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.

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.

Impact of the early COVID-19 pandemic on adult mental health-related dispensed medications, hospitalizations and specialist outpatient visits in Norway and Sweden: Interrupted time series analysis.

AIMS: Norway and Sweden had different early pandemic responses that may have impacted mental health management. The aim was to assess the impact of the early COVID-19 pandemic on mental health-related care. METHODS: We used national registries in Norway and Sweden (1 January 2018-31 December 2020) to define 2 cohorts: (i) general adult population; and (ii) mental health adult population. Interrupted times series regression analyses evaluated step and slope changes compared to prepandemic levels for monthly rates of medications (antidepressants, antipsychotics, anxiolytics, hypnotics/sedatives, lithium, opioid analgesics, psychostimulants), hospitalizations (for anxiety, bipolar, depressive/mood, eating and schizophrenia/delusional disorders) and specialist outpatient visits. RESULTS: In Norway, immediate reductions occurred in the general population for medications (-12% antidepressants to -7% hypnotics/sedatives) except for antipsychotics; and hospitalizations (-33% anxiety disorders to -17% bipolar disorders). Increasing slope change occurred for all medications except psychostimulants (+1.1%/month hypnotics/sedatives to +1.7%/month antidepressants); and hospitalization for anxiety disorders (+5.5%/month), depressive/mood disorders (+1.7%/month) and schizophrenia/delusional disorders (+2%/month). In Sweden, immediate reductions occurred for antidepressants (-7%) and opioids (-10%) and depressive/mood disorder hospitalizations (-11%) only with increasing slope change in psychostimulant prescribing of (0.9%/month). In contrast to Norway, increasing slope changes occurred in specialist outpatient visits for depressive/mood disorders, eating disorders and schizophrenia/delusional disorders (+1.5, +1.9 and +2.3%/month, respectively). Similar changes occurred in the pre-existing mental health cohorts. CONCLUSION: Differences in early COVID-19 policy response may have contributed to differences in adult mental healthcare provision in Norway and Sweden.

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.

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.

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.

Ticagrelor or prasugrel vs. clopidogrel in patients with atrial fibrillation undergoing percutaneous coronary intervention for myocardial infarction.

Aims: The efficacy and safety of ticagrelor or prasugrel vs. clopidogrel in patients with atrial fibrillation (AF) on oral anticoagulation (OAC) undergoing percutaneous coronary intervention (PCI) for myocardial infarction (MI) have not been established. Methods and results: This was a nationwide cohort study of patients on OAC for AF who underwent PCI for MI from 2011 through 2019 and were prescribed a P2Y12 inhibitor at discharge. The primary efficacy outcome was major adverse cardiovascular events (MACE), defined as a composite of death from any cause, stroke, recurrent MI, or repeat revascularization. The primary safety outcome was cerebral, gastrointestinal, or urogenital bleeding requiring hospitalization. Absolute and relative risks for outcomes at 1 year were calculated through multivariable logistic regression with average treatment effect modelling. Outcomes were standardized for the individual components of the CHA2DS2-VASc and HAS-BLED scores as well as type of OAC, aspirin, and proton pump inhibitor use. We included 2259 patients of whom 1918 (84.9%) were prescribed clopidogrel and 341 (15.1%) ticagrelor or prasugrel. The standardized risk of MACE was significantly lower in the ticagrelor or prasugrel group compared with the clopidogrel group (standardized absolute risk, 16.3% vs. 19.4%; relative risk, 0.84, 95% confidence interval, 0.70-0.98; P = 0.02), while the risk of bleeding did not differ (standardized absolute risk, 5.5% vs. 5.1%; relative risk, 1.07, 95% confidence interval, 0.73-1.41; P = 0.69). Conclusion: In patients with AF on OAC who underwent PCI for MI, treatment with ticagrelor or prasugrel vs. clopidogrel was associated with reduced ischaemic risk, without a concomitantly increased bleeding risk.

Persistence and adherence with Latanoprost: A Danish register-based cohort study in older patients with glaucoma.

