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AimsTo develop a disease risk score for COVID-19-related hospitalization and mortality in Sweden and externally validate it in Norway.MethodWe 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.ResultsDuring 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.ConclusionThe disease risk score showed moderately good performance to predict COVID-19-related mortality but performed poorly in predicting hospitalization when externally validated.

ImportanceThere 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.ObjectivesTo 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 participantsAll cases of myocarditis and pericarditis from VAERS reported up to July 28, 2021.ExposureSingle-dose Ad26.COV2.S, BNT162b2 mRNA, or mRNA-1273 SARS-CoV-2 vaccinations.Main outcomes and measuresReporting 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.ResultsBy 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 relevanceWe 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.

AimTo 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 criteriaCohort, 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 sourcesArticles recorded in Ovid MEDLINE from 01/01/2019 to 22/08/2022 were screened.Data extractionWe extracted information on data sources, AI models, and epidemiological aspects of retrieved studies.Bias assessmentA bias assessment of AI models was done using PROBAST.ParticipantsPatients tested positive for COVID-19.ResultsWe 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 0.7. According to the assessment with PROBAST, all models had a high risk of bias and/or concern regarding applicability.ConclusionsA 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.

Purpose: There is a lack of available evidence regarding the treatment pattern of switches and add-ons for individuals aged 65 years or older with epilepsy during the first years from the time they received their first anti-seizure medication because of the lack of valid methods. Therefore, this study aimed to develop an algorithm for identifying switches and add-ons using secondary data sources for anti-seizure medication users.Methods: Danish nationwide databases were used as data sources. Residents in Denmark between 1996 and 2018 who were diagnosed with epilepsy and redeemed their first prescription for anti-seizure medication after epilepsy diagnosis were followed up for 730 days until the end of the follow-up period, death, or emigration to assess switches and add-ons occurred during the follow-up period. The study outcomes were the overall accuracy of the classification of switch or add-on of the newly developed algorithm.Results: In total, 15870 individuals were included in the study population with a median age of 72.9 years, of whom 52.0% were male and 48.0% were female. A total of 988 of the 15879 patients from the study population were present during the 730-day follow-up period, and 988 individuals (6.2%) underwent a total of 1485 medication events with co-exposure to two or more anti-seizure medications. The newly developed algorithmic method correctly identified 9 out of 10 add-ons (overall accuracy 92%) and 9 out of 10 switches (overall accuracy 88%).Conclusion: The majority of switches and add-ons occurred early during the first 2 years of disease and according to clinical recommendations. The newly developed algorithm correctly identified 9 out of 10 switches/add-ons.

Aim: To summarize the evidence on the performance of artificial intelligence vs. traditional pharmacoepidemiological techniques.Methods: Ovid MEDLINE (01/1950 to 05/2019) was searched to identify observational studies, meta-analyses, and clinical trials using artificial intelligence techniques having a drug as the exposure or the outcome of the study. Only studies with an available full text in the English language were evaluated.Results: In all, 72 original articles and five reviews were identified via Ovid MEDLINE of which 19 (26.4%) compared the performance of artificial intelligence techniques with traditional pharmacoepidemiological methods. In total, 44 comparisons have been performed in articles that aimed at 1) predicting the needed dosage given the patient’s characteristics (31.8%), 2) predicting the clinical response following a pharmacological treatment (29.5%), 3) predicting the occurrence/severity of adverse drug reactions (20.5%), 4) predicting the propensity score (9.1%), 5) identifying subpopulation more at risk of drug inefficacy (4.5%), 6) predicting drug consumption (2.3%), and 7) predicting drug-induced lengths of stay in hospital (2.3%). In 22 out of 44 (50.0%) comparisons, artificial intelligence performed better than traditional pharmacoepidemiological techniques. Random forest (seven out of 11 comparisons; 63.6%) and artificial neural network (six out of 10 comparisons; 60.0%) were the techniques that in most of the comparisons outperformed traditional pharmacoepidemiological methods.Conclusion: Only a small fraction of articles compared the performance of artificial intelligence techniques with traditional pharmacoepidemiological methods and not all artificial intelligence techniques have been compared in a Pharmacoepidemiological setting. However, in 50% of comparisons, artificial intelligence performed better than pharmacoepidemiological techniques.

