Teaching resources for the European Open Platform for Prescribing Education (EurOP2E) : a nominal group technique study

© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.The European Open Platform for Prescribing Education (EurOP2E) seeks to improve and harmonize European clinical pharmacology and therapeutics (CPT) education by facilitating international collaboration and sharing problem-based, online, open educational resources. The COVID-19 pandemic forced teachers to switch to virtual modalities, highlighting the need for high-quality online teaching materials. The goal of this study was to establish the online problem-based teaching resources needed to sustain prescribing education during the pandemic and thereafter. A nominal group technique study was conducted with prescribing teachers from 15 European countries. Results were analyzed through thematic analysis. In four meetings, 20 teachers from 15 countries proposed and ranked 35 teaching materials. According to the participants, the most necessary problem-based-online teaching materials related to three overarching themes. Related to learning outcomes for CPT, participants proposed creating prescription scenarios, including materials focusing on background knowledge and resources on personalized medicine and topical/ethical issues such as the prescription's impact on planetary health. Second, related to teaching, they proposed online case discussions, gamification and decision support systems. Finally, in relation to faculty development, they recommend teacher courses, a repository of reusable exam questions and harmonized formularies. Future work will aim to collaboratively produce such materials.This study was funded by the European Union under Erasmus+ grant 2020-1-NL01-KA203-083098info:eu-repo/semantics/publishedVersio

Risk-based decision making: estimands for sequential prediction under interventions

Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an intervention while those at low risk are advised to refrain from it. Standard prediction models do not always provide risks that are relevant to inform such decisions: e.g., an individual may be estimated to be at low risk because similar individuals in the past received an intervention which lowered their risk. Therefore, prediction models supporting decisions should target risks belonging to defined intervention strategies. Previous works on prediction under interventions assumed that the prediction model was used only at one time point to make an intervention decision. In clinical practice, intervention decisions are rarely made only once: they might be repeated, deferred and re-evaluated. This requires estimated risks under interventions that can be reconsidered at several potential decision moments. In the current work, we highlight key considerations for formulating estimands in sequential prediction under interventions that can inform such intervention decisions. We illustrate these considerations by giving examples of estimands for a case study about choosing between vaginal delivery and cesarean section for women giving birth. Our formalization of prediction tasks in a sequential, causal, and estimand context provides guidance for future studies to ensure that the right question is answered and appropriate causal estimation approaches are chosen to develop sequential prediction models that can inform intervention decisions.Comment: 32 pages, 2 figure

Identifying adults with acute rhinosinusitis in primary care that benefit most from antibiotics : protocol of an individual patient data meta-analysis using multivariable risk prediction modelling

Introduction Acute rhinosinusitis (ARS) is a prime reason for doctor visits and among the conditions with highest antibiotic overprescribing rates in adults. To reduce inappropriate prescribing, we aim to predict the absolute benefit of antibiotic treatment for individual adult patients with ARS by applying multivariable risk prediction methods to individual patient data (IPD) of multiple randomised placebo-controlled trials. Methods and analysis This is an update and re-analysis of a 2008 IPD meta-analysis on antibiotics for adults with clinically diagnosed ARS. First, the reference list of the 2018 Cochrane review on antibiotics for ARS will be reviewed for relevant studies published since 2008. Next, the systematic searches of CENTRAL, MEDLINE and Embase of the Cochrane review will be updated to 1 September 2020. Methodological quality of eligible studies will be assessed using the Cochrane Risk of Bias 2 tool. The primary outcome is cure at 8-15 days. Regression-based methods will be used to model the risk of being cured based on relevant predictors and treatment, while accounting for clustering. Such model allows for risk predictions as a function of treatment and individual patient characteristics and hence gives insight into individualised absolute benefit. Candidate predictors will be based on literature, clinical reasoning and availability. Calibration and discrimination will be evaluated to assess model performance. Resampling techniques will be used to assess internal validation. In addition, internal-external cross-validation procedures will be used to inform on between-study differences and estimate out-of-sample model performance. Secondarily, we will study possible heterogeneity of treatment effect as a function of outcome risk. Ethics and dissemination In this study, no identifiable patient data will be used. As such, the Medical Research Involving Humans Subject Act (WMO) does not apply and official ethical approval is not required. Results will be submitted for publication in international peer-reviewed journals. PROSPERO registration number CRD42020220108.Peer reviewe

Mechanistic Studies of the Transdermal Iontophoretic Delivery of 5-OH-DPAT In Vitro

A characterization and optimization of the in vitro transdermal iontophoretic transport of 5-hydroxy-2-(N,N,-di-n-propylamino)tetralin (5-OH-DPAT) is presented. The utility of acetaminophen as a marker of electroosmotic flow was studied as well. The following parameters of iontophoretic transport of 5-OH-DPAT were examined: drug donor concentration, electroosmotic contribution, influence of co-ions, current density, and composition of the acceptor phase. The steady-state flux (Flux(ss)) of acetaminophen was linearly correlated with the donor concentration and co-iontophoresis of acetaminophen did not influence the iontophoretic flux of 5-OH-DPAT, indicating that acetaminophen is an excellent marker of electroosmotic flow. Lowering the Na(+) concentration from 78 to 10 mM in the donor phase, resulted in a 2.5-fold enhancement of the Flux(ss). The Flux(ss) showed a nonlinear relation with the drug donor concentration and an excellent linear correlation with the current density. Reducing the pH of the acceptor phase from 7.4 to 6.2 resulted in a dramatic decrease of the Flux(ss) of 5-OH-DPAT, explained by a reduced electroosmotic flow and an increased counter-ion flow. Optimization of the conditions resulted in a maximum Flux(ss) of 5-OH-DPAT of 1.0 mu mol . cm(-2) h(-1) demonstrating the potential of the iontophoretic delivery of this dopamine agonist for the symptomatic treatment of Parkinson's disease. (C) 2009 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 99:275-285, 201