PURPOSE: This study aims to assess the association between switching patterns and adherence/persistence in Danish patients over the age of 65, who started their first-ever glaucoma treatment with latanoprost eye drops. METHODS: Patients were assigned to three different cohorts: (1) switchers, (2) non-switchers, and (3) preservative-free latanoprost (Monoprost®) users. Patients were followed for 1 year until the end of data coverage or censoring. Study covariates were used to compute the propensity score. In the adjusted analysis, the propensity score was added to the model as an independent variable. The Cox regression model was used to calculate the hazard ratio (HR) of discontinuation for the three cohorts (the non-switchers cohort was the reference level) in both adjusted and unadjusted analyses. RESULTS: Non-switchers had a statistically significant lower adherence (proportion of days covered, PDC 92%) than switchers (PDC 96%; p < 0.001) and users of Monoprost® (PDC 99%; p < 0.001). Switchers had a 53% lower risk of treatment discontinuation compared to the reference group within 1 year after the first redemption of latanoprost in both unadjusted (HR 0.47; 95% Confidence interval, 95% CI: 0.41-0.53; p < 0.001) and adjusted (HR 0.47; 95% CI: 0.42-0.53; p < 0.001) analyses. In comparison to the non-switchers, Monoprost® users had a 78% lower risk for the above result in both unadjusted (HR 0.22; 95% CI: 0.17-0.28; p < 0.001) and adjusted (HR 0.22; 95% CI: 0.17-0.29; p < 0.001) analyses. CONCLUSION: This study found increased adherence and persistence in latanoprost users among those who redeemed preservative-free latanoprost (Monoprost®) and among those who switched between different latanoprost formulations.

Optimizing Signal Management in a Vaccine Adverse Event Reporting System: A Proof-of-Concept with COVID-19 Vaccines Using Signs, Symptoms, and Natural Language Processing.

INTRODUCTION: The Vaccine Adverse Event Reporting System (VAERS) has already been challenged by an extreme increase in the number of individual case safety reports (ICSRs) after the market introduction of coronavirus disease 2019 (COVID-19) vaccines. Evidence from scientific literature suggests that when there is an extreme increase in the number of ICSRs recorded in spontaneous reporting databases (such as the VAERS), an accompanying increase in the number of disproportionality signals (sometimes referred to as 'statistical alerts') generated is expected. OBJECTIVES: The objective of this study was to develop a natural language processing (NLP)-based approach to optimize signal management by excluding disproportionality signals related to listed adverse events following immunization (AEFIs). COVID-19 vaccines were used as a proof-of-concept. METHODS: The VAERS was used as a data source, and the Finding Associated Concepts with Text Analysis (FACTA+) was used to extract signs and symptoms of listed AEFIs from MEDLINE for COVID-19 vaccines. Disproportionality analyses were conducted according to guidelines and recommendations provided by the US Centers for Disease Control and Prevention. By using signs and symptoms of listed AEFIs, we computed the proportion of disproportionality signals dismissed for COVID-19 vaccines using this approach. Nine NLP techniques, including Generative Pre-Trained Transformer 3.5 (GPT-3.5), were used to automatically retrieve Medical Dictionary for Regulatory Activities Preferred Terms (MedDRA PTs) from signs and symptoms extracted from FACTA+. RESULTS: Overall, 17% of disproportionality signals for COVID-19 vaccines were dismissed as they reported signs and symptoms of listed AEFIs. Eight of nine NLP techniques used to automatically retrieve MedDRA PTs from signs and symptoms extracted from FACTA+ showed suboptimal performance. GPT-3.5 achieved an accuracy of 78% in correctly assigning MedDRA PTs. CONCLUSION: Our approach reduced the need for manual exclusion of disproportionality signals related to listed AEFIs and may lead to better optimization of time and resources in signal management.

Myocarditis and pericarditis in individuals exposed to the Ad26.COV2.S, BNT162b2 mRNA, or mRNA-1273 SARS-CoV-2 vaccines.

