Growth to early adulthood following extremely preterm birth: the EPICure study.

OBJECTIVE: To investigate growth trajectories from age 2.5 to 19 years in individuals born before 26 weeks of gestation (extremely preterm; EP) compared with term-born controls. METHODS: Multilevel modelling of growth data from the EPICure study, a prospective 1995 birth cohort of 315 EP participants born in the UK and Ireland and 160 term-born controls recruited at school age. Height, weight, head circumference and body mass index (BMI) z-scores were derived from UK standards at ages 2.5, 6, 11 and 19 years. RESULTS: 129 (42%) EP children were assessed at 19 years. EP individuals were on average 4.0 cm shorter and 6.8 kg lighter with a 1.5 cm smaller head circumference relative to controls at 19 years. Relative to controls, EP participants grew faster in weight by 0.06 SD per year (95% CI 0.05 to 0.07), in head circumference by 0.04 SD (95% CI 0.03 to 0.05), but with no catch-up in height. For the EP group, because of weight catch-up between 6 and 19 years, BMI was significantly elevated at 19 years to +0.32 SD; 23.4% had BMI >25 kg/m2 and 6.3% >30 kg/m2 but these proportions were similar to those in control subjects. EP and control participants showed similar pubertal development in early adolescence, which was not associated with height at 19 years in either study group. Growth through childhood was related to birth characteristics and to neonatal feeding practices. CONCLUSIONS: EP participants remained shorter and lighter and had smaller head circumferences than reference data or controls in adulthood but had elevated BMI

Clinical use of HIV integrase inhibitors : a systematic review and meta-analysis

Background: Optimal regimen choice of antiretroviral therapy is essential to achieve long-term clinical success. Integrase inhibitors have swiftly been adopted as part of current antiretroviral regimens. The purpose of this study was to review the evidence for integrase inhibitor use in clinical settings. Methods: MEDLINE and Web-of-Science were screened from April 2006 until November 2012, as were hand-searched scientific meeting proceedings. Multiple reviewers independently screened 1323 citations in duplicate to identify randomized controlled trials, nonrandomized controlled trials and cohort studies on integrase inhibitor use in clinical practice. Independent, duplicate data extraction and quality assessment were conducted. Results: 48 unique studies were included on the use of integrase inhibitors in antiretroviral therapy-naive patients and treatment-experienced patients with either virological failure or switching to integrase inhibitors while virologically suppressed. On the selected studies with comparable outcome measures and indication (n = 16), a meta-analysis was performed based on modified intention-to-treat (mITT), on-treatment (OT) and as-treated (AT) virological outcome data. In therapy-naive patients, favorable odds ratios (OR) for integrase inhibitor-based regimens were observed, (mITT OR 0.71, 95% CI 0.59-0.86). However, integrase inhibitors combined with protease inhibitors only did not result in a significant better virological outcome. Evidence further supported integrase inhibitor use following virological failure (mITT OR 0.27; 95% CI 0.11-0.66), but switching to integrase inhibitors from a high genetic barrier drug during successful treatment was not supported (mITT OR 1.43; 95% CI 0.89-2.31). Integrase inhibitor-based regimens result in similar immunological responses compared to other regimens. A low genetic barrier to drug-resistance development was observed for raltegravir and elvitegravir, but not for dolutegravir. Conclusion: In first-line therapy, integrase inhibitors are superior to other regimens. Integrase inhibitor use after virological failure is supported as well by the meta-analysis. Careful use is however warranted when replacing a high genetic barrier drug in treatment-experienced patients switching successful treatment

The effect of feedback to general practitioners on quality of care for people with type 2 diabetes. A systematic review of the literature

<p>Abstract</p> <p>Background</p> <p>There have been numerous efforts to improve and assure the quality of treatment and follow-up of people with Type 2 diabetes (PT2D) in general practice. Facilitated by the increasing usability and validity of guidelines, indicators and databases, feedback on diabetes care is a promising tool in this aspect. Our goal was to assess the effect of feedback to general practitioners (GPs) on the quality of care for PT2D based on the available literature.</p> <p>Methods</p> <p>Systematic review searches were conducted using October 2008 updates of Medline (Pubmed), Cochrane library and Embase databases. Additional searches in reference lists and related articles were conducted. Papers were included if published in English, performed as randomized controlled trials, studying diabetes, having general practice as setting and using feedback to GPs on diabetes care. The papers were assessed according to predefined criteria.</p> <p>Results</p> <p>Ten studies complied with the inclusion criteria. Feedback improved the care for PT2D, particularly process outcomes such as foot exams, eye exams and Hba1c measurements. Clinical outcomes like lowering of blood pressure, Hba1c and cholesterol levels were seen in few studies. Many process and outcome measures did not improve, while none deteriorated. Meta analysis was unfeasible due to heterogeneity of the studies included. Two studies used electronic feedback.</p> <p>Conclusion</p> <p>Based on this review, feedback seems a promising tool for quality improvement in diabetes care, but more research is needed, especially of electronic feedback.</p

Hookworm Infection and Environmental Factors in Mbeya Region, Tanzania: A Cross-sectional, Population-based study.

