Artificial intelligence for the optimal management of community-acquired pneumonia.

PURPOSE OF REVIEW: This timely review explores the integration of artificial intelligence (AI) into community-acquired pneumonia (CAP) management, emphasizing its relevance in predicting the risk of hospitalization. With CAP remaining a global public health concern, the review highlights the need for efficient and reliable AI tools to optimize resource allocation and improve patient outcomes. RECENT FINDINGS: Challenges in CAP management delve into the application of AI in predicting CAP-related hospitalization risks, and complications, and mortality. The integration of AI-based risk scores in managing CAP has the potential to enhance the accuracy of predicting patients at higher risk, facilitating timely intervention and resource allocation. Moreover, AI algorithms reduce variability associated with subjective clinical judgment, promoting consistency in decision-making, and provide real-time risk assessments, aiding in the dynamic management of patients with CAP. SUMMARY: The development and implementation of AI-tools for hospitalization in CAP represent a transformative approach to improving patient outcomes. The integration of AI into healthcare has the potential to revolutionize the way we identify and manage individuals at risk of severe outcomes, ultimately leading to more efficient resource utilization and better overall patient care.

A novel approach for pharmacological substantiation of safety signals using plasma concentrations of medication and administrative/healthcare databases: A case study using Danish registries for an FDA warning on lamotrigine.

PHARMACOM-EPI is a novel framework to predict plasma concentrations of drugs at the time of occurrence of clinical outcomes. In early 2021, the U.S. Food and Drug Administration (FDA) issued a warning on the antiseizure drug lamotrigine claiming that it has the potential to increase the risk of arrhythmias and related sudden cardiac death due to a pharmacological sodium channel-blocking effect. We hypothesized that the risk of arrhythmias and related death is due to toxicity. We used the PHARMACOM-EPI framework to investigate the relationship between lamotrigine's plasma concentrations and the risk of death in older patients using real-world data. Danish nationwide administrative and healthcare registers were used as data sources and individuals aged 65 years or older during the period 1996 - 2018 were included in the study. According to the PHARMACOM-EPI framework, plasma concentrations of lamotrigine were predicted at the time of death and patients were categorized into non-toxic and toxic groups based on the therapeutic range of lamotrigine (3-15 mg/L). Over 1 year of treatment, the incidence rate ratio (IRR) of all-cause mortality was calculated between the propensities score matched toxic and non-toxic groups. In total, 7286 individuals were diagnosed with epilepsy and were exposed to lamotrigine, 432 of which had at least one plasma concentration measurement The pharmacometric model by Chavez et al. was used to predict lamotrigine's plasma concentrations considering the lowest absolute percentage error among identified models (14.25 %, 95 % CI: 11.68-16.23). The majority of lamotrigine associated deaths were cardiovascular-related and occurred among individuals with plasma concentrations in the toxic range. The IRR of mortality between the toxic group and non-toxic group was 3.37 [95 % CI: 1.44-8.32] and the cumulative incidence of all-cause mortality exponentially increased in the toxic range. Application of our novel framework PHARMACOM-EPI provided strong evidence to support our hypothesis that the increased risk of all-cause and cardiovascular death was associated with a toxic plasma concentration level of lamotrigine among older lamotrigine users.

Exploring the underlying mechanisms of drug-induced impulse control disorders: a pharmacovigilance-pharmacodynamic study.

INTRODUCTION: Impulse control disorders (e.g. pathological gambling, hypersexuality) may develop as adverse reactions to drugs. Pathogenetic hypotheses have mainly focused on D3-receptor agonism, and switching to alternatives with different pharmacologic mechanisms represents a common management strategy. Nonetheless, treatment failure is common and gaining pathophysiological insights is needed. AIM: We aimed to identify targets potentially contributing to pathologic impulsivity. METHOD: We performed a pharmacovigilance-pharmacodynamic study on dopamine agonists and antipsychotics using the Food and Drug Administration Adverse Event Reporting System (January 2004-December 2021). We estimated disproportionate reporting using the Bayesian information component. Using online public databases (IUPHAR, ChEMBL, PDSP, DrugBank), we calculated drug occupancies. To identify the targets potentially contributing to impulsivity, we fitted univariate regression models interpolating information components and occupancies within dopamine agonists and antipsychotics. Sensitivity analyses were performed to check for the robustness of the results. RESULTS: Among 19 887 reports of impulsivity, 5898 recorded an antipsychotic, and 3100 a dopamine agonist. The more robust signals concerned aripiprazole (N = 3091; median information component [95% confidence interval] = 4.51[4.45-4.55]) and brexpiprazole (229; 4.00[3.78-4.16]) for antipsychotics, pergolide (105; 5.82[5.50-6.06]) and pramipexole (2009; 5.43[5.36-5.48]) for dopamine agonists. Robust, significant positive associations between drug occupancy and impulsivity reporting were found for D3 within dopamine agonists (beta = 1.52; P-value = 0.047) and 5-HT1a within antipsychotics (1.92, 0.029). CONCLUSION: Our results supported the role of D3-receptor agonism in inducing impulsivity in dopamine receptor agonists and identified a potential role of 5-HT1a receptor agonism in antipsychotics. Investigating these receptors may drive towards a better management of drug-induced impulsivity.

