Holger Schwender

Promoting brain health is vital for well-being and reducing healthcare burdens. Brain health as measured with the Brain Age Gap (BAG) - the difference between chronological and predicted brain age- relates to many factors. However, a holistic view, integrating the range of factors an individual brain is exposed to, is missing for understanding how the exposome shapes brain health. After computing BAG as an indicator of grey matter (GM) health, we predicted it using machine learning based on 261 exposome variables (spanning biomedical, environmental, lifestyle, socio-affective, and early life domains) in UK Biobank participants. Exposome data can predict GM health with factors pertaining to cardiovascular and bone health, along with alcohol and smoking, nutrition and diabetes showing greater contribution to the prediction. In such domains, life period and duration of exposure appeared crucial. These findings call for early prevention in cardiovascular and metabolic health to promote life-long brain health.

Background: Genomic or proteomic data are frequently affected by noise or are convolutions of different biological signals. This is, e.g., particularly relevant in skin aging research, in which intrinsic aging, driven by genetic factors, and extrinsic aging, caused by environmental exposure, are both investigated considering, e.g., the proteome of skin fibroblasts. Since all skin areas are affected by intrinsic aging, isolating the pure extrinsic signal from the mixture of the intrinsic and extrinsic effects is crucial to investigate extrinsic aging. In such settings, deconvolution methods can be used to estimate the density of the target component. However, existing nonparametric deconvolution approaches often fail when the variance of the mixed distribution is substantially greater than that of the target distribution, a common issue in genomic and proteomic data. Results: We introduce a new nonparametric deconvolution method called N-power Fourier deconvolution (NPFD) that deals with this problem of differing variances by employing the N -th power of the Fourier transforms of the densities. Leveraging Fourier inversion and key properties of density transforms, NPFD reduces numerical instability, resulting in smooth and accurate density estimates. NPDF is able to effectively resolve smoothness- and variance-related challenges and performs comparably or better than existing methods in almost all considered scenarios, as a comprehensive simulation study shows. Moreover, applications to real proteomic data from skin fibroblasts demonstrate how NPFD can be employed to estimate the pure extrinsic aging signal. Conclusions: NPFD represents a new conceptual framework for nonparametric density deconvolution by exploiting the properties of Fourier transforms in a novel way. Its ability to address smoothness- and variance-related challenges makes it a versatile and powerful tool for deconvolving complex biological signals on the level of density functions across diverse applications.

Background: Clinical decision support systems (CDSSs) already support community pharmacists in conducting medication reviews (MRs) by identifying important information on interactions and suggesting clinical solutions. However, their impact in terms of quality and time savings is widely unexplored. The aim of our study was to investigate whether MRs are performed faster and better with or without using a CDSS. Methods: In a randomized controlled study with a cross-over design, 71 pharmacists performed a total of four MRs, two with and two without the use of a CDSS. The primary endpoint was defined as the time required for the MRs. The secondary endpoints were the number of predefined relevant drug-related problems (DRPs) detected and pharmacist satisfaction. Results: Without the use of a CDSS, pharmacists needed between 25.7% and 30.7% more time to perform a MR than with a CDSS. In addition, significantly more relevant DRPs were detected in the MRs with CDSS than without CDSS (70% vs. 50%; p = 0.0037). Furthermore, participants stated that they felt more confident using a CDSS for MRs than without. Conclusions: Our results demonstrate that MRs can be performed both faster and better when using a CDSS than without. Consequently, community pharmacists benefit from the use of CDSSs for MRs, as do patients in terms of their drug therapy safety.

Recently, there has been a remarkable move towards interprofessional collaboration in response to the COVID-19 pandemic and the care of comorbidities. In Germany, there has been a gradual increase in interprofessional learning in medical and pharmacy education, aiming to enhance patient care. To adapt the pharmacy curriculum for collaborative practice between pharmacy and medical students, we developed an immersive interprofessional collaboration course for pharmacy students using adult and pediatric high-fidelity simulators (HFS) to assess and train medication consultation skills. In a randomized controlled trial, we investigated whether interprofessional training between pharmacy and medical students results in differences in pharmacy students’ performance of medication therapy consultation compared to the case of mono-professional training of pharmacy students only. Before and after inter/mono-professional training, each pharmacy student performed an objective structured clinical examination (OSCE) and completed a self-assessment questionnaire. Additionally, an attitude survey towards interprofessional learning was completed by pharmacy and medical students at the end of the training. As expected, interprofessional as well as mono-professional training showed a statistically significant increase in medication consultation skills. Of importance, the performance in the interprofessional training group was significantly better than in the mono-professional group, particularly in drug therapy counselling and consultation behaviors. There was a significant difference between the intervention and control groups in self-assessment scores, and all study participants had positive attitudes toward interprofessional collaboration and training. Therefore, interprofessional training using HFS has been shown to appropriately train pharmacy students for collaborative practice and consultation skills.

