Identifying Community Pharmacists' Readiness to Participate in Transitions of Care

Objective: To determine the readiness of community pharmacists to participate in transitions of care and to identify barriers related to participation. Methods: A questionnaire was developed to identify community pharmacists’ willingness, attitudes, and barriers to participating in transitions of care programs. The questionnaire was piloted with community pharmacists prior to implementation. The questionnaire was distributed to a population of chain, supermarket/grocery, and independent community pharmacists (n=200). Descriptive statistics, correlations, and chi square tests were used to analyze demographic trends and final results. Results: Of the 200 questionnaires distributed, 147 pharmacists responded (73.5%). Community pharmacists agreed transitions of care services have the potential to improve patients’ understanding of medications (97.3%), decrease medication-related errors (95.9%), and enhance relationships with patients (96.6%). The largest pharmacist perceived barriers included time (69.7%), communication/lack of patient data (14.6%), and lack of physician acceptance (6.7%). Of the respondents who indicated time as the greatest barrier to participation, 76.9% of pharmacists were still willing to participate in transitions of care given the opportunity. Willingness to participate in transitions of care did not differ by gender (p=0.139), years in practice (p=0.133), or degree of education (p=0.382). Conclusion: Community pharmacists are in a unique position to widely impact patient health outcomes and decrease hospital readmission rates through improved care coordination. This research demonstrates community pharmacists’ readiness and willingness to participate in transitions of care. Awareness of perceived barriers can allow stakeholders to address these areas when designing and implementing transitions of care programs with pharmacists.   Type: Original Researc

Identifying Community Pharmacists' Readiness to Participate in Transitions of Care

Objective: To determine the readiness of community pharmacists to participate in transitions of care and to identify barriers related to participation. Methods: A questionnaire was developed to identify community pharmacists’ willingness, attitudes, and barriers to participating in transitions of care programs. The questionnaire was piloted with community pharmacists prior to implementation. The questionnaire was distributed to a population of chain, supermarket/grocery, and independent community pharmacists (n=200). Descriptive statistics, correlations, and chi square tests were used to analyze demographic trends and final results. Results: Of the 200 questionnaires distributed, 147 pharmacists responded (73.5%). Community pharmacists agreed transitions of care services have the potential to improve patients’ understanding of medications (97.3%), decrease medication-related errors (95.9%), and enhance relationships with patients (96.6%). The largest pharmacist perceived barriers included time (69.7%), communication/lack of patient data (14.6%), and lack of physician acceptance (6.7%). Of the respondents who indicated time as the greatest barrier to participation, 76.9% of pharmacists were still willing to participate in transitions of care given the opportunity. Willingness to participate in transitions of care did not differ by gender (p=0.139), years in practice (p=0.133), or degree of education (p=0.382). Conclusion: Community pharmacists are in a unique position to widely impact patient health outcomes and decrease hospital readmission rates through improved care coordination. This research demonstrates community pharmacists’ readiness and willingness to participate in transitions of care. Awareness of perceived barriers can allow stakeholders to address these areas when designing and implementing transitions of care programs with pharmacists.   Type: Original Researc

Use of temporally validated machine learning models to predict outcomes of percutaneous nephrolithotomy using data from the British Association of Urological Surgeons percutaneous nephrolithotomy audit