Hookworm disease is one of the most common infections and cause of a high disease burden in the tropics and subtropics. Remotely sensed ecological data and model-based geostatistics have been used recently to identify areas in need for hookworm control. Cross-sectional interview data and stool samples from 6,375 participants from nine different sites in Mbeya region, south-western Tanzania, were collected as part of a cohort study. Hookworm infection was assessed by microscopy of duplicate Kato-Katz thick smears from one stool sample from each participant. A geographic information system was used to obtain remotely sensed environmental data such as land surface temperature (LST), vegetation cover, rainfall, and elevation, and combine them with hookworm infection data and with socio-demographic and behavioral data. Uni- and multivariable logistic regression was performed on sites separately and on the pooled dataset. Univariable analyses yielded significant associations for all ecological variables. Five ecological variables stayed significant in the final multivariable model: population density (odds ratio (OR) = 0.68; 95% confidence interval (CI) = 0.63-0.73), mean annual vegetation density (OR = 0.11; 95% CI = 0.06-0.18), mean annual LST during the day (OR = 0.81; 95% CI = 0.75-0.88), mean annual LST during the night (OR = 1.54; 95% CI = 1.44-1.64), and latrine coverage in household surroundings (OR = 1.02; 95% CI = 1.01-1.04). Interaction terms revealed substantial differences in associations of hookworm infection with population density, mean annual enhanced vegetation index, and latrine coverage between the two sites with the highest prevalence of infection. This study supports previous findings that remotely sensed data such as vegetation indices, LST, and elevation are strongly associated with hookworm prevalence. However, the results indicate that the influence of environmental conditions can differ substantially within a relatively small geographic area. The use of large-scale associations as a predictive tool on smaller scales is therefore problematic and should be handled with care

Health care use and costs of adverse drug events emerging from outpatient treatment in Germany: A modelling approach

<p>Abstract</p> <p>Background</p> <p>This study's aim was to develop a first quantification of the frequency and costs of adverse drug events (ADEs) originating in ambulatory medical practice in Germany.</p> <p>Methods</p> <p>The frequencies and costs of ADEs were quantified for a base case, building on an existing cost-of-illness model for ADEs. The model originates from the U.S. health care system, its structure of treatment probabilities linked to ADEs was transferred to Germany. Sensitivity analyses based on values determined from a literature review were used to test the postulated results.</p> <p>Results</p> <p>For Germany, the base case postulated that about 2 million adults ingesting medications have will have an ADE in 2007. Health care costs related to ADEs in this base case totalled 816 million Euros, mean costs per case were 381 Euros. About 58% of costs resulted from hospitalisations, 11% from emergency department visits and 21% from long-term care. Base case estimates of frequency and costs of ADEs were lower than all estimates of the sensitivity analyses.</p> <p>Discussion</p> <p>The postulated frequency and costs of ADEs illustrate the possible size of the health problems and economic burden related to ADEs in Germany. The validity of the U.S. treatment structure used remains to be determined for Germany. The sensitivity analysis used assumptions from different studies and thus further quantified the information gap in Germany regarding ADEs.</p> <p>Conclusions</p> <p>This study found costs of ADEs in the ambulatory setting in Germany to be significant. Due to data scarcity, results are only a rough indication.</p

Development and validation of a risk model for predicting adverse drug reactions in older people during hospital stay: Brighton Adverse Drug Reactions Risk (BADRI) model

BACKGROUND: Older patients are at an increased risk of developing adverse drug reactions (ADR). Of particular concern are the oldest old, which constitute an increasingly growing population. Having a validated clinical tool to identify those older patients at risk of developing an ADR during hospital stay would enable healthcare staff to put measures in place to reduce the risk of such an event developing. The current study aimed to (1) develop and (2) validate an ADR risk prediction model. METHODS: We used a combination of univariate analysis and multivariate binary logistic regression to identify clinical risk factors for developing an ADR in a population of older people from a UK teaching hospital. The final ADR risk model was then validated in a European population (European dataset). RESULTS: Six-hundred-ninety patients (median age 85 years) were enrolled in the development stage of the study. Ninety-five reports of ADR were confirmed by independent review in these patients. Five clinical variables were identified through multivariate analysis and included in our final model; each variable was attributed a score of 1. Internal validation produced an AUROC of 0.74, a sensitivity of 80%, and specificity of 55%. During the external validation stage the AUROC was 0.73, with sensitivity and specificity values of 84% and 43% respectively. CONCLUSIONS: We have developed and successfully validated a simple model to use ADR risk score in a population of patients with a median age of 85, i.e. the oldest old. The model is based on 5 clinical variables (≥8 drugs, hyperlipidaemia, raised white cell count, use of anti-diabetic agents, length of stay ≥12 days), some of which have not been previously reported

Paclitaxel alters the expression and specific activity of deoxycytidine kinase and cytidine deaminase in non-small cell lung cancer cell lines

