The response of a small stream in the Lesni potok forested catchment, central Czech Republic, to a short-term in-stream acidification

International audienceLesni Potok stream drains a forested headwater catchment in the central Czech Republic. It was artificially acidified with hydrochloric acid (HC1) for four hours to assess the role of stream substrate in acid-neutralisation and recovery. The pH was lowered from 4.7 to 3.2. Desorption of Ca and Mg and desorption or solution of Al dominated acid-neutralisation; Al mobilisation was more important later. The stream substrate released 4,542 meq Ca, 1,184 meq Mg, and 2,329 meq Al over a 45 m long and 1 m wide stream segment; smaller amounts of Be, Cd, Fe, and Mn were released. Adsorption of SO42- and desorption of F? occurred during the acidification phase of the experiment. The exchange reactions were rapidly reversible for Ca, Mg and SO42-; but not symmetric as the substrate resorbed 1083, 790 and 0 meq Ca, Mg, and Al, respectively, in a 4-hour recovery period. Desorption of SO42-; occurred during the resorption of Ca and Mg. These exchange and dissolution reactions delay acidification, diminish the pH depression and retard recovery from episodic acidification. The behaviour of the stream substrate-water interaction resembles that for soil?soil water interactions. A mathematical dynamic mass-balance based model, MASS (Modelling Acidification of Stream pediments), was developed which simulates the adsorption and desorption of base cations during the experiment and was successfully calibrated to the experimental data. Keywords: Al, Ca, Mg, base cations, acid-neutralisation, stream acidification, recovery, stream sediment, experiment, modelling, adsorption, desorption, adsorption, Czech Republic, Lesni Poto

Clinical Outcomes of Zirconia Dental Implants: A Systematic Review

To determine the survival rate and marginal bone loss (MBL) of zirconia dental implants restored with single crowns or fixed dental prostheses. An electronic search was conducted up to November 2015 (without any restriction regarding the publication time) through the databases MEDLINE (PubMed), Cochrane Library, and EMBASE to identify randomized controlled clinical trials and prospective clinical trials including >15 patients. Primary outcomes were survival rate and MBL. Furthermore, the influence of several covariates on MBL was evaluated. Qualitative assessment and statistical analyses were performed. This review was conducted according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines for systematic reviews. With the applied search strategy, 4,196 titles could be identified. After a screening procedure, 2 randomized controlled clinical trials and 7 prospective clinical trials remained for analyses. In these trials, a total of 326 patients received 398 implants. The follow-up ranged from 12 to 60 mo. Implant loss was mostly reported within the first year, especially within the healing period. Thereafter, nearly constant survival curves could be observed. Therefore, separate meta-analyses were performed for the first and subsequent years, resulting in an implant survival rate of 95.6% (95% confidence interval: 93.3% to 97.9%) after 12 mo and, thereafter, an expected decrease of 0.05% per year (0.25% after 5 y). Additionally, a meta-analysis was conducted for the mean MBL after 12 mo, resulting in 0.79 mm (95% confidence interval: 0.73 to 0.86 mm). Implant bulk material and design, restoration type, and the application of minor augmentation procedures during surgery, as well as the modes of temporization and loading, had no statistically significant influence on MBL. The short-term cumulative survival rates and the MBL of zirconia implants in the presented systematic review are promising. However, additional data are still needed to confirm the long-term predictability of these implants

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR

The impact of post-procedural complications on reimbursement, length of stay and mechanical ventilation among patients undergoing transcatheter aortic valve implantation in Germany