Background and objective: Machine learning (ML) is a subset of artificial intelligence that uses data to build algorithms to predict specific outcomes. Few ML studies have examined percutaneous nephrolithotomy (PCNL) outcomes. Our objective was to build, streamline, temporally validate, and use ML models for prediction of PCNL outcomes (intensive care admission, postoperative infection, transfusion, adjuvant treatment, postoperative complications, visceral injury, and stone-free status at follow-up) using a comprehensive national database (British Association of Urological Surgeons PCNL). Methods: This was an ML study using data from a prospective national database. Extreme gradient boosting (XGB), deep neural network (DNN), and logistic regression (LR) models were built for each outcome of interest using complete cases only, imputed, and oversampled and imputed/oversampled data sets. All validation was performed with complete cases only. Temporal validation was performed with 2019 data only. A second round used a composite of the most important 11 variables in each model to build the final model for inclusion in the shiny application. We report statistics for prognostic accuracy. Key findings and limitations: The database contains 12 810 patients. The final variables included were age, Charlson comorbidity index, preoperative haemoglobin, Guy’s stone score, stone location, size of outer sheath, preoperative midstream urine result, primary puncture site, preoperative dimercapto-succinic acid scan, stone size, and image guidance (https://endourology.shinyapps.io/PCNL_Demographics/). The areas under the receiver operating characteristic curve was >0.6 in all cases. Conclusions and clinical implications: This is the largest ML study on PCNL outcomes to date. The models are temporally valid and therefore can be implemented in clinical practice for patient-specific risk profiling. Further work will be conducted to externally validate the models. Patient summary: We applied artificial intelligence to data for patients who underwent a keyhole surgery to remove kidney stones and developed a model to predict outcomes for this procedure. Doctors could use this tool to advise patients about their risk of complications and the outcomes they can expect after this surgery

Structural Determinants of the APOBEC3G N-Terminal Domain for HIV-1 RNA Association

APOBEC3G (A3G) is a cellular protein that inhibits HIV-1 infection through virion incorporation. The interaction of the A3G N-terminal domain (NTD) with RNA is essential for A3G incorporation in the HIV-1 virion. The interaction between A3G-NTD and RNA is not completely understood. The A3G-NTD is also recognized by HIV-1 Viral infectivity factor (Vif) and A3G-Vif binding leads to A3G degradation. Therefore, the A3G-Vif interaction is a target for the development of antiviral therapies that block HIV-1 replication. However, targeting the A3G-Vif interactions could disrupt the A3G-RNA interactions that are required for A3G's antiviral activity. To better understand A3G-RNA binding, we generated in silico docking models to simulate the RNA-binding propensity of A3G-NTD. We simulated the A3G-NTD residues with high RNA-binding propensity, experimentally validated our prediction by testing A3G-NTD mutations, and identified structural determinants of A3G-RNA binding. In addition, we found a novel amino acid residue, I26 responsible for RNA interaction. The new structural insights provided here will facilitate the design of pharmaceuticals that inhibit A3G-Vif interactions without negatively impacting A3G-RNA interactions

The clinical and cost effectiveness of surgical interventions for stones in the lower pole of the kidney : the percutaneous nephrolithotomy, flexible ureterorenoscopy and extracorporeal shockwave lithotripsy for lower pole kidney stones randomised controlled trial (PUrE RCT) protocol

PUrE is a UK Collaborative Trial funded by the National Institute for Health Research Health Technology Assessment (NIHR HTA) Programme (project no. 13/152/02). The Health Services Research Unit is funded by the Chief Scientists Office of the Scottish Government Health Directorates. The views and opinions expressed herein are those of the authors and do not necessarily reflect those of the Health Technology Assessment Programme, the National Institute of Health Research, the National Health Service or the Department of Health. The funder (through their peer-review and funding board review process) approved the study proposal but had no role in the collection, analysis or interpretation of data or writing of the report.Peer reviewedPublisher PD

