Literature DB >> 27418265

Development, implementation and outcome analysis of semi-automated alerts for metformin dose adjustment in hospitalized patients with renal impairment.

David Niedrig1,2, Regina Krattinger1, Annika Jödicke1, Carmen Gött1, Guido Bucklar3, Stefan Russmann4,5,6,7.   

Abstract

PURPOSE: Overdosing of the oral antidiabetic metformin in impaired renal function is an important contributory cause to life-threatening lactic acidosis. The presented project aimed to quantify and prevent this avoidable medication error in clinical practice.
METHODS: We developed and implemented an algorithm into a hospital's clinical information system that prospectively identifies metformin prescriptions if the estimated glomerular filtration rate is below 60 mL/min. Resulting real-time electronic alerts are sent to clinical pharmacologists and pharmacists, who validate cases in electronic medical records and contact prescribing physicians with recommendations if necessary.
RESULTS: The screening algorithm has been used in routine clinical practice for 3 years and generated 2145 automated alerts (about 2 per day). Validated expert recommendations regarding metformin therapy, i.e., dose reduction or stop, were issued for 381 patients (about 3 per week). Follow-up was available for 257 cases, and prescribers' compliance with recommendations was 79%. Furthermore, during 3 years, we identified eight local cases of lactic acidosis associated with metformin therapy in renal impairment that could not be prevented, e.g., because metformin overdosing had occurred before hospitalization.
CONCLUSIONS: Automated sensitive screening followed by specific expert evaluation and personal recommendations can prevent metformin overdosing in renal impairment with high efficiency and efficacy. Repeated cases of metformin-associated lactic acidosis in renal impairment underline the clinical relevance of this medication error. Our locally developed and customized alert system is a successful proof of concept for a proactive clinical drug safety program that is now expanded to other clinically and economically relevant medication errors.
Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Entities:  

Keywords:  clinical decision support; drug safety; interventional pharmacoepidemiology; metformin; pharmacoepidemiology; renal impairment

Mesh:

Substances:

Year:  2016        PMID: 27418265     DOI: 10.1002/pds.4062

Source DB:  PubMed          Journal:  Pharmacoepidemiol Drug Saf        ISSN: 1053-8569            Impact factor:   2.890


  4 in total

1.  Medication safety alert fatigue may be reduced via interaction design and clinical role tailoring: a systematic review.

Authors:  Mustafa I Hussain; Tera L Reynolds; Kai Zheng
Journal:  J Am Med Inform Assoc       Date:  2019-10-01       Impact factor: 4.497

2.  Severe CNS depression with duloxetine, ciprofloxacin and CYP2D6 deficiency-role and recognition of drug-drug-gene interactions.

Authors:  Matthias Hoffmann; Stefan Russmann; David F Niedrig
Journal:  Eur J Clin Pharmacol       Date:  2022-01-17       Impact factor: 2.953

3.  Designing and evaluating an automated system for real-time medication administration error detection in a neonatal intensive care unit.

Authors:  Yizhao Ni; Todd Lingren; Eric S Hall; Matthew Leonard; Kristin Melton; Eric S Kirkendall
Journal:  J Am Med Inform Assoc       Date:  2018-05-01       Impact factor: 4.497

4.  Using Machine Learning to Identify Patients at High Risk of Inappropriate Drug Dosing in Periods with Renal Dysfunction.

Authors:  Benjamin Skov Kaas-Hansen; Cristina Leal Rodríguez; Davide Placido; Hans-Christian Thorsen-Meyer; Anna Pors Nielsen; Nicolas Dérian; Søren Brunak; Stig Ejdrup Andersen
Journal:  Clin Epidemiol       Date:  2022-02-22       Impact factor: 4.790

  4 in total

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