О развитии экспертных возможностей для рассмотрения проектов оборудования и технологических решений

The purpose of the research described in the article is to formulate and substantiate the methodology for analyzing quantitative patterns of parameters of projects, devices, systems and technologies, that can be used for relative or rating comparison of projected, planned or applied investigated objects without using long-term experimental or operational data.The proposed approach to the analysis of quantitative patterns makes it possible to conduct a primary assessment of the advantages of innovative technologies, acomparative assessment of the safety of technical complexes, a targeted pre-operational assessment of equipment parameters, and to form planned distinctive parameters for the implementation of experimental and commercial technical projects.Цель исследований, описанных в статье, сформулировать и обосновать методологию анализа количественных закономерностей параметров проектов, устройств, систем и технологий, которые могут быть использованы для относительного или рейтингового сравнения проектируемых, планируемых или применяемых объектов изучения без использования длительной наработки экспериментальных или эксплуатационных данных.Предложенный подход анализа количественных закономерностей позволяет провести первичную оценку преимуществ инновационных технологий, сравнительную оценку безопасности технических комплексов, целевую предэксплуатационую оценку параметров оборудования, сформировать планируемые отличительные параметры для реализации экспериментальных и коммерческих технических проектов

Interleukin-6, age, and corpus callosum integrity.

The contribution of inflammation to deleterious aging outcomes is increasingly recognized; however, little is known about the complex relationship between interleukin-6 (IL-6) and brain structure, or how this association might change with increasing age. We examined the association between IL-6, white matter integrity, and cognition in 151 community dwelling older adults, and tested whether age moderated these associations. Blood levels of IL-6 and vascular risk (e.g., homocysteine), as well as health history information, were collected. Processing speed assessments were administered to assess cognitive functioning, and we employed tract-based spatial statistics to examine whole brain white matter and regions of interest. Given the association between inflammation, vascular risk, and corpus callosum (CC) integrity, fractional anisotropy (FA) of the genu, body, and splenium represented our primary dependent variables. Whole brain analysis revealed an inverse association between IL-6 and CC fractional anisotropy. Subsequent ROI linear regression and ridge regression analyses indicated that the magnitude of this effect increased with age; thus, older individuals with higher IL-6 levels displayed lower white matter integrity. Finally, higher IL-6 levels were related to worse processing speed; this association was moderated by age, and was not fully accounted for by CC volume. This study highlights that at older ages, the association between higher IL-6 levels and lower white matter integrity is more pronounced; furthermore, it underscores the important, albeit burgeoning role of inflammatory processes in cognitive aging trajectories

Predicting disease progression in progressive supranuclear palsy in multicenter clinical trials

INTRODUCTION: Clinical and MRI measurements can track disease progression in PSP, but many have not been extensively evaluated in multicenter clinical trials. We identified optimal measures to capture clinical decline and predict disease progression in multicenter PSP trials. METHODS: Longitudinal clinical rating scales, neuropsychological test scores, and volumetric MRI data from an international, phase 2/3 clinical trial of davunetide for PSP (intent to treat population, n = 303) were used to identify measurements with largest effect size, strongest correlation with clinical change, and best ability to predict dropout or clinical decline over one year as measured by PSP Rating Scale (PSPRS). RESULTS: Baseline cognition as measured by Repeatable Battery for Assessing Neuropsychological Status (RBANS) was associated with attrition, but had only a small effect. PSPRS and Clinical Global Impression (CGI) had the largest effect size for measuring change. Annual change in CGI, RBANS, color trails, and MRI midbrain and ventricular volumes were most strongly correlated with annual PSPRS and had the largest effect sizes for detecting annual change. At baseline, shorter disease duration, more severe depression, and lower performance on RBANS and executive function tests were associated with faster worsening of the PSPRS in completers. With dropouts included, SEADL, RBANS, and executive function tests had significant effect on PSPRS trajectory of change. CONCLUSION: Baseline cognitive status and mood influence the rate of disease progression in PSP. Multiple clinical, neuropsychological, and volumetric MRI measurements are sensitive to change over one year in PSP and appropriate for use in multicenter clinical trials

Protocol for the 'e-Nudge trial' : a randomised controlled trial of electronic feedback to reduce the cardiovascular risk of individuals in general practice [ISRCTN64828380]

