Polypharmacy and emergency readmission to hospital after critical illness:a population-level cohort study

From PubMed via Jisc Publications RouterPolypharmacy is common and closely linked to drug interactions. The impact of polypharmacy has not been previously quantified in survivors of critical illness who have reduced resilience to stressors. Our aim was to identify factors associated with preadmission polypharmacy and ascertain whether polypharmacy is an independent risk factor for emergency readmission to hospital after discharge from a critical illness. A population-wide cohort study consisting of patients admitted to all Scottish general ICUs between January 1, 2011 and December 31, 2013, whom survived their ICU stay. Patients were stratified by presence of preadmission polypharmacy, defined as being prescribed five or more regular medications. The primary outcome was emergency hospital readmission within 1 yr of discharge from index hospital stay. Of 23 844 ICU patients, 29.9% were identified with polypharmacy (n=7138). Factors associated with polypharmacy included female sex, increasing age, and social deprivation. Emergency 1-yr hospital readmission was significantly higher in the polypharmacy cohort (51.8% vs 35.8%, P<0.001). After confounder adjustment, patients with polypharmacy had a 22% higher hazard of emergency 1-yr readmission (adjusted hazard ratio 1.22, 95% confidence interval 1.16-1.28, P<0.001). On a linear scale of polypharmacy each additional prescription conferred a 3% increase in hazard of emergency readmission by 1 yr (adjusted hazard ratio 1.03, 95% confidence interval 1.02-1.03, P<0.001). This national cohort study of ICU survivors demonstrates that preadmission polypharmacy is an independent risk factor for emergency readmission. In an ever-growing era of polypharmacy, this risk factor may represent a substantial burden in the at-risk post-intensive care population.126pubpub

PReventing early unplanned hOspital readmission aFter critical ILlnEss (PROFILE): protocol and analysis framework for a mixed methods study

Introduction Survivors of critical illness experience multidimensional disabilities that reduce quality of life, and 25–30% require unplanned hospital readmission within 3 months following index hospitalisation. We aim to understand factors associated with unplanned readmission; develop a risk model to identify intensive care unit (ICU) survivors at highest readmission risk; understand the modifiable and non-modifiable readmission drivers; and develop a risk assessment tool for identifying patients and areas for early intervention. Methods and analysis We will use mixed methods with concurrent data collection. Quantitative data will comprise linked healthcare records for adult Scottish residents requiring ICU admission (1 January 2000–31 December 2013) who survived to hospital discharge. The outcome will be unplanned emergency readmission within 90 days of index hospital discharge. Exposures will include pre-ICU demographic data, comorbidities and health status, and critical illness variables representing illness severity. Regression analyses will be used to identify factors associated with increased readmission risk, and to develop and validate a risk prediction model. Qualitative data will comprise recorded/transcribed interviews with up to 60 patients and carers recently experiencing unplanned readmissions in three health board regions. A deductive and inductive thematic analysis will be used to identify factors contributing to readmissions and how they may interact. Through iterative triangulation of quantitative and qualitative data, we will develop a construct/taxonomy that captures reasons and drivers for unplanned readmission. We will validate and further refine this in focus groups with patients/carers who experienced readmissions in six Scottish health board regions, and in consultation with an independent expert group. A tool will be developed to screen for ICU survivors at risk of readmission and inform anticipatory interventions

Predicting risk of unplanned hospital readmission in survivors of critical illness: a population-level cohort study

BackgroundIntensive care unit survivors experience high levels of morbidity after hospital discharge and are at high risk of unplanned hospital readmission. Identifying those at highest-risk before hospital discharge may allow targeting of novel risk reduction strategies. We aimed to identify risk factors for unplanned 90-day readmission, develop a risk prediction model and assess its performance to screen for ICU survivors at highest readmission risk.MethodsPopulation cohort study linking registry data for patients discharged from general ICUs in Scotland (2005-2013). Independent risk factors for 90-day readmission and discriminant ability (c-index) of groups of variables were identified using multivariable logistic regression. Derivation and validation risk prediction models were constructed using a time-based split.ResultsOf 55,975 ICU survivors, 24.1% (95%CI 23.7%,24.4%) had unplanned 90-day readmission. Pre-existing health factors were fair discriminators of readmission (c-index 0.63,95%CI 0.63,0.64), but better than acute illness factors (0.60) or demographics (0.54). In a subgroup of those with no comorbidity, acute illness factors (0.62) were better discriminators than pre-existing health factors (0.56). Overall model performance and calibration in the validation cohort was fair (0.65,95%CI 0.64,0.66) but did not perform sufficiently well as a screening tool, demonstrating high false positive/false negative rates at clinically relevant thresholds.ConclusionsUnplanned 90-day hospital readmission is common. Pre-existing illness indices are better predictors of readmission than acute illness factors. Identifying additional patient-centred drivers of readmission may improve risk prediction models. Improved understanding of risk factors that are amenable to intervention could improve the clinical and cost-effectiveness of post-ICU care and rehabilitation

