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Abstract Background/Objective: Despite the intuitive attractiveness of bringing research to participants rather than making them come to central study sites, widespread decentralized enrollment has not been common in clinical trials. Methods: The need for clinical research in the context of the COVID-19 pandemic, along with innovations in technology, led us to use a decentralized trial approach in our Phase 2 COVID-19 trial. We used real-time acquisition and transmission of health-related data using home-based monitoring devices and mobile applications to assess outcomes. This approach not only avoids spreading COVID-19 but it also can support inclusion of participants in more diverse socioeconomic circumstances and in rural settings. Results: Our team developed and deployed a decentralized trial platform to support patient engagement and adverse event reporting. Clinicians, engineers, and informaticians on our research team developed a Clinical-Trial-in-a-Box tool to optimally collect and analyze data from multiple decentralized platforms. Conclusion: Applying the decentralized model in Long COVID, using digital health technology and personal devices integrated with our telehealth platform, we share the lessons learned from our work, along with challenges and future possibilities.

Background: Widespread antimicrobial use in dogs and cats drives antimicrobial resistance in both animals and humans. Knowledge of the factors associated with antimicrobial use is limited in veterinary medicine. We examined factors associated with antimicrobial drug prescription among inpatient dogs and cats at an academic veterinary hospital. Methods: A veterinary-adapted observational medical outcomes partnership common data model was utilized to extract demographic, clinical, and prescription data from the electronic medical record system in this descriptive observational study. Using generalized estimating equations, we assessed the association between demographic and clinical factors and systemic antimicrobial drug prescription among inpatient dogs and cats at a small-animal teaching hospital between 2018 and 2020. Results: Across 11,685 dogs with 14,328 admissions (mean age, 7.4 years; 47% females), the following factors were associated with increased odds of any antimicrobial drug prescription: female, longer admission, a history of chemotherapy within 30 days of hospital admission, surgery upon admission or within the last 30 days, urinary catheterization, ICU admission, and oxygen support. In 3,371 cats with 4,088 admissions (mean age, 8.6 years; 39% females), the following factors were associated with increased odds of any antimicrobial drug prescription: female, longer admission, increased age (>8 years), admission into the ICU, surgery upon admission, and feline that did not require oxygen support or urinary catheterization. Conclusions: This study identifies multiple patient and clinical factors associated with increased risk of antimicrobial drug use in inpatient dogs and cats that can inform veterinary antimicrobial stewardship efforts and may be useful for antimicrobial use benchmarking on an institutional or multi-institutional scale.

AbstractIntroduction:Shared patient–clinician decision-making is central to choosing between medical treatments. Decision support tools can have an important role to play in these decisions. We developed a decision support tool for deciding between nonsurgical treatment and surgical total knee replacement for patients with severe knee osteoarthritis. The tool aims to provide likely outcomes of alternative treatments based on predictive models using patient-specific characteristics. To make those models relevant to patients with knee osteoarthritis and their clinicians, we involved patients, family members, patient advocates, clinicians, and researchers as stakeholders in creating the models.Methods:Stakeholders were recruited through local arthritis research, advocacy, and clinical organizations. After being provided with brief methodological education sessions, stakeholder views were solicited through quarterly patient or clinician stakeholder panel meetings and incorporated into all aspects of the project.Results:Participating in each aspect of the research from determining the outcomes of interest to providing input on the design of the user interface displaying outcome predications, 86% (12/14) of stakeholders remained engaged throughout the project. Stakeholder engagement ensured that the prediction models that form the basis of the Knee Osteoarthritis Mathematical Equipoise Tool and its user interface were relevant for patient–clinician shared decision-making.Conclusions:Methodological research has the opportunity to benefit from stakeholder engagement by ensuring that the perspectives of those most impacted by the results are involved in study design and conduct. While additional planning and investments in maintaining stakeholder knowledge and trust may be needed, they are offset by the valuable insights gained.

AbstractBackground:To identify potential participants for clinical trials, electronic health records (EHRs) are searched at potential sites. As an alternative, we investigated using medical devices used for real-time diagnostic decisions for trial enrollment.Methods:To project cohorts for a trial in acute coronary syndromes (ACS), we used electrocardiograph-based algorithms that identify ACS or ST elevation myocardial infarction (STEMI) that prompt clinicians to offer patients trial enrollment. We searched six hospitals’ electrocardiograph systems for electrocardiograms (ECGs) meeting the planned trial’s enrollment criterion: ECGs with STEMI or > 75% probability of ACS by the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI). We revised the ACI-TIPI regression to require only data directly from the electrocardiograph, the e-ACI-TIPI using the same data used for the original ACI-TIPI (development set n = 3,453; test set n = 2,315). We also tested both on data from emergency department electrocardiographs from across the US (n = 8,556). We then used ACI-TIPI and e-ACI-TIPI to identify potential cohorts for the ACS trial and compared performance to cohorts from EHR data at the hospitals.Results:Receiver-operating characteristic (ROC) curve areas on the test set were excellent, 0.89 for ACI-TIPI and 0.84 for the e-ACI-TIPI, as was calibration. On the national electrocardiographic database, ROC areas were 0.78 and 0.69, respectively, and with very good calibration. When tested for detection of patients with > 75% ACS probability, both electrocardiograph-based methods identified eligible patients well, and better than did EHRs.Conclusion:Using data from medical devices such as electrocardiographs may provide accurate projections of available cohorts for clinical trials.

