Systematic identification and classification of adverse events in human research

Abstract

Widely accepted standards and safeguards for research participants now include systematic surveillance and recording of adverse events. In the absence of a uniform regulation or structure for such reporting, each institution must now establish suitable yet efficient procedures to accomplish this task. We report herein our single center experience with a customized data collection, storage and review system specifically designed to identify and react appropriately to adverse events. Adverse events are classified by each investigator using three criteria in specific order: seriousness, expectedness and relatedness to the investigational intervention. Once classified, events are entered into an online database that includes collation, retrieval and search capabilities. Events meeting specified criteria are reviewed and adjudicated on a weekly basis by The University of Connecticut Research Adverse Events Committee, which makes advisory recommendations to the hospital's two Institutional Research Boards ranging from modification of informed consent to study suspension. Three hundred and seventy-one serious adverse events from > 900 studies were reviewed in the previous academic year. Our system, which combines timely on-line reporting with regular surveillance, provides a potential model that meets the need for comprehensive yet practical adverse events assessment and reporting.