Background: Deprescribing has been recommended for managing polypharmacy but deprescribing preventive medication in older patients is still uncommon. We aimed to investigate older patients' barriers to and enablers of deprescribing cardiometabolic medication. Methods: Two focus groups were conducted among patients ≥70 years with polypharmacy, including cardiometabolic medication. Purposive sampling through four community pharmacies was used in two regions in the Netherlands. A topic list was developed using literature and the theoretical domains framework (TDF). The meetings were audio recorded, transcribed verbatim and coded using thematic coding, attribute coding and the TDF. In addition, patients were categorized on attitudes towards medication and willingness to stop.

Background Overtreatment with cardiometabolic medication in older patients can lead to major adverse events. Timely deprescribing of these medications is therefore essential. Self-reported willingness to stop medication is usually high among older people, still overtreatment with cardiometabolic medication is common and deprescribing is rarely initiated. An important barrier for deprescribing reported by general practitioners is the patients’ unwillingness to stop the medication. More insights are needed into the influence of patients’ characteristics on their attitudes towards deprescribing and differences in these attitudes between cardiometabolic medication groups. Methods A survey in older people using cardiometabolic medication using the revised Patients’ Attitudes Towards Deprescribing (rPATD) questionnaire was performed. Participants completed the general rPATD and an adapted version for four medication groups. Linear and ordinal logistic regression were used to assess the influence of age, sex, therapeutic area and number of medications used on the patients’ general attitudes towards deprescribing. Univariate analysis was used to compare differences in deprescribing attitudes towards sulfonylureas, insulins, antihypertensive medication and statins. Results Overall, 314 out of 1143 invited participants completed the survey (median age 76 years, 54% female). Most participants (80%) were satisfied with their medication and willing to stop medications if their doctor said it was possible (88%). Age, sex and therapeutic area had no influence on the general attitudes towards deprescribing. Taking more than ten medicines was significantly associated with a higher perceived medication burden. Antihypertensive medication and insulin were considered more appropriate than statins, and insulin was considered more appropriate than sulfonylureas not favouring deprescribing. Conclusions The majority of older people using cardiometabolic medication are willing to stop one of their medicines if their doctor said it was possible. Health care providers should take into account that patients perceive some of their medication as more appropriate than other medication when discussing deprescribing.

Introduction Benefits and risks of preventive medication change over time for ageing patients and deprescribing of medication may be needed. Deprescribing of cardiovascular and antidiabetic drugs can be challenging and is not widely implemented in daily practice. Objective The aim of this study was to identify barriers and enablers of deprescribing cardiometabolic medication as seen by healthcare providers (HCPs) of different disciplines, and to explore their views on their specific roles in the process of deprescribing. Methods Three focus groups with five general practitioners, eight pharmacists, three nurse practitioners, two geriatricians, and two elder care physicians were conducted in three cities in The Netherlands. Interviews were recorded and transcribed verbatim. Directed content analysis was performed on the basis of the Theoretical Domains Framework. Two researchers independently coded the data. Results Most HCPs agreed that deprescribing of cardiometabolic medication is relevant but that barriers include lack of evidence and expertise, negative beliefs and fears, poor communication and collaboration between HCPs, and lack of resources. Having a guideline was considered an enabler for the process of deprescribing of cardiometabolic medication. Some HCPs feared the consequences of discontinuing cardiovascular or antidiabetic medication, while others were not motivated to deprescribe when the patients experienced no problems with their medication. HCPs of all disciplines stated that adequate patient communication and involving the patients and relatives in the decision making enables deprescribing. Barriers to deprescribing included the use of medication initiated by specialists, the poor exchange of information, and the amount of time it takes to deprescribe cardiometabolic medication. The HCPs were uncertain about each other's roles and responsibilities. A multidisciplinary approach including the pharmacist and nurse practitioner was seen as the best way to support the process of deprescribing and address barriers related to resources. Conclusion HCPs recognized the importance of deprescribing cardiometabolic medication as a medical decision that can only be made in close cooperation with the patient. To successfully accomplish the process of deprescribing they strongly recommended a multidisciplinary approach.

Background and Purpose Asthma is a heterogeneous chronic inflammatory disease, characterized by the development of structural changes (airway remodelling). β‐catenin, a transcriptional co‐activator, is fundamentally involved in airway smooth muscle growth and may be a potential target in the treatment of airway smooth muscle remodelling. Experimental Approach We assessed the ability of small‐molecule compounds that selectively target β‐catenin breakdown or its interactions with transcriptional co‐activators to inhibit airway smooth muscle remodelling in vitro and in vivo. Key Results ICG‐001, a small‐molecule compound that inhibits the β‐catenin/CREB‐binding protein (CBP) interaction, strongly and dose‐dependently inhibited serum‐induced smooth muscle growth and TGFβ1‐induced production of extracellular matrix components in vitro. Inhibition of β‐catenin/p300 interactions using IQ‐1 or inhibition of tankyrase 1/2 using XAV‐939 had considerably less effect. In a mouse model of allergic asthma, β‐catenin expression in the smooth muscle layer was found to be unaltered in control versus ovalbumin‐treated animals, a pattern that was found to be similar in smooth muscle within biopsies taken from asthmatic and non‐asthmatic donors. However, β‐catenin target gene expression was highly increased in response to ovalbumin; this effect was prevented by topical treatment with ICG‐001. Interestingly, ICG‐001 dose‐dependently reduced airway smooth thickness after repeated ovalbumin challenge, but had no effect on the deposition of collagen around the airways, mucus secretion or eosinophil infiltration. Conclusions and Implications Together, our findings highlight the importance of β‐catenin/CBP signalling in the airways and suggest ICG‐001 may be a new therapeutic approach to treat airway smooth muscle remodelling in asthma.

