Background and Objective Proprotein convertase subtilisin/kexin type 9 inhibition with monoclonal antibodies such as alirocumab significantly reduces low-density lipoprotein-cholesterol levels ± other lipid-lowering therapies. We aimed to develop and qualify a population pharmacokinetics (PopPK) model for alirocumab in healthy subjects and patients, taking into account the mechanistic target-mediated drug disposition (TMDD) process. Methods This TMDD model was developed using a subset of the alirocumab clinical trial database, including nine phase I/II/III studies (n = 527); the model was subsequently expanded to a larger data set of 13 studies (n = 2870). Potential model parameters and covariate relationships were explored, and predictive ability was qualified using a visual predictive check. Results The TMDD model was built using the quasi-steady-state approximation. The final TMDD–quasi-steady-state model included a significant relationship between distribution volume of the central compartment and disease state: distribution volume of the central compartment was 1.56-fold higher in patients vs. healthy subjects. Separately, application of the model to the expanded data set revealed a significant relationship between linear clearance and statin co-administration: linear clearance was 1.27-fold higher with statins. The good predictive performance of the TMDD model was assessed based on graphical and numerical quality criteria, together with the visual predictive check and comparison of the predictions to those from a PopPK model with parallel linear and Michaelis–Menten clearances (i.e., simplification of the TMDD PopPK model). Conclusions This mechanistic TMDD PopPK model integrates the interaction of alirocumab with its target and accurately predicts both alirocumab and total proprotein convertase subtilisin/kexin type 9 concentrations in healthy subjects and patients.

Adverse drug events (ADEs) are a major public health concern, given their consequences in terms of morbi-mortality and associated healthcare costs. Many studies have focused on the elderly, who are considered particularly vulnerable in this respect. We aimed to determine and compare the frequency, characteristics, and predictive factors of ADEs according to age in an adult population. A prospective seven-year cross-sectional study was conducted in a university hospital emergency department. Structured medication reviews and ADE detection were performed. Patient data and ADE characteristics were collected. Descriptive statistics and logistic regression were performed in two age groups: Group 1 (age < 65 years) and 2 (age ≥ 65 years). Among the 13,653 patients included, 18.4% in Group 1 and 22.6% in Group 2 experienced an ADE. Differences were identified in terms of the ADE type (more ADEs due to noncompliance in Group 1) and ADE symptoms (greater bleeding in Group 2). In the multivariable analysis, several specific predictive factors were identified, including kidney failure and antidiabetic drug use in Group 1 and inappropriate prescription and antithrombotic treatment in Group 2. Analysis by age provided a more refined vision of ADEs as we identified distinct profiles of iatrogenesis. These results will lead to a better detection of ADEs.

The objective of this study was to assess the impact of the COVID-19 pandemic on patients’ perceptions regarding infection risk and vaccination in subjects suffering from chronic diseases. A prospective observational multicentric study conducted from December 2020 to April 2021 in three French University Hospitals. Patients with chronic diseases were proposed to complete a questionnaire regarding the impact of the COVID-19 pandemic on infectious risk knowledge and vaccination. A total of 1151 patients were included and analyzed (62% of which were people with diabetes). The COVID-19 pandemic increased awareness of infectious risks by 19.3%, significantly more in people with diabetes (23.2%, from 54.4% to 67.0%, p < 0.01) when compared to the other high-risk patients (12.5%, from 50.5% to 56.8%, p = 0.06). Respectively, 30.6% and 16.5% of patients not up-to-date for pneumococcal and flu vaccines reported wanting to update their vaccination due to the COVID-19 pandemic. By contrast, the proportion of patients against vaccines increased during the COVID-19 pandemic (6.0% vs. 9.5%, p < 0.01). The COVID-19 pandemic has led to a small increase in awareness regarding the risks of infection in patients with chronic diseases, including people with diabetes, but without any change in willingness to be vaccinated. This underlines the urgent need to sensibilize people with diabetes to infection risk and the importance of vaccination.

