Integrating the care of the complex COPD patient

The European Seminars in Respiratory Medicine has represented an outstanding series updating new science in respiratory disease from the 1990\u2019s up to the early beginning of this 21st century [1,2]. Its aim is to update issues and current science, focusing on the multidisciplinary approach to patients with respiratory disease. As such, it represents a unique opportunity for specialists in Respiratory Medicine involved in Basic and Clinical Research to discuss topical and debated problems in medical care, at a top level forum guided by an expert panel of authors. The structure of the seminar is based on the following pillars: \u2022 Attendance at the Seminars is strictly limited: selection of participants is based, in order of priority, on scientific curriculum, age (younger specialists are privileged), and early receipt of the application form. \u2022 Each topic is allotted considerable time for presentation and discussion. The first section is devoted to a series of presentations (with adequate time allocated for discussion) by an expert panel of researchers and clinicians. In the second section involves discussions of controversial issues, in a smaller audience format encouraging interaction between the panel and audience. \u2022 \u201cMeet the expert\u201d seminars discuss topical subjects in more depth, utilizing an interactive tutorial

Imputation strategies for missing binary outcomes in cluster randomized trials

<p>Abstract</p> <p>Background</p> <p>Attrition, which leads to missing data, is a common problem in cluster randomized trials (CRTs), where groups of patients rather than individuals are randomized. Standard multiple imputation (MI) strategies may not be appropriate to impute missing data from CRTs since they assume independent data. In this paper, under the assumption of missing completely at random and covariate dependent missing, we compared six MI strategies which account for the intra-cluster correlation for missing binary outcomes in CRTs with the standard imputation strategies and complete case analysis approach using a simulation study.</p> <p>Method</p> <p>We considered three within-cluster and three across-cluster MI strategies for missing binary outcomes in CRTs. The three within-cluster MI strategies are logistic regression method, propensity score method, and Markov chain Monte Carlo (MCMC) method, which apply standard MI strategies within each cluster. The three across-cluster MI strategies are propensity score method, random-effects (RE) logistic regression approach, and logistic regression with cluster as a fixed effect. Based on the community hypertension assessment trial (CHAT) which has complete data, we designed a simulation study to investigate the performance of above MI strategies.</p> <p>Results</p> <p>The estimated treatment effect and its 95% confidence interval (CI) from generalized estimating equations (GEE) model based on the CHAT complete dataset are 1.14 (0.76 1.70). When 30% of binary outcome are missing completely at random, a simulation study shows that the estimated treatment effects and the corresponding 95% CIs from GEE model are 1.15 (0.76 1.75) if complete case analysis is used, 1.12 (0.72 1.73) if within-cluster MCMC method is used, 1.21 (0.80 1.81) if across-cluster RE logistic regression is used, and 1.16 (0.82 1.64) if standard logistic regression which does not account for clustering is used.</p> <p>Conclusion</p> <p>When the percentage of missing data is low or intra-cluster correlation coefficient is small, different approaches for handling missing binary outcome data generate quite similar results. When the percentage of missing data is large, standard MI strategies, which do not take into account the intra-cluster correlation, underestimate the variance of the treatment effect. Within-cluster and across-cluster MI strategies (except for random-effects logistic regression MI strategy), which take the intra-cluster correlation into account, seem to be more appropriate to handle the missing outcome from CRTs. Under the same imputation strategy and percentage of missingness, the estimates of the treatment effect from GEE and RE logistic regression models are similar.</p

The effectiveness of the Austrian disease management programme for type 2 diabetes: a cluster-randomised controlled trial

