Clinical impact of medication review and deprescribing in older inpatients: A systematic review and meta-analysis.

BACKGROUND: Polypharmacy is a primary risk factor for the prescription of potentially inappropriate medications (PIMs), drug-drug interactions (DDIs), and ultimately, adverse drug reactions (ADRs). Medication review and deprescribing represent effective strategies to simplify therapeutic regimens, minimize risks, and reduce PIM prescriptions. This systematic review and meta-analysis of experimental and observational studies aimed to evaluate the impact of different medication review and deprescribing interventions in hospitalized older patients. METHODS: Experimental and observational prospective cohort studies evaluating the clinical effects of medication review and deprescribing strategies in older hospitalized patients were searched in the bibliographic databases, PubMed, Embase, and Scopus, from inception until January 8, 2024. A narrative synthesis of the results was provided, along with a meta-analysis of dichotomous data (i.e., re-hospitalizations and mortality). RESULTS: Overall, 21 randomized controlled trials, 7 non-randomized interventional studies, and 2 prospective cohort studies were included in the systematic review. Of these, 14 (46.7%) assessed medication appropriateness as the primary outcome, while the remaining evaluated clinical outcomes (e.g., length of hospital stay, hospital readmissions, emergency department visits, and incidence of ADRs) and/or quality of life. The meta-analysis revealed a slight but statistically significant 8% reduction in hospital readmissions (HR: 0.92; 95% CI: 0.85-0.99) following medication review and deprescribing, but no significant impact on mortality (HR: 0.98; 95% CI: 0.96-1.00). Of the 30 included studies, 21 were considered at high risk of bias, mostly due to potential deviations from intended interventions and randomization processes. The remaining nine studies had "some concerns" (eight studies) or were considered at "low" risk of bias (one study). CONCLUSION: Medication review and deprescribing are associated with potential benefits in reducing hospital readmission rates among hospitalized older patients, particularly through the reduction of PIM prescriptions. The integration of thorough medication review and deprescribing protocols in hospital settings may improve post-discharge outcomes and reduce overall healthcare costs.

Pharmacological and Benefit-Risk Profile of Once-Weekly Basal Insulin Administration (Icodec): Addressing Patients' Unmet Needs and Exploring Future Applications.

Diabetes mellitus (DM) is a chronic metabolic disease affecting over 500 million people worldwide, which leads to severe complications and to millions of deaths yearly. When therapeutic goals are not reached with diet, physical activity, or non-insulin drugs, starting/adding insulin treatment is recommended by international guidelines. A novel recombinant insulin is icodec, a once-weekly insulin that successfully completed phase III trials and that has recently obtained the marketing authorization approval from the European Medicines Agency. This narrative review aims to assess icodec pharmacological and clinical features concerning evidence on benefit-risk profile, as compared to other basal insulins, addressing the potential impact on patients' unmet needs. Icodec is a full agonist, recombinant human insulin analogue characterized by an ultra-long half-life (196 h), enabling its use in once-weekly administration. Phase III randomized clinical trials involving more than 4000 diabetic patients, mostly type 2 DM, documented non-inferiority of icodec, as compared to currently available basal insulins, in terms of estimated mean reduction of glycated hemoglobin levels; a superiority of icodec, compared to control, was confirmed in insulin-naïve patients (ONWARDS 1, 3, and 5), and in patients previously treated with basal insulin (ONWARDS 2). Icodec safety profile was comparable to the currently available basal insulins. Once-weekly icodec has the potential to improve patients' adherence, thus positively influencing patients' treatment satisfaction as well as quality of life, especially in type 2 DM insulin-naïve patients. An improved adherence might positively influence glycemic target achievement, reduce overall healthcare costs and overcome some of the unmet patients' needs. Icodec has the potential to emerge as a landmark achievement in the evolution of insulin therapy, with a positive impact also for the National Health Services and the whole society.

Epidemiological analysis to identify predictors of X-linked hypophosphatemia (XLH) diagnosis in an Italian pediatric population: the EPIX project.

