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Active safety monitoring of newly marketed medications in a distributed data network: application of a semi-automated monitoring system.

ABSTRACT: We developed a semi-automated active monitoring system that uses sequential matched-cohort analyses to assess drug safety across a distributed network of longitudinal electronic health-care data. In a retrospective analysis, we show that the system would have identified cerivastatin-induced rhabdomyolysis. In this study, we evaluated whether the system would generate alerts for three drug-outcome pairs: rosuvastatin and rhabdomyolysis (known null association), rosuvastatin and diabetes mellitus, and telithromycin and hepatotoxicity (two examples for which alerting would be questionable). Over >5 years of monitoring, rate differences (RDs) in comparisons of rosuvastatin with atorvastatin were -0.1 cases of rhabdomyolysis per 1,000 person-years (95% confidence interval (CI): -0.4, 0.1) and -2.2 diabetes cases per 1,000 person-years (95% CI: -6.0, 1.6). The RD for hepatotoxicity comparing telithromycin with azithromycin was 0.3 cases per 1,000 person-years (95% CI: -0.5, 1.0). In a setting in which false positivity is a major concern, the system did not generate alerts for the three drug-outcome pairs.

SUBMITTER: Gagne JJ

PROVIDER: S-EPMC3947906 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature

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Active safety monitoring of newly marketed medications in a distributed data network: application of a semi-automated monitoring system.

Gagne J J JJ Glynn R J RJ Rassen J A JA Walker A M AM Daniel G W GW Sridhar G G Schneeweiss S S

Clinical pharmacology and therapeutics 20120516 1

We developed a semi-automated active monitoring system that uses sequential matched-cohort analyses to assess drug safety across a distributed network of longitudinal electronic health-care data. In a retrospective analysis, we show that the system would have identified cerivastatin-induced rhabdomyolysis. In this study, we evaluated whether the system would generate alerts for three drug-outcome pairs: rosuvastatin and rhabdomyolysis (known null association), rosuvastatin and diabetes mellitus, ...[more]

PMID: 22588606

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Project description:The aim of this study was to develop and test a semi-automated process for conducting routine active safety monitoring for new drugs in a network of electronic healthcare databases.We built a modular program that semi-automatically performs cohort identification, confounding adjustment, diagnostic checks, aggregation and effect estimation across multiple databases, and application of a sequential alerting algorithm. During beta-testing, we applied the system to five databases to evaluate nine examples emulating prospective monitoring with retrospective data (five pairs for which we expected signals, two negative controls, and two examples for which it was uncertain whether a signal would be expected): cerivastatin versus atorvastatin and rhabdomyolysis; paroxetine versus tricyclic antidepressants and gastrointestinal bleed; lisinopril versus angiotensin receptor blockers and angioedema; ciprofloxacin versus macrolide antibiotics and Achilles tendon rupture; rofecoxib versus non-selective non-steroidal anti-inflammatory drugs (ns-NSAIDs) and myocardial infarction; telithromycin versus azithromycin and hepatotoxicity; rosuvastatin versus atorvastatin and diabetes and rhabdomyolysis; and celecoxib versus ns-NSAIDs and myocardial infarction.We describe the program, the necessary inputs, and the assumed data environment. In beta-testing, the system generated four alerts, all among positive control examples (i.e., lisinopril and angioedema; rofecoxib and myocardial infarction; ciprofloxacin and tendon rupture; and cerivastatin and rhabdomyolysis). Sequential effect estimates for each example were consistent in direction and magnitude with existing literature.Beta-testing across nine drug-outcome examples demonstrated the feasibility of the proposed semi-automated prospective monitoring approach. In retrospective assessments, the system identified an increased risk of myocardial infarction with rofecoxib and an increased risk of rhabdomyolysis with cerivastatin years before these drugs were withdrawn from the market.

