Examining variations in prescribing safety in UK general practice: cross sectional study using the Clinical Practice Research Datalink

Study question: What is the prevalence of different types of potentially hazardous prescribing in general practice in the United Kingdom, and what is the variation between practices? Methods: A cross sectional study included all adult patients potentially at risk of a prescribing or monitoring error defined by a combination of diagnoses and prescriptions in 526 general practices contributing to the Clinical Practice Research Datalink (CPRD) up to 1 April 2013. Primary outcomes were the prevalence of potentially hazardous prescriptions of anticoagulants, anti-platelets, NSAIDs, β blockers, glitazones, metformin, digoxin, antipsychotics, combined hormonal contraceptives, and oestrogens and monitoring by blood test less frequently than recommended for patients with repeated prescriptions of angiotensin converting enzyme inhibitors and loop diuretics, amiodarone, methotrexate, lithium, or warfarin. Study answer and limitations: 49 927 of 949 552 patients at risk triggered at least one prescribing indicator (5.26%, 95% confidence interval 5.21% to 5.30%) and 21 501 of 182 721 (11.8%, 11.6% to 11.9%) triggered at least one monitoring indicator. The prevalence of different types of potentially hazardous prescribing ranged from almost zero to 10.2%, and for inadequate monitoring ranged from 10.4% to 41.9%. Older patients and those prescribed multiple repeat medications had significantly higher risks of triggering a prescribing indicator whereas younger patients with fewer repeat prescriptions had significantly higher risk of triggering a monitoring indicator. There was high variation between practices for some indicators. Though prescribing safety indicators describe prescribing patterns that can increase the risk of harm to the patient and should generally be avoided, there will always be exceptions where the indicator is clinically justified. Furthermore there is the possibility that some information is not captured by CPRD for some practices—for example, INR results in patients receiving warfarin. What this study adds: The high prevalence for certain indicators emphasises existing prescribing risks and the need for their appropriate consideration within primary care, particularly for older patients and those taking multiple medications. The high variation between practices indicates potential for improvement through targeted practice level intervention. Funding, competing interests, data sharing: National Institute for Health Research through the Greater Manchester Primary Care Patient Safety Translational Research Centre (grant No GMPSTRC-2012-1). Data from CPRD cannot be shared because of licensing restrictions

Kidney DNA methylation as a driver of genetic change in the kidney

Objectives: Hypertension is associated with various physiological changes that result in an increased risk of stroke, cardiovascular events and chronic kidney disease. Here we conducted a genome-wide analysis of methylation changes in the human kidney to identify epigenetic signatures of hypertension using the largest collection of apparently healthy human renal tissue. As the first analysis of its kind in the human kidney we also determined whether these changes have functional consequences at the gene expression level. Methods: We examined DNA extracted from a total of 94 human kidneys to investigate methylation patterns in hypertension. We also examined RNA -sequencing to characterise the transcriptome of 180 human kidneys to uncover interactions between DNA methylation and gene expressi. Results: Our methylation analysis identified one hypertension-associated CpG site, three systolic blood pressure-associated CpG sites and 19 diastolic blood pressure -associated CpG sites; including four CpG sites previously identified in peripheral blood studies of hypertension. DNA methylation is a known regulator of gene expression; therefore, we investigated whether differential DNA methylation in proximity to hypertension-associated renal genes correlated with their renal expression. Methylation of two genes (FAM50B, PC) showed an association with renal expression. The transcriptome analysis of 180 kidneys revealed 14 hypertension-associated genes, 1 gene associated with systolic blood pressure and 6 genes associated with diastolic blood pressure; including those involved in smooth muscle response to blood pressure fluctuation and blood pressure response to salt intake in humans. Conclusion: Our study uncovered DNA methylation as a new regulatory mechanism underpinning hypertension-related changes in renal gene expression

Primary care medication safety surveillance with integrated primary and secondary care electronic health records: a cross-sectional study

Introduction: The extent of preventable medication-related hospital admissions and medication-related issues in primary care is significant enough to justify developing decision support systems for medication safety surveillance. The prerequisite for such systems is defining a relevant set of medication safety-related indicators and understanding the influence of both patient and general practice characteristics on medication prescribing and monitoring. Objective: The aim of the study was to investigate the feasibility of linked primary and secondary care electronic health record data for surveillance of medication safety, examining not only prescribing but also monitoring, and associations with patient- and general practice-level characteristics. Methods: A cross-sectional study was conducted using linked records of patients served by one hospital and over 50 general practices in Salford, UK. Statistical analysis consisted of mixed-effects logistic models, relating prescribing safety indicators to potential determinants. Results: The overall prevalence (proportion of patients with at least one medication safety hazard) was 5.45 % for prescribing indicators and 7.65 % for monitoring indicators. Older patients and those on multiple medications were at higher risk of prescribing hazards, but at lower risk of missed monitoring. The odds of missed monitoring among all patients were 25 % less for males, 50 % less for patients in practices that provide general practitioner training, and threefold higher in practices serving the most deprived compared with the least deprived areas. Practices with more prescribing hazards did not tend to show more monitoring issues. Conclusions:Systematic collection, collation, and analysis of linked primary and secondary care records produce plausible and useful information about medication safety for a health system. Medication safety surveillance systems should pay close attention to patient age and polypharmacy with respect to both prescribing and monitoring failures; treat prescribing and monitoring as different statistical processes, rather than a combined measure of prescribing safety; and audit the socio-economic equity of missed monitoring

Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals

Genetic studies of blood pressure (BP) to date have mainly analyzed common variants (minor allele frequency > 0.05). In a meta-analysis of up to ~1.3 million participants, we discovered 106 new BP-associated genomic regions and 87 rare (minor allele frequency ≤ 0.01) variant BP associations (P < 5 × 10−8), of which 32 were in new BP-associated loci and 55 were independent BP-associated single-nucleotide variants within known BP-associated regions. Average effects of rare variants (44% coding) were ~8 times larger than common variant effects and indicate potential candidate causal genes at new and known loci (for example, GATA5 and PLCB3). BP-associated variants (including rare and common) were enriched in regions of active chromatin in fetal tissues, potentially linking fetal development with BP regulation in later life. Multivariable Mendelian randomization suggested possible inverse effects of elevated systolic and diastolic BP on large artery stroke. Our study demonstrates the utility of rare-variant analyses for identifying candidate genes and the results highlight potential therapeutic targets. © 2020, The Author(s), under exclusive licence to Springer Nature America, Inc. There are 286 authors of this articles not all are listed in this record

Author Correction: Promoter interactome of human embryonic stem cell-derived cardiomyocytes connects GWAS regions to cardiac gene networks (Nature Communications, (2018), 9, 1, (2526), 10.1038/s41467-018-04931-0)

In the original version of the Article, the gene symbol for tissue factor pathway inhibitor was inadvertently given as ‘TFP1’ instead of ‘TFPI’. This has now been corrected in both the PDF and HTML versions of the Article

Publisher Correction:Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals (Nature Genetics, (2020), 52, 12, (1314-1332), 10.1038/s41588-020-00713-x)

In the version of this article originally published, the e-mail address of corresponding author Patricia B. Munroe was incorrect. The error has been corrected in the HTML and PDF versions of the article

Probability elicitation : predictive approach

Probability elicitation is an important area of research with a wide scope for investigation and experimentation. The existing literature on the subject is vast and spread over many disciplines. This indicates the importance of the subject and the ubiquitous nature of the concept of probability. In this thesis, we focus on a probability elicitation method known as predictive elicitation. Predictive elicitation is a method for estimating hyperparameters of prior distributions by inverting corresponding prior predictive distributions. The uncertainty associated with prior predictive distributions is the uncertainty associated with socalled observable quantities. This uncertainty is generally accepted to be fundamentally more robust for elicitation than the uncertainty about unobservable parameters associated with prior distributions. Although predictive elicitation is the most natural way for eliciting probabilities for Bayesian models, it has two major difficulties for practical implementation. The first of these difficulties is related to inverting integral equations. Here, we deal with this difficulty by restricting the space of possible classes of prior distributions into three families, namely the beta, gamma and normal families as suggested by Percy (2002- 2004). The second difficulty is the problem of constraints on eliciting quantiles of the prior predictive distribution. In this thesis, we propose a method for identifying such constraints for single parameter models. We also propose a computational algorithm that makes predictive elicitation accessible for two-parameter models. We demonstrate that using the proposed elicitation method for two-parameter models it is possible to identify associated constraints. In summary, we extend the current literature related to predictive elicitation by adding to it the following main points: We propose a method for identifying constraints on the elicitation of quantiles for single parameter models. We propose the use of a new hybrid elicitation procedure for two-parameter models. We also investigate a method for identifying constraints on the elicitation process posed by the hybrid elicitation strategy. We provide numerical algorithms, programmed using MathCAD software, that enable full implementation of predictive elicitation for single parameter models. We also provide similar programs for selected two-parameter models that enable implementation of the proposed hybrid elicitation method. These algorithms can be used as bases for developing generic software for implementing predictive elicitation. Further research is needed to address the issue of the practical applicability of predictive elicitation to multi-parameter and multivariate models. The advancements made in this thesis provide foundations and an approach for dealing with the problem of constraints that can be extended to solve similar problems for multi-parameter and multivariate models.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Support vector regression for warranty claim forecasting

Forecasting the number of warranty claims is vitally important for manufacturers/warranty providers in preparing fiscal plans. In existing literature, a number of techniques such as log-linear Poisson models, Kalman filter, time series models, and artificial neural network models have been developed. Nevertheless, one might find two weaknesses existing in these approaches: (1) they do not consider the fact that warranty claims reported in the recent months might be more important in forecasting future warranty claims than those reported in the earlier months, and (2) they are developed based on repair rates (i.e., the total number of claims divided by the total number of products in service), which can cause information loss through such an arithmetic-mean operation. To overcome the above two weaknesses, this paper introduces two different approaches to forecasting warranty claims: the first is a weighted support vector regression (SVR) model and the second is a weighted SVR-based time series model. These two approaches can be applied to two scenarios: when only claim rate data are available and when original claim data are available. Two case studies are conducted to validate the two modelling approaches. On the basis of model evaluation over six months ahead forecasting, the results show that the proposed models exhibit superior performance compared to that of multilayer perceptrons, radial basis function networks and ordinary support vector regression models.Support vector regression Radial basis function network Warranty claims Neural networks Multilayer perceptron

Forecasting Warranty Claims for Recently Launched Products

Forecasting warranty claims for recently launched products that have short histories of claim records is vitally important for manufacturers in preparing their fiscal plans. Since the amount of historical claim data for such products is not large enough, developing forecasting models with good performance has been a difficult problem. The objective of this paper is to develop an algorithm for forecasting the number of warranty claims of recently launched products. A two-phase modelling algorithm is developed: in Phase I, we estimate the upper and the lower bounds of the warranty claim rates of the reference products that have been in the market for a longer time; in Phase II, we build forecasting models for the recently launched products and assume that their future claim rates are subject to the bound constraints derived from Phase I. Based on this algorithm, we use the NHPP (non-homogeneous Poisson process) and the constrained maximum likelihood estimation to build forecasting models on artificially generated data as well as warranty claim data collected from an electronics manufacturer. The results show that the proposed algorithm outperforms commonly used NHPP models. © 2012 Elsevier Ltd