In order to increase vaccination rates, the Government of Germany introduced vaccination against influenza and COVID-19 into the regular care administered by pharmacists. However, vaccination training is yet not integrated into the German pharmacy curriculum. Therefore, the Institute for Clinical Pharmacy and Pharmacotherapy in Duesseldorf had developed an innovative vaccination course using high-fidelity simulation for students. To investigate the acceptance further, the course was carried out at three different German universities (Bonn, Duesseldorf, Greifswald). Students were asked to give their self-assessment before and after and satisfaction only after the training course. Responses from 33 participants from the University of Bonn, 42 from the University of Duesseldorf and 49 from the University of Greifswald were analyzed. Every participant at the respective universities showed a significant increase in their self-assessment and indicated a high level of satisfaction with the course. The results also did not differ significantly between the respective universities. Consequently, the results lead to the hypothesis that the satisfaction of pharmacy students with this kind of training using high-fidelity simulation is very high and attractive, and can be recommended for other German universities. The integration of such vaccination training into the German pharmacy curriculum might be a future step.

Interactions between predictors play an important role in many applications. Popular and successful tree-based supervised learning methods such as random forests or logic regression can incorporate interactions associated with the considered outcome without specifying which variables might interact. Nonetheless, these algorithms suffer from certain drawbacks such as limited interpretability of model predictions and difficulties with negligible marginal effects in the case of random forests or not being able to incorporate interactions with continuous variables, being restricted to additive structures between Boolean terms, and not directly considering conjunctions that reveal the interactions in the case of logic regression. We, therefore, propose a novel method called logic decision trees (logicDT) that is specifically tailored to binary input data and helps to overcome the drawbacks of existing methods. The main idea consists of considering sets of Boolean conjunctions, using these terms as input variables for decision trees, and searching for the best performing model. logicDT is also accompanied by a framework for estimating the importance of identified terms, i.e., input variables and interactions between input variables. This new method is compared to other popular statistical learning algorithms in simulations and real data applications. As these evaluations show, logicDT is able to yield high prediction performances while maintaining interpretability.

Digital Medication Review Tools (DMRTs) are increasingly important in pharmacy practice. To ensure that young pharmacists are sufficiently competent to perform medication reviews after graduation, the introduction of DMRTs teaching in academic education is necessary. The aim of our study was to demonstrate the effect of DMRTs use on pharmacy students’ performance when conducting a medication review (MR) in a randomized controlled pre-post design. Forty-one pharmacy students were asked to complete a MR within 60 min, followed by a 10-min consultation with (intervention group) and without a DMRT (control group). The MR performance was subdivided into four categories: communication skills, subjective and objective patient data, assessment, and plan. Performance was assessed using objective structured clinical examinations (OSCEs) and analytical checklists. With the use of DMRTs, the overall performance was improved by 17.0% compared to the control group (p < 0.01). Improvement through DMRTs was seen in the subcategories “Assessment” and “Plan”. Furthermore, pharmacy students liked using DMRTs and felt more confident overall. Our study results demonstrate that DMRTs improve the performance of MRs, hence DMRTs should become an integral part of pharmacy curriculum. Consequently, digitally enabled pharmacists using DMRTs will be better prepared for their professional careers in pharmacy practice.

Introduction Recently, pharmacists in Germany were allowed to administer influenza and COVID-19 vaccines for people aged 12 years and older in order to increase vaccination coverage rates. To adapt pharmacy curriculum for clinical practice, an innovative, high level vaccination training course comprising clinical skills, techniques required for level of competence was developed with participants interacting either with a high-fidelity simulator or low-fidelity injection pad. Clinical scenarios to manage adverse events were also implemented. Methods A randomized controlled trial using a pre-post-design with pharmacy undergraduates alongside with a theoretical part was performed. The intervention group interacted with a high-fidelity simulator, while the control group was trained with low-fidelity injection pads. Before and after the respective training each participant went through an objective structured clinical examination (OSCE) and each participant completed a self-assessment questionnaire and knowledge quiz. Results OSCE Score were raised through an analytical checklist examining skills in anamnesis, patient information, vaccination process, and handling emergency case. Both training methods showed a significant (pConclusions High fidelity simulation proves to be an appropriate tool to train pharmacy students for vaccine administration, as a new pharmaceutical service and enable the students to recognize and manage adverse events.