Long-term COVID symptoms, work ability and fitness to work in healthcare workers hospitalized for Sars-CoV-2 infection.

BACKGROUND: COVID-19 can affect the persistence of symptoms and work ability (WA), hence the fitness to work of healthcare workers (HCW). We describe the effects of COVID-19 in hospitalized HCWs of a large Hospital in Lombardy and their implications on WA and fitness to work. METHODS: Fifty-six HCWs of Fatebenefratelli-Sacco Hospital have been hospitalized for COVID-19 since March 2020. Clinical and fitness-to-work data were acquired from Occupational Health Surveillance Program. A structured questionnaire was administered to 53/56 HCWs 18 months after infection to investigate Long-COVID symptoms and WA. RESULTS: Symptoms most reported at recovery (rhino-pharyngeal swab-NPS-negative) were exertional dyspnea (86.8%), asthenia (86.8%), arthro-myalgia (71.7%), sleep disorders (64.2%), resting dyspnea (62.3%), cough (56.6%). 69.6% underwent evaluation at out-patient clinics experienced in Long-COVID. Ten months after recovery, symptoms related to physical well-being decreased while memory and anxiety/depression were more persistent. At recovery, the WA score decreased from 10 to 8, and then an improvement from 8 to 9 was noted during the survey. At the return-to-work examination, fit-to-work judgements with restrictions increased from 31.4% to 58.7%; then, a slight decrease in the rate of judgements with restrictions was observed at the survey's time. CONCLUSION: Post-COVID-19 symptoms can persist for a long time and could impact WA and fitness-to-work of HCW. Adequate health surveillance protocols should guarantee the health protection of HCW with persistent disorders after COVID-19.

Hyponatremia Following Antipsychotic Treatment: In Silico Pharmacodynamics Analysis of Spontaneous Reports From the US Food and Drug Administration Adverse Event Reporting System Database and an Updated Systematic Review.

BACKGROUND: Hyponatremia associated with antipsychotic drugs is a rare but potentially life-threatening adverse drug reaction; the underlying pharmacological mechanism has not yet been explained. METHODS: We investigated the relationship between pharmacological targets of antipsychotic drugs and the occurrence of hyponatremia by conducting a nested case-control study using the Food and Drug Administration Adverse Event Reporting System database. Multiple logistic regression was used to determine the associations between antipsychotics receptor occupancy and hyponatremia. We also performed a systematic review of clinical studies on this association. RESULTS: Of 139 816 reports involving at least 1 antipsychotic, 1.1% reported hyponatremia. Olanzapine was the most frequently suspected drug (27%). A significant positive association was found between dopamine D3, D4, and hyponatremia, while adrenergic α 1, serotonin 5-HT1A, and 5-HT2A receptor occupancies were negatively associated. A multivariable stepwise regression model showed that dopamine D3 (adj. odds ratio = 1.21; 95% CI = 1.09-1.34; P < .05) predicted the risk for hyponatremia (P < .05), while serotonin 5-HT2A occupancy (Adj. odds ratio = 0.78; 95% CI = 0.68-0.90; P < .01) exhibited a protective effect against hyponatremia. Among the 11 studies included in the systematic review, incidence rates of hyponatremia diverged between 0.003% and 86%, whereas the odds of developing hyponatremia from effect studies ranged between 0.83 and 3.47. CONCLUSIONS: Antipsychotic drugs having a combined modest occupancy for D3 and 5-HT2A receptors and higher levels of D3 receptor occupancy correspond to different degrees of risk for hyponatremia. Based on the few, relatively large-scale available studies, atypical antipsychotics have a more attenuated risk profile for hyponatremia.

