Dataset Information

A modular, prospective, semi-automated drug safety monitoring system for use in a distributed data environment.

ABSTRACT: 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infarction with rofecoxib and an increased risk of rhabdomyolysis with cerivastatin years before these drugs were withdrawn from the market.

SUBMITTER: Gagne JJ

PROVIDER: S-EPMC4159708 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

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A modular, prospective, semi-automated drug safety monitoring system for use in a distributed data environment.

Gagne Joshua J JJ Wang Shirley V SV Rassen Jeremy A JA Schneeweiss Sebastian S

Pharmacoepidemiology and drug safety 20140430 6

<h4>Purpose</h4>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.<h4>Methods</h4>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 fi ...[more]

PMID: 24788694

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Project description:Quantifying, controlling, and monitoring image quality is an essential prerequisite for ensuring the validity and reproducibility of many types of neuroimaging data analyses. Implementation of quality control (QC) procedures is the key to ensuring that neuroimaging data are of high-quality and their validity in the subsequent analyses. We introduce the QC system of the Laboratory of Neuro Imaging (LONI): a web-based system featuring a workflow for the assessment of various modality and contrast brain imaging data. The design allows users to anonymously upload imaging data to the LONI-QC system. It then computes an exhaustive set of QC metrics which aids users to perform a standardized QC by generating a range of scalar and vector statistics. These procedures are performed in parallel using a large compute cluster. Finally, the system offers an automated QC procedure for structural MRI, which can flag each QC metric as being 'good' or 'bad.' Validation using various sets of data acquired from a single scanner and from multiple sites demonstrated the reproducibility of our QC metrics, and the sensitivity and specificity of the proposed Auto QC to 'bad' quality images in comparison to visual inspection. To the best of our knowledge, LONI-QC is the first online QC system that uniquely supports the variety of functionality where we compute numerous QC metrics and perform visual/automated image QC of multi-contrast and multi-modal brain imaging data. The LONI-QC system has been used to assess the quality of large neuroimaging datasets acquired as part of various multi-site studies such as the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study and the Alzheimer's Disease Neuroimaging Initiative (ADNI). LONI-QC's functionality is freely available to users worldwide and its adoption by imaging researchers is likely to contribute substantially to upholding high standards of brain image data quality and to implementing these standards across the neuroimaging community.

Dataset Information

A modular, prospective, semi-automated drug safety monitoring system for use in a distributed data environment.

Publications

A modular, prospective, semi-automated drug safety monitoring system for use in a distributed data environment.

Similar Datasets

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