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- W319081738 abstract "The safety profile of anticoagulants, which are being used with increasing frequency in pediatric populations, is not well studied. Automatic triggers built into electronic health record systems (EHR) have been shown to be an effective way to monitor for and identify medication errors. Anticoagulant-associated adverse events were examined through the use of an anticoagulant trigger panel.In a retrospective, five-year (September 2007-September 2012) observational study, four automated triggers were used to detect anticoagulant-related adverse events: activated partial thromboplastin time (aPTT) > 100 seconds in patients on an unfractionated heparin (UFH) infusion, International Normalized Ratio (INR) > 4, anti-factor Xa (anti-FXa) >1.5U/mL for patients on enoxaparin, and the documented use of protamine.For the 1,664 triggers evaluated, 12 were associated with the aPTT trigger, only 1 of which was preventable. Receiver operator characteristic curve analysis indicated that increasing the aPTT trigger > 140 seconds would optimize sensitivity and specificity. The INR trigger identified four outpatients with adverse events. No adverse events were associated with the anti-FXa trigger. The protamine trigger identified 12 adverse events and was associated with more severe events. Minimal overlap was found with protamine and aPTT triggers.Laboratory- and medication-based triggers can be effective monitoring tools for anticoagulants. For patients receiving a UFH infusion, an aPTT cutoff value of > 140 seconds is more precise. We also found that protamine use as a trigger adds value to a trigger-based anticoagulant monitoring system. Continued improvement in the logic algorithms associated with the EHR-based trigger tool will allow expanded use of this tool in a clinical manner." @default.
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- W319081738 date "2015-03-01" @default.
- W319081738 modified "2023-09-28" @default.
- W319081738 title "Monitoring the Harm Associated with Use of Anticoagulants in Pediatric Populations Through Trigger-Based Automated Adverse-Event Detection" @default.
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- W319081738 doi "https://doi.org/10.1016/s1553-7250(15)41015-3" @default.
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