and ranked. It represents a comprehensive review of the available software's characteristics. Numbers of drugs handled vary widely and 8 programs offer the ability to the user to add its own drug model. 10 computer programs are able to compute Bayesian dosage adaptation based on a blood concentration (a posteriori adjustment) while 9 are also able to suggest a priori dosage regimen (prior to any blood concentration measurement), based on individual patient covariates, such as age, gender, weight. Among those applying Bayesian analysis, one uses the non-parametric approach. The top 2 software emerging from this benchmark are MwPharm and TCIWorks. Other programs evaluated have also a good potential but are less sophisticated (e.g. in terms of storage or report generation) or less user-friendly.¦Conclusion: Whereas 2 integrated programs are at the top of the ranked listed, such complex tools would possibly not fit all institutions, and each software tool must be regarded with respect to individual needs of hospitals or clinicians. Interest in computing tool to support therapeutic monitoring is still growing. Although developers put efforts into it the last years, there is still room for improvement, especially in terms of institutional information system interfacing, user-friendliness, capacity of data storage and report generation.

Benchmarking therapeutic drug monitoring software: A systematic evaluation of available computer tools

Objectives: Therapeutic drug monitoring (TDM) aims at optimizing treatment by individualizing dosage regimen based on blood concentrations measurement. Maintaining concentrations within a target range requires pharmacokinetic (PK) and clinical capabilities. Bayesian calculation represents a gold standard in TDM approach but requires computing assistance. The aim of this benchmarking was to assess and compare computer tools designed to support TDM clinical activities.¦Methods: Literature and Internet were searched to identify software. Each program was scored against a standardized grid covering pharmacokinetic relevance, user-friendliness, computing aspects, interfacing, and storage. A weighting factor was applied to each criterion of the grid to consider its relative importance. To assess the robustness of the software, six representative clinical vignettes were also processed through all of them.¦Results: 12 software tools were identified, tested and ranked. It represents a comprehensive review of the available software characteristics. Numbers of drugs handled vary from 2 to more than 180, and integration of different population types is available for some programs. Nevertheless, 8 programs offer the ability to add new drug models based on population PK data. 10 computer tools incorporate Bayesian computation to predict dosage regimen (individual parameters are calculated based on population PK models). All of them are able to compute Bayesian a posteriori dosage adaptation based on a blood concentration while 9 are also able to suggest a priori dosage regimen, only based on individual patient covariates. Among those applying Bayesian analysis, MM-USC*PACK uses a non-parametric approach. The top 2 programs emerging from this benchmark are MwPharm and TCIWorks. Others programs evaluated have also a good potential but are less sophisticated or less user-friendly.¦Conclusions: Whereas 2 software packages are ranked at the top of the list, such complex tools would possibly not fit all institutions, and each program must be regarded with respect to individual needs of hospitals or clinicians. Programs should be easy and fast for routine activities, including for non-experienced users. Although interest in TDM tools is growing and efforts were put into it in the last years, there is still room for improvement, especially in terms of institutional information system interfacing, user-friendliness, capability of data storage and automated report generation.

Munc18-1 mutations that strongly impair SNARE-complex binding support normal synaptic transmission.

Synaptic transmission depends critically on the Sec1p/Munc18 protein Munc18-1, but it is unclear whether Munc18-1 primarily operates as a integral part of the fusion machinery or has a more upstream role in fusion complex assembly. Here, we show that point mutations in Munc18-1 that interfere with binding to the free Syntaxin1a N-terminus and strongly impair binding to assembled SNARE complexes all support normal docking, priming and fusion of synaptic vesicles, and normal synaptic plasticity in munc18-1 null mutant neurons. These data support a prevailing role of Munc18-1 before/during SNARE-complex assembly, while its continued association to assembled SNARE complexes is dispensable for synaptic transmission.

The InterActive Choice Aid: A new approach to supporting online consumer decision making

Interactive Choice Aid (ICA) is a decision aid, introduced in this paper, that systematically assists consumers with online purchase decisions. ICA integrates aspects from prescriptive decision theory, insights from descriptive decision research, and practical considerations; thereby combining pre-existing best practices with novel features. Instead of imposing an objectively ideal but unnatural decision procedure on the user, ICA assists the natural process of human decision-making by providing explicit support for the execution of the user's decision strategies. The application contains an innovative feature for in-depth comparisons of alternatives through which users' importance ratings are elicited interactively and in a playful way. The usability and general acceptance of the choice aid was studied; results show that ICA is a promising contribution and provides insights that may further improve its usability.

Cost-effectiveness analysis of immune-modulating nutritional support for gastrointestinal cancer patients.