AimTo perform a systematic review on the application of artificial intelligence (AI) based knowledge discovery techniques in pharmacoepidemiology.Study Eligibility CriteriaClinical trials, meta-analyses, narrative/systematic review, and observational studies using (or mentioning articles using) artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded.Data SourcesArticles recorded from 1950/01/01 to 2019/05/06 in Ovid MEDLINE were screened.ParticipantsStudies including humans (real or simulated) exposed to a drug.ResultsIn total, 72 original articles and 5 reviews were identified via Ovid MEDLINE. Twenty different knowledge discovery methods were identified, mainly from the area of machine learning (66/72; 91.7%). Classification/regression (44/72; 61.1%), classification/regression + model optimization (13/72; 18.0%), and classification/regression + features selection (12/72; 16.7%) were the three most frequent tasks in reviewed literature that machine learning methods has been applied to solve. The top three used techniques were artificial neural networks, random forest, and support vector machines models.ConclusionsThe use of knowledge discovery techniques of artificial intelligence techniques has increased exponentially over the years covering numerous sub-topics of pharmacoepidemiology.Systematic Review RegistrationSystematic review registration number in PROSPERO: CRD42019136552.

Purpose: Gender differences exist in the prevalence of asthma and allergic diseases, partially due to the effects of sex hormones on the development of allergic manifestations. Women, compared with men, are more prone to suffer allergic asthma, experience difficulties in controlling asthma symptoms, and show adverse responses to drugs. However, there are knowledge gaps on the effectiveness of anti-leukotrienes drugs on lung function, symptoms, and pulmonary and systemic inflammation in adult asthmatic women compared with men. We conducted a prospective cohort study to characterize the effectiveness of an anti-leukotrienes drug, montelukast (MS), in asthmatic adult women and men.Methods: Twenty-one asthmatic subjects (11 women and 10 men), who were on low-dose inhaled corticosteroids (ICS), were treated with MS. The optimal control of the symptoms was achieved in both groups according to the Global Initiative for Asthma guidelines. At enrollment, and after 13 weeks from the beginning of MS, pulmonary function tests and asthma control tests were performed, and the fraction of exhaled nitric oxide and blood eosinophils levels were measured.Results: From baseline until the end of the study, women treated with MS + ICS had better control of the asthmatic symptoms, defined as higher asthma control test (ACT) score (17.00 ± 1.07 to 23.36 ± 0.45; p < 0.0015), improved pulmonary function [with higher forced expiratory volume in 1 s (from 77.25 ± 6.79 to 103.88 ± 6.24; p < 0.0077)], and forced vital capacity (from 91.95 ± 6.81 to 113.17 ± 4.79; p < 0.0183) compared with men. Interestingly, MS + ICS-treated women had significantly lower levels of blood eosinophils (from 5.27 ± 0.30 to 3.30 ± 0.31; p < 0.0449) and exhaled nitric oxide (from 44.70 ± 7.30 to 25.20 ± 3.90; p < 0.0294) compared with men.Conclusion: The treatment with MS, added to ICS, in women leads to better control of symptoms, better management of lung function, and decreased inflammation levels compared with ICS + MS treatment in men.

BackgroundDespite the risk of bleeding is a well-known adverse effect of oral anticoagulants, there is scarce evidence on the preventability of oral anticoagulant-induced bleedings. Therefore, we investigated the potential risk factors related to preventable cases of oral anticoagulant-induced bleedings.MethodsWe performed a study using Individual Case Safety Reports (ICSRs) with an oral anticoagulant as suspected drug among those reported through the spontaneous reporting system of Campania Region from 1 July 2012 to 31 December 2017. The P-method was used for the preventability assessment of all cases of bleeding.ResultsIn total, 58 cases out of 253 (22.9%) were preventable, and the most reported suspected drug was an indirect oral anticoagulant (warfarin). Sixty-eight critical criteria for preventability were identified, all related to healthcare professionals’ practices. The most detected risk factor related to healthcare professionals’ practices was the labeled drug–drug interaction for both direct and indirect oral anticoagulants.ConclusionOur findings describe the most reported risk factors for preventability of oral anticoagulant-induced bleedings. These factors may be useful for targeting interventions to improve pharmacovigilance activities in our regional territory and to reduce the burden of medication errors and inappropriate prescription.