Glucocerebrosidase: Functions in and Beyond the Lysosome

Glucocerebrosidase (GCase) is a retaining β-glucosidase with acid pH optimum metabolizing the glycosphingolipid glucosylceramide (GlcCer) to ceramide and glucose. Inherited deficiency of GCase causes the lysosomal storage disorder named Gaucher disease (GD). In GCase-deficient GD patients the accumulation of GlcCer in lysosomes of tissue macrophages is prominent. Based on the above, the key function of GCase as lysosomal hydrolase is well recognized, however it has become apparent that GCase fulfills in the human body at least one other key function beyond lysosomes. Crucially, GCase generates ceramides from GlcCer molecules in the outer part of the skin, a process essential for optimal skin barrier property and survival. This review covers the functions of GCase in and beyond lysosomes and also pays attention to the increasing insight in hitherto unexpected catalytic versatility of the enzyme

Skin barrier dysfunction in non-lesional atopic eczema: the role of stratum corneum lipids

Dermatology and Venereolog

Teaching resources for the European Open Platform for Prescribing Education (EurOP2E)—a nominal group technique study

The European Open Platform for Prescribing Education (EurOP2E) seeks to improve and harmonize European clinical pharmacology and therapeutics (CPT) education by facilitating international collaboration and sharing problem-based, online, open educational resources. The COVID-19 pandemic forced teachers to switch to virtual modalities, highlighting the need for high-quality online teaching materials. The goal of this study was to establish the online problem-based teaching resources needed to sustain prescribing education during the pandemic and thereafter. A nominal group technique study was conducted with prescribing teachers from 15 European countries. Results were analyzed through thematic analysis. In four meetings, 20 teachers from 15 countries proposed and ranked 35 teaching materials. According to the participants, the most necessary problem-based-online teaching materials related to three overarching themes. Related to learning outcomes for CPT, participants proposed creating prescription scenarios, including materials focusing on background knowledge and resources on personalized medicine and topical/ethical issues such as the prescription’s impact on planetary health. Second, related to teaching, they proposed online case discussions, gamification and decision support systems. Finally, in relation to faculty development, they recommend teacher courses, a repository of reusable exam questions and harmonized formularies. Future work will aim to collaboratively produce such materials

Problems in detecting misfit of latent class models in diagnostic research without a gold standard were shown

OBJECTIVES: The objective of this study was to evaluate the performance of goodness-of-fit testing to detect relevant violations of the assumptions underlying the criticized 'standard' 2-class latent class model. Often used to obtain sensitivity and specificity estimates for diagnostic tests in the absence of a gold reference standard, this model relies on assuming that diagnostic test errors are independent. When this assumption is violated, accuracy estimates may be biased: goodness-of-fit testing is often used to evaluate the assumption and prevent bias. STUDY DESIGN AND SETTING: We investigate the performance of goodness-of-fit testing by Monte Carlo simulation. The simulation scenarios are based on three empirical examples. RESULTS: Goodness-of-fit tests lack power to detect relevant misfit of the standard 2-class latent class model at sample sizes that are typically found in empirical diagnostic studies. The goodness-of-fit tests that are based on asymptotic theory are not robust to the sparseness of data. A parametric bootstrap procedure improves the evaluation of goodness-of-fit in the case of sparse data. CONCLUSION: Our simulation study suggests that relevant violation of the local independence assumption underlying the standard 2-class latent class model may remain undetected in empirical diagnostic studies, potentially leading to biased estimates of sensitivity and specificity

Problems in detecting misfit of latent class models in diagnostic research without a gold standard were shown

OBJECTIVES: The objective of this study was to evaluate the performance of goodness-of-fit testing to detect relevant violations of the assumptions underlying the criticized 'standard' 2-class latent class model. Often used to obtain sensitivity and specificity estimates for diagnostic tests in the absence of a gold reference standard, this model relies on assuming that diagnostic test errors are independent. When this assumption is violated, accuracy estimates may be biased: goodness-of-fit testing is often used to evaluate the assumption and prevent bias. STUDY DESIGN AND SETTING: We investigate the performance of goodness-of-fit testing by Monte Carlo simulation. The simulation scenarios are based on three empirical examples. RESULTS: Goodness-of-fit tests lack power to detect relevant misfit of the standard 2-class latent class model at sample sizes that are typically found in empirical diagnostic studies. The goodness-of-fit tests that are based on asymptotic theory are not robust to the sparseness of data. A parametric bootstrap procedure improves the evaluation of goodness-of-fit in the case of sparse data. CONCLUSION: Our simulation study suggests that relevant violation of the local independence assumption underlying the standard 2-class latent class model may remain undetected in empirical diagnostic studies, potentially leading to biased estimates of sensitivity and specificity