Background: Proton pump inhibitors (PPIs) are some of the most frequently prescribed medications, but they are often used inappropriately, either being prescribed without a clear indication or continued for longer than necessary. In such cases, deprescribing is recommended. However, despite its proven effectiveness, the implementation of deprescribing in clinical practice remains inconsistent and varied, making it challenging to identify the most effective strategies. The goal is to provide a comprehensive outline of deprescribing interventions for PPI therapy implemented across various settings and by different healthcare professionals. Methods: The study is designed to be a systematic review of the published literature. PubMed, Embase, and Web of Science databases were searched from 1 January 1989 (the first PPI on the market) to 30 September 2024 for articles assessing PPI deprescribing in adult patients, focusing on the implementation rate (primary outcome) or effects on symptoms (secondary outcome). Results: After screening, 66 studies were included, predominantly pragmatic trials (N = 32) or randomized controlled trials (N = 25). We found a variety of interventions promoting PPI deprescription. Collaborative efforts involving multiple healthcare professionals, the use of algorithms for clinical decision-making, and patient involvement have proven to be key elements in the most effective strategies. Discontinuing therapy may not be advisable in cases of recurrent symptoms, suggesting that on-demand therapy could be a recommended approach. Deprescribing is particularly relevant for individuals with mild illnesses and symptoms, where tapering can effectively mitigate the rebound symptoms often associated with abrupt discontinuation. Conclusions: Given the current prevalence of inappropriate PPI prescribing, it is imperative to raise awareness among both physicians and patients about the importance of the deprescribing process, which should be tailored to the specific needs of each patient, considering his/her medical history, current health status, and personal preferences.

Background Safety signals for potential drug-induced adverse events (AEs) typically emerge from multiple data sources, primarily spontaneous reporting systems, despite known limitations. Increasingly, real-world data from sources such as electronic health records (EHRs) and administrative databases are leveraged for signal detection. Although network analysis has shown promise in mapping relationships between clinical attributes for signal detection in spontaneous reporting system databases, its application in real-world data from EHRs and administrative databases remains limited. Objective This study aimed to evaluate the performance of network analysis in detecting safety signals within Italian administrative databases, using drug-induced acute myocardial infarction (AMI) as a proof of concept. Methods We employed a case–crossover design to explore the association between drug exposure and AMI using the Healthcare Administrative Database of Mantova, Italy, from 2014 to 2018. Patients with their first AMI hospitalization were identified after a 365-day washout period to exclude prior hospitalizations. We constructed a network to analyse the relationships between prescribed drugs and diagnoses, represented as nodes, with undirected edges illustrating their interactions. For each patient with AMI, we identified all diagnoses and drugs recorded or redeemed within 365 days of the first AMI episode and generated various drug–diagnosis, drug–drug, and diagnosis–diagnosis pairs. We calculated the frequency of these pairs, and three types of edge weights quantified the strength of connections. We identified outlier drug–AMI pairs using a predictive score (F) based on frequency (C) and full edge weights (W_F), with validation for known AMI associations. We prioritized signals using the F score, C of AMI, and W_F, analysed through k-means clustering to identify patterns in the data. Results From 2014 to 2018, a total of 3918 patients had an AMI, with 4686 AMI diagnoses. Of those, 2866 had prescriptions in the previous year, totalling 498,591 prescriptions. A network analysis identified 2968 unique nodes, revealing 529,935 diagnosis–diagnosis connections, 235,380 drug–diagnosis connections, and 102,831 drug–drug connections. The median number of connections (C) was 404 (Q1–Q3: 194–671) for drug nodes and 380 (Q1–Q3: 216–664) for diagnosis nodes. The median W_F was 11.8 (Q1–Q3: 9–14), and the median F score across pairs was 0.1 (Q1–Q3: 0.1–0.3). A total of 249 potential safety signals were detected, with 63.4% aligning with known AEs. Among the remaining signals, 80 were prioritized, and five emerged as the highest priority: terazosin, tamsulosin, allopurinol, esomeprazole, and omeprazole. Conclusions Overall, our novel method demonstrates that network analysis is a valuable tool for signal detection and prioritization in drug-induced AEs based on EHRs and administrative databases.

Background: Elevated blood pressure is a major risk factor for severe medical conditions. Adherence to antihypertensive medication, especially in free-dose combinations, poses a significant challenge. This study aims to develop a novel method for assessing co-exposure to free-dose antihypertensive medications using secondary data sources. Methods: A register-based cohort study was conducted on individuals aged 65 years or older in Denmark who initiated antihypertensive therapy from 1996 to 2016 and followed for 730 days from the index date. A new method was developed to assess co-exposure to antihypertensive medications through redeemed prescriptions, treatment episodes, and overlapping medication events. The method's accuracy was evaluated using a random sample of 400 individuals. Results: A total of 1,021,819 individuals were included in the study, with a mean age of 68.8 years, and 53.7% were women. The method achieved 100% accuracy in identifying co-exposure periods. During the early stage of the follow-up (0–180 days), 54.1% of individuals were co-exposed to at least two antihypertensive medications, while 37.5% were co-exposed during the late stage of the follow-up period (181–730 days). The most frequent antihypertensive combinations included bendroflumethiazide and potassium with either amlodipine or enalapril in the early (13.2% and 12.5% of patients, respectively) and late stages (16.9% and 15.0% of patients, respectively). Conclusions: The newly developed method effectively assesses co-exposure to antihypertensive medications, overcoming previous limitations. The findings reveal common co-exposure combinations and evolving trends in antihypertensive medication use among older individuals, reflecting changes in clinical practice and guidelines over two decades.

