Data bases for the assessment of medical technologies: examples from Europe.

The assessment of medical technologies has to answer several questions ranging from safety and effectiveness to complex economical, social, and health policy issues. The type of data needed to carry out such evaluation depends on the specific questions to be answered, as well as on the stage of development of a technology. Basically two types of data may be distinguished: (a) general demographic, administrative, or financial data which has been collected not specifically for technology assessment; (b) the data collected with respect either to a specific technology or to a disease or medical problem. On the basis of a pilot inquiry in Europe and bibliographic research, the following categories of type (b) data bases have been identified: registries, clinical data bases, banks of factual and bibliographic knowledge, and expert systems. Examples of each category are discussed briefly. The following aims for further research and practical goals are proposed: criteria for the minimal data set required, improvement to the registries and clinical data banks, and development of an international clearinghouse to enhance information diffusion on both existing data bases and available reports on medical technology assessments.

Importance of databases for technology assessment.

The data indispensable for carrying out the comprehensive, multi-faceted process of medical technology assessment (MTA) should be collected from a variety of sources. The authors distinguish between type "A" general data, useful for assessment but collected without this specific aim, and type "B" data. Registries of health care procedures or of diseases, as well as clinical data bases are quoted as examples of type "B" data, specifically relating to MTA. Since demographic methods are of importance for the evaluation of long-term effects of medical technologies, examples of sources of type "A" data are presented. Their significance for health policy making is discussed.

Four years later: a clinical update on latanoprost.

PURPOSE: Almost five years have elapsed since the introduction of latanoprost on several markets and considering the large number of publications dealing with it, the authors felt that it was worth re-evaluating the drug. METHODS: The criterion used to select trials for inclusion in the review was: all articles mentioning the drug in common electronic data-bases; these were then screened and considered, on the basis of methodological quality. RESULTS: Experimental data suggest that latanoprost acts by remodeling the extracellular matrix in the ciliary muscle, thus increasing the flow of aqueous humor through the ciliary muscle bundles of the uveoscleral pathway. POAG: Latanoprost persistently improves the pulsatile ocular blood flow in primary open angle glaucoma (POAG). Recent trials confirmed the greater IOP-lowering efficacy of latanoprost vs. timolol, dorzolamide, brimonidine and unoprostone. Trials lasting up to 24 months showed that latanoprost is effective in long-term treatment of POAG and ocular hypertension (OH), with no signs of loss of efficacy when compared to timolol or dorzolamide. Latanoprost provides better control of circadian IOP. Non-responders to beta-blockers should preferably be switched to latanoprost monotherapy before a combination therapy is started. The possibility of a fixed combination of latanoprost and timolol has been explored, with promising results. NTG: Latanoprost is effective in normal tension glaucoma (NTG), lowering IOP, improving pulsatile ocular blood flow and increasing ocular perfusion pressure. OTHER GLAUCOMAS: Latanoprost may provide effective IOP control in angle-closure glaucoma after iridectomy, in pigmentary glaucoma, glaucoma after cataract extraction and steroid-induced glaucoma. However, latanoprost was effective in only a minority of pediatric cases of glaucoma and is contraindicated in all forms of uveitic glaucoma. SAFETY: In the articles reviewed, new or duration-related adverse events were reported.

Development of a cross-European virtual sample repository and HIV resistance database

