Literature DB >> 22130215

Predictive value of alert triggers for identification of developing adverse drug events.

Carlton Moore1, Jiang Li, Chang-Chiao Hung, John Downs, Jonathan R Nebeker.   

Abstract

OBJECTIVE: Adverse drug events (ADEs) are the most common type of iatrogenic injury in hospitalized patients. However; the ability of electronic triggers to identify patients at high risk for inpatient ADEs before they occur has not been well studied. The objective of this study was to assess the positive predictive value of event triggers to detect developing ADEs.
METHODS: We conducted a prospective observational study in patients at a university-based teaching hospital during a 5-month period. Patients were monitored using electronic triggers designed to detect patients at increased risk for 4 types of ADEs: hypoglycemia, hypokalemia, hyperkalemia, and thrombocytopenia. Each patient for whom a trigger fired was followed to determine whether a drug-induced markedly abnormal laboratory result occurred between 1 and 72 hours after the initial trigger firing.
RESULTS: Overall, the triggers fired 611 times on 456 patients. Of the 456 patients, 101 experienced 1 or more related ADEs between 1 and 72 hours after the initial trigger firing. The positive predictive value of the triggers and median time from trigger firing to ADE was 31% and 11.6 hours for hypoglycemia, 4.0% and 17 hours for hypokalemia, 31% and 25.4 hours for hyperkalemia, and 21% and 48.4 hours for thrombocytopenia.
CONCLUSION: Computerized triggers have sufficient predictive value to detect developing ADEs and can help clinicians avert ADEs. More research is required to determine whether real-time, primary-prevention alerts may reduce the incidence of ADEs.

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Year:  2009        PMID: 22130215     DOI: 10.1097/PTS.0b013e3181bc05e5

Source DB:  PubMed          Journal:  J Patient Saf        ISSN: 1549-8417            Impact factor:   2.844


  6 in total

1.  Computerized detection of adverse drug reactions in the medical intensive care unit.

Authors:  Sandra L Kane-Gill; Shyam Visweswaran; Melissa I Saul; An-Kwok Ian Wong; Louis E Penrod; Steven M Handler
Journal:  Int J Med Inform       Date:  2011-05-31       Impact factor: 4.046

2.  Utility of an adverse drug event trigger tool in Veterans Affairs nursing facilities.

Authors:  Zachary A Marcum; Kelly L Arbogast; Michael C Behrens; Michael W Logsdon; Susan Dove Francis; Sean M Jeffery; Sherrie L Aspinall; Joseph T Hanlon; Steven M Handler
Journal:  Consult Pharm       Date:  2013-02

3.  Technical considerations for evaluating clinical prediction indices: a case study for predicting code blue events with MEWS.

Authors:  Kais Gadhoumi; Alex Beltran; Christopher G Scully; Ran Xiao; David O Nahmias; Xiao Hu
Journal:  Physiol Meas       Date:  2021-06-17       Impact factor: 2.688

Review 4.  Clinical decision support for drug related events: Moving towards better prevention.

Authors:  Sandra L Kane-Gill; Archita Achanta; John A Kellum; Steven M Handler
Journal:  World J Crit Care Med       Date:  2016-11-04

Review 5.  Quality of Decision Support in Computerized Provider Order Entry: Systematic Literature Review.

Authors:  Delphine Carli; Guillaume Fahrni; Pascal Bonnabry; Christian Lovis
Journal:  JMIR Med Inform       Date:  2018-01-24

6.  Trigger Tool-Based Automated Adverse Event Detection in Electronic Health Records: Systematic Review.

Authors:  Sarah N Musy; Dietmar Ausserhofer; René Schwendimann; Hans Ulrich Rothen; Marie-Madlen Jeitziner; Anne Ws Rutjes; Michael Simon
Journal:  J Med Internet Res       Date:  2018-05-30       Impact factor: 5.428

  6 in total

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