PURPOSE: X-linked hypophosphatemia (XLH) is a rare multi-systemic disease characterized by low plasma phosphate levels. The aim of this study was to investigate the annual XLH prevalence and internally evaluate predictive algorithms' application performance for the early diagnosis of XLH. METHODS: The PediaNet database, containing data on more than 400,000 children aged up to 14 years, was used to identify a cohort of XLH patients, which were matched with up to 10 controls by date of birth and gender. The annual prevalence of XLH cases per 100,000 patients registered in PediaNet database was estimated. To identify possible predictors associated with XLH diagnosis, a logistic regression model and two machine learning algorithms were applied. Predictive analyses were separately carried out including patients with at least 1 or 2 years of database history in PediaNet. RESULTS: Among 431,021 patients registered in the PediaNet database between 2007-2020, a total of 12 cases were identified with a mean annual prevalence of 1.78 cases per 100,000 patients registered in PediaNet database. Overall, 8 cases and 60 matched controls were included in the analysis. The random forest algorithm achieved the highest area under the receiver operating characteristic curve (AUC) value both in the one-year prior ID (AUC = 0.99, 95% CI = 0.99-1.00) and the two-year prior ID (AUC = 1.00, 95% CI = 1.00-1.00) analysis. Overall, the XLH predictors selected by the three predictive methods were: the number of vitamin D prescriptions, the number of recorded diagnoses of acute respiratory infections, the number of prescriptions of antihistamine for systemic use, the number of prescriptions of X-ray of the lower limbs and pelvis and the number of allergology visits. CONCLUSION: Findings showed that data-driven machine learning models may play a prominent role for the prediction of the diagnosis of rare diseases such as XLH.

Machine learning-based algorithms applied to drug prescriptions and other healthcare services in the Sicilian claims database to identify acromegaly as a model for the earlier diagnosis of rare diseases.

Acromegaly is a rare disease characterized by a diagnostic delay ranging from 5 to 10 years from the symptoms' onset. The aim of this study was to develop and internally validate machine-learning algorithms to identify a combination of variables for the early diagnosis of acromegaly. This retrospective population-based study was conducted between 2011 and 2018 using data from the claims databases of Sicily Region, in Southern Italy. To identify combinations of potential predictors of acromegaly diagnosis, conditional and unconditional penalized multivariable logistic regression models and three machine learning algorithms (i.e., the Recursive Partitioning and Regression Tree, the Random Forest and the Support Vector Machine) were used, and their performance was evaluated. The random forest (RF) algorithm achieved the highest Area under the ROC Curve value of 0.83 (95% CI 0.79-0.87). The sensitivity in the test set, computed at the optimal threshold of predicted probabilities, ranged from 28% for the unconditional logistic regression model to 69% for the RF. Overall, the only diagnosis predictor selected by all five models and algorithms was the number of immunosuppressants-related pharmacy claims. The other predictors selected by at least two models were eventually combined in an unconditional logistic regression to develop a meta-score that achieved an acceptable discrimination accuracy (AUC = 0.71, 95% CI 0.66-0.75). Findings of this study showed that data-driven machine learning algorithms may play a role in supporting the early diagnosis of rare diseases such as acromegaly.

Pharmacological Therapies of Spinal Muscular Atrophy: A Narrative Review of Preclinical, Clinical-Experimental, and Real-World Evidence.

Spinal muscular atrophy (SMA) is a rare neuromuscular disease, with an estimated incidence of about 1 in 10,000 live births. To date, three orphan drugs have been approved for the treatment of SMA: nusinersen, onasemnogene abeparvovec, and risdiplam. The aim of this narrative review was to provide an overview of the pre- and post-marketing evidence on the pharmacological treatments approved for the treatment of SMA by identifying preclinical and clinical studies registered in clinicaltrials.gov and in the EU PAS register from their inception until the 4 January 2023. The preclinical evidence on the drugs approved for SMA allowed a significant acceleration in the experimental phase of these drugs. However, since these drugs had been authorized through accelerated programs, the conduction of post-marketing studies was requested as a condition of their marketing approval to better understand their risk-benefit profiles in real-world settings. As of the 4 January 2023, a total of 69 post-marketing studies concerning the three orphan drugs approved for SMA were identified in clinicaltrials.gov (N = 65; 94.2%) and in the EU PAS register (N = 4; 5.8%). Currently, ongoing studies are primarily aimed at providing evidence concerning the risk-benefit profile of the three drugs in specific populations that were not included in the pivotal trials and to investigate the long-term safety and clinical benefits of these drugs. Real-world data sources collecting information regarding the natural history of the disease and post-marketing surveillance of the available therapies are increasingly becoming essential for generating real-world evidence on this rare disease and its orphan drugs.