Association between the glyco-metabolic adverse effects of antipsychotic drugs and their chemical and pharmacological profile: a network meta-analysis and regression.

BACKGROUND: Glyco-metabolic deteriorations are the most limiting adverse reactions to antipsychotics in the long term. They have been incompletely investigated and the properties of antipsychotics that determine their magnitude are not clarified.To rank antipsychotics by the magnitude of glyco-metabolic alterations and to associate it to their pharmacological and chemical properties, we conducted a network meta-analysis. METHODS: We searched PubMed, Embase, and Psycinfo on 10 September 2020. We selected studies containing the endpoint-baseline difference or the distinct values of at least one outcome among glucose, HbA1c, insulin, HOMA-IR, triglycerides, total/HDL/LDL cholesterols. Of 2094 articles, 46 were included in network meta-analysis. Study quality was assessed by the RoB 2 and ROBINS-I tools. Mean differences (MD) were obtained by random-effects network meta-analysis; relations between MD and antipsychotic properties were analyzed by linear regressions. Antipsychotic properties investigated were acidic and basic pKa, polar surface area, polarizability, and occupancies of D2, H1, M1, M3, α1A, α2A, 5-HT1A, 5-HT2A, 5-HT2C receptors. RESULTS: We meta-analyzed 46 studies (11 464 patients); on average, studies lasted 15.47 weeks, patients had between 17.68 and 61.06 years of mean age and 61.64% were males. Olanzapine and clozapine associated with greater deteriorations, aripiprazole and ziprasidone with smaller deteriorations. Higher polarizability and 5-HT1A receptor occupancy were associated with smaller deteriorations, H1, M1, and M3 receptor occupancies with larger deteriorations. CONCLUSIONS: Drug rankings may guide antipsychotic switching toward metabolically safer drugs. Mechanistic insights may suggest improvements for combination therapies and drug development. More data are required regarding newer antipsychotics.

The use of biological agents in pregnant women affected by autoimmune disorders: Why we need more research of this neglected area.

Women of childbearing age are largely affected by several autoimmune disorders (the estimates range between 1.5 and 10 per 10,000). The increasing number of effective biological agents has dramatically revolutionized the treatment of these clinical conditions, ameliorating the patient's quality of life. The use of these agents by women during pregnancy is growing to ensure the disease activity control and avoid adverse health outcomes. However, for many newer biological agents, the degree of information concerning their use in pregnancy is often incomplete to perform a conclusive risk assessment on fetal and maternal health given the exclusion of this specific population from pharmacological clinical trials. More recently, the COVID-19 pandemic has confirmed the unacceptable inequities of pharmacological research and medical treatment for pregnant and lactating women, exacerbating the need for filling the gaps of quantitative and qualitative pharmacology data in this sensitive population. ere we summarize (i) what is already known about safety and effectiveness of biological agents in this understudied population (with specific focus on pregnancy-related health outcomes), and what we are going to learn from the on-going studies among pregnant women treated with biological agents; (ii) the methodological and ethical considerations that characterize the pharmacological research in pregnancy, also discussing emerging evidence on the use of therapeutic drug monitoring (TDM) in this clinical setting.

Beta-blocker-associated hypoglycaemia: New insights from a real-world pharmacovigilance study.

AIMS: To investigate the statistical association between hypoglycaemia and ß-blocker use and to define what patient and drug characteristics could potentially increase the risk for its occurrence. METHODS: We investigated the relationship between pharmacological parameters of ß-blockers and the occurrence of hypoglycaemia by conducting a case/non case analysis using the Food and Drug Administration Adverse Event Reporting System database. Pharmacological properties that could represent a predictive factor for hypoglycaemia were analysed through a multilinear binary logistic regression (null hypothesis rejected for values of P < .05). We also performed a systematic review of clinical studies on this association. RESULTS: Of 83 954 selected reports, 1465 cases (1.75%) of hypoglycaemia were identified. The association was found statistically significant for nadolol (reporting odds ratio [95% confidence interval]: 6.98 [5.40-9.03]), celiprolol (2.35 [1.35-4.10]), propranolol (2.14 [1.87-2.46]) and bisoprolol (1.42 [1.25-1.61]). Paediatric cases (n = 310) showed a positive association with hypoglycaemia for long half-life drugs (odds ratio [95% confidence interval]: 2.232 [1.398-3.563]) and a negative association for ß1-selectivity (0.644 [0.414-0.999]). Seven papers were included in the systematic review. Because of great heterogeneity in study design and demographics, hypoglycaemia incidence rates varied greatly among studies, occurring in 1.73% of the cases for propranolol treatment (n total participants = 575), 6.6% for atenolol (n = 30) and 10% for carvedilol (n = 20). CONCLUSION: Nadolol appears to be the ß-blocker significantly most associated with hypoglycaemia and children represent the most susceptible sample. Furthermore, long half-life and nonselective ß-blockers seem to increase the risk for its occurrence.