BACKGROUND: The impact of various post-procedural complications after transcatheter aortic valve implantation (TAVI) on resource use and their consequences in the German reimbursement system has still not been properly quantified. METHODS: In a retrospective observational study, we use data from the German DRG statistic on patient characteristics and in-hospital outcomes of all isolated TAVI procedures in 2013 (N = 9147). The impact of post-procedural complications on reimbursement, length of stay and mechanical ventilation was analyzed using both unadjusted and risk-adjusted linear and logistic regression analyses. RESULTS: A total of 235 (2.57%) strokes, 583 (6.37%) bleeding events, 474 (5.18%) cases of acute kidney injury and 1428 (15.61%) pacemaker implantations were documented. The predicted reimbursement of an uncomplicated TAVI procedure was €33,272, and bleeding events were associated with highest additional reimbursement (€12,839, p 48 h: OR 6.93, p 48 h: OR 5.73, p < 0.001). Pacemaker implantations, in contrast, were associated with comparably small increases in reimbursement (€662, p = 0.006) and length of stay (3.54 days, p = 0.006) and no impaired likelihood of mechanical ventilation more than 48 h (OR 1.22, p = 0.156). Interestingly, these complication-related consequences remain mostly unchanged after baseline risk-adjustment. CONCLUSIONS: Post procedural complications such as bleeding events, acute kidney injuries and strokes are associated with increased resource use and substantial amounts of additional reimbursement in Germany, which has important implications for decision making outside of the usual clinical sphere

Using data linkage to electronic patient records to assess the validity of selected mental health diagnoses in English Hospital Episode Statistics (HES)

<div><p>Background</p><p>Administrative data can be used to support research, such as in the UK Biobank. Hospital Episode Statistics (HES) are national data for England that include contain ICD-10 diagnoses for inpatient mental healthcare episodes, but the validity of these diagnoses for research purposes has not been assessed.</p><p>Methods</p><p>250 peoples' HES records were selected based on a HES recorded inpatient stay at the South London and Maudsley NHS Foundation Trust with a diagnosis of schizophrenia, a wider schizophrenia spectrum disorder, bipolar affective disorder or unipolar depression. A gold-standard research diagnosis was made using Clinical Records Interactive Search pseudonymised electronic patient records using, and the OPCRIT+ algorithm.</p><p>Results</p><p>Positive predictive value at the level of lifetime psychiatric disorder was 100%, and at the level of lifetime diagnosis in the four categories of schizophrenia, wider schizophrenia spectrum, bipolar or unipolar depression was 73% (68–79). Agreement varied by diagnosis, with schizophrenia having the highest PPV at 90% (80–96). Each person had an average of five psychiatric HES records. An algorithm that looked at the last recorded psychiatric diagnosis led to greatest overall agreement with the research diagnosis.</p><p>Discussion</p><p>For people who have a HES record from a psychiatric admission with a diagnosis of schizophrenia spectrum disorder, bipolar affective disorder or unipolar depression, HES records appear to be a good indicator of a mental disorder, and can provide a diagnostic category with reasonable certainty. For these diagnoses, HES records can be an effective way of ascertaining psychiatric diagnosis.</p></div

Measuring agreement of administrative data with chart data using prevalence unadjusted and adjusted kappa

YesFunding provided by the Open Access Authors Fund

Multidimensional severity assessment in bronchiectasis:An analysis of 7 European cohorts.

INTRODUCTION: Bronchiectasis is a multidimensional disease associated with substantial morbidity and mortality. Two disease-specific clinical prediction tools have been developed, the Bronchiectasis Severity Index (BSI) and the FACED score, both of which stratify patients into severity risk categories to predict the probability of mortality. METHODS: We aimed to compare the predictive utility of BSI and FACED in assessing clinically relevant disease outcomes across seven European cohorts independent of their original validation studies. RESULTS: The combined cohorts totalled 1612. Pooled analysis showed that both scores had a good discriminatory predictive value for mortality (pooled area under the curve (AUC) 0.76, 95% CI 0.74 to 0.78 for both scores) with the BSI demonstrating a higher sensitivity (65% vs 28%) but lower specificity (70% vs 93%) compared with the FACED score. Calibration analysis suggested that the BSI performed consistently well across all cohorts, while FACED consistently overestimated mortality in 'severe' patients (pooled OR 0.33 (0.23 to 0.48), p<0.0001). The BSI accurately predicted hospitalisations (pooled AUC 0.82, 95% CI 0.78 to 0.84), exacerbations, quality of life (QoL) and respiratory symptoms across all risk categories. FACED had poor discrimination for hospital admissions (pooled AUC 0.65, 95% CI 0.63 to 0.67) with low sensitivity at 16% and did not consistently predict future risk of exacerbations, QoL or respiratory symptoms. No association was observed with FACED and 6 min walk distance (6MWD) or lung function decline. CONCLUSION: The BSI accurately predicts mortality, hospital admissions, exacerbations, QoL, respiratory symptoms, 6MWD and lung function decline in bronchiectasis, providing a clinically relevant evaluation of disease severity