Assisted Reproductive Technology and Disease Management in Infertile Women with Multiple Sclerosis.

Multiple sclerosis (MS) predominantly affects women of fertile age. Various aspects of MS could impact on fertility, such as sexual dysfunction, endocrine alterations, autoimmune imbalances, and disease-modifying therapies (DMTs). The proportion of women with MS (wMS) requesting infertility management and assisted reproductive technology (ART) is increasing over time. In this review, we report on data regarding ART in wMS and address safety issues. We also discuss the clinical aspects to consider when planning a course of treatment for infertility, and provide updated recommendations to guide neurologists in the management of wMS undergoing ART, with the goal of reducing the risk of disease activation after this procedure. According to most studies, there is an increase in relapse rate and magnetic resonance imaging activity after ART. Therefore, to reduce the risk of relapse, ART should be considered in wMS with stable disease. In wMS, especially those with high disease activity, fertility issues should be discussed early as the choice of DMT, and fertility preservation strategies might be proposed in selected cases to ensure both disease control and a safe pregnancy. For patients with stable disease taking DMTs compatible with pregnancy, treatment should not be interrupted before ART. If the ongoing therapy is contraindicated in pregnancy, then it should be switched to a compatible therapy. Prior to beginning fertility treatments in wMS, it would be reasonable to assess vitamin D serum levels, thyroid function and its antibody serum levels; start folic acid supplementation; and ensure smoking and alcohol cessation, adequate sleep, and food hygiene. Cervico-vaginal swabs for Ureaplasma urealyticum, Mycoplasma hominis, and Chlamydia trachomatis, as well as serology for viral hepatitis, HIV, syphilis, and cytomegalovirus, should be performed. Steroids could be administered under specific indications. Although the available data do not clearly show a definite raised relapse risk associated with a specific ART protocol, it seems reasonably safe to prefer the use of gonadotropin-releasing hormone (GnRH) antagonists for ovarian stimulation. Close clinical and radiological monitoring is reasonably recommended, particularly after hormonal stimulation and in case of pregnancy failure.

Infection Rates and Impact of Glucose Lowering Medications on the Clinical Course of COVID-19 in People with Type 2 Diabetes: A Retrospective Observational Study.

Purpose: Diabetes is a risk factor for COVID-19 severity, but the role played by glucose lowering medications (GLM) is still unclear. The aim of this study was to assess infection rates and outcomes of COVID-19 (hospitalization and mortality) in adults with diabetes assisted by the Local Health Unit of Padua (North-East Italy) according to the ongoing GLM. Patients and Methods: People with diabetes were identified using administrative claims, while those with SARS-CoV-2 infection were detected by cross referencing with the local COVID-19 surveillance registry. A multivariate logistic regression model was used to verify the association between GLM classes and the outcome. Results: SARS-CoV-2 infection rates were marginally but significantly higher in individuals with diabetes as compared to those without diabetes (RR 1.04, p = 0.043), though such relative 4% increase may be irrelevant from a clinical and epidemiological perspective. 1923 individuals with GLM-treated diabetes were diagnosed with COVID-19; 456 patients were hospitalized and 167 died. Those treated with insulin had a significantly higher risk of hospitalizations for COVID-19 (OR 1.48 p < 0.01) as were those treated with sulphonylureas/glinides (OR 1.34, p = 0.02). Insulin use was also significantly associated with higher mortality (OR 1.90, p < 0.01). Use of metformin was significantly associated with lower death rates (OR 0.62, p = 0.02). The association of other GLM classes with the outcome was not significant. Conclusion: Diabetes does not appear to modify the risk of SARS-CoV-2 infection in a clinically meaningful way, but strongly increases the rates of hospitalization and death. Insulin use was associated with worse outcomes, whereas metformin use was associated with lower mortality.