Application of a Language Model Tool for COVID-19 Vaccine Adverse Event Monitoring Using Web and Social Media Content: Algorithm Development and Validation Study.

Background: Spontaneous pharmacovigilance reporting systems are the main data source for signal detection for vaccines. However, there is a large time lag between the occurrence of an adverse event (AE) and the availability for analysis. With global mass COVID-19 vaccination campaigns, social media, and web content, there is an opportunity for real-time, faster monitoring of AEs potentially related to COVID-19 vaccine use.

Considerations for practical use of tree-based scan statistics for signal detection using electronic healthcare data: a case study with insulin glargine.

Background: Hypothesis-free signal detection (HFSD) methods such as tree-based scan statistics (TBSS) applied to longitudinal electronic healthcare data (EHD) are increasingly used in safety monitoring. However, challenges may arise in interpreting HFSD results alongside results from disproportionality analysis of spontaneous reporting.

Research Design And Methods: Using the anti-diabetes drug insulin glargine (Lantus®) we apply two different tree-based scan designs using TreeScan™ software on retrospective EHD and compare the results to one another as well as to results from a disproportionality analysis using SRD.

Risk of Anaphylaxis Among New Users of GLP-1 Receptor Agonists: A Cohort Study.

Objective: To assess risk of anaphylaxis among patients with type 2 diabetes mellitus who are initiating therapy with a glucagon-like peptide 1 receptor agonist (GLP-1 RA), with a focus on those starting lixisenatide therapy.

Research Design And Methods: A cohort study was conducted in three large, U.S.

Suitability of Preference Methods Across the Medical Product Lifecycle: A Multicriteria Decision Analysis.

Objectives: This study aimed to understand the importance of criteria describing methods (eg, duration, costs, validity, and outcomes) according to decision makers for each decision point in the medical product lifecycle (MPLC) and to determine the suitability of a discrete choice experiment, swing weighting, probabilistic threshold technique, and best-worst scale cases 1 and 2 at each decision point in the MPLC.

Methods: Applying multicriteria decision analysis, an online survey was sent to MPLC decision makers (ie, industry, regulatory, and health technology assessment representatives). They ranked and weighted 19 methods criteria from an existing performance matrix about their respective decisions across the MPLC.

Incidence of diabetic ketoacidosis and its trends in patients with type 1 diabetes mellitus identified using a U.S. claims database, 2007-2019.

Diabetic ketoacidosis (DKA) is a common complication of type 1 diabetes mellitus (T1DM). We found that the incidence of DKA was 55.5 per 1000 person-years in US commercially insured patients with T1DM; age-sex-standardized incidence decreased at an average annual rate of 6.

Development and validation of a predictive model algorithm to identify anaphylaxis in adults with type 2 diabetes in U.S. administrative claims data.

Purpose: To use medical record adjudication and predictive modeling methods to develop and validate an algorithm to identify anaphylaxis among adults with type 2 diabetes (T2D) in administrative claims.

Methods: A conventional screening algorithm that prioritized sensitivity to identify potential anaphylaxis cases was developed and consisted of diagnosis codes for anaphylaxis or relevant signs and symptoms. This algorithm was applied to adults with T2D in the HealthCore Integrated Research Database (HIRD) from 2016 to 2018.

Evaluating the Risk of Digitalis Intoxication Associated With Concomitant Use of Dronedarone and Digoxin Using Real-World Data.

Purpose: Dronedarone may increase digoxin plasma levels through inhibition of P-glycoprotein. Using real-world data, we evaluated the risk of digitalis intoxication in concomitant users of dronedarone and digoxin compared digoxin-alone users.

Methods: We used the Clinformatics DataMart, a US claims database, to identify adult patients with atrial fibrillation (AF) or atrial flutter (AFL) who concomitantly used dronedarone and digoxin and those who used digoxin alone between July 2009 and March 2016.

Risk of interstitial lung disease in patients treated for atrial fibrillation with dronedarone versus other antiarrhythmics.

Purpose: To compare risks of interstitial lung disease (ILD) between patients treated with dronedarone versus other antiarrhythmics.