Severe hypoglycemia in children treated with nadolol: A case report and FDA adverse event reporting system (FAERS) database analysis.

OBJECTIVE: We describe a case of severe hypoglycemia in a 14-month-old child as a suspected adverse drug reaction (ADR) to nadolol, and we performed an analysis of the FDA Adverse Event Reporting System (FAERS) database. Although previous reports have identified the risk of severe hypoglycemia in children during treatment with ß-blockers, little is known about hypoglycemia as an ADR in infants treated with nadolol. Moreover, the pharmacodynamic and pharmacokinetic profiles of nadolol in children aged less than 1 year old are still not fully known. MATERIALS AND METHODS: We extracted all ADR reports involving nadolol from the FAERS database; in order to reduce the risk of bias, we only considered cases that exclusively reported nadolol as the suspect drug. We then selected cases of hypoglycemia in the pediatric population and conducted a manual deduplication. RESULTS: Upon FAERS database analysis, a total of 2,674 suspected ADR reports to nadolol were found. Of these, 1,950 (73%) were solely attributed to nadolol, and 63 of them were hypoglycemic events. A total of 47 reports included the relevant pediatric age (74.6%). After deduplication, we identified 25 cases (mean age: 3.65 years old); all of these reports were categorized as serious, and hospitalization was required in 15 cases. CONCLUSION: Hypoglycemia is a reported life-threatening ADR associated with nadolol, especially in infants, in whom this drug should be used with caution.

New data-driven method to predict the therapeutic indication of redeemed prescriptions in secondary data sources: a case study on antiseizure medications users aged ≥65 identified in Danish registries.

OBJECTIVES: We aimed to develop a new data-driven method to predict the therapeutic indication of redeemed prescriptions in secondary data sources using antiepileptic drugs among individuals aged ≥65 identified in Danish registries. DESIGN: This was an incident new-user register-based cohort study using Danish registers. SETTING: The study setting was Denmark and the study period was 2005-2017. PARTICIPANTS: Participants included antiepileptic drug users in Denmark aged ≥65 with a confirmed diagnosis of epilepsy. PRIMARY AND SECONDARY OUTCOME MEASURES: Sensitivity served as the performance measure of the algorithm. RESULTS: The study population comprised 8609 incident new users of antiepileptic drugs. The sensitivity of the algorithm in correctly predicting the therapeutic indication of antiepileptic drugs in the study population was 65.3% (95% CI 64.4 to 66.2). CONCLUSIONS: The algorithm demonstrated promising properties in terms of overall sensitivity for predicting the therapeutic indication of redeemed antiepileptic drugs by older individuals with epilepsy, correctly identifying the therapeutic indication for 6 out of 10 individuals using antiepileptic drugs for epilepsy.

Enhancing Transparency in Defining Studied Drugs: The Open-Source Living DiAna Dictionary for Standardizing Drug Names in the FAERS.

INTRODUCTION: In refining drug safety signals, defining the object of study is crucial. While research has explored the effect of different event definitions, drug definition is often overlooked. The US FDA Adverse Event Reporting System (FAERS) records drug names as free text, necessitating mapping to active ingredients. Although pre-mapped databases exist, the subjectivity and lack of transparency of the mapping process lead to a loss of control over the object of study. OBJECTIVE: We implemented the DiAna dictionary, systematically mapping individual free-text instances to their corresponding active ingredients and linking them to the World Health Organization Anatomical Therapeutic Chemical (WHO-ATC) classification. METHODS: We retrieved all drug names reported to the FAERS (2004-December 2022). Using existing vocabularies and string editing, we automatically mapped free text to ingredients. We manually revised the mapping and linked it to the ATC classification. RESULTS: We retrieved 18,151,842 reports, with 74,143,411 drug entries. We manually checked the first 14,832 terms, up to terms occurring over 200 times (96.88% of total drug entries), to 6282 unique active ingredients. Automatic unchecked translations extend the standardization to 346,854 terms (98.94%). The DiAna dictionary showed a higher sensitivity compared with RxNorm alone, particularly for specific drugs (e.g., rimegepant, adapalene, drospirenone, umeclidinium). The most prominent drug classes in the FAERS were immunomodulating (37.40%) and neurologic drugs (29.19%). CONCLUSION: The DiAna dictionary, as a dynamic open-source tool, provides transparency and flexibility, enabling researchers to actively shape drug definitions during the mapping phase. This empowerment enhances accuracy, reproducibility, and interpretability of results.