<p>Abstract</p> <p>Background</p> <p>Disease management programmes (DMPs) are costly and impose additional work load on general practitioners (GPs). Data on their effectiveness are inconclusive. We therefore conducted a cluster-randomised controlled trial to evaluate the effectiveness of the Austrian DMP for diabetes mellitus type 2 on HbA1c and quality of care for adult patients in primary care.</p> <p>Methods</p> <p>All GPs of Salzburg-province were invited to participate. After cluster-randomisation by district, all patients with diabetes type 2 were recruited consecutively from 7-11/2007. The DMP, consisting mainly of physician and patient education, standardised documentation and agreement on therapeutic goals, was implemented in the intervention group while the control group received usual care. We aimed to show superiority of the intervention regarding metabolic control and process quality. The primary outcome measure was a change in HbA1c after one year. Secondary outcomes were days in the hospital, blood pressure, lipids, body mass index (BMI), enrolment in patient education and regular guideline-adherent examination. Blinding was not possible.</p> <p>Results</p> <p>92 physicians recruited 1489 patients (649 intervention, 840 control). After 401 ± 47 days, 590 intervention-patients and 754 controls had complete data. In the intention to treat analysis (ITT) of all 1489 patients, HbA1c decreased 0.41% in the intervention group and 0.28% in controls. The difference of -0.13% (95% CI -0.24; -0.02) was significant at p = 0.026. Significance was lost in mixed models adjusted for baseline value and cluster-effects (adjusted mean difference -0.03 (95% CI -0.15; 0.09, p = 0.607). Of the secondary outcome measures, BMI and cholesterol were significantly reduced in the intervention group compared to controls in ITT after adjustments (-0.53 kg/m²; 95% CI -1.03;-0.02; p = 0.014 and -0.10 mmol/l; 95% CI -0.21; -0.003; p = 0.043). Additionally, more patients received patient education (49.5% vs. 20.1%, p < 0.0001), eye- (71.0% vs. 51.2%, p < 0.0001), foot examinations (73.8% vs. 45.1%, p < 0.0001), and regular HbA1c checks (44.1% vs. 36.0%, p < 0.01) in the intervention group.</p> <p>Conclusion</p> <p>The Austrian DMP implemented by statutory health insurance improves process quality and enhances weight reduction, but does not significantly improve metabolic control for patients with type 2 diabetes mellitus. Whether the small benefit seen in secondary outcome measures leads to better patient outcomes, remains unclear.</p> <p>Trial Registration</p> <p>Current Controlled trials Ltd., ISRCTN27414162.</p

Using Population Genetic Theory and DNA Sequences for Species Detection and Identification in Asexual Organisms

It is widely agreed that species are fundamental units of biology, but there is little agreement on a definition of species or on an operational criterion for delimiting species that is applicable to all organisms.We focus on asexual eukaryotes as the simplest case for investigating species and speciation. We describe a model of speciation in asexual organisms based on basic principles of population and evolutionary genetics. The resulting species are independently evolving populations as described by the evolutionary species concept or the general lineage species concept. Based on this model, we describe a procedure for using gene sequences from small samples of individuals to assign them to the same or different species. Using this method of species delimitation, we demonstrate the existence of species as independent evolutionary units in seven groups of invertebrates, fungi, and protists that reproduce asexually most or all of the time.This wide evolutionary sampling establishes the general existence of species and speciation in asexual organisms. The method is well suited for measuring species diversity when phenotypic data are insufficient to distinguish species, or are not available, as in DNA barcoding and environmental sequencing. We argue that it is also widely applicable to sexual organisms

Genetic Association and Risk Scores in a Chronic Obstructive Pulmonary Disease Meta-analysis of 16,707 Subjects

The heritability of chronic obstructive pulmonary disease (COPD) cannot be fully explained by recognized genetic risk factors identified as achieving genome-wide significance. In addition, the combined contribution of genetic variation to COPD risk has not been fully explored. We sought to determine: (1) whether studies of variants from previous studies of COPD or lung function in a larger sample could identify additional associated variants, particularly for severe COPD; and (2) the impact of genetic risk scores on COPD. We genotyped 3,346 single-nucleotide polymorphisms (SNPs) in 2,588 cases (1,803 severe COPD) and 1,782 control subjects from four cohorts, and performed association testing with COPD, combining these results with existing genotyping data from 6,633 cases (3,497 severe COPD) and 5,704 control subjects. In addition, we developed genetic risk scores from SNPs associated with lung function and COPD and tested their discriminatory power for COPD-related measures. We identified significant associations between SNPs near PPIC (P = 1.28 X 10-8) and PPP4R4/SERPINA1 (P = 1.0131028) and severe COPD; the latter association may be driven by recognized variants in SERPINA1. Genetic risk scores based on SNPs previously associated with COPD and lung function had a modest ability to discriminate COPD (area under the curve, ~0.6), and accounted for a mean 0.9–1.9% lower forced expiratory volume in 1 second percent predicted for each additional risk allele. In a large genetic association analysis, we identified associations with severe COPD near PPIC and SERPINA1. A risk score based on combining genetic variants had modest, but significant, effects on risk of COPD and lung function

