Article

Computerized Surveillance for Adverse Drug Events in a Pediatric Hospital

Department of Internal Medicine, Washington University School of Medicine, St Louis, MO
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium 09/2009; 16(5):607-612. DOI: 10.1197/jamia.M3167
Source: DBLP

ABSTRACT

There are limited data on adverse drug event rates in pediatrics. The authors describe the implementation and evaluation of an automated surveillance system modified to detect adverse drug events (ADEs) in pediatric patients. The authors constructed an automated surveillance system to screen admissions to a large pediatric hospital. Potential ADEs identified by the system were reviewed by medication safety pharmacists and a physician and scored for causality and severity. Over the 6 month study period, 6,889 study children were admitted to the hospital for a total of 40,250 patient-days. The ADE surveillance system generated 1226 alerts, which yielded 160 true ADEs. This represents a rate of 2.3 ADEs per 100 admissions or 4 per 1,000 patient-days. Medications most frequently implicated were diuretics, antibiotics, immunosuppressants, narcotics, and anticonvulsants. The composite positive predictive value of the ADE surveillance system was 13%. Automated surveillance can be an effective method for detecting ADEs in hospitalized children.

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Available from: Richard M Reichley, Nov 29, 2015
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    • "In [5]–[9] some NLP approaches have been proposed for the extraction of ADRs from text. In [10], the authors collect narrative discharge summaries from the Clinical Information System at New York Presbyterian Hospital. MedLEE, an NLP system, is applied to this collection, to identify medication events and entities, which could be potential adverse drug events. "
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    ABSTRACT: Pharmacovigilance is the field of science devoted to the collection, analysis and prevention of Adverse Drug Reactions (ADRs). Efficient strategies for the extraction of information about ADRs from free text resources are essential to support the work of experts, employed in the crucial task of detecting and classifying unexpected pathologies possibly related to drug assumptions. Narrative ADR descriptions may be collected in several way, e.g. by monitoring social networks or through the so called spontaneous reporting, the main method pharmacovigilance adopts in order to identify ADRs. The encoding of free-text ADR descriptions according to MedDRA standard terminology is central for report analysis. It is a complex work, which has to be manually implemented by the pharmacovigilance experts. The manual encoding is expensive (in terms of time). Moreover, a problem about the accuracy of the encoding may occur, since the number of reports is growing up day by day. In this paper, we propose MagiCoder, an efficient Natural Language Processing algorithm able to automatically derive MedDRA terminologies from free-text ADR descriptions. MagiCoder is part of VigiWork, a web application for online ADR reporting and analysis. From a practical view-point, MagiCoder radically reduces the revision time of ADR reports: the pharmacologist has simply to revise and validate the automatic solution versus the hard task of choosing solutions in the 70k terms of MedDRA. This improvement of the expert work efficiency has a meaningful impact on the quality of data analysis. Moreover, our procedure is general purpose. We developed MagiCoder for the Italian pharmacovigilance language, but preliminarily analyses show that it is robust to language and dictionary changes.
    Full-text · Conference Paper · Oct 2015
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    • "In [5]–[9] some NLP approaches have been proposed for the extraction of ADRs from text. In [10], the authors collect narrative discharge summaries from the Clinical Information System at New York Presbyterian Hospital. MedLEE, an NLP system, is applied to this collection, to identify medication events and entities, which could be potential adverse drug events. "
    Dataset: ICHI15Final

    Full-text · Dataset · Oct 2015
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