Importance: There is a high level of public and professional interest related to potential safety issues of the COVID-19 vaccines; however, no serious adverse cardiovascular events were reported in phase 3 randomized controlled trials of their safety and efficacy. Moreover, none of the case series from the United States (US) of these potential complications have been population-based. Objectives: To estimate the reporting rates of myocarditis and pericarditis in the US using the Vaccine Adverse Event Reporting System (VAERS), and to assess if these adverse events were disproportionally reported among the different COVID-19 vaccines. Design setting and participants: All cases of myocarditis and pericarditis from VAERS reported up to July 28, 2021. Exposure: Single-dose Ad26.COV2.S, BNT162b2 mRNA, or mRNA-1273 SARS-CoV-2 vaccinations. Main outcomes and measures: Reporting rates were computed by dividing the total number of cases of myocarditis and pericarditis (combined) by the total number of vaccine doses administered. Disproportionality analyses were performed to evaluate disproportional reporting of myocarditis and pericarditis for the Ad26.COV2.S and mRNA-1273 vaccines vs. the BNT162b2 mRNA vaccine. Results: By July 28, 2021, 1392, 699, and 68 cases of myocarditis or pericarditis had been reported out of 1.91, 1.38, and 1.33 million administered doses of the BNT162b2 mRNA, mRNA-1273, and Ad26.COV2.S COVID-19 vaccines, respectively. Median times to event were 3 days, 3 days, and 9 days for the BNT162b2 mRNA, mRNA-1273, and Ad26.COV2.S COVID-19 vaccines. The reporting rates for myocarditis or pericarditis were 0.00073 (95% confidence interval, 95% CI 0.00069-0.00077), 0.00051 (95% CI 0.00047-0.00055), and 0.00005 events per dose (95% CI 0.00004-0.00006) for the BNT162b2 mRNA, mRNA-1273, and Ad26.COV2.S COVID-19 vaccines, respectively. Myocarditis and pericarditis were disproportionally reported following the BNT162b2 mRNA vaccine when compared with the other vaccines, using both disproportionality measures. Conclusions and relevance: We found reporting rates of myocarditis and pericarditis to be less than 0.1% after COVID-19 vaccination. Rates were highest for the BNT162b2 mRNA vaccine, followed by the mRNA-1273 and Ad26.COV2.S, respectively. However, the reporting rates of myocarditis and pericarditis secondary to vaccination remains less common than those seen for SARS-CoV-2 infection.

Machine learning-driven development of a disease risk score for COVID-19 hospitalization and mortality: a Swedish and Norwegian register-based study.

Aims: To develop a disease risk score for COVID-19-related hospitalization and mortality in Sweden and externally validate it in Norway. Method: We employed linked data from the national health registries of Sweden and Norway to conduct our study. We focused on individuals in Sweden with confirmed SARS-CoV-2 infection through RT-PCR testing up to August 2022 as our study cohort. Within this group, we identified hospitalized cases as those who were admitted to the hospital within 14 days of testing positive for SARS-CoV-2 and matched them with five controls from the same cohort who were not hospitalized due to SARS-CoV-2. Additionally, we identified individuals who died within 30 days after being hospitalized for COVID-19. To develop our disease risk scores, we considered various factors, including demographics, infectious, somatic, and mental health conditions, recorded diagnoses, and pharmacological treatments. We also conducted age-specific analyses and assessed model performance through 5-fold cross-validation. Finally, we performed external validation using data from the Norwegian population with COVID-19 up to December 2021. Results: During the study period, a total of 124,560 individuals in Sweden were hospitalized, and 15,877 individuals died within 30 days following COVID-19 hospitalization. Disease risk scores for both hospitalization and mortality demonstrated predictive capabilities with ROC-AUC values of 0.70 and 0.72, respectively, across the entire study period. Notably, these scores exhibited a positive correlation with the likelihood of hospitalization or death. In the external validation using data from the Norwegian COVID-19 population (consisting of 53,744 individuals), the disease risk score predicted hospitalization with an AUC of 0.47 and death with an AUC of 0.74. Conclusion: The disease risk score showed moderately good performance to predict COVID-19-related mortality but performed poorly in predicting hospitalization when externally validated.