Classifying and scoring of molecules with the NGN: new datasets, significance tests, and generalization

<p>Abstract</p> <p/> <p>This paper demonstrates how a Neural Grammar Network learns to classify and score molecules for a variety of tasks in chemistry and toxicology. In addition to a more detailed analysis on datasets previously studied, we introduce three new datasets (BBB, FXa, and toxicology) to show the generality of the approach. A new experimental methodology is developed and applied to both the new datasets as well as previously studied datasets. This methodology is rigorous and statistically grounded, and ultimately culminates in a Wilcoxon significance test that proves the effectiveness of the system. We further include a complete generalization of the specific technique to arbitrary grammars and datasets using a mathematical abstraction that allows researchers in different domains to apply the method to their own work.</p> <p>Background</p> <p>Our work can be viewed as an alternative to existing methods to solve the quantitative structure-activity relationship (QSAR) problem. To this end, we review a number approaches both from a methodological and also a performance perspective. In addition to these approaches, we also examined a number of chemical properties that can be used by generic classifier systems, such as feed-forward artificial neural networks. In studying these approaches, we identified a set of interesting benchmark problem sets to which many of the above approaches had been applied. These included: ACE, AChE, AR, BBB, BZR, Cox2, DHFR, ER, FXa, GPB, Therm, and Thr. Finally, we developed our own benchmark set by collecting data on toxicology.</p> <p>Results</p> <p>Our results show that our system performs better than, or comparatively to, the existing methods over a broad range of problem types. Our method does not require the expert knowledge that is necessary to apply the other methods to novel problems.</p> <p>Conclusions</p> <p>We conclude that our success is due to the ability of our system to: 1) encode molecules losslessly before presentation to the learning system, and 2) leverage the design of molecular description languages to facilitate the identification of relevant structural attributes of the molecules over different problem domains.</p

Nomenclature for kidney function and disease: report of a Kidney Disease:Improving Global Outcomes (KDIGO) Consensus Conference

The worldwide burden of kidney disease is rising, but public awareness remains limited, underscoring the need for more effective communication by stakeholders in the kidney health community. Despite this need for clarity, the nomenclature for describing kidney function and disease lacks uniformity. In June 2019, Kidney Disease: Improving Global Outcomes (KDIGO) convened a Consensus Conference with the goal of standardizing and refining the nomenclature used in the English language to describe kidney function and disease, and of developing a glossary that could be used in scientific publications. Guiding principles of the conference were that the revised nomenclature should be patient-centered, precise, and consistent with nomenclature used in the KDIGO guidelines. Conference attendees reached general consensus on the following recommendations: (i) to use "kidney" rather than "renal" or "nephro-" when referring to kidney disease and kidney function; (ii) to use "kidney failure" with appropriate descriptions of presence or absence of symptoms, signs, and treatment, rather than "end-stage kidney disease"; (iii) to use the KDIGO definition and classification of acute kidney diseases and disorders (AKD) and acute kidney injury (AKI), rather than alternative descriptions, to define and classify severity of AKD and AKI; (iv) to use the KDIGO definition and classification of chronic kidney disease (CKD) rather than alternative descriptions to define and classify severity of CKD; and (v) to use specific kidney measures, such as albuminuria or decreased glomerular filtration rate (GFR), rather than "abnormal" or "reduced" kidney function to describe alterations in kidney structure and function. A proposed 5-part glossary contains specific items for which there was general agreement. Conference attendees acknowledged limitations of the recommendations and glossary, but they considered standardization of scientific nomenclature to be essential for improving communication

Prebiotic potential of a maize-based soluble fibre and impact of dose on the human gut microbiota