BACKGROUND: Several European HIV observational data bases have, over the last decade, accumulated a substantial number of resistance test results and developed large sample repositories, There is a need to link these efforts together, We here describe the development of such a novel tool that allows to bind these data bases together in a distributed fashion for which the control and data remains with the cohorts rather than classic data mergers.METHODS: As proof-of-concept we entered two basic queries into the tool: available resistance tests and available samples. We asked for patients still alive after 1998-01-01, and between 180 and 195 cm of height, and how many samples or resistance tests there would be available for these patients, The queries were uploaded with the tool to a central web server from which each participating cohort downloaded the queries with the tool and ran them against their database, The numbers gathered were then submitted back to the server and we could accumulate the number of available samples and resistance tests.RESULTS: We obtained the following results from the cohorts on available samples/resistance test: EuResist: not availableI11,194; EuroSIDA: 20,71611,992; ICONA: 3,751/500; Rega: 302/302; SHCS: 53,78311,485, In total, 78,552 samples and 15,473 resistance tests were available amongst these five cohorts. Once these data items have been identified, it is trivial to generate lists of relevant samples that would be usefuI for ultra deep sequencing in addition to the already available resistance tests, Saon the tool will include small analysis packages that allow each cohort to pull a report on their cohort profile and also survey emerging resistance trends in their own cohort,CONCLUSIONS: We plan on providing this tool to all cohorts within the Collaborative HIV and Anti-HIV Drug Resistance Network (CHAIN) and will provide the tool free of charge to others for any non-commercial use, The potential of this tool is to ease collaborations, that is, in projects requiring data to speed up identification of novel resistance mutations by increasing the number of observations across multiple cohorts instead of awaiting single cohorts or studies to reach the critical number needed to address such issues.

Probabilistic base calling of Solexa sequencing data.

BACKGROUND: Solexa/Illumina short-read ultra-high throughput DNA sequencing technology produces millions of short tags (up to 36 bases) by parallel sequencing-by-synthesis of DNA colonies. The processing and statistical analysis of such high-throughput data poses new challenges; currently a fair proportion of the tags are routinely discarded due to an inability to match them to a reference sequence, thereby reducing the effective throughput of the technology. RESULTS: We propose a novel base calling algorithm using model-based clustering and probability theory to identify ambiguous bases and code them with IUPAC symbols. We also select optimal sub-tags using a score based on information content to remove uncertain bases towards the ends of the reads. CONCLUSION: We show that the method improves genome coverage and number of usable tags as compared with Solexa's data processing pipeline by an average of 15%. An R package is provided which allows fast and accurate base calling of Solexa's fluorescence intensity files and the production of informative diagnostic plots.

Data-driven clustering : new methods and applications

Abstract : This work is concerned with the development and application of novel unsupervised learning methods, having in mind two target applications: the analysis of forensic case data and the classification of remote sensing images. First, a method based on a symbolic optimization of the inter-sample distance measure is proposed to improve the flexibility of spectral clustering algorithms, and applied to the problem of forensic case data. This distance is optimized using a loss function related to the preservation of neighborhood structure between the input space and the space of principal components, and solutions are found using genetic programming. Results are compared to a variety of state-of--the-art clustering algorithms. Subsequently, a new large-scale clustering method based on a joint optimization of feature extraction and classification is proposed and applied to various databases, including two hyperspectral remote sensing images. The algorithm makes uses of a functional model (e.g., a neural network) for clustering which is trained by stochastic gradient descent. Results indicate that such a technique can easily scale to huge databases, can avoid the so-called out-of-sample problem, and can compete with or even outperform existing clustering algorithms on both artificial data and real remote sensing images. This is verified on small databases as well as very large problems. Résumé : Ce travail de recherche porte sur le développement et l'application de méthodes d'apprentissage dites non supervisées. Les applications visées par ces méthodes sont l'analyse de données forensiques et la classification d'images hyperspectrales en télédétection. Dans un premier temps, une méthodologie de classification non supervisée fondée sur l'optimisation symbolique d'une mesure de distance inter-échantillons est proposée. Cette mesure est obtenue en optimisant une fonction de coût reliée à la préservation de la structure de voisinage d'un point entre l'espace des variables initiales et l'espace des composantes principales. Cette méthode est appliquée à l'analyse de données forensiques et comparée à un éventail de méthodes déjà existantes. En second lieu, une méthode fondée sur une optimisation conjointe des tâches de sélection de variables et de classification est implémentée dans un réseau de neurones et appliquée à diverses bases de données, dont deux images hyperspectrales. Le réseau de neurones est entraîné à l'aide d'un algorithme de gradient stochastique, ce qui rend cette technique applicable à des images de très haute résolution. Les résultats de l'application de cette dernière montrent que l'utilisation d'une telle technique permet de classifier de très grandes bases de données sans difficulté et donne des résultats avantageusement comparables aux méthodes existantes.