Comparing major and mild cognitive impairment risks in older type-2 diabetic patients: a Danish register-based study on dipeptidyl peptidase-4 inhibitors vs. glucagon-like peptide-1 analogues.

INTRODUCTION: The prevalence of major and mild cognitive impairment (CI) in type-2 diabetes older patients is 15-25% and 30-60%, respectively, thus affecting quality of life and health outcomes. There is, therefore, the need of head-to-head studies aiming at identifying the optimal treatment for individuals with type-2 diabetes at increased risk of mild and major CI. This study focuses on the risk of developing mild and major CI in Danish patients treated with dipeptidyl peptidase-4 inhibitors (DPP-4i) and glucagon-like peptide-1 analogues (GLP-1a) using administrative and healthcare registers. METHODS: An active comparator design with a 3-year follow-up period was used. The main outcome was the hospital admission with a diagnosis of mild CI or major CI. Multivariate Cox Regression analysis was performed using the high-dimensional propensity score to obtain adjusted Hazard Ratio (HR) estimates. Inverse probability of treatment weighting (IPTW) and marginal structured model were used to calculate risk differences while accounting for the variations of confounders throughout the follow-up period. RESULTS: Our results show a significant higher risk of major CI between DPP-4i and GLP-1a in unadjusted [HR (95% CI) = 3.13 (2.45-4.00), p < 0.001] and adjusted analyses [HR (95% CI) = 1.58 (1.22-2.06), p = 0.001]. No statistically significant differences were observed for mild CI. IPTW resulted stable throughout the follow-up period. Marginal structure modeling (ß (95% CI) = 0.022 (0.020-0.024), p < 0.001) resulted in a higher risk of major CI for DPP-4i when compared to GLP-1a. DISCUSSION: DPP-4i was associated with an increased risk of developing major CI when compared to GLP-1a among older individuals with type-2 diabetes.

Timing Matters: A Machine Learning Method for the Prioritization of Drug-Drug Interactions Through Signal Detection in the FDA Adverse Event Reporting System and Their Relationship with Time of Co-exposure.

INTRODUCTION: Current drug-drug interaction (DDI) detection methods often miss the aspect of temporal plausibility, leading to false-positive disproportionality signals in spontaneous reporting system (SRS) databases. OBJECTIVE: This study aims to develop a method for detecting and prioritizing temporally plausible disproportionality signals of DDIs in SRS databases by incorporating co-exposure time in disproportionality analysis. METHODS: The method was tested in the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The CRESCENDDI dataset of positive controls served as the primary source of true-positive DDIs. Disproportionality analysis was performed considering the time of co-exposure. Temporal plausibility was assessed using the flex point of cumulative reporting of disproportionality signals. Potential confounders were identified using a machine learning method (i.e. Lasso regression). RESULTS: Disproportionality analysis was conducted on 122 triplets with more than three cases, resulting in the prioritization of 61 disproportionality signals (50.0%) involving 13 adverse events, with 61.5% of these included in the European Medicine Agency's (EMA's) Important Medical Event (IME) list. A total of 27 signals (44.3%) had at least ten cases reporting the triplet of interest, and most of them (n = 19; 70.4%) were temporally plausible. The retrieved confounders were mainly other concomitant drugs. CONCLUSIONS: Our method was able to prioritize disproportionality signals with temporal plausibility. This finding suggests a potential for our method in pinpointing signals that are more likely to be furtherly validated.

Exploring the impact of co-exposure timing on drug-drug interactions in signal detection through spontaneous reporting system databases: a scoping review.

