Safety during the monitoring of diabetic patients: trial teaching course on health professionals and diabetics - SEGUDIAB study

<p>Abstract</p> <p>Background</p> <p>Safety for diabetic patients means providing the most suitable treatment for each type of diabetic in order to improve monitoring and to prevent the adverse effects of drugs and complications arising from the disease. The aim of this study is to analyze the effect of imparting educational interventions to health professionals regarding the safety of patients with Diabetes Mellitus (DM).</p> <p>Methods</p> <p><it>Design</it>: A cluster randomized trial with a control group.</p> <p><it>Setting and sample</it>: The study analyzed ten primary healthcare centres (PHC) covering approximately 150,000 inhabitants. Two groups of 5 PHC were selected on the basis of their geographic location (urban, semi-urban and rural), their socio-economic status and the size of their PHC, The interventions and control groups were assigned at random. The study uses computerized patient records to individually assess subjects aged 45 to 75 diagnosed with type 1 and type 2 DM, who met the inclusion conditions and who had the variables of particular interest to the study.</p> <p><it>Trial</it>: The educational interventions consisted of a standardized teaching course aimed at doctors and nurses. The course lasted 6 hours and was split into three 2-hour blocks with subsequent monthly refresher courses.</p> <p><it>Measurement</it>: For the health professionals, the study used the <it>Diabetes Attitude Scale </it>(DAS-3) to assess their attitudes and motivation when monitoring diabetes. For the patients, the study assessed factors related to their degree of control over the disease at onset, 6, 12 and 24 months.</p> <p><it>Main variables</it>: levels of HbA1c.</p> <p><it>Analysis</it>: The study analyzed the effect of the educational interventions both on the attitudes and motivations of health professionals and on the degree of control over the diabetes in both groups.</p> <p>Discussion</p> <p>Imparting educational interventions to health professionals would improve the monitoring of diabetic patients. The most effective model involves imparting the course to both doctors and nurses. However, these models have not been tested on our Spanish population within the framework of primary healthcare.</p> <p>Trial registration</p> <p>ClinicalTrials.gov: <a href="http://www.clinicaltrials.gov/ct2/show/NCT01087541">NCT01087541</a></p

Toward a unifying strategy for the structure-based prediction of toxicological endpoints

Most computational methods used for the prediction of toxicity endpoints are based on the assumption that similar compounds have similar biological properties. This principle can be exploited using computational methods like read across or quantitative structure-activity relationships. However, there is no general agreement about which method is the most appropriate for quantifying compound similarity neither for exploiting the similarity principle in order to obtain reliable estimations of the compound properties. Moreover, optimal similarity metrics and modeling methods might depend on the characteristics of the endpoints and training series used in each case. This study describes a comparative analysis of the predictive performance of diverse similarity metrics and modeling methods in toxicological applications. A collection of two quantitative (n = 660, n = 1114) and three qualitative (n = 447, n = 905, n = 1220) datasets representing very different endpoints of interest in drug safety evaluation and rigorous methods were used to estimate the external predictive ability in each case. The results confirm that no single approach produces the best results in all instances, and the best predictions were obtained using different tools in different situations. The trends observed in this study were exploited to propose a unifying strategy allowing the use of the most suitable method for every compound. A comparison of the quality of the predictions obtained by the unifying strategy with those obtained by standard prediction methods confirmed the usefulness of the proposed approach.The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking, under Grant Agreement No. 115002 (eTOX), resources of which are composed of a financial contribution from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies’ in kind contribution