Background: Cardiovascular disease (including coronary heart disease and stroke) is a major cause of death and disability in the United Kingdom, and is to a large extent preventable, by lifestyle modification and drug therapy. The recent standardisation of electronic codes for cardiovascular risk variables through the United Kingdom's new General Practice contract provides an opportunity for the application of risk algorithms to identify high risk individuals. This randomised controlled trial will test the benefits of an automated system of alert messages and practice searches to identify those at highest risk of cardiovascular disease in primary care databases. Design: Patients over 50 years old in practice databases will be randomised to the intervention group that will receive the alert messages and searches, and a control group who will continue to receive usual care. In addition to those at high estimated risk, potentially high risk patients will be identified who have insufficient data to allow a risk estimate to be made. Further groups identified will be those with possible undiagnosed diabetes, based either on elevated past recorded blood glucose measurements, or an absence of recent blood glucose measurement in those with established cardiovascular disease. Outcome measures: The intervention will be applied for two years, and outcome data will be collected for a further year. The primary outcome measure will be the annual rate of cardiovascular events in the intervention and control arms of the study. Secondary measures include the proportion of patients at high estimated cardiovascular risk, the proportion of patients with missing data for a risk estimate, and the proportion with undefined diabetes status at the end of the trial

NETIMIS: Dynamic Simulation of Health Economics Outcomes Using Big Data

Many healthcare organizations are now making good use of electronic health record (EHR) systems to record clinical information about their patients and the details of their healthcare. Electronic data in EHRs is generated by people engaged in complex processes within complex environments, and their human input, albeit shaped by computer systems, is compromised by many human factors. These data are potentially valuable to health economists and outcomes researchers but are sufficiently large and complex enough to be considered part of the new frontier of ‘big data’. This paper describes emerging methods that draw together data mining, process modelling, activity-based costing and dynamic simulation models. Our research infrastructure includes safe links to Leeds hospital’s EHRs with 3 million secondary and tertiary care patients. We created a multidisciplinary team of health economists, clinical specialists, and data and computer scientists, and developed a dynamic simulation tool called NETIMIS (Network Tools for Intervention Modelling with Intelligent Simulation; http://www.netimis.com) suitable for visualization of both human-designed and data-mined processes which can then be used for ‘what-if’ analysis by stakeholders interested in costing, designing and evaluating healthcare interventions. We present two examples of model development to illustrate how dynamic simulation can be informed by big data from an EHR. We found the tool provided a focal point for multidisciplinary team work to help them iteratively and collaboratively ‘deep dive’ into big data

Rates Of Amyloid Imaging Positivity In Patients With Primary Progressive Aphasia

IMPORTANCE The ability to predict the pathology underlying different neurodegenerative syndromes is of critical importance owing to the advent of molecule-specific therapies. OBJECTIVE To determine the rates of positron emission tomography (PET) amyloid positivity in the main clinical variants of primary progressive aphasia (PPA). DESIGN, SETTING, AND PARTICIPANTS This prospective clinical-pathologic case series was conducted at a tertiary research clinic specialized in cognitive disorders. Patients were evaluated as part of a prospective, longitudinal research study between January 2002 and December 2015. Inclusion criteria included clinical diagnosis of PPA; availability of complete speech, language, and cognitive testing; magnetic resonance imaging performed within 6 months of the cognitive evaluation; and PET carbon 11-labeled Pittsburgh Compound-B or florbetapir F 18 brain scan results. Of 109 patients referred for evaluation of language symptoms who underwent amyloid brain imaging, 3 were excluded because of incomplete language evaluations, 5 for absence of significant aphasia, and 12 for presenting with significant initial symptoms outside of the language domain, leaving a cohort of 89 patients with PPA. MAIN OUTCOMES AND MEASURES Clinical, cognitive, neuroimaging, and pathology results. RESULTS Twenty-eight cases were classified as imaging-supported semantic variant PPA (11 women [39.3%]; mean [SD] age, 64 [7] years), 31 nonfluent/agrammatic variant PPA (22 women [71.0%]; mean [SD] age, 68 [7] years), 26 logopenic variant PPA (17 women [65.4%]; mean [SD] age, 63 [8] years), and 4 mixed PPA cases. Twenty-four of 28 patients with semantic variant PPA (86%) and 28 of 31 patients with nonfluent/agrammatic variant PPA (90%) had negative amyloid PET scan results, while 25 of 26 patients with logopenic variant PPA (96%) and 3 of 4 mixed PPA cases (75%) had positive scan results. The amyloid positive semantic variant PPA and nonfluent/agrammatic variant PPA cases with available autopsy data (2 of 4 and 2 of 3, respectively) all had a primary frontotemporal lobar degeneration and secondary Alzheimer disease pathologic diagnoses, whereas autopsy of 2 patients with amyloid PET-positive logopenic variant PPA confirmed Alzheimer disease. One mixed PPA patient with a negative amyloid PET scan had Pick disease at autopsy. CONCLUSIONS AND RELEVANCE Primary progressive aphasia variant diagnosis according to the current classification scheme is associated with Alzheimer disease biomarker status, with the logopenic variant being associated with carbon 11-labeled Pittsburgh Compound-B positivity in more than 95% of cases. Furthermore, in the presence of a clinical syndrome highly predictive of frontotemporal lobar degeneration pathology, biomarker positivity for Alzheimer disease may be associated more with mixed pathology rather than primary Alzheimer disease