INTRODUCTION: Drug-drug interactions (DDIs) are defined as the pharmacological effects produced by the concomitant administration of two or more drugs. To minimize false positive signals and ensure their validity when analyzing Spontaneous Reporting System (SRS) databases, it has been suggested to incorporate key pharmacological principles, such as temporal plausibility. AREAS COVERED: The scoping review of the literature was completed using MEDLINE from inception to March 2023. Included studies had to provide detailed methods for identifying DDIs in SRS databases. Any methodological approach and adverse event were accepted. Descriptive analyzes were excluded as we focused on automatic signal detection methods. The result is an overview of all the available methods for DDI signal detection in SRS databases, with a specific focus on the evaluation of the co-exposure time of the interacting drugs. It is worth noting that only a limited number of studies (n = 3) have attempted to address the issue of overlapping drug administration times. EXPERT OPINION: Current guidelines for signal validation focus on factors like the number of reports and temporal association, but they lack guidance on addressing overlapping drug administration times, highlighting a need for further research and method development.

Evaluating the 6-month formulation of paliperidone palmitate: a twice-yearly injectable treatment for schizophrenia in adults.

INTRODUCTION: Paliperidone Palmitate is the only antipsychotic that has been developed in three different intramuscular long-acting injectable (LAI) dosing regimen: monthly (PP1M), quarterly (PP3M), and from 2020 also twice-yearly (PP6M). The latter was approved for the maintenance treatment of adults with schizophrenia and clinically stabilized with PP1M or PP3M. AREAS COVERED: Data from studies evaluating efficacy in the maintenance treatment of schizophrenia with PP6M are reviewed. Since no post-marketing safety studies are currently available, data from spontaneous reporting system databases, FAERS and Eudravigilance, are analyzed and the reported treatment-emergent adverse events of PP6M are discussed. EXPERT OPINION: The efficacy of PP6M is comparable to that of PP3M in terms of relapses prevention in patients with schizophrenia previously stabilized on PP3M or PP1M. Also, the maintenance of clinical efficacy in the long term has been demonstrated. Data from pharmacovigilance analyses, as well as from phase 3 studies, show that PP6M is generally well tolerated, consistently with PP3M safety data. PP6M allows a longer dosing interval than any other LAI antipsychotics, potentially reducing nonadherence and disease relapses. In future, an increase in the prescription rates of PP6M is expected and real-world efficacy and tolerability studies will be conducted.

Umbrella Review: Association Between Antipsychotic Drugs and Metabolic Syndrome Hallmarks in Children and Adolescents.

OBJECTIVE: To summarize the available evidence on metabolic parameters indicating metabolic adverse effects and risk of metabolic syndrome in children and adolescents treated with antipsychotics, following a pre-specified protocol (PROSPERO ID 252336). METHOD: We searched PubMed, Embase and PsycINFO until May 14, 2021, to identify systematic reviews (SR), meta-analyses (MA) and network meta-analyses (NMA) examining symptoms associated to metabolic syndrome in patients <18 years of age who required treatment with oral antipsychotic drugs. Evidence from quantitative analyses for all outcomes related to anthropometric, glyco-metabolic, and blood pressure parameters (measured from baseline to intervention-end and/or follow-up, in subjects exposed to antipsychotics and placebo) was reported on the basis of their metrics (median difference [medianD], mean difference [MD], standardized mean difference [SMD], odds ratio [OR], risk ratio ([RR]). A qualitative synthesis was also made. A formal quality assessment of the included studies was carried out by using the AMSTAR 2. We also provided a hierarchical stratification of the evidence from meta-analyses based on the class of evidence. RESULTS: A total of 23 articles (13 MA, 4 NMA and 6 SR) were included for review. As compared with placebo, an increase in triglyceride levels was associated with olanzapine (medianD [95% CI]: 37 [12.27, 61.74] mg/dL; MD [95% CI]: 38.57 [21.44, 55.77] mg/dL) and quetiapine (medianD [95% CI]: 21.58 [95% CI]: 4.27, 38.31 mg/dL; MD [95% CI]: 34.87 [20.08, 49.67] mg/dL; SMD [95% CI]: 0.37 [0.06, 0.068]), whereas decreased triglyceride levels were found for lurasidone. Increased total cholesterol level was associated with asenapine (medianD [95% CI]: 9.1 [1.73, 16.44] mg/dL), quetiapine (medianD [95% CI]: 15.60 [7.30, 24.05] mg/dL; olanzapine (MD [95% CI] from 3.67 [1.43, 5.92] mg/dL to 20.47 [13.97, 26.94] mg/dL]; and lurasidone (medianD [95% CI]: 8.94 [1.27, 16.90] mg/dL). Change in glucose levels did not differ among antipsychotics or placebo. Lurasidone, molindone, and ziprasidone were the best tolerated in terms of weight gain. According to the AMSTAR 2 scoring system, 13 (56.5%) reviews were rated as very low quality. According to classes of evidence, most MA were level 4, especially because of their limited total sample size. CONCLUSION: By collating meta-analyses assessing biochemical markers of metabolic syndrome in antipsychotic-treated children, we conclude that olanzapine should not be the antipsychotic of choice in patients at risk for hypertriglyceridemia or hypercholesterolemia. Aripiprazole and lurasidone appear to be better tolerated in terms of metabolic adverse events. Insufficient meta-analytic data are available to provide a precise risk estimate of metabolic syndrome, and, overall, the quality of evidence is low. STUDY REGISTRATION INFORMATION: Association between the use of antipsychotic drugs and alterations of the parameters defining the Metabolic Syndrome (MetS) in children and adolescents: an umbrella review; https://www.crd.york.ac.uk/prospero/; CRD42021252336.