Methods: Parallel retrospective cohort studies were conducted in the United States Department of Defense Military Health System database (DoD) and the HealthCore Integrated Research Database (HIRD). Study patients were treated for atrial fibrillation (AF) with dronedarone, amiodarone, sotalol, or flecainide.

Performance assessment of different machine learning approaches in predicting diabetic ketoacidosis in adults with type 1 diabetes using electronic health records data.

Purpose: To assess the performance of different machine learning (ML) approaches in identifying risk factors for diabetic ketoacidosis (DKA) and predicting DKA.

Methods: This study applied flexible ML (XGBoost, distributed random forest [DRF] and feedforward network) and conventional ML approaches (logistic regression and least absolute shrinkage and selection operator [LASSO]) to 3400 DKA cases and 11 780 controls nested in adults with type 1 diabetes identified from Optum® de-identified Electronic Health Record dataset (2007-2018). Area under the curve (AUC), accuracy, sensitivity and specificity were computed using fivefold cross validation, and their 95% confidence intervals (CI) were established using 1000 bootstrap samples.

Structured benefit-risk evaluation for medicinal products: review of quantitative benefit-risk assessment findings in the literature.

Unlabelled: A favorable benefit-risk profile remains an essential requirement for marketing authorization of medicinal drugs and devices. Furthermore, prior subjective, implicit and inconsistent ad hoc benefit-risk assessment methods have rightly evolved towards more systematic, explicit or "structured" approaches. Contemporary structured benefit-risk evaluation aims at providing an objective assessment of the benefit-risk profile of medicinal products and a higher transparency for decision making purposes.

Age- and sex-specific incidence of non-traumatic lower limb amputation in patients with type 2 diabetes mellitus in a U.S. claims database.

Aim: To estimate age- and sex-specific incidence rates (IRs) of non-traumatic lower limb amputations (LLA) in patients with type 2 diabetes mellitus (T2DM) using a claims database from the United States (US).

Methods: Patients with T2DM 18 years and older were identified using the Truven Health MarketScan database from January 1, 2007 to September 30, 2018. The overall and age- and sex-specific IRs of all non-traumatic LLA, minor LLA (amputation at or below the ankle), and major LLA (amputation above ankle) were calculated.

Appraising patient preference methods for decision-making in the medical product lifecycle: an empirical comparison.

Background: Incorporating patient preference (PP) information into decision-making has become increasingly important to many stakeholders. However, there is little guidance on which patient preference assessment methods, including preference exploration (qualitative) and elicitation (quantitative) methods, are most suitable for decision-making at different stages in the medical product lifecycle (MPLC). This study aimed to use an empirical approach to assess which attributes of PP assessment methods are most important, and to identify which methods are most suitable, for decision-makers' needs during different stages in the MPLC.

Design, Conduct, and Use of Patient Preference Studies in the Medical Product Life Cycle: A Multi-Method Study.

To investigate stakeholder perspectives on how patient preference studies (PPS) should be designed and conducted to allow for inclusion of patient preferences in decision-making along the medical product life cycle (MPLC), and how patient preferences can be used in such decision-making. Two literature reviews and semi-structured interviews (n = 143) with healthcare stakeholders in Europe and the US were conducted; results of these informed the design of focus group guides. Eight focus groups were conducted with European patients, industry representatives and regulators, and with US regulators and European/Canadian health technology assessment (HTA) representatives.

Factors and Situations Affecting the Value of Patient Preference Studies: Semi-Structured Interviews in Europe and the US.

Patient preference information (PPI) is gaining recognition among the pharmaceutical industry, regulatory authorities, and health technology assessment (HTA) bodies/payers for use in assessments and decision-making along the medical product lifecycle (MPLC). This study aimed to identify factors and situations that influence the value of patient preference studies (PPS) in decision-making along the MPLC according to different stakeholders. Semi-structured interviews (n = 143) were conducted with six different stakeholder groups (physicians, academics, industry representatives, regulators, HTA/payer representatives, and a combined group of patients, caregivers, and patient representatives) from seven European countries (the United Kingdom, Sweden, Italy, Romania, Germany, France, and the Netherlands) and the United States.

Opportunities and challenges for the inclusion of patient preferences in the medical product life cycle: a systematic review.

