104 research outputs found

    CIS-based registration of quality of life in a single source approach

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    Background: Documenting quality of life (QoL) in routine medical care and using it both for treatment and for clinical research is not common, although such information is absolutely valuable for physicians and patients alike. We therefore aimed at developing an efficient method to integrate quality of life information into the clinical information system (CIS) and thus make it available for clinical care and secondary use. Methods: We piloted our method in three different medical departments, using five different QoL questionnaires. In this setting we used structured interviews and onsite observations to perform workflow and form analyses. The forms and pertinent data reports were implemented using the integrated tools of the local CIS. A web-based application for mobile devices was developed based on XML schemata to facilitate data import into the CIS. Data exports of the CIS were analysed with statistical software to perform an analysis of data quality. Results: The quality of life questionnaires are now regularly documented by patients and physicians. The resulting data is available in the Electronic Health Record (EHR) and can be used for treatment purposes and communication as well as research functionalities. The completion of questionnaires by the patients themselves using a mobile device (iPad) and the import of the respective data into the CIS forms were successfully tested in a pilot installation. The quality of data is rendered high by the use of automatic score calculations as well as the automatic creation of forms for follow-up documentation. The QoL data was exported to research databases for use in scientific analysis. Conclusion: The CIS-based QoL is technically feasible, clinically accepted and provides an excellent quality of data for medical treatment and clinical research. Our approach with a commercial CIS and the web-based application is transferable to other sites

    Rare Copy Number Deletions Predict Individual Variation in Intelligence

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    Phenotypic variation in human intellectual functioning shows substantial heritability, as demonstrated by a long history of behavior genetic studies. Many recent molecular genetic studies have attempted to uncover specific genetic variations responsible for this heritability, but identified effects capture little variance and have proven difficult to replicate. The present study, motivated an interest in “mutation load” emerging from evolutionary perspectives, examined the importance of the number of rare (or infrequent) copy number variations (CNVs), and the total number of base pairs included in such deletions, for psychometric intelligence. Genetic data was collected using the Illumina 1MDuoBeadChip Array from a sample of 202 adult individuals with alcohol dependence, and a subset of these (N = 77) had been administered the Wechsler Abbreviated Scale of Intelligence (WASI). After removing CNV outliers, the impact of rare genetic deletions on psychometric intelligence was investigated in 74 individuals. The total length of the rare deletions significantly and negatively predicted intelligence (r = −.30, p = .01). As prior studies have indicated greater heritability in individuals with relatively higher parental socioeconomic status (SES), we also examined the impact of ethnicity (Anglo/White vs. Other), as a proxy measure of SES; these groups did not differ on any genetic variable. This categorical variable significantly moderated the effect of length of deletions on intelligence, with larger effects being noted in the Anglo/White group. Overall, these results suggest that rare deletions (between 5% and 1% population frequency or less) adversely affect intellectual functioning, and that pleotropic effects might partly account for the association of intelligence with health and mental health status. Significant limitations of this research, including issues of generalizability and CNV measurement, are discussed

    The sleep EEG spectrum is a sexually dimorphic marker of general intelligence

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    The shape of the EEG spectrum in sleep relies on genetic and anatomical factors and forms an individual “EEG fingerprint”. Spectral components of EEG were shown to be connected to mental ability both in sleep and wakefulness. EEG sleep spindle correlates of intelligence, however, exhibit a sexual dimorphism, with a more pronounced association to intelligence in females than males. In a sample of 151 healthy individuals, we investigated how intelligence is related to spectral components of full-night sleep EEG, while controlling for the effects of age. A positive linear association between intelligence and REM anterior beta power was found in females but not males. Transient, spindle-like “REM beta tufts” are described in the EEG of healthy subjects, which may reflect the functioning of a recently described cingular-prefrontal emotion and motor regulation network. REM sleep frontal high delta power was a negative correlate of intelligence. NREM alpha and sigma spectral power correlations with intelligence did not unequivocally remain significant after multiple comparisons correction, but exhibited a similar sexual dimorphism. These results suggest that the neural oscillatory correlates of intelligence in sleep are sexually dimorphic, and they are not restricted to either sleep spindles or NREM sleep

    Biomedical informatics and translational medicine

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    Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams
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