Are Anticholinergic Symptoms a Risk Factor for Falls in Older General Practice Patients With Polypharmacy?: Study Protocol for the Development and Validation of a Prognostic Model

Background: Cumulative anticholinergic exposure, also known as anticholinergic burden, is associated with a variety of adverse outcomes. However, studies show that anticholinergic effects tend to be underestimated by prescribers, and anticholinergics are the most frequently prescribed potentially inappropriate medication in older patients. The grading systems and drugs included in existing scales to quantify anticholinergic burden differ considerably and do not adequately account for patients' susceptibility to medications. Furthermore, their ability to link anticholinergic burden with adverse outcomes such as falls is unclear. This study aims to develop a prognostic model that predicts falls in older general practice patients, to assess the performance of several anticholinergic burden scales, and to quantify the added predictive value of anticholinergic symptoms in this context. Methods: Data from two cluster-randomized controlled trials investigating medication optimization in older general practice patients in Germany will be used. One trial (RIME, n = 1,197) will be used for the model development and the other trial (PRIMUM, n = 502) will be used to externally validate the model. A priori, candidate predictors will be selected based on a literature search, predictor availability, and clinical reasoning. Candidate predictors will include socio-demographics (e.g. age, sex), morbidity (e.g. single conditions), medication (e.g. polypharmacy, anticholinergic burden as defined by scales), and well-being (e.g. quality of life, physical function). A prognostic model including sociodemographic and lifestyle-related factors, as well as variables on morbidity, medication, health status, and well-being, will be developed, whereby the prognostic value of extending the model to include additional patient-reported symptoms will be also assessed. Logistic regression will be used for the binary outcome, which will be defined as "no falls" vs. "≥1 fall" within six months of baseline, as reported in patient interviews. Discussion: As the ability of different anticholinergic burden scales to predict falls in older patients is unclear, this study may provide insights into their relative importance as well as into the overall contribution of anticholinergic symptoms and other patient characteristics. The results may support general practitioners in their clinical decision-making and in prescribing fewer medications with anticholinergic properties

Convergent functional genomic studies of omega-3 fatty acids in stress reactivity, bipolar disorder and alcoholism

Omega-3 fatty acids have been proposed as an adjuvant treatment option in psychiatric disorders. Given their other health benefits and their relative lack of toxicity, teratogenicity and side effects, they may be particularly useful in children and in females of child-bearing age, especially during pregnancy and postpartum. A comprehensive mechanistic understanding of their effects is needed. Here we report translational studies demonstrating the phenotypic normalization and gene expression effects of dietary omega-3 fatty acids, specifically docosahexaenoic acid (DHA), in a stress-reactive knockout mouse model of bipolar disorder and co-morbid alcoholism, using a bioinformatic convergent functional genomics approach integrating animal model and human data to prioritize disease-relevant genes. Additionally, to validate at a behavioral level the novel observed effects on decreasing alcohol consumption, we also tested the effects of DHA in an independent animal model, alcohol-preferring (P) rats, a well-established animal model of alcoholism. Our studies uncover sex differences, brain region-specific effects and blood biomarkers that may underpin the effects of DHA. Of note, DHA modulates some of the same genes targeted by current psychotropic medications, as well as increases myelin-related gene expression. Myelin-related gene expression decrease is a common, if nonspecific, denominator of neuropsychiatric disorders. In conclusion, our work supports the potential utility of omega-3 fatty acids, specifically DHA, for a spectrum of psychiatric disorders such as stress disorders, bipolar disorder, alcoholism and beyond