Dietary management of the human gut microbiota towards a more beneficial composition is one approach that may improve host health. To date, a large number of human intervention studies have demonstrated that dietary consumption of certain food products can result in significant changes in the composition of the gut microbiota i.e. the prebiotic concept. Thus the prebiotic effect is now established as a dietary approach to increase beneficial gut bacteria and it has been associated with modulation of health biomarkers and modulation of the immune system. Promitor™ Soluble Corn Fibre (SCF) is a well-known maize-derived source of dietary fibre with potential selective fermentation properties. Our aim was to determine the optimum prebiotic dose of tolerance, desired changes to microbiota and fermentation of SCF in healthy adult subjects. A double-blind, randomised, parallel study was completed where volunteers (n = 8/treatment group) consumed 8, 14 or 21 g from SCF (6, 12 and 18 g/fibre delivered respectively) over 14-d. Over the range of doses studied, SCF was well tolerated Numbers of bifidobacteria were significantly higher for the 6 g/fibre/day compared to 12g and 18g/fibre delivered/day (mean 9.25 and 9.73 Log10 cells/g fresh faeces in the pre-treatment and treatment periods respectively). Such a numerical change of 0.5 Log10 bifidobacteria/g fresh faeces is consistent with those changes observed for inulin-type fructans, which are recognised prebiotics. A possible prebiotic effect of SCF was therefore demonstrated by its stimulation of bifidobacteria numbers in the overall gut microbiota during a short-term intervention

Recent Surveys in the Forests of Ulu Segama Malua, Sabah, Malaysia, Show That Orang-utans (P. p. morio) Can Be Maintained in Slightly Logged Forests

BACKGROUND: Today the majority of wild great ape populations are found outside of the network of protected areas in both Africa and Asia, therefore determining if these populations are able to survive in forests that are exploited for timber or other extractive uses and how this is managed, is paramount for their conservation. METHODOLOGY/PRINCIPAL FINDINGS: In 2007, the "Kinabatangan Orang-utan Conservation Project" (KOCP) conducted aerial and ground surveys of orang-utan (Pongo pygmaeus morio) nests in the commercial forest reserves of Ulu Segama Malua (USM) in eastern Sabah, Malaysian Borneo. Compared with previous estimates obtained in 2002, our recent data clearly shows that orang-utan populations can be maintained in forests that have been lightly and sustainably logged. However, forests that are heavily logged or subjected to fast, successive coupes that follow conventional extraction methods, exhibit a decline in orang-utan numbers which will eventually result in localized extinction (the rapid extraction of more than 100 m(3) ha(-1) of timber led to the crash of one of the surveyed sub-populations). Nest distribution in the forests of USM indicates that orang-utans leave areas undergoing active disturbance and take momentarily refuge in surrounding forests that are free of human activity, even if these forests are located above 500 m asl. Displaced individuals will then recolonize the old-logged areas after a period of time, depending on availability of food sources in the regenerating areas. CONCLUSION/SIGNIFICANCE: These results indicate that diligent planning prior to timber extraction and the implementation of reduced-impact logging practices can potentially be compatible with great ape conservation

Design and integration of lower ports for ITER diagnostic systems

All around the ITER vacuum vessel, forty-four ports will provide access to the vacuum vessel for remotehandling operations, diagnostic systems, heating, and vacuum systems: 18 upper ports, 17 equatorialports, and 9 lower ports. Among the lower ports, three of them will be used for the remote handlinginstallation of the ITER divertor. Once the divertor is in place, these ports will host various diagnosticsystems mounted in the so-called diagnostic racks. The diagnostic racks must allow the support andcooling of the diagnostics, extraction of the required diagnostic signals, and providing access and main-tainability while minimizing the leakage of radiation toward the back of the port where the humans areallowed to enter. A fully integrated inner rack, carrying the near plasma diagnostic components, will bean stainless steel structure, 4.2 m long, with a maximum weight of 10 t. This structure brings water forcooling and baking at maximum temperature of 240?C and provides connection with gas, vacuum andelectric services. Additional racks (placed away from plasma and not requiring cooling) may be requiredfor the support of some particular diagnostic components. The diagnostics racks and its associated exvessel structures, which are in its conceptual design phase, are being designed to survive the lifetimeof ITER of 20 years. This paper presents the current state of development including interfaces, diagnos-tic integration, operation and maintenance, shielding requirements, remote handling, loads cases anddiscussion of the main challenges coming from the severe environment and engineering requirements

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Tackling antibiotic resistance: the environmental framework

Antibiotic resistance is a threat to human and animal health worldwide, and key measures are required to reduce the risks posed by antibiotic resistance genes that occur in the environment. These measures include the identification of critical points of control, the development of reliable surveillance and risk assessment procedures, and the implementation of technological solutions that can prevent environmental contamination with antibiotic resistant bacteria and genes. In this Opinion article, we discuss the main knowledge gaps, the future research needs and the policy and management options that should be prioritized to tackle antibiotic resistance in the environment