BACKGROUND & AIM: Immune-modulating nutritional formula containing arginine, omega-3 fatty acids and nucleotides has been demonstrated to decrease complications and length of stay in surgical patients. This study aims at assessing the impact of immune-modulating formula on hospital costs in gastrointestinal cancer surgical patients in Switzerland. METHOD: Based on a previously published meta-analysis, the relative risks of overall and infectious complications with immune-modulating versus standard nutrition formula were computed. Swiss hospital costs of patients undergoing gastrointestinal cancer surgery were retrieved. A method was developed to compute the patients' severity level, not taking into account the complications from the surgery. Incremental costs of complications were computed for both treatment groups, and sensitivity analyses were carried out. RESULTS: Relative risk of complications with pre-, peri- and post-operative use of immune-modulating formula was 0.69 (95%CI 0.58-0.83), 0.62 (95%CI 0.53-0.73) and 0.73 (95%CI 0.35-0.96) respectively. The estimated average contribution of complications to the cost of stay was CHF 14,949 (euro10,901) per patient (95%CI 10,712-19,186), independently of case's severity. Based on this cost, immune-modulating nutritional support decreased costs of hospital stay by CHF 1638 to CHF 2488 per patient (euro1195-euro1814). Net hospital savings were present for baseline complications rates as low as 5%. CONCLUSION: Immune-modulating nutritional solution is a cost-saving intervention in gastrointestinal cancer patients. The additional cost of immune-modulating formula are more than offset by savings associated with decreased treatment of complications.

Mynodbcsv: Lightweight Zero-Config Database Solution for Handling Very Large CSV Files.

Volumes of data used in science and industry are growing rapidly. When researchers face the challenge of analyzing them, their format is often the first obstacle. Lack of standardized ways of exploring different data layouts requires an effort each time to solve the problem from scratch. Possibility to access data in a rich, uniform manner, e.g. using Structured Query Language (SQL) would offer expressiveness and user-friendliness. Comma-separated values (CSV) are one of the most common data storage formats. Despite its simplicity, with growing file size handling it becomes non-trivial. Importing CSVs into existing databases is time-consuming and troublesome, or even impossible if its horizontal dimension reaches thousands of columns. Most databases are optimized for handling large number of rows rather than columns, therefore, performance for datasets with non-typical layouts is often unacceptable. Other challenges include schema creation, updates and repeated data imports. To address the above-mentioned problems, I present a system for accessing very large CSV-based datasets by means of SQL. It's characterized by: "no copy" approach - data stay mostly in the CSV files; "zero configuration" - no need to specify database schema; written in C++, with boost [1], SQLite [2] and Qt [3], doesn't require installation and has very small size; query rewriting, dynamic creation of indices for appropriate columns and static data retrieval directly from CSV files ensure efficient plan execution; effortless support for millions of columns; due to per-value typing, using mixed text/numbers data is easy; very simple network protocol provides efficient interface for MATLAB and reduces implementation time for other languages. The software is available as freeware along with educational videos on its website [4]. It doesn't need any prerequisites to run, as all of the libraries are included in the distribution package. I test it against existing database solutions using a battery of benchmarks and discuss the results.

Monitoring network optimisation for spatial data classification using support vector machines

The paper presents a novel method for monitoring network optimisation, based on a recent machine learning technique known as support vector machine. It is problem-oriented in the sense that it directly answers the question of whether the advised spatial location is important for the classification model. The method can be used to increase the accuracy of classification models by taking a small number of additional measurements. Traditionally, network optimisation is performed by means of the analysis of the kriging variances. The comparison of the method with the traditional approach is presented on a real case study with climate data.

The thalamocortical projection systems in primate: an anatomical support for multisensory and sensorimotor interplay.

Multisensory and sensorimotor integrations are usually considered to occur in superior colliculus and cerebral cortex, but few studies proposed the thalamus as being involved in these integrative processes. We investigated whether the organization of the thalamocortical (TC) systems for different modalities partly overlap, representing an anatomical support for multisensory and sensorimotor interplay in thalamus. In 2 macaque monkeys, 6 neuroanatomical tracers were injected in the rostral and caudal auditory cortex, posterior parietal cortex (PE/PEa in area 5), and dorsal and ventral premotor cortical areas (PMd, PMv), demonstrating the existence of overlapping territories of thalamic projections to areas of different modalities (sensory and motor). TC projections, distinct from the ones arising from specific unimodal sensory nuclei, were observed from motor thalamus to PE/PEa or auditory cortex and from sensory thalamus to PMd/PMv. The central lateral nucleus and the mediodorsal nucleus project to all injected areas, but the most significant overlap across modalities was found in the medial pulvinar nucleus. The present results demonstrate the presence of thalamic territories integrating different sensory modalities with motor attributes. Based on the divergent/convergent pattern of TC and corticothalamic projections, 4 distinct mechanisms of multisensory and sensorimotor interplay are proposed.

Traumatologie de la personne âgée : une urgence nutritionnelle ? [Elderly orthopedic patients: Is nutritional support needed?]