Aim: To develop and pilot an algorithm to select older people for different types of medication review based on their case complexity.Methods: Experts rated complexity of patient cases through a Delphi-consensus method. The case characteristics were included in a regression model predicting complexity to develop a criteria-based algorithm. The algorithm was piloted in four community pharmacies with 38 patients of high and low complexity. Pharmacists conducted medication reviews according to their personal judgment and rated the patients' complexity. Time needed for reviewing and number of interventions (proposed and implemented) were assessed. Feasibility was evaluated with in-depth interviews.Results: We developed the algorithm with 75 cases proceeding from patients in average 79 years old and using 10 prescribed medications. The regression model (adjusted R2 = 0.726, P < 0.0001) resulted in the following criteria for the algorithm: “number of medications” × 1 + “number of prescribers” × 3 + “recent fall incident” × 7 + “does not collect own medication” × 4. The pharmacists performed advanced medication reviews with all patients. The time needed to perform the medication review did not differ significantly according to case complexity (76.9 min for high complexity; 66.1 min for low complexity). Agreement between the algorithm scores and the pharmacists' ratings on complexity degree was slight to moderate (Kappa 0.16–0.42). The pharmacists had mixed opinions about the feasibility of applying the algorithm, particularly regarding the criterion “fall incidents.”Conclusion: We developed an algorithm with four criteria that distinguished between high and low complexity patients as rated by experts. Additional validation steps are needed before implementation.

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Background Hypoglycaemia is a common and potentially avoidable adverse event in people with type 2 diabetes (T2D). It can reduce quality of life, increase healthcare costs, and reduce treatment success. We investigated self-management issues associated with hypoglycaemia and self-identified causes of hypoglycaemia in these patients. Methods In this mixed methods study qualitative semi-structured interviews were performed, which informed a subsequent quantitative survey in T2D patients. All interviews were audio recorded, transcribed verbatim and coded independently by two coders using directed content analysis, guided by the Theoretical Domains Framework. Descriptive statistics were used to quantify the self-management issues and causes of hypoglycaemia collected in the survey for the respondents that had experienced at least one hypoglycaemic event in the past. Results Sixteen participants were interviewed, aged 59–84 years. Participants perceived difficulties in managing deviations from routine, and they sometimes lacked procedural knowledge to adjust medication, nutrition or physical activity to manage their glucose levels. Grief and loss of support due to the loss of a partner interfered with self-management and lead to hypoglycaemic events. Work ethic lead some participant to overexerting themselves, which in turn lead to hypoglycaemic events. The participants had difficulties preventing hypoglycaemic events, because they did not know the cause, suffered from impaired hypoglycaemia awareness and/or did not want to regularly measure their blood glucose. When they did recognise a cause, they identified issues with nutrition, physical activity, stress or medication. In total, 40% of respondents reported regular stress as an issue, 24% reported that they regularly overestimated their physical abilities, and 22% indicated they did not always know how to adjust their medication. Around 16% of patients could not always remember whether they took their medication, and 42% always took their medication at regular times. Among the 83 respondents with at least one hypoglycaemic event, common causes for hypoglycaemia mentioned were related to physical activity (67%), low food intake (52%), deviations from routine (35%) and emotional burden (28%). Accidental overuse of medication was reported by 10%. Conclusion People with T2D experience various issues with self-managing their glucose levels. This study underlines the importance of daily routine and being able to adjust medication in relation to more physical activity or less food intake as well as the ability to reduce and manage stress to prevent hypoglycaemic events.

Introduction: In primary care, identifying patients with type 2 diabetes (T2D) who are at increased risk of hypoglycaemia is important for the prevention of hypoglycaemic events. We aimed to develop a screening tool based on machine learning to identify such patients using routinely available demographic and medication data. Methods:We used a cohort study design and the Groningen Initiative to ANalyse Type 2 diabetes Treatment (GIANTT) medical record database to develop models for hypoglycaemia risk. The first hypoglycaemic event in the observation period (2007)(2008)(2009)(2010)(2011)(2012)(2013) was the outcome. Demographic and medication data were used as predictor variables to train machine learning models. The performance of the models was compared with a model using additional clinical data using fivefold cross validation with the area under the receiver operator characteristic curve (AUC) as a metric.Results: We included 13,876 T2D patients. The best performing model including only demographic and medication data was logistic regression with least absolute shrinkage and selection operator, with an AUC of 0.71. Ten variables were included (odds ratio): male gender (0.997), age (0.990), total drug count (1.012), glucoselowering drug count (1.039), sulfonylurea use (1.62), insulin use (1.769), pre-mixed insulin use (1.109), insulin count (1.827), insulin duration (1.193), and antidepressant use (1.05). The proposed model obtained a similar performance to the model using additional clinical data. Conclusion:Using demographic and medication data, a model for identifying patients at increased risk of hypoglycaemia was developed using machine learning. This model can be used as a tool in primary care to screen for patients with T2D who may need additional attention to prevent or reduce hypoglycaemic events. K E Y W O R D S artificial intelligence, hypoglycaemia, type 2 diabetesThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.