Background: General practitioners (GPs) should regularly review patients’ medications and, when they are potentially harmful or no longer necessary, implement deprescribing approach. We aimed to assess the perceptions and potential barriers to deprescribing among Italian GPs. Methods: GPs were invited to participate in an observational cross-sectional study through an online survey containing 20 questions addressing attitudes towards deprescribing, including physicians’ perceptions, potential barriers, and how this process is addressed in daily clinical practice. The survey, accessible for responses from 4th May 2023 to 15th January 2024, was distributed through social media, networks, medical associations, and involving primary care departments of local health authorities. Results: Over 8 months, 617 answers were collected. Less than 2% ( n = 11) reported to not implement deprescribing interventions in daily practice, primarily due to perceived insufficient experience ( n = 7) or lack of specific education ( n = 6). Conversely, 23.1%( n = 142) of respondents reported frequently or very frequently implementing deprescribing. Among barriers, GPs reported difficulties in dealing with specialists ( n = 438, 71.7%), distrust of patients in drug discontinuation ( n = 326, 53.4%), poor availability of deprescribing guidelines ( n = 231, 37.8%), and time constraints ( n = 213, 34.9%). Guidelines and targeted training were mostly demanded (by 66.1% and 59.6%, respectively). Regarding specific drug classes, the proportion of GPs reporting to frequently implement deprescribing for proton pump inhibitors was 51.8% ( n = 313), while percentage was lower for benzodiazepines ( n = 166, 27.4%), bisphosphonates ( n = 147, 24.3%), statins ( n = 128, 21.2%), antihypertensives ( n = 108, 17.9%), and antidepressants ( n = 96, 15.9%). Conclusion: The study shows that while GPs recognize the importance of deprescribing, however, they face significant barriers, including a lack in targeted education and specific guidelines to enhance their confidence and knowledge in implementing this process effectively in daily clinical practice.

Background Falls are a leading cause of hospitalisation among older adults, with substantial impacts on quality of life and healthcare costs. Fall-risk-increasing drugs represent an important risk factor. Objective This study aimed to evaluate the association between fall-risk-increasing drug use and the risk of fall-related hospitalisation. Methods A nested case-control study was conducted using administrative healthcare data from a sample of Italian community-dwelling older adults. Cases included individuals aged ≥65 years hospitalised for fall-related injuries in 2018, matched 1:1 by sex and age with controls who had no fall-related injuries. Fall-risk-increasing drug exposure was assessed by drug class, duration and recency of use. Adjusted odds ratios (aORs) were estimated using logistic regression, controlling for polypharmacy and comorbidities. Results Among 16,118 cases and 16,118 controls (68.77% female, mean age 79.76 years), cases showed a higher prevalence of polypharmacy (five to nine drugs: 42.03 vs 37.16%; ten or more drugs: 7.02 vs 5.44%; p < 0.0001) and multimorbidity (three or more diseases: 6.42 vs 2.82%; p < 0.0001). Fall-risk-increasing drug use was higher in cases (37.70 vs 26.49%, p < 0.0001), with a 51% increased risk of fall-related hospitalisation (aOR 1.51, 95% confidence interval [CI] 1.44–1.59). Opioids (aOR 1.40, 95% CI 1.31–1.50) and antidepressants (aOR 1.45, 95% CI 1.36–1.54) were associated with the highest risk. Prolonged fall-risk-increasing drug exposure (>9 months; aOR 1.69, 95% CI 1.57–1.83) and current exposure (within 30 days before hospitalisation; aOR 1.78, 95% CI 1.68–1.89) showed higher odds. Conclusions Fall-risk-increasing drug use was associated with a higher risk of fall-related hospitalisations in older adults. These findings support the importance of optimising prescribing and managing polypharmacy as potential strategies to improve patient safety and reduce the healthcare burden.

Introduction: Optimizing drug treatments in older adults is essential for improving health outcomes and reducing drug-related issues. Objective: This study aimed to develop an updated and comprehensive explicit indicator of potentially inappropriate medications in older adults, and to evaluate its association with all-cause hospitalization. Methods: Administrative healthcare data from Local Health Units (LHUs) in Lombardy, Italy (2015–2018) were used to identify individuals aged ≥65 years who were exposed to potentially inappropriate medications (PIMs), identified through an explicit criteria-based list. Risk-set matching was used to select unexposed controls, and logistic regression models adjusted for high-dimensional propensity scores were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for all-cause hospitalization within 90 days after the first exposure date. Key findings: Among 641 230 older patients, between 52.8% and 56.3% were exposed to at least one PIM. After matching (128 063 pairs) hospitalization rates were higher among the exposed (8.3%–10.2%) compared with the not exposed (5.1%–6.0%) across the LHUs. Pooled estimates showed an increased risk of hospitalization for those exposed (ORs 1.55, 95% CIs 1.48–1.62), with the highest risk observed in adults aged 65–74 years (1.67, 1.59–1.76) and in women (1.62, 1.55–1.70). Conclusions: Over half of older individuals were exposed to at least one PIM, with a 55% increase in short-term hospitalization risk. These findings highlight the urgent need for improved prescribing practices tailored to older populations.