Unintended consequences of existential quantifications in biomedical ontologies

<p>Abstract</p> <p>Background</p> <p>The Open Biomedical Ontologies (OBO) Foundry is a collection of freely available ontologically structured controlled vocabularies in the biomedical domain. Most of them are disseminated via both the OBO Flatfile Format and the semantic web format Web Ontology Language (OWL), which draws upon formal logic. Based on the interpretations underlying OWL description logics (OWL-DL) semantics, we scrutinize the OWL-DL releases of OBO ontologies to assess whether their logical axioms correspond to the meaning intended by their authors.</p> <p>Results</p> <p>We analyzed ontologies and ontology cross products available via the OBO Foundry site <url>http://www.obofoundry.org</url> for existential restrictions (<it>someValuesFrom</it>), from which we examined a random sample of 2,836 clauses.</p> <p>According to a rating done by four experts, 23% of all existential restrictions in OBO Foundry candidate ontologies are suspicious (Cohens' <it>κ </it>= 0.78). We found a smaller proportion of existential restrictions in OBO Foundry cross products are suspicious, but in this case an accurate quantitative judgment is not possible due to a low inter-rater agreement (<it>κ </it>= 0.07). We identified several typical modeling problems, for which satisfactory ontology design patterns based on OWL-DL were proposed. We further describe several usability issues with OBO ontologies, including the lack of ontological commitment for several common terms, and the proliferation of domain-specific relations.</p> <p>Conclusions</p> <p>The current OWL releases of OBO Foundry (and Foundry candidate) ontologies contain numerous assertions which do not properly describe the underlying biological reality, or are ambiguous and difficult to interpret. The solution is a better anchoring in upper ontologies and a restriction to relatively few, well defined relation types with given domain and range constraints.</p

Is the association of birth weight with premenopausal breast cancer risk mediated through childhood growth?

Several studies have found positive associations between birth weight and breast cancer risk at premenopausal ages. The mechanisms underlying this association are not known, but it is possible that it may be mediated through childhood growth. We examined data from a British cohort of 2176 women born in 1946 and for whom there were prospective measurements of birth weight and of body size throughout life. In all, 59 breast cancer cases occurred during follow-up, 21 of whom were known to be premenopausal. Women who weighed at least 4 kg at birth were five times (relative risk (RR)=5.03; 95% confidence interval=1.13, 22.5) more likely to develop premenopausal breast cancer than those who weighed less than 3 kg (P-value for linear trend=0.03). This corresponded to an RR of 2.31 (0.95, 5.64) per 1 kg increase in birth weight. Birth weight was also a predictor of postnatal growth, that is, women who were heavy at birth remained taller and heavier throughout their childhood and young adulthood. However, the effect of birth weight on premenopausal breast cancer risk was only reduced slightly after simultaneous adjustment for height and body mass index (BMI) at age 2 years and height and BMI velocities throughout childhood and adolescence (adjusted RR=1.94 (0.74, 5.14) per 1 kg increase in birth weight). The pathways through which birth weight is associated with premenopausal breast cancer risk seem to be largely independent of those underlying the relation of postnatal growth to risk