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Active safety monitoring of new medical products using electronic healthcare data: selecting alerting rules.

ABSTRACT: Active medical-product-safety surveillance systems are being developed to monitor many products and outcomes simultaneously in routinely collected longitudinal electronic healthcare data. These systems will rely on algorithms to generate alerts about potential safety concerns.We compared the performance of 5 classes of algorithms in simulated data using a sequential matched-cohort framework, and applied the results to 2 electronic healthcare databases to replicate monitoring of cerivastatin-induced rhabdomyolysis. We generated 600,000 simulated scenarios with varying expected event frequency in the unexposed, alerting threshold, and outcome risk in the exposed, and compared the alerting algorithms in each scenario type using an event-based performance metric.We observed substantial variation in algorithm performance across the groups of scenarios. Relative performance varied by the event frequency and by user-defined preferences for sensitivity versus specificity. Type I error-based statistical testing procedures achieved higher event-based performance than other approaches in scenarios with few events, whereas statistical process control and disproportionality measures performed relatively better with frequent events. In the empirical data, we observed 6 cases of rhabdomyolysis among 4294 person-years of follow-up, with all events occurring among cerivastatin-treated patients. All selected algorithms generated alerts before the drug was withdrawn from the market.For active medical-product-safety monitoring in a sequential matched cohort framework, no single algorithm performed best in all scenarios. Alerting algorithm selection should be tailored to particular features of a product-outcome pair, including the expected event frequencies and trade-offs between false-positive and false-negative alerting.

SUBMITTER: Gagne JJ

PROVIDER: S-EPMC3285473 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

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Active safety monitoring of new medical products using electronic healthcare data: selecting alerting rules.

Gagne Joshua J JJ Rassen Jeremy A JA Walker Alexander M AM Glynn Robert J RJ Schneeweiss Sebastian S

Epidemiology (Cambridge, Mass.) 20120301 2

<h4>Background</h4>Active medical-product-safety surveillance systems are being developed to monitor many products and outcomes simultaneously in routinely collected longitudinal electronic healthcare data. These systems will rely on algorithms to generate alerts about potential safety concerns.<h4>Methods</h4>We compared the performance of 5 classes of algorithms in simulated data using a sequential matched-cohort framework, and applied the results to 2 electronic healthcare databases to replic ...[more]

PMID: 22266893

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Project description:IntroductionThe rapid expansion of the Internet and computing power in recent years has opened up the possibility of using social media for pharmacovigilance. While this general concept has been proposed by many, central questions remain as to whether social media can provide earlier warnings for rare and serious events than traditional signal detection from spontaneous report data.ObjectiveOur objective was to examine whether specific product-adverse event pairs were reported via social media before being reported to the US FDA Adverse Event Reporting System (FAERS).MethodsA retrospective analysis of public Facebook and Twitter data was conducted for 10 recent FDA postmarketing safety signals at the drug-event pair level with six negative controls. Social media data corresponding to two years prior to signal detection of each product-event pair were compiled. Automated classifiers were used to identify each 'post with resemblance to an adverse event' (Proto-AE), among English language posts. A custom dictionary was used to translate Internet vernacular into Medical Dictionary for Regulatory Activities (MedDRA®) Preferred Terms. Drug safety physicians conducted a manual review to determine causality using World Health Organization-Uppsala Monitoring Centre (WHO-UMC) assessment criteria. Cases were also compared with those reported in FAERS.FindingsA total of 935,246 posts were harvested from Facebook and Twitter, from March 2009 through October 2014. The automated classifier identified 98,252 Proto-AEs. Of these, 13 posts were selected for causality assessment of product-event pairs. Clinical assessment revealed that posts had sufficient information to warrant further investigation for two possible product-event associations: dronedarone-vasculitis and Banana Boat Sunscreen--skin burns. No product-event associations were found among the negative controls. In one of the positive cases, the first report occurred in social media prior to signal detection from FAERS, whereas the other case occurred first in FAERS.ConclusionsAn efficient semi-automated approach to social media monitoring may provide earlier insights into certain adverse events. More work is needed to elaborate additional uses for social media data in pharmacovigilance and to determine how they can be applied by regulatory agencies.

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.

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Active safety monitoring of new medical products using electronic healthcare data: selecting alerting rules.

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Active safety monitoring of new medical products using electronic healthcare data: selecting alerting rules.

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