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Background: Cumulative anticholinergic exposure, also known as anticholinergic burden, is associated with a variety of adverse outcomes. However, studies show that anticholinergic effects tend to be underestimated by prescribers, and anticholinergics are the most frequently prescribed potentially inappropriate medication in older patients. The grading systems and drugs included in existing scales to quantify anticholinergic burden differ considerably and do not adequately account for patients’ susceptibility to medications. Furthermore, their ability to link anticholinergic burden with adverse outcomes such as falls is unclear. This study aims to develop a prognostic model that predicts falls in older general practice patients, to assess the performance of several anticholinergic burden scales, and to quantify the added predictive value of anticholinergic symptoms in this context.
Methods: Data from two cluster-randomized controlled trials investigating medication optimization in older general practice patients in Germany will be used. One trial (RIME, n = 1,197) will be used for the model development and the other trial (PRIMUM, n = 502) will be used to externally validate the model. A priori, candidate predictors will be selected based on a literature search, predictor availability, and clinical reasoning. Candidate predictors will include socio-demographics (e.g. age, sex), morbidity (e.g. single conditions), medication (e.g. polypharmacy, anticholinergic burden as defined by scales), and well-being (e.g. quality of life, physical function). A prognostic model including sociodemographic and lifestyle-related factors, as well as variables on morbidity, medication, health status, and well-being, will be developed, whereby the prognostic value of extending the model to include additional patient-reported symptoms will be also assessed. Logistic regression will be used for the binary outcome, which will be defined as “no falls” vs. “≥1 fall” within six months of baseline, as reported in patient interviews. Discussion: As the ability of different anticholinergic burden scales to predict falls in older patients is unclear, this study may provide insights into their relative importance as well as into the overall contribution of anticholinergic symptoms and other patient characteristics. The results may support general practitioners in their clinical decision-making and in prescribing fewer medications with anticholinergic properties.
Background: In the past, protease inhibitors (PIs) and the reverse transcriptase inhibitor abacavir were identified increasing the risk for thromboembolic complications and cardiovascular events (CVE) of HIV infected patients taking a combination antiretroviral therapy (cART). Results of the previous HIV-PLA I-study lead to the assumption that platelet activation could play a substantial role in increasing CVE risks.
Methods: The open label, monocentric HIV-PLA II-study investigated HIV-1-infected, therapy-naïve adults (n=45) starting with cART, consisting either of boosted PI (atazanavir, n= 6, darunavir, n=11), NNRTI (efavirenz, n=14) or integrase inhibitor (raltegravir, n=14), each plus tenofovir/emtricitabine co-medication. Main exclusion criteria were tobacco smoking, the intake of NSAIDs or abacavir or past CVE. Platelet adhesive molecule p-selectin (CD62P) and FITC anti-human Integrin α-IIb/Integrin β-3 (CD41/CD61) antibody (PAC-1) binding, monocyte CD11b/monocyte-associated CD41 expression and the endogenous thrombin potential (ETP) were assessed ex vivo-in vitro at baseline, weeks 4, 12 and 24. Therapy regimens were blinded to the investigators for laboratory and statistical analyses.
Results: CD11b and ETP showed no significant changes or differences between all study groups. In contrast, the mean + SD mean fluorescence units (MFI) of CD62P and PAC-1 increased significantly in patients taking PI, indicating an enhanced potential for thrombocyte activation and aggregation.
Conclusion: CD62P expression, detecting the ɑ-platelet degranulation of pro-inflammatory and pro-thrombotic factors and adhesive proteins, and PAC-1 expression, representing a marker for conformation changes of the GIIb/IIIa receptor, increased significantly in patients taking HIV protease inhibitors. The findings of this study revealed a yet unknown pathway of platelet activation, possibly contributing to the increased risk for CVE under HIV protease inhibitor containing cART.
Clinical Trial Registration No.: DRKS00000288.
Introduction: Clinically complex patients often require multiple medications. Polypharmacy is associated with inappropriate prescriptions, which may lead to negative outcomes. Few effective tools are available to help physicians optimise patient medication. This study assesses whether an electronic medication management support system (eMMa) reduces hospitalisation and mortality and improves prescription quality/safety in patients with polypharmacy. Methods and analysis: Planned design: pragmatic, parallel cluster-randomised controlled trial; general practices as randomisation unit; patients as analysis unit. As practice recruitment was poor, we included additional data to our primary endpoint analysis for practices and quarters from October 2017 to March 2021. Since randomisation was performed in waves, final study design corresponds to a stepped-wedge design with open cohort and step-length of one quarter. Scope: general practices, Westphalia-Lippe (Germany), caring for BARMER health fund-covered patients. Population: patients (≥18 years) with polypharmacy (≥5 prescriptions). Sample size: initially, 32 patients from each of 539 practices were required for each study arm (17 200 patients/arm), but only 688 practices were randomised after 2 years of recruitment. Design change ensures that 80% power is nonetheless achieved. Intervention: complex intervention eMMa. Follow-up: at least five quarters/cluster (practice). recruitment: practices recruited/randomised at different times; after follow-up, control group practices may access eMMa. Outcomes: primary endpoint is all-cause mortality and hospitalisation; secondary endpoints are number of potentially inappropriate medications, cause-specific hospitalisation preceded by high-risk prescribing and medication underuse. Statistical analysis: primary and secondary outcomes are measured quarterly at patient level. A generalised linear mixed-effect model and repeated patient measurements are used to consider patient clusters within practices. Time and intervention group are considered fixed factors; variation between practices and patients is fitted as random effects. Intention-to-treat principle is used to analyse primary and key secondary endpoints.