A national clinical decision support infrastructure to enable the widespread and consistent practice of genomic and personalized medicine

<p>Abstract</p> <p>Background</p> <p>In recent years, the completion of the Human Genome Project and other rapid advances in genomics have led to increasing anticipation of an era of genomic and personalized medicine, in which an individual's health is optimized through the use of all available patient data, including data on the individual's genome and its downstream products. Genomic and personalized medicine could transform healthcare systems and catalyze significant reductions in morbidity, mortality, and overall healthcare costs.</p> <p>Discussion</p> <p>Critical to the achievement of more efficient and effective healthcare enabled by genomics is the establishment of a robust, nationwide clinical decision support infrastructure that assists clinicians in their use of genomic assays to guide disease prevention, diagnosis, and therapy. Requisite components of this infrastructure include the standardized representation of genomic and non-genomic patient data across health information systems; centrally managed repositories of computer-processable medical knowledge; and standardized approaches for applying these knowledge resources against patient data to generate and deliver patient-specific care recommendations. Here, we provide recommendations for establishing a national decision support infrastructure for genomic and personalized medicine that fulfills these needs, leverages existing resources, and is aligned with the <it>Roadmap for National Action on Clinical Decision Support </it>commissioned by the U.S. Office of the National Coordinator for Health Information Technology. Critical to the establishment of this infrastructure will be strong leadership and substantial funding from the federal government.</p> <p>Summary</p> <p>A national clinical decision support infrastructure will be required for reaping the full benefits of genomic and personalized medicine. Essential components of this infrastructure include standards for data representation; centrally managed knowledge repositories; and standardized approaches for leveraging these knowledge repositories to generate patient-specific care recommendations at the point of care.</p

Evaluation of an online interactive Diabetes Needs Assessment Tool (DNAT) versus online self-directed learning: a randomised controlled trial

<p>Abstract</p> <p>Background</p> <p>Methods for the dissemination, understanding and implementation of clinical guidelines need to be examined for their effectiveness to help doctors integrate guidelines into practice. The objective of this randomised controlled trial was to evaluate the effectiveness of an interactive online Diabetes Needs Assessment Tool (DNAT) (which constructs an e-learning curriculum based on individually identified knowledge gaps), compared with self-directed e-learning of diabetes guidelines.</p> <p>Methods</p> <p>Health professionals were randomised to a 4-month learning period and either given access to diabetes learning modules alone (control group) or DNAT plus learning modules (intervention group). Participants completed knowledge tests before and after learning (primary outcome), and surveys to assess the acceptability of the learning and changes to clinical practice (secondary outcomes).</p> <p>Results</p> <p>Sixty four percent (677/1054) of participants completed both knowledge tests. The proportion of nurses (5.4%) was too small for meaningful analysis so they were excluded. For the 650 doctors completing both tests, mean (SD) knowledge scores increased from 47.4% (12.6) to 66.8% (11.5) [intervention group (n = 321, 64%)] and 47.3% (12.9) to 67.8% (10.8) [control group (n = 329, 66%)], (ANCOVA p = 0.186). Both groups were satisfied with the usability and usefulness of the learning materials. Seventy seven percent (218/284) of the intervention group reported combining the DNAT with the recommended reading materials was "<it>very useful"/"useful"</it>. The majority in both groups (184/287, 64.1% intervention group and 206/299, 68.9% control group) [95% CI for the difference (-2.8 to 12.4)] reported integrating the learning into their clinical practice.</p> <p>Conclusions</p> <p>Both groups experienced a similar and significant improvement in knowledge. The learning materials were acceptable and participants incorporated the acquired knowledge into practice.</p> <p>Trial registration</p> <p>ISRCTN: <a href="http://www.controlled-trials.com/ISRCTN67215088">ISRCTN67215088</a></p