Rationale: Long-term clinical implications of beta-blockade in obstructive airway diseases remains controversial. We investigated if within the first 5 years of treatment patients with heart failure and obstructive airway diseases using non β1-adrenoreceptor selective beta-blockers have an increased risk of being hospitalized for all-causes, heart failure, and chronic obstructive pulmonary disease (COPD) when compared to patient using selective beta-blockers.Methods: Carvedilol users were propensity matched 1:1 for co-treatments, age, gender, and year of inclusion in the cohort with metoprolol/bisoprolol/nebivolol users. Cox proportional hazard regression model was used to compare all causes, COPD, and heart failure hospitalization or the beta-blocker discontinuation between cohorts. For statistically significant associations, we computed the rate difference and the attributable risk.Results: Overall, 11,844 patients out of the 51,214 (23.1%) were exposed to carvedilol and 39,370 (76.9%) to metoprolol/bisoprolol/nebivolol. Carvedilol users had a higher hazard for heart failure hospitalization (HR 1.29; 95% Confidence Interval [CI] 1.18–1.40) with 106 (95%CI 76–134; p-value < 0.001) additional cases of heart failure hospitalization per 10000 person-years if compared to metoprolol/bisoprolol/nebivolol users. In all, 26.8% (95%CI 22.5–30.9%; p-value < 0.001) of heart failure hospitalizations in the study population could be attributed to being exposed to carvedilol. Carvedilol users had a higher hazard (HR 1.06; 95%CI 1.02–1.10) of discontinuing the pharmacological treatment with 131 (95%CI 62–201; p-value < 0.001) additional cases of beta-blocker discontinuation per 10000 person-years metoprolol/bisoprolol/nebivolol users. In all, 6.5% (95%CI 3.9–9.0%; p-value < 0.001) of beta-blocker discontinuation could be attributed to being exposed to carvedilol.Conclusion: On long-term follow-up period, carvedilol was associated with a higher risk of heart failure hospitalization and discontinuation if compared to metoprolol/bisoprolol/nebivolol users among patients with heart failure and obstructive airway diseases.

Rationale: Gender-related differences in asthma prevalence, pathophysiology and clinical features induced by sex steroids have been investigated, however, how gender influences response to asthma treatments in routine clinical practice have not yet been elucidated fully. This aspect is crucial for montelukast considering the jeopardization of asthmatic patients that benefit from this treatment and the existence of evidence of gender differences in leukotriene levels. Therefore, to fulfill this medical need, we investigated the role of gender on a set of montelukast' effectiveness surrogates in adults and pediatric patients with asthma.Methods: The study settings were Napoli 2 Local Health Unit (southern Italy) and the entire Danish territory. The study population was composed of adult and pediatric patients with asthma. Cumulative incidence curves, unadjusted and adjusted Cox regression were used as statistical models to compare aforementioned outcomes between genders.Results: Adult Italian male users of montelukast had a statistically lower persistence in montelukast treatment compared to female users. In the adjusted analyses, they had a higher hazard of montelukast' withdrawal (Hazard Ratio [HR] 1.07; 95% Confidence Interval [CI] 1.01–1.14), add-on/switch to a long-term treatment for asthma following montelukast withdrawal (HR 1.72; 95%CI 1.39–2.12), and rescue therapy with short-acting β2 agonist (HR 1.24; 95%CI 1.04–1.47). In the adult Danish cohort, we also found that male users had higher a hazard of rescue therapy with oral corticosteroids (HR 1.10; 95%CI 1.04–1.16). In the pediatric cohorts, no statistically significant differences were observed between genders for aforementioned outcomes.Conclusions: In adults, male gender was associated with increased hazards of montelukast discontinuation, add-on/switch to a long-term treatment for asthma following montelukast withdrawal, and rescue therapy with oral corticosteroids or short-acting β2 agonist when compared to the female gender. As expected, these associations were reversed or absent in pediatric patients.

Rationale: The aim of this study is to investigate if following the implementation of the Regulation EU/1235/2010 and the Directive 2010/84/EU there was an increase of individual case safety reports (ICSRs) deriving from a medication error, abuse, misuse, overdose, or occupational exposure. Other objectives are the identification of drugs mostly involved in such cases, to establish if the codification of aforementioned conditions is performed correctly and, whenever codification errors exist, to identify predictors of codification errors. Finally, we estimated the magnitude of these errors on signal detection activities.Methods: ICSRs sent through Campania Region (Italy) spontaneous reporting system from July 2nd 2012 to December 31th 2017 were used as data source. A multivariable logistic regression model was used to identify predictors of codification errors. Four measures of disproportionality were used to investigate the magnitude of codification errors on a known safety signal or rather the association between benzodiazepines derivatives and abuse.Results: In all, 358 (1.4%) out of 25610 ICSRs reported “non-normal use” of drugs, mainly as cases of abuse. Drugs mostly involved in abuse were “Benzodiazepines derivatives” (171/358; 47.8%). For medication errors instead, “Other antiseptics and disinfectants” (9/358; 2.5%). At the first quality control, 125 (34.9%) out of 358 ICSRs did not have a codification of “non-normal use” or codifications were performed wrongly. Codification errors included misclassification of abuse as overdose (10/125; 8.0%) and misclassification of medication error as overdose (7/125; 5.6%) or abuse (7/125; 5.6%). Compared to pharmaceutical companies, patients/citizens (as reporters) had a 24.88 higher odd (Reporting Odds Ratio 24.88, 95%CI 1.82–449.95; p-value: 0.02) of performing un-classification or misclassification of aforementioned codifications. Codification errors were associated with the underestimation of measure of disproportionality' estimates in the identification of the safety signal “Benzodiazepine derivatives /abuse”.Conclusion: In conclusion, this study found that in Campania Region (southern Italy) there was an exponential increase of ICSR reporting “non-normal use,” mainly as cases of abuse, with an improvable proportion of cases misclassified/unclassified. Moreover, this study found that ICSRs sent by patients/citizens were associated with an increased odd of un-classification or misclassification that had a relevant impact on signal detection activities.