Background: The inclusion of patient preferences (PP) in the medical product life cycle is a topic of growing interest to stakeholders such as academics, Health Technology Assessment (HTA) bodies, reimbursement agencies, industry, patients, physicians and regulators. This review aimed to understand the potential roles, reasons for using PP and the expectations, concerns and requirements associated with PP in industry processes, regulatory benefit-risk assessment (BRA) and marketing authorization (MA), and HTA and reimbursement decision-making.

Methods: A systematic review of peer-reviewed and grey literature published between January 2011 and March 2018 was performed.

Benefit-risk evaluation: the past, present and future.

In the last two decades there has been a shift in the approach to evaluating the benefit-risk (BR) profiles of medicinal products from an unstructured, subjective, and inconsistent, to a more structured and objective, process. This article describes that shift from a historical perspective; the past, the present, and the future, and highlights key events that played critical roles in changing the field.

Comparison of text processing methods in social media-based signal detection.

Purpose: Adverse event (AE) identification in social media (SM) can be performed using various types of natural language processing (NLP) and machine learning (ML). These methods can be categorized by complexity and precision level. Co-occurrence-based ML methods are rather basic, as they identify simultaneous appearance of drugs and clinical events in a single post.

Patient Preferences in the Medical Product Life Cycle: What do Stakeholders Think? Semi-Structured Qualitative Interviews in Europe and the USA.

Background: Patient preferences (PP), which are investigated in PP studies using qualitative or quantitative methods, are a growing area of interest to the following stakeholders involved in the medical product lifecycle: academics, health technology assessment bodies, payers, industry, patients, physicians, and regulators. However, the use of PP in decisions along the medical product lifecycle remains limited. As the adoption of PP heavily relies on these stakeholders, knowledge of their perceptions of PP is critical.

Methods for exploring and eliciting patient preferences in the medical product lifecycle: a literature review.

Preference studies are becoming increasingly important within the medical product decision-making context. Currently, there is limited understanding of the range of methods to gain insights into patient preferences. We developed a compendium and taxonomy of preference exploration (qualitative) and elicitation (quantitative) methods by conducting a systematic literature review to identify these methods.

Proximal HbA1C Level and First Hypoglycemia Hospitalization in Adults With Incident Type 2 Diabetes.

Context: Hemoglobin A1C (HbA1C) is an important predictor of severe hypoglycemia.

Objective: To determine the association of proximal HbA1C level with first hypoglycemia hospitalization (HH) in adults with incident type 2 diabetes (T2D).

Design, Setting, And Participants: A nested case-control study was designed using linked data from the Clinical Practice Research Datalink and Hospital Episode Statistics in England in 1997 to 2014.

Web-Based Signal Detection Using Medical Forums Data in France: Comparative Analysis.

Background: While traditional signal detection methods in pharmacovigilance are based on spontaneous reports, the use of social media is emerging. The potential strength of Web-based data relies on their volume and real-time availability, allowing early detection of signals of disproportionate reporting (SDRs).

Objective: This study aimed (1) to assess the consistency of SDRs detected from patients' medical forums in France compared with those detected from the traditional reporting systems and (2) to assess the ability of SDRs in identifying earlier than the traditional reporting systems.

Factors and situations influencing the value of patient preference studies along the medical product lifecycle: a literature review.

Industry, regulators, health technology assessment (HTA) bodies, and payers are exploring the use of patient preferences in their decision-making processes. In general, experience in conducting and assessing patient preference studies is limited. Here, we performed a systematic literature search and review to identify factors and situations influencing the value of patient preference studies, as well as applications throughout the medical product lifecyle.

Assessing the Risk for Peripheral Neuropathy in Patients Treated With Dronedarone Compared With That in Other Antiarrhythmics.

Purpose: There are few data on the risk for peripheral neuropathy associated with dronedarone, a newer antiarrhythmic medicine. The objective of this study was to assess whether dronedarone is potentially associated with an increased risk for peripheral neuropathy compared with other antiarrhythmics, including amiodarone, sotalol, flecainide, and propafenone.

Methods: The MarketScan database was used for identifying patients who were at least 18 years of age, had atrial fibrillation or flutter, and had not been diagnosed with peripheral neuropathy in the 180-day period prior to or on the date of the first prescription of an antiarrhythmic between July 20, 2009, and December 31, 2011.