The expanding availability of real-world data (RWD) has led to an increase in both the interest and possibilities for using this information in postmarketing safety analyses and signal management. While there is enormous potential value from the safety insights generated through RWD, the analysis preparation, execution, and communication required to reliably deliver the evidence can be time consuming. Since the safety signal assessment process is a regulated and timebound process, any supporting RWD analyses require a rapid turnaround of well-designed and informative results. To address this challenge, a TransCelerate BioPharma working group was formed and developed a framework to help teams responsible for safety signal assessment overcome the challenges of working with RWD rapidly to deliver analyses within regulatory timelines. Here, a previously performed safety assessment was evaluated within the context of the developed framework to illustrate how the framework may be adopted in practice., (© 2024. The Author(s).)

Pre-exposure prophylaxis (PrEP) availability through the Pharmaceutical Benefits Scheme provides real potential for the elimination of HIV transmission in Australia, as evidenced by a rapid decline in HIV incidence among gay and bisexual men (GBM). However, HIV elimination will not be possible without also extending PrEP to other populations, including cisgender women. We conducted a scoping review to examine the extent to which PrEP access for cisgender women has been considered in Australia. A comprehensive search across five databases, grey literature, and hand search of references was conducted. A single reviewer conducted title and abstract screening and two reviewers completed full-text screening and data extraction. Nineteen documents were included in the final review and included both peer-reviewed journal articles and guidelines and strategies. Focused discussion of cisgender women's use of PrEP was largely missing from the literature and, although their use of PrEP is supported in some relevant guidelines, little has been done to actively develop strategies to inform cisgender women about PrEP as a precursor to prescribing for HIV prevention. Healthcare providers' narrow view of PrEP as being the domain of GBM further limits cisgender women's potential access. If HIV elimination in Australia is to be a reality, we need to develop mechanisms to specifically engage with cisgender women about PrEP., (© 2024. The Author(s).)

Purpose: TransCelerate BioPharma surveyed its member biopharmaceutical companies to understand current practices and identify opportunities to complement safety signal assessment with rapid real-world data (RWD) analysis., Methods: A voluntary 30-question questionnaire regarding the use of RWD in safety signal assessment was disseminated to subject matter experts at all TransCelerate member companies in July 2022. Responses were blinded, aggregated, summarized, and presented., Results: Eighteen of 20 member companies provided responses to the questionnaire. Sixteen (89%) companies reported actively leveraging RWD in their signal assessment processes. Of 18 respondent companies, 8 (44%) routinely use rapid approaches to RWD analysis, 7 (39%) utilize rapid RWD analysis non-routinely or in a pilot setting, 2 (11%) are considering using rapid RWD analysis, and 1 (6%) has no plans to use rapid RWD analysis for their signal assessment. Most companies reported that RWD adds context to and improves quality of signal assessments. To conduct RWD analysis for signal assessment, 16 of 17 (94%) respondent companies utilize or plan to utilize internally available data, 8 (47%) utilize both internal and external data, and 3 (18%) utilize data networks. Respondents identified key challenges to rapidly performing RWD analyses, including data access/availability, time for analysis execution, and uncertainties regarding acceptance of minimal or non-protocolized approaches by health authorities., Conclusion: Biopharmaceutical companies reported that they see value in the use of rapid RWD analyses for complementing signal assessments. Future work is recommended to offer a framework and process for use of rapid use of RWD analyses in signal assessment., (© 2024. The Author(s).)

In pharmacovigilance, disproportionality analyses based on individual case safety reports are widely used to detect safety signals. Unfortunately, publishing disproportionality analyses lacks specific guidelines, often leading to incomplete and ambiguous reporting, and carries the risk of incorrect conclusions when data are not placed in the correct context. The REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance (READUS-PV) statement was developed to address this issue by promoting transparent and comprehensive reporting of disproportionality studies. While the statement paper explains in greater detail the procedure followed to develop these guidelines, with this explanation paper we present the 14 items retained for READUS-PV guidelines, together with an in-depth explanation of their rationale and bullet points to illustrate their practical implementation. Our primary objective is to foster the adoption of the READUS-PV guidelines among authors, editors, peer reviewers, and readers of disproportionality analyses. Enhancing transparency, completeness, and accuracy of reporting, as well as proper interpretation of their results, READUS-PV guidelines will ultimately facilitate evidence-based decision making in pharmacovigilance., (© 2024. The Author(s).)

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., (© 2024. The Author(s).)