Venous thromboembolism with use of hormonal contraception and non-steroidal anti-inflammatory drugs: nationwide cohort study.

OBJECTIVE: To study the influence of concomitant use of hormonal contraception and non-steroidal anti-inflammatory drugs (NSAIDs) on the risk of venous thromboembolism. DESIGN: Nationwide cohort study. SETTING: Denmark through national registries. PARTICIPANTS: All 15-49 year old women living in Denmark between 1996 and 2017 with no medical history of any venous or arterial thrombotic event, cancer, thrombophilia, hysterectomy, bilateral oophorectomy, sterilisation, or infertility treatment (n=2 029 065). MAIN OUTCOME MEASURE: A first time discharge diagnosis of lower limb deep venous thrombosis or pulmonary embolism. RESULTS: Among 2.0 million women followed for 21.0 million person years, 8710 venous thromboembolic events occurred. Compared with non-use of NSAIDs, use of NSAIDs was associated with an adjusted incidence rate ratio of venous thromboembolism of 7.2 (95% confidence interval 6.0 to 8.5) in women not using hormonal contraception, 11.0 (9.6 to 12.6) in women using high risk hormonal contraception, 7.9 (5.9 to 10.6) in those using medium risk hormonal contraception, and 4.5 (2.6 to 8.1) in users of low/no risk hormonal contraception. The corresponding numbers of extra venous thromboembolic events per 100 000 women over the first week of NSAID treatment compared with non-use of NSAIDs were 4 (3 to 5) in women not using hormonal contraception, 23 (19 to 27) in women using high risk hormonal contraception, 11 (7 to 15) in those using medium risk hormonal contraception, and 3 (0 to 5) in users of low/no risk hormonal contraception. CONCLUSIONS: NSAID use was positively associated with the development of venous thromboembolism in women of reproductive age. The number of extra venous thromboembolic events with NSAID use compared with non-use was significantly larger with concomitant use of high/medium risk hormonal contraception compared with concomitant use of low/no risk hormonal contraception. Women needing both hormonal contraception and regular use of NSAIDs should be advised accordingly.

Trends in Medical and Device Therapies Following Incident Heart Failure in Denmark during 1996-2019: A Nationwide Register-Based Follow-Up Study.

Introduction: Data on temporal trends in guideline-based medical and device therapies in real-world chronic heart failure (HF) patients are lacking. Methods: Register-based nationwide follow-ups of temporal trends in characteristics, guideline-recommended therapies, one-year all-cause mortality, and HF rehospitalizations in incident HF patients in Denmark during 1996-2019. Results: Among 291,720 incident HF patients, the age at the onset of HF was stable over time. While initially fairly equal, the sex distribution markedly changed over time with more incidents occurring in men overall. Hypertension and diabetes increased significantly over time, while other comorbidities remained stable. Between 1996 and 2019, significant increases in angiotensin-converting enzyme inhibitor and angiotensin II-receptor blocker (ACEi/ARB) therapy (38.2% to 69.9%), beta-blocker therapy (15.5% to 70.6%), and mineralocorticoid receptor antagonist (MRA) therapy (11.8% to 34.5%) were seen. Angiotensin receptor-neprilysin inhibitor (ARNI) and sodium-glucose cotransporter-2 inhibitors (SGLT2i) were introduced in the middle of the past decade, with minor increases but overall low uses: ARNI (2015: 0.1% vs. 2019: 3.9%) and SGLT2i (2012: <0.1% vs. 2019: 3.9%). Between 1999 and 2019, implantable cardioverter-defibrillator (ICD) use increased significantly: 0.1% to 3-4%. Cardiac resynchronization therapy (CRT) use similarly increased between 2000 and 2019: 0.2% to 2.3%. Between 1996 and 2019, one-year all-cause mortality decreased significantly: 34.6% to 20.9%, as did HF rehospitalizations (6% to 1.3%). Conclusions: Among 291,720 incident HF patients in Denmark during 1996-2019, significant increases in the use of ACEi/ARB, beta-blockers, MRAs, and devices were seen, with concurrent significant decreases in the one-year all-cause mortality and HF rehospitalization rates. The use of CRT, ARNI, and SGLT2i remained low, and MRAs were relatively underutilized, thereby representing future targets to potentially further improve HF prognoses.