Assessing the use of very high resolution data to predict species distribution in a mountain environment

Nowadays, Species Distribution Models (SDMs) are a widely used tool. Using different statistical approaches these models reconstruct the realized niche of a species using presence data and a set of variables, often topoclimatic. There utilization range is quite large from understanding single species requirements, to the creation of nature reserve based on species hotspots, or modeling of climate change impact, etc... Most of the time these models are using variables at a resolution of 50km x 50km or 1 km x 1 km. However in some cases these models are used with resolutions below the kilometer scale and thus called high resolution models (100 m x 100 m or 25 m x 25 m). Quite recently a new kind of data has emerged enabling precision up to lm x lm and thus allowing very high resolution modeling. However these new variables are very costly and need an important amount of time to be processed. This is especially the case when these variables are used in complex calculation like models projections over large areas. Moreover the importance of very high resolution data in SDMs has not been assessed yet and is not well understood. Some basic knowledge on what drive species presence-absences is still missing. Indeed, it is not clear whether in mountain areas like the Alps coarse topoclimatic gradients are driving species distributions or if fine scale temperature or topography are more important or if their importance can be neglected when balance to competition or stochasticity. In this thesis I investigated the importance of very high resolution data (2-5m) in species distribution models using either very high resolution topographic, climatic or edaphic variables over a 2000m elevation gradient in the Western Swiss Alps. I also investigated more local responses of these variables for a subset of species living in this area at two precise elvation belts. During this thesis I showed that high resolution data necessitates very good datasets (species and variables for the models) to produce satisfactory results. Indeed, in mountain areas, temperature is the most important factor driving species distribution and needs to be modeled at very fine resolution instead of being interpolated over large surface to produce satisfactory results. Despite the instinctive idea that topographic should be very important at high resolution, results are mitigated. However looking at the importance of variables over a large gradient buffers the importance of the variables. Indeed topographic factors have been shown to be highly important at the subalpine level but their importance decrease at lower elevations. Wether at the mountane level edaphic and land use factors are more important high resolution topographic data is more imporatant at the subalpine level. Finally the biggest improvement in the models happens when edaphic variables are added. Indeed, adding soil variables is of high importance and variables like pH are overpassing the usual topographic variables in SDMs in term of importance in the models. To conclude high resolution is very important in modeling but necessitate very good datasets. Only increasing the resolution of the usual topoclimatic predictors is not sufficient and the use of edaphic predictors has been highlighted as fundamental to produce significantly better models. This is of primary importance, especially if these models are used to reconstruct communities or as basis for biodiversity assessments. -- Ces dernières années, l'utilisation des modèles de distribution d'espèces (SDMs) a continuellement augmenté. Ces modèles utilisent différents outils statistiques afin de reconstruire la niche réalisée d'une espèce à l'aide de variables, notamment climatiques ou topographiques, et de données de présence récoltées sur le terrain. Leur utilisation couvre de nombreux domaines allant de l'étude de l'écologie d'une espèce à la reconstruction de communautés ou à l'impact du réchauffement climatique. La plupart du temps, ces modèles utilisent des occur-rences issues des bases de données mondiales à une résolution plutôt large (1 km ou même 50 km). Certaines bases de données permettent cependant de travailler à haute résolution, par conséquent de descendre en dessous de l'échelle du kilomètre et de travailler avec des résolutions de 100 m x 100 m ou de 25 m x 25 m. Récemment, une nouvelle génération de données à très haute résolution est apparue et permet de travailler à l'échelle du mètre. Les variables qui peuvent être générées sur la base de ces nouvelles données sont cependant très coûteuses et nécessitent un temps conséquent quant à leur traitement. En effet, tout calcul statistique complexe, comme des projections de distribution d'espèces sur de larges surfaces, demande des calculateurs puissants et beaucoup de temps. De plus, les facteurs régissant la distribution des espèces à fine échelle sont encore mal connus et l'importance de variables à haute résolution comme la microtopographie ou la température dans les modèles n'est pas certaine. D'autres facteurs comme la compétition ou la stochasticité naturelle pourraient avoir une influence toute aussi forte. C'est dans ce contexte que se situe mon travail de thèse. J'ai cherché à comprendre l'importance de la haute résolution dans les modèles de distribution d'espèces, que ce soit pour la température, la microtopographie ou les variables édaphiques le long d'un important gradient d'altitude dans les Préalpes vaudoises. J'ai également cherché à comprendre l'impact local de certaines variables potentiellement négligées en raison d'effets confondants le long du gradient altitudinal. Durant cette thèse, j'ai pu monter que les variables à haute résolution, qu'elles soient liées à la température ou à la microtopographie, ne permettent qu'une amélioration substantielle des modèles. Afin de distinguer une amélioration conséquente, il est nécessaire de travailler avec des jeux de données plus importants, tant au niveau des espèces que des variables utilisées. Par exemple, les couches climatiques habituellement interpolées doivent être remplacées par des couches de température modélisées à haute résolution sur la base de données de terrain. Le fait de travailler le long d'un gradient de température de 2000m rend naturellement la température très importante au niveau des modèles. L'importance de la microtopographie est négligeable par rapport à la topographie à une résolution de 25m. Cependant, lorsque l'on regarde à une échelle plus locale, la haute résolution est une variable extrêmement importante dans le milieu subalpin. À l'étage montagnard par contre, les variables liées aux sols et à l'utilisation du sol sont très importantes. Finalement, les modèles de distribution d'espèces ont été particulièrement améliorés par l'addition de variables édaphiques, principalement le pH, dont l'importance supplante ou égale les variables topographique lors de leur ajout aux modèles de distribution d'espèces habituels.