Irreducible uncertainty in near-term climate projections

Model simulations of the next few decades are widely used in assessments of climate change impacts and as guidance for adaptation. Their non-linear nature reveals a level of irreducible uncertainty which it is important to understand and quantify, especially for projections of near-term regional climate. Here we use large idealised initial condition ensembles of the FAMOUS global climate model with a 1 %/year compound increase in CO2 levels to quantify the range of future temperatures in model-based projections. These simulations explore the role of both atmospheric and oceanic initial conditions and are the largest such ensembles to date. Short-term simulated trends in global temperature are diverse, and cooling periods are more likely to be followed by larger warming rates. The spatial pattern of near-term temperature change varies considerably, but the proportion of the surface showing a warming is more consistent. In addition, ensemble spread in inter-annual temperature declines as the climate warms, especially in the North Atlantic. Over Europe, atmospheric initial condition uncertainty can, for certain ocean initial conditions, lead to 20 year trends in winter and summer in which every location can exhibit either strong cooling or rapid warming. However, the details of the distribution are highly sensitive to the ocean initial condition chosen and particularly the state of the Atlantic meridional overturning circulation. On longer timescales, the warming signal becomes more clear and consistent amongst different initial condition ensembles. An ensemble using a range of different oceanic initial conditions produces a larger spread in temperature trends than ensembles using a single ocean initial condition for all lead times. This highlights the potential benefits from initialising climate predictions from ocean states informed by observations. These results suggest that climate projections need to be performed with many more ensemble members than at present, using a range of ocean initial conditions, if the uncertainty in near-term regional climate is to be adequately quantified

Human Cerebral Neuropathology of Type 2 Diabetes Mellitus

The cerebral neuropathology of Type 2 diabetes (CNDM2) has not been positively defined. This review includes a description of CNDM2 research from before the ‘Pubmed Era’. Recent neuroimaging studies have focused on cerebrovascular and white matter pathology. These and prior studies about cerebrovascular histopathology in diabetes are reviewed. Evidence is also described for and against the link between CNDM2 and Alzheimer\u27s disease pathogenesis. To study this matter directly, we evaluated data from University of Kentucky Alzheimer\u27s Disease Center (UK ADC) patients recruited while non-demented and followed longitudinally. Of patients who had come to autopsy (N = 234), 139 met inclusion criteria. These patients provided the basis for comparing the prevalence of pathological and clinical indices between well-characterized cases with (N = 50) or without (N = 89) the premortem diagnosis of diabetes. In diabetics, cerebrovascular pathology was more frequent and Alzheimer-type pathology was less frequent than in non-diabetics. Finally, a series of photomicrographs demonstrates histopathological features (including clinical–radiographical correlation) observed in brains of persons that died after a history of diabetes. These preliminary, correlative, and descriptive studies may help develop new hypotheses about CNDM2. We conclude that more work should be performed on human material in the context of CNDM2

Dynamic legislative policy making

Abstract We prove existence of stationary Markov perfect equilibria in an infinite-horizon model of legislative policy making in which the policy outcome in one period determines the status quo for the next. We allow for a multidimensional policy space and arbitrary smooth stage utilities, and we assume preferences and the status quo are subject to arbitrarily small shocks. We prove that all such equilibria are essentially in pure strategies and that proposal strategies are continuous almost everywhere. We establish upper hemicontinuity of the equilibrium correspondence, and we derive conditions under which each equilibrium of our model determines a unique invariant distribution characterizing long run policy outcomes. We provide a convergence theorem giving conditions under which the invariant distributions generated by stationary equilibria must be close to the core in a canonical spatial model

Explaining Institutional Change: Why Elected Politicians Implement Direct Democracy

In existing models of direct democratic institutions, the median voter benefits, but representative politicians are harmed since their policy choices can be overridden. This is a puzzle, since representative politicians were instrumental in creating these institutions. I build a model of direct democracy that explains why a representative might benefit from tying his or her own hands in this way. The key features are (1) that voters are uncertain about their representative's preferences; (2) that direct and representative elections are complementary ways for voters to control outcomes. The model shows that some politicians benefit from the introduction of direct democracy, since they are more likely to survive representative elections: direct democracy credibly prevents politicians from realising extreme outcomes. Historical evidence from the introduction of the initiative, referendum and recall in America broadly supports the theory, which also explains two empirical results that have puzzled scholars: legislators are trusted less, but reelected more, in US states with direct democracy. I conclude by discussing the potential for incomplete information and signaling models to improve our understanding of institutional change more generally