AI-based disease risk score for community-acquired pneumonia hospitalization.

Community-acquired pneumonia (CAP) is an acute infection involving the parenchyma of the lungs, which is acquired outside of the hospital. Population-wide real-world data and artificial intelligence (AI) were used to develop a disease risk score for CAP hospitalization among older individuals. The source population included residents in Denmark aged 65 years or older in the period January 1, 1996, to July 30, 2018. 137344 individuals were hospitalized for pneumonia during the study period for which, 5 controls were matched leading to a study population of 620908 individuals. The disease risk had an average accuracy of 0.79 based on 5-fold cross-validation in predicting CAP hospitalization. The disease risk score can be useful in clinical practice to identify individuals at higher risk of CAP hospitalization and intervene to minimize their risk of being hospitalized for CAP.

Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review.

Aim: To perform a systematic review on the use of Artificial Intelligence (AI) techniques for predicting COVID-19 hospitalization and mortality using primary and secondary data sources. Study eligibility criteria: Cohort, clinical trials, meta-analyses, and observational studies investigating COVID-19 hospitalization or mortality using artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded. Data sources: Articles recorded in Ovid MEDLINE from 01/01/2019 to 22/08/2022 were screened. Data extraction: We extracted information on data sources, AI models, and epidemiological aspects of retrieved studies. Bias assessment: A bias assessment of AI models was done using PROBAST. Participants: Patients tested positive for COVID-19. Results: We included 39 studies related to AI-based prediction of hospitalization and death related to COVID-19. The articles were published in the period 2019-2022, and mostly used Random Forest as the model with the best performance. AI models were trained using cohorts of individuals sampled from populations of European and non-European countries, mostly with cohort sample size <5,000. Data collection generally included information on demographics, clinical records, laboratory results, and pharmacological treatments (i.e., high-dimensional datasets). In most studies, the models were internally validated with cross-validation, but the majority of studies lacked external validation and calibration. Covariates were not prioritized using ensemble approaches in most of the studies, however, models still showed moderately good performances with Area under the Receiver operating characteristic Curve (AUC) values >0.7. According to the assessment with PROBAST, all models had a high risk of bias and/or concern regarding applicability. Conclusions: A broad range of AI techniques have been used to predict COVID-19 hospitalization and mortality. The studies reported good prediction performance of AI models, however, high risk of bias and/or concern regarding applicability were detected.

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.

Developing an Artificial Intelligence-Guided Signal Detection in the Food and Drug Administration Adverse Event Reporting System (FAERS): A Proof-of-Concept Study Using Galcanezumab and Simulated Data.