Project description:BackgroundA commercial biotyping system (Taxa Profile™, Merlin Diagnostika) testing the metabolization of various substrates by bacteria was used to determine if a set of phenotypic features will allow the identification of members of the genus Brucella and their differentiation into species and biovars.ResultsA total of 191 different amines, amides, amino acids, other organic acids and heterocyclic and aromatic substrates (Taxa Profile™ A), 191 different mono-, di-, tri- and polysaccharides and sugar derivates (Taxa Profile™ C) and 95 amino peptidase- and protease-reactions, 76 glycosidase-, phosphatase- and other esterase-reactions, and 17 classic reactions (Taxa Profile™ E) were tested with the 23 reference strains representing the currently known species and biovars of Brucella and a collection of 60 field isolates. Based on specific and stable reactions a 96-well "Brucella identification and typing" plate (Micronaut™) was designed and re-tested in 113 Brucella isolates and a couple of closely related bacteria.Brucella species and biovars revealed characteristic metabolic profiles and each strain showed an individual pattern. Due to their typical metabolic profiles a differentiation of Brucella isolates to the species level could be achieved. The separation of B. canis from B. suis bv 3, however, failed. At the biovar level, B. abortus bv 4, 5, 7 and B. suis bv 1-5 could be discriminated with a specificity of 100%. B. melitensis isolates clustered in a very homogenous group and could not be resolved according to their assigned biovars.ConclusionsThe comprehensive testing of metabolic activity allows cluster analysis within the genus Brucella. The biotyping system developed for the identification of Brucella and differentiation of its species and biovars may replace or at least complement time-consuming tube testing especially in case of atypical strains. An easy to handle identification software facilitates the applicability of the Micronaut™ system for microbiology laboratories.

Project description:Appropriate wound management shortens the healing times and reduces the management costs, benefiting the patient in physical terms and potentially reducing the healthcare system’s economic burden. Among the instrumental measurement methods, the image analysis of a wound area is becoming one of the cornerstones of chronic ulcer management. Our study aim is to develop a solid AI method based on a convolutional neural network to segment the wounds efficiently to make the work of the physician more efficient, and subsequently, to lay the foundations for the further development of more in-depth analyses of ulcer characteristics. In this work, we introduce a fully automated model for identifying and segmenting wound areas which can completely automatize the clinical wound severity assessment starting from images acquired from smartphones. This method is based on an active semi-supervised learning training of a convolutional neural network model. In our work, we tested the robustness of our method against a wide range of natural images acquired in different light conditions and image expositions. We collected the images using an ad hoc developed app and saved them in a database which we then used for AI training. We then tested different CNN architectures to develop a balanced model, which we finally validated with a public dataset. We used a dataset of images acquired during clinical practice and built an annotated wound image dataset consisting of 1564 ulcer images from 474 patients. Only a small part of this large amount of data was manually annotated by experts (ground truth). A multi-step, active, semi-supervised training procedure was applied to improve the segmentation performances of the model. The developed training strategy mimics a continuous learning approach and provides a viable alternative for further medical applications. We tested the efficiency of our model against other public datasets, proving its robustness. The efficiency of the transfer learning showed that after less than 50 epochs, the model achieved a stable DSC that was greater than 0.95. The proposed active semi-supervised learning strategy could allow us to obtain an efficient segmentation method, thereby facilitating the work of the clinician by reducing their working times to achieve the measurements. Finally, the robustness of our pipeline confirms its possible usage in clinical practice as a reliable decision support system for clinicians.

Project description:BackgroundVaccine safety surveillance is important because it is related to vaccine hesitancy, which affects vaccination rate. To increase confidence in vaccination, the active monitoring of vaccine adverse events is important. For effective active surveillance, we developed and verified a machine learning-based active surveillance system using national claim data.MethodsWe used two databases, one from the Korea Disease Control and Prevention Agency, which contains flu vaccination records for the elderly, and another from the National Health Insurance Service, which contains the claim data of vaccinated people. We developed a case-crossover design based machine learning model to predict the health outcome of interest events (anaphylaxis and agranulocytosis) using a random forest. Feature importance values were evaluated to determine candidate associations with each outcome. We investigated the relationship of the features to each event via a literature review, comparison with the Side Effect Resource, and using the Local Interpretable Model-agnostic Explanation method.ResultsThe trained model predicted each health outcome of interest with a high accuracy (approximately 70%). We found literature supporting our results, and most of the important drug-related features were listed in the Side Effect Resource database as inducing the health outcome of interest. For anaphylaxis, flu vaccination ranked high in our feature importance analysis and had a positive association in Local Interpretable Model-Agnostic Explanation analysis. Although the feature importance of vaccination was lower for agranulocytosis, it also had a positive relationship in the Local Interpretable Model-Agnostic Explanation analysis.ConclusionWe developed a machine learning-based active surveillance system for detecting possible factors that can induce adverse events using health claim and vaccination databases. The results of the study demonstrated a potentially useful application of two linked national health record databases. Our model can contribute to the establishment of a system for conducting active surveillance on vaccination.

Dataset Information

Active safety monitoring of newly marketed medications in a distributed data network: application of a semi-automated monitoring system.

Publications

Active safety monitoring of newly marketed medications in a distributed data network: application of a semi-automated monitoring system.

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