La fracture de hanche chez la personne âgée reste un problème de santé publique. Elle est la conséquence d'une chute neuf fois sur dix et survient chez des personnes fragilisées par une ostéoporose, une sarcopénie, une dénutrition. Dans un service de traumatologie, la dénutrition protéino-énergétique est fréquente. Présente dès l'admission chez environ un patient sur deux, elle va souvent s'aggraver pendant le séjour hospitalier et favoriser la survenue de complications. Une prise en charge nutritionnelle doit impliquer une équipe multidisciplinaire qu'il faut coordonner. Elle doit être envisagée précocement pendant le séjour hospitalier et privilégier la voie orale. L'assistance nutritionnelle sous forme de CNO a prouvé son efficacité dans la réduction de la morbidité postopératoire. Son impact sur la mortalité, sur le pronostic fonctionnel et social reste discuté. Il faudra attendre l'étude de nouvelles cohortes dans lesquelles la compliance au traitement est nettement améliorée avant de conclure de manière définitive.

Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data.

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.

High dose chemotherapy with autologous hematopoietic stem cell support for solid tumors other than breast cancer in adults.

Since the early 1980s high dose chemotherapy with autologous hematopoietic stem cell support was adopted by many oncologists as a potentially curative option for solid tumors, supported by a strong rationale from laboratory studies and apparently convincing results of early phase II studies. As a result, the number and size of randomized trials comparing this approach with conventional chemotherapy initiated (and often abandoned before completion) to prove or disprove its value was largely insufficient. In fact, with the possible exception of breast carcinoma, the benefit of a greater escalation of dose of chemotherapy with stem cell support in solid tumors is still unsettled and many oncologists believe that this approach should cease. In this article, we critically review and comment on the data from studies of high dose chemotherapy so far reported in adult patients with small cell lung cancer, ovarian cancer, germ cell tumors and sarcomas.

Support for a colleague

As collaborators of Anders Pape Møller, we were shocked and surprised to read that he was accused of data fabrication ("Ecologists roiled by misconduct case," G. Vogel, F. Proffitt, R. Stone, News of the Week, 30 Jan., p. 606). We have never had cause to be concerned about any aspect of our collaborations with Møller. He is an amazing scientist, and his great organizational skills are a model for how to be productive in the face of competing time demands. Most of us are capable of much more than we actually accomplish, but we lack the dedication and self-discipline to follow through like Anders Møller. This is the secret of his phenomenal effectiveness that has been so puzzling to the scientific community. His achievements may have caused negative responses from some of his competitors. We would like to see a full, objective, and independent inquiry into the allegations. Our experience tells us that Anders Møller has an exceptionally complete focus on any task at hand, be it fieldwork, data analysis, or paper writing; this, combined with more than a little natural talent, is sufficient to explain his exceptional productivity. We have worked with him on a variety of projects, including collecting data, sometimes under arduous conditions, and in all our dealings with him, his behavior has been beyond reproach. We would ask colleagues to restrain from further public condemnation until such time as any allegations have been proven beyond doubt.

ConnectomeViewer - Multi-Modal Multi-Level Network Visualization and Analysis.

Introduction: The field of Connectomic research is growing rapidly, resulting from methodological advances in structural neuroimaging on many spatial scales. Especially progress in Diffusion MRI data acquisition and processing made available macroscopic structural connectivity maps in vivo through Connectome Mapping Pipelines (Hagmann et al, 2008) into so-called Connectomes (Hagmann 2005, Sporns et al, 2005). They exhibit both spatial and topological information that constrain functional imaging studies and are relevant in their interpretation. The need for a special-purpose software tool for both clinical researchers and neuroscientists to support investigations of such connectome data has grown. Methods: We developed the ConnectomeViewer, a powerful, extensible software tool for visualization and analysis in connectomic research. It uses the novel defined container-like Connectome File Format, specifying networks (GraphML), surfaces (Gifti), volumes (Nifti), track data (TrackVis) and metadata. Usage of Python as programming language allows it to by cross-platform and have access to a multitude of scientific libraries. Results: Using a flexible plugin architecture, it is possible to enhance functionality for specific purposes easily. Following features are already implemented: * Ready usage of libraries, e.g. for complex network analysis (NetworkX) and data plotting (Matplotlib). More brain connectivity measures will be implemented in a future release (Rubinov et al, 2009). * 3D View of networks with node positioning based on corresponding ROI surface patch. Other layouts possible. * Picking functionality to select nodes, select edges, get more node information (ConnectomeWiki), toggle surface representations * Interactive thresholding and modality selection of edge properties using filters * Arbitrary metadata can be stored for networks, thereby allowing e.g. group-based analysis or meta-analysis. * Python Shell for scripting. Application data is exposed and can be modified or used for further post-processing. * Visualization pipelines using filters and modules can be composed with Mayavi (Ramachandran et al, 2008). * Interface to TrackVis to visualize track data. Selected nodes are converted to ROIs for fiber filtering The Connectome Mapping Pipeline (Hagmann et al, 2008) processed 20 healthy subjects into an average Connectome dataset. The Figures show the ConnectomeViewer user interface using this dataset. Connections are shown that occur in all 20 subjects. The dataset is freely available from the homepage (connectomeviewer.org). Conclusions: The ConnectomeViewer is a cross-platform, open-source software tool that provides extensive visualization and analysis capabilities for connectomic research. It has a modular architecture, integrates relevant datatypes and is completely scriptable. Visit www.connectomics.org to get involved as user or developer.