INTRODUCTION: Time- and resource-demanding activities related to processing individual case safety reports (ICSRs) include manual procedures to evaluate individual causality with the final goal of dismissing false-positive safety signals. Eminent experts and a representative from pharmaceutical industries and regulatory agencies have highlighted the need to automatize time- and resource-demanding procedures in signal detection and validation. However, to date there is a sparse availability of automatized tools for such purposes. OBJECTIVES: ICSRs recorded in spontaneous reporting databases have been and continue to be the cornerstone and the most important data source in signal detection. Despite the richness of this data source, the incessantly increased amount of ICSRs recorded in spontaneous reporting databases has generated problems in signal detection and validation due to the increase in resources and time needed to process cases. This study aimed to develop a new artificial intelligence (AI)-based framework to automate resource- and time-consuming steps of signal detection and signal validation, such as (1) the selection of control groups in disproportionality analyses and (2) the identification of co-reported drugs serving as alternative causes, to look to dismiss false-positive disproportionality signals and therefore reduce the burden of case-by-case validation. METHODS: The Summary of Product Characteristics (SmPC) and the Anatomical Therapeutic Chemical (ATC) classification system were used to automatically identify control groups within and outside the chemical subgroup of the proof-of-concept drug under investigation, galcanezumab. Machine learning, specifically conditional inference trees, has been used to identify alternative causes in disproportionality signals. RESULTS: By using conditional inference trees, the framework was able to dismiss 20.00% of erenumab, 14.29% of topiramate, and 13.33% of amitriptyline disproportionality signals on the basis of purely alternative causes identified in cases. Furthermore, of the disproportionality signals that could not be dismissed purely on the basis of the alternative causes identified, we estimated a 15.32%, 25.39%, and 26.41% reduction in the number of galcanezumab cases to undergo manual validation in comparison with erenumab, topiramate, and amitriptyline, respectively. CONCLUSION: AI could significantly ease some of the most time-consuming and labor-intensive steps of signal detection and validation. The AI-based approach showed promising results, however, future work is needed to validate the framework.

Ubrogepant and rimegepant: signal detection using spontaneous reports of adverse events from the Food and Drug Administration Adverse Event Reporting System.

BACKGROUND: In this study, we fill this gap in knowledge by updating the safety profile of ubrogepant and rimegepant via disproportionality analysis in the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), a US-based database registering spontaneous reports. RESEARCH DESIGN AND METHODS: ASCII files of quarterly extraction of FAERS data were downloaded from the FDA website up to the 3rd quarter (Q3) of 2021 (last accessed 03/02/2022). Disproportionality analysis was done using the Reporting Odds Ratio (ROR) as a disproportionality measure. RORs of all AEs related to ubrogepant and rimegepant in FAERS were calculated in comparison with those related to erenumab. Drug-event pairs with a frequency ≤ 2, were removed according to European Medicine Agency (EMA)'s procedures. RESULTS: In total, 2010 and 3691 individual case safety reports (ICSRs) recorded in FAERS reported ubrogepant and rimegepant, respectively, as suspect drugs. Ten disproportionality signals for ubrogepant and 25 disproportionality signals for rimegepant were identified; these were mostly related to psychiatric, neurological, gastrointestinal, skin, vascular, and infectious type of adverse events. CONCLUSIONS: New safety aspects related to the treatment of ubrogepant and rimegepant using disproportionality analysis from spontaneous reporting databases were identified. Further studies are needed to confirm these findings.

Ubrogepant and rimegepant: systematic review, meta-analysis, and meta-regression of clinical studies.

OBJECTIVES: This study aimed at providing pooled estimates of the incidence of adverse drug reactions (ADRs) of ubrogepant and rimegepant and to use meta-regression to identify correlations between the occurrence of selected ADRs, socio-demographic, and clinical characteristics from data published in clinical studies. METHODS: Ovid MEDLINE (up to 03/02/2022) was searched along with the references listed in the reviews identified with the research query. Random intercept and slope logistic regression models were used to estimate the logit transformation of the pooled incidence. To examine how selected clinical and socio-demographic characteristics correlated with the pooled incidence rates, we performed random-effects meta-regression. RESULTS: Significant heterogeneity of incidence estimates was observed in clinical studies along with correlations between ADRs and the sociodemographic and clinical characteristics of patients exposed to ubrogepant. In particular, we observed a correlation between ubrogepant dosage and muscle strain and between Body Mass Index (BMI) and liver function values. For rimegepant, significant correlations were observed between age and infections and having aura symptoms at baseline and nausea/dizziness/diarrhea/muscle strain. CONCLUSION: This study provided pooled incidence estimates of ubrogepant and rimegepant's ADRs and highlighted new safety aspects of the pharmacological treatment with ubrogepants and rimigepants from correlations obtained from the meta-regression.