<p>Abstract</p> <p>Background</p> <p>We observed that paclitaxel altered the pharmacokinetic properties of gemcitabine in patients with non-small cell lung cancer (NSCLC) and limited the accumulation of gemcitabine and its metabolites in various primary and immortalized human cells. Therefore, we classified the drug-drug interaction and the effects of paclitaxel on deoxycytidine kinase (dCK) and cytidine deaminase (CDA) in three NSCLC cell lines. These enzymes are responsible for the metabolism of gemcitabine to its deaminated metabolite dFdU (80% of the parent drug) and the phosphorylated metabolites dFdCMP, dFdCDP and dFdCTP. These metabolites appear to relate to sensitivity and tolerability of gemcitabine based on previous animal and laboratory studies.</p> <p>Methods</p> <p>Three immortalized human cells representative of the most common histological subtypes identified in patients with advanced NSCLC were exposed to the individual drugs or combinations to complete a multiple drug effect analysis. These same cell lines were exposed to vehicle-control or paclitaxel and the mRNA levels, protein expression and specific activity of dCK and CDA were compared. Comparisons were made using a two-tailed paired t-test or analysis of variance with a P value of < 0.05 considered significant.</p> <p>Results</p> <p>The multiple drug effect analysis indicated synergy for H460, H520 and H838 cells independent of sequence. As anticipated, paclitaxel-gemcitabine increased the number of G2/M cells, whereas gemcitabine-paclitaxel increased the number of G0/G1 or S cells. Paclitaxel significantly decreased dCK and CDA mRNA levels in H460 and H520 cells (40% to 60%, P < 0.05) and lowered dCK protein (24% to 56%, P < 0.05) without affecting CDA protein. However, paclitaxel increased both dCK (10% to 50%) and CDA (75% to 153%) activity (P < 0.05). Paclitaxel caused substantial declines in the accumulation of the deaminated and phosphorylated metabolites in H520 cells (P < 0.05); the metabolites were not measurable in the remaining two cell lines. The ratio of dCK to CDA mRNA levels corresponded to the combination index (CI) estimated for sequential paclitaxel-gemcitabine.</p> <p>Conclusion</p> <p>In summary, paclitaxel altered the mRNA levels and specific activity of dCK and CDA and these effects could be dependent on histological subtype. More cell and animal studies are needed to further characterize the relationship between mRNA levels and the overall drug-drug interaction and the potential to use histological subtype as a predictive factor in the selection of an appropriate anticancer drug regimen.</p

Epidemiology of Classical Hodgkin Lymphoma and Its Association with Epstein Barr Virus in Northern China

BACKGROUND: The incidence of classical Hodgkin lymphoma (cHL) and its association with Epstein-Barr virus (EBV) varies significantly with age, sex, ethnicity and geographic location. This is the first report on epidemiological features of cHL patients from Northern regions of China. These features are compared to data from a previously published Dutch cHL population. METHODOLOGY/PRINCIPAL FINDINGS: 157 cHL patients diagnosed between 1997 and 2008 in the North of China were included after histopathological re-evaluation. The Dutch population-based cohort consisted of 515 cHL patients diagnosed between 1987 and 2000. EBV status was determined by in situ hybridization of EBV- encoded small RNAs. In the Chinese population, tumor cells of 39% of the cHL patients were EBV+ and this was significantly associated with male sex, mixed cellularity subtype and young age (<20 y). The median age of the Chinese patients was 9 years younger than that of the Dutch patients (28 y vs. 37 y). In addition, the age distribution between the two populations was strikingly different in both the EBV+ subgroups (p<0.001) and the EBV- subgroups (p = 0.01). The mixed cellularity subtype was almost 3x more frequent amongst the Chinese (p<0.001). CONCLUSION/SIGNIFICANCE: CHL patients from Northern regions of China show a distinctive age distribution pattern with a striking incidence peak of EBV+ mixed cellularity cases among children and adolescents and another high incidence peak of EBV- nodular sclerosis cases in young adults. In comparison to Dutch cHL patients there are pronounced differences in age distribution, subtype and EBV status, presumably caused by complex gene-environmental interactions

Increasing consistency of disease biomarker prediction across datasets

Microarray studies with human subjects often have limited sample sizes which hampers the ability to detect reliable biomarkers associated with disease and motivates the need to aggregate data across studies. However, human gene expression measurements may be influenced by many non-random factors such as genetics, sample preparations, and tissue heterogeneity. These factors can contribute to a lack of agreement among related studies, limiting the utility of their aggregation. We show that it is feasible to carry out an automatic correction of individual datasets to reduce the effect of such 'latent variables' (without prior knowledge of the variables) in such a way that datasets addressing the same condition show better agreement once each is corrected. We build our approach on the method of surrogate variable analysis but we demonstrate that the original algorithm is unsuitable for the analysis of human tissue samples that are mixtures of different cell types. We propose a modification to SVA that is crucial to obtaining the improvement in agreement that we observe. We develop our method on a compendium of multiple sclerosis data and verify it on an independent compendium of Parkinson's disease datasets. In both cases, we show that our method is able to improve agreement across varying study designs, platforms, and tissues. This approach has the potential for wide applicability to any field where lack of inter-study agreement has been a concern. © 2014 Chikina, Sealfon