Background and objectives: Renal failure is a contraindication for some glucose-lowering drugs and requires dosage adjustment for others, particularly biguanides, sulfonylureas, and inhibitors of dipeptidyl peptidase 4. In this study, we assessed adherence to prescription recommendations for glucose-lowering drugs according to renal function in hospitalized diabetic subjects. Materials and Methods: This prospective cohort study was carried out over a 2-year period in a university hospital. Glomerular filtration rate (GFR) was determined by averaging all measurements performed during hospitalization. Glucose-lowering drug dosages were analyzed according to the recommendations of the relevant medical societies. Results: In total, 2071 diabetic patients (53% hospitalized in cardiology units) were examined. GFR was 60 in 53.3%. Inappropriate oral glucose-lowering treatments were administered to 273 (13.2%) patients, including 53 (2.6%) with a contraindication. In cardiology units, 53.1% and 14.3% of patients had GFRs of <60 and <30 mL/min/1.73 m², respectively, and 179 (15.4%) patients had a contraindication or were prescribed an excessive dose of glucose-lowering drugs. Conclusions: We showed that the burden of inappropriate prescriptions is high in diabetic patients. Given the high number of patients receiving these medications, particularly in cardiology units, a search for potential adverse effects related to these drugs should be performed.

Background and Objectives: Vaccination coverage is suboptimal in people living with diabetes. The objectives of this study were to determine the impact of hospitalization on vaccination coverage and the variables associated with vaccination during hospital stay. Materials and Methods: This observational study was conducted from May 2019 to December 2019 in the Endocrinology-Nutrition-Diabetes Department of the University Hospital of Montpellier, France. This department encompasses three medical units, two of which have a full-time clinical pharmacist involved in the multidisciplinary management of patients. All adult diabetic patients who completed a questionnaire about vaccines were prospectively included by a clinical pharmacist and followed until department discharge. Coverage at the time of admission for the tetanus, diphtheria, pertussis (Tdap), pneumococcal, influenza, and herpes zoster vaccines was assessed from patient interviews and/or contact with the general practitioner and/or with the community pharmacist. Multivariable logistic regression analysis was performed to identify the factors associated with a vaccination update during the hospital stay. Results: A total of 222 patients were included (mean age: 59.4 years, 68.5% type 2 diabetes). Vaccination coverage increased by 26.7% (47.3% to 59.9%), 188.0% (10.8% to 31.1%) and 8.9% (45.9% to 50.0%), respectively, for the Tdap, pneumococcal and influenza vaccines during hospital stay. Female sex, admission to a diabetes care unit with a full-time pharmacist, favorable feelings about vaccination, unknown immunization coverage for pneumococcal vaccines, and evaluation and recording of vaccine coverage at admission in the patient medical records were associated with at least one vaccination during hospital stay. Conclusions: Our real-life study highlights that hospitalization and multidisciplinary management (i.e., physician-pharmacist) may be key points in the diabetes care pathway to improve vaccination coverage, especially for patients with advanced diabetes and comorbidities.

Adverse drug events (ADEs) constitute a major clinical and economic burden in Europe. While hospital pharmacy activities improve prescription safety, pharmacists cannot review all orders in time and must prioritize high-risk patients. Rule-based clinical decision support systems (CDSS) offer an additional preventive strategy but often generate excessive, low-relevance alerts. To develop rules for detecting iatrogenic risk in accordance with methodological standards reported in the literature, using ADEs identified in a prospective cohort of adult patients admitted to the emergency department of a French healthcare institution (2,600-bed tertiary care center). ADEs were identified through a structured medication history interview conducted by a trained clinical pharmacist upon the patient's admission to the emergency department. To focus on the most critical situations, drug classes defined at the fourth level of the Anatomical Therapeutic Chemical (ATC) classification system (ATC4) and associated with the highest risk were identified by considering prescription frequency, ADE occurrence, and ADE severity. For each selected ATC level 4 class, logistic regression models were used to assess the association between ADE probability and specific explanatory factors. These factors were then operationalized into rules designed to detect iatrogenic risk. A total of 245 ATC4 classes were involved in at least one ADE. Among these, 22 classes were identified as high iatrogenic risk, accounting for approximately 50% of prescriptions leading to an ADE, with vitamin K antagonists and heparins showing the highest risk. Regression analyses resulted in 58 distinct rules: 31 (53.4%) combined prescription data with at least one laboratory parameter, 8 (13.8%) incorporated demographic variables (age or sex), and 19 (32.8%) were based solely on medication prescription data. The clinical and pharmaceutical relevance of the proposed rules must be further evaluated to reduce excessive alert generation, which may lead to disengagement from both pharmacists and prescribers. The institutional health data warehouse could provide an appropriate environment for this evaluation.