Objectives: To investigate the occurrence of adverse events (AEs) in naïve patients receiving biotech drugs.Design: A prospective observational study.Setting: Onco-hematology, Hepato-gastroenterology, Rheumatology, Dermatology, and Neurology Units in Campania Region (Italy).Participants: 775 patients (53.81% female) with mean age 56.0 (SD 15.2). The mean follow-up/patient was 3.48 (95% confidence interval 3.13–3.84).Main outcome measures: We collected all AEs associated to biotech drugs, including serious infections and malignancies. Serious AEs were defined according to the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use, clinical safety data management: definitions and standards for expedited reporting E2A guideline.Results: The majority of the study population was enrolled in Onco-hematology and Rheumatology Units and the most common diagnosis were hematological malignancies, followed by rheumatoid arthritis, colorectal cancer, breast cancer, and psoriatic arthritis. The most commonly prescribed biotech drugs were rituximab, bevacizumab, infliximab, trastuzumab, adalimumab, and cetuximab. Out of 775 patients, 320 experienced at least one AE. Most of patients experienced AEs to cetuximab therapy, rituximab and trastuzumab. Comparing female and male population, our findings highlighted a statistically significant difference in terms of AEs for adalimumab (35.90% vs. 7.41%, p < 0.001) and etanercept (27.59% vs. 10.00%, p = 0.023). Considering all biotech drugs, we observed a peak for all AEs occurrence at follow-up 91–180 days category. Bevacizumab, brentuximab, rituximab, trastuzumab and cetuximab were more commonly associated to serious adverse events; most of these were possibly related to biotech drugs, according to causality assessment. Three cases of serious infections occurred.Conclusions: The results of our study demonstrated that the majority of AEs were not serious and expected. Few cases of serious infections occurred, while no case of malignancy did. Overall, the safety profile of biotech drugs used in our population was similar to those observed in pivotal trials. Notwithstanding the positive results of our study, some safety concerns still remain unresolved. In order to collect more effectiveness and safety data on biotech drugs, the collection and analysis of real world data should be endorsed as well as the management of post-authorization studies.

Purpose: This study aims to investigate preventability criteria of adverse drug reactions (ADRs) involving non-steroidal anti-inflammatory drugs (NSAIDs) by analyzing individual case safety reports (ICSRs) sent through Campania region (Italy) spontaneous reporting system from July 2012 to October 2016.Methods: For all the ICSRs that reported NSAIDs as suspected drug, a trained multidisciplinary team of Campania Pharmacovigilance Regional Centre composed of clinical pharmacologists and pharmacists with pluriannual experience in Pharmacovigilance assessed preventability by using the P-method.Results: In all 19,039 ICSRs were sent to Campania Pharmacovigilance Regional Centre, of which 550 reported NSAIDs as suspected drug. In total, 94 cases (17.1%) out of 550 ICSRs were preventable. In the 94 preventable cases, 201 critical criteria were detected of which 182/201 (90.5%) related to healthcare professionals' practices, 0/201 (0.0%) to drug quality, and 19/201 (9.5%) to patient behavior. The most detected critical criteria were the necessary medication not given (52/182; 28.6%), labeled drug–drug interaction (36/182; 19.7%), incorrect drug administration duration (31/182; 16.9%), wrong indication (26/182; 14.2%), therapeutic duplication (18/182; 10.0%), and documented hypersensitivity to administered drug or drug class (10/182; 5.6%). In seventeen (18.1%) preventable cases, there were 19 critical criteria involving non-compliance (15/19 critical criteria; 78.9%) and self-medication with the non-over-the-counter drugs (4/19 critical criteria; 21.1%). In all, 17 out 94 (18.1%) preventable cases involved over-the-counter drugs.Conclusion: A call for action for Campania Pharmacovigilance Regional Centre is necessary in order to promote initiatives to increase the awareness of healthcare professionals and citizens on the risk associated with inappropriate use of NSAIDs.