Care related pain in rehabilitation after orthopedic trauma: An exploratory study with qualitative data.

OBJECTIVE: Care related pain (CRP) is generally under-estimated and rarely studied in rehabilitation as well as in general medecine. Beliefs about pain influence psychological distress, adjustment to pain and physical disability. In this sense, perceptions of CRP could limit recovery. This exploratory study aims to understand patients' and caregivers' subjective perceptions and beliefs about CRP. PATIENTS AND METHODS: Questionnaires about CRP were submitted to members of the interdisciplinary team of a rehabilitation hospital and to patients with musculoskeletal complaints (cross-sectional design). Twenty patients were also individually interviewed (qualitative data). Four topics were addressed: frequency of CRP, situations and procedures causing CRP, beliefs about CRP and means used to deal with CRP. RESULTS: Seventy-five caregivers and 50 patients replied to the questionnaire. CRP is a very common experience in rehabilitation and it is recognized by both groups. Generally, the situations causing CRP reflect the specificity of rehabilitation (mobilization...) and are similarly perceived by patients and caregivers, with patients considering them as more painful. Beliefs about CRP are clearly different from those usually associated with pain. Both groups point out the utilitarian and the inevitable character of CRP. They differ on that, that patients had a more positive view about CRP. They associate it more often with progress and see it as acceptable at least until a certain limit. They are also able to perceive the richness of means used by physiotherapists to help them coping with CRP. CONCLUSION: Our data may suggest new keys to motivate patient to be active in rehabilitation for example in choosing carefully arguments or words which may fit theirs' beliefs about CRP, or in using various means to manage CRP. Promoting the use of relational competences with chronic pain patients and of a patient-centred approach may also be a concern in training caregivers.