Introduction: Signal validation in pharmacovigilance is the process of evaluating data to decide whether evidence is sufficient to justify further assessment of a detected signal. During the signal validation process, safety experts in our organization are required to review signals of disproportionate reporting (SDRs) and classify them into one of six predefined categories., Objective: This experiment explored the extent to which predictive machine learning (ML) models can support the decision making of safety experts by accurately identifying the most appropriate predefined signal validation category., Methods: We extracted cumulative data for six medicinal products, consisting of historic SDR validations and Individual Case Safety Reports, from the company's safety database for training and testing of the ML model. We implemented a decision tree-based supervised multiclass classifier model termed Gradient Boosted Trees followed by a SHapley Additive exPlanations (SHAP) analysis to mitigate the "black box" effect of the ensemble model by identifying the key predicting features in the model. Following a retrospective analysis, a prospective experiment was conducted to test the model accuracy and user acceptance in a real-life setting., Results: The prediction accuracy of our ML model ranged from 83 to 86% over 3 months for the six medicinal products. The applicability of the model was confirmed by the company's safety experts. Additionally, the systematic predictions provided valuable information to the safety experts and assisted them in reviewing the SDRs efficiently and consistently., Conclusions: This experiment demonstrated that it is possible to train a multiclass classification model to accurately predict signal validation categories for SDRs. More importantly, the transparency of the predictions provided by the SHAP analysis led to high acceptance by the safety experts., (© 2022. The Author(s).)

Over a period of 5 years, the Innovative Medicines Initiative PROTECT (Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium) project has addressed key research questions relevant to the science of safety signal detection. The results of studies conducted into quantitative signal detection in spontaneous reporting, clinical trial and electronic health records databases are summarised and 39 recommendations have been formulated, many based on comparative analyses across a range of databases (e.g. regulatory, pharmaceutical company). The recommendations point to pragmatic steps that those working in the pharmacovigilance community can take to improve signal detection practices, whether in a national or international agency or in a pharmaceutical company setting. PROTECT has also pointed to areas of potentially fruitful future research and some areas where further effort is likely to yield less.

Introduction: The goal of signal detection in pharmacovigilance (PV) is to detect unknown causal associations between medicines and unexpected events. Statistical methods serve to detect signals and supplement traditional PV methods. Statistical signal detection (SSD) requires decisions about various settings that influence the quality and efficiency of SSD, as shown in several studies. To our knowledge, the effects of SSD periodicity and resignalling criteria on the quality and workload of routine SSD have not been published before., Objective: To analyse the effects of different periodicities and resignalling criteria on signal detection quality and signal validation workload, and to test the impact of changing the signal threshold for number of cases., Methods: We calculated signals of disproportionate reporting (SDRs) using thresholds of number of cases (N) ≥3, proportional reporting ratio ≥2 and Chi(2) ≥ 4. We retrospectively simulated recurrent SDR calculation and validation with varying periodicity (quarterly vs. monthly), resignalling criteria, and N ≥ 3 vs. N ≥ 5., Results: Changing the periodicity from quarterly to monthly increased the workload by 46.6 % (0 % signal loss). More restrictive resignalling criteria reduced the workload between 36.3 % (0 % signal loss) and 74.1 % (50 % signal loss). For N ≥ 3, the most efficient monthly SSD resignalling criterion reduced the workload by 36.3 % and detected all true signals earlier than quarterly SSD. N ≥ 5 reduced the workload between 13.8 and 21.4 % (0 % signal loss)., Conclusions: In real-life PV practice, signal detection and validation are recurrent periodic activities. Some true signals are only discovered upon resignalling. Our results demonstrate resignalling criteria with high signal detection quality and high efficiency. We found potential earlier detection of true signals using monthly SSD. Additional studies about resignalling should be performed to complement our findings.

Background: Most pharmacovigilance departments maintain a system to identify adverse drug reactions (ADRs) through analysis of spontaneous reports. The signal detection algorithms (SDAs) and the nature of the reporting databases vary between operators and it is unclear whether any algorithm can be expected to provide good performance in a wide range of environments., Objective: The objective of this study was to compare the performance of commonly used algorithms across spontaneous reporting databases operated by pharmaceutical companies and national and international pharmacovigilance organisations., Methods: 220 products were chosen and a reference set of ADRs was compiled. Within four company, one national and two international databases, 15 SDAs based on five disproportionality methods were tested. Signals of disproportionate reporting (SDRs) were calculated at monthly intervals and classified by comparison with the reference set. These results were summarised as sensitivity and precision for each algorithm in each database., Results: Different algorithms performed differently between databases but no method dominated all others. Performance was strongly dependent on the thresholds used to define a statistical signal. However, the different disproportionality statistics did not influence the achievable performance. The relative performance of two algorithms was similar in different databases. Over the lifetime of a product there is a reduction in precision for any method., Conclusions: In designing signal detection systems, careful consideration should be given to the criteria that are used to define an SDR. The choice of disproportionality statistic does not appreciably affect the achievable range of signal detection performance and so this can primarily be based on ease of implementation, interpretation and minimisation of computing resources. The changes in sensitivity and precision obtainable by replacing one algorithm with another are predictable. However, the absolute performance of a method is specific to the database and is best assessed directly on that database. New methods may be required to gain appreciable improvements.