Drug Repurposing in Crohn's Disease Using Danish Real-World Data.

Aim: Drug repurposing, utilizing electronic healthcare records (EHRs), offers a promising alternative by repurposing existing drugs for new therapeutic indications, especially for patients lacking effective therapies. Intestinal fibrosis, a severe complication of Crohn's disease (CD), poses significant challenges, increasing morbidity and mortality without available pharmacological treatments. This article focuses on identifying medications associated with an elevated or reduced risk of fibrosis in CD patients through a population-wide real-world data and artificial intelligence (AI) approach. Methods: Patients aged 65 or older with a diagnosis of CD from 1996 to 2019 in the Danish EHRs were followed for up to 24 years. The primary outcome was the need of specific surgical procedures, namely proctocolectomy with ileostomy and ileocecal resection as proxies of intestinal fibrosis. The study explored drugs linked to an increased or reduced risk of the study outcome through machine-learning driven survival analysis. Results: Among the 9179 CD patients, 1029 (11.2%) underwent surgery, primarily men (58.5%), with a mean age of 76 years, 10 drugs were linked to an elevated risk of surgery for proctocolectomy with ileostomy and ileocecal resection. In contrast, 10 drugs were associated with a reduced risk of undergoing surgery for these conditions. Conclusion: This study focuses on repurposing existing drugs to prevent surgery related to intestinal fibrosis in CD patients, using Danish EHRs and advanced statistical methods. The findings offer valuable insights into potential treatments for this condition, addressing a critical unmet medical need. Further research and clinical trials are warranted to validate the effectiveness of these repurposed drugs in preventing surgery related to intestinal fibrosis in CD patients.

The Reporting of a Disproportionality Analysis for Drug Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Development and Statement.

BACKGROUND AND AIM: Disproportionality analyses using reports of suspected adverse drug reactions are the most commonly used quantitative methods for detecting safety signals in pharmacovigilance. However, their methods and results are generally poorly reported in published articles and existing guidelines do not capture the specific features of disproportionality analyses. We here describe the development of a guideline (REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance [READUS-PV]) for reporting the results of disproportionality analyses in articles and abstracts. METHODS: We established a group of 34 international experts from universities, the pharmaceutical industry, and regulatory agencies, with expertise in pharmacovigilance, disproportionality analyses, and assessment of safety signals. We followed a three-step process to develop the checklist: (1) an open-text survey to generate a first list of items; (2) an online Delphi method to select and rephrase the most important items; (3) a final online consensus meeting. RESULTS: Among the panel members, 33 experts responded to round 1 and 30 to round 2 of the Delphi and 25 participated to the consensus meeting. Overall, 60 recommendations for the main body of the manuscript and 13 recommendations for the abstracts were retained by participants after the Delphi method. After merging of some items together and the online consensus meeting, the READUS-PV guidelines comprise a checklist of 32 recommendations, in 14 items, for the reporting of disproportionality analyses in the main body text and four items, comprising 12 recommendations, for abstracts. CONCLUSIONS: The READUS-PV guidelines will support authors, editors, peer-reviewers, and users of disproportionality analyses using individual case safety report databases. Adopting these guidelines will lead to more transparent, comprehensive, and accurate reporting and interpretation of disproportionality analyses, facilitating the integration with other sources of evidence.