Literature DB >> 22067631

Impact of real-time electronic alerting of acute kidney injury on therapeutic intervention and progression of RIFLE class.

Kirsten Colpaert1, Eric A Hoste, Kristof Steurbaut, Dominique Benoit, Sofie Van Hoecke, Filip De Turck, Johan Decruyenaere.   

Abstract

OBJECTIVE: To evaluate whether a real-time electronic alert system or "AKI sniffer," which is based on the RIFLE classification criteria (Risk, Injury and Failure), would have an impact on therapeutic interventions and acute kidney injury progression.
DESIGN: Prospective intervention study.
SETTING: Surgical and medical intensive care unit in a tertiary care hospital. PATIENTS: A total of 951 patients having in total 1,079 admission episodes were admitted during the study period (prealert control group: 227, alert group: 616, and postalert control group: 236).
INTERVENTIONS: Three study phases were compared: A 1.5-month prealert control phase in which physicians were blinded for the acute kidney injury sniffer and a 3-month intervention phase with real-time alerting of worsening RIFLE class through the Digital Enhanced Cordless Technology telephone system followed by a second 1.5-month postalert control phase.
MEASUREMENTS AND MAIN RESULTS: A total of 2593 acute kidney injury alerts were recorded with a balanced distribution over all study phases. Most acute kidney injury alerts were RIFLE class risk (59.8%) followed by RIFLE class injury (34.1%) and failure (6.1%). A higher percentage of patients in the alert group received therapeutic intervention within 60 mins after the acute kidney injury alert (28.7% in alert group vs. 7.9% and 10.4% in the pre- and postalert control groups, respectively, p μ .001). In the alert group, more patients received fluid therapy (23.0% vs. 4.9% and 9.2%, p μ .01), diuretics (4.2% vs. 2.6% and 0.8%, p μ .001), or vasopressors (3.9% vs. 1.1% and 0.8%, p μ .001). Furthermore, these patients had a shorter time to intervention (p μ .001). A higher proportion of patients in the alert group showed return to a baseline kidney function within 8 hrs after an acute kidney injury alert "from normal to risk" compared with patients in the control group (p = .048).
CONCLUSIONS: The real-time alerting of every worsening RIFLE class by the acute kidney injury sniffer increased the number and timeliness of early therapeutic interventions. The borderline significant improvement of short-term renal outcome in the RIFLE class risk patients needs to be confirmed in a large multicenter trial.

Entities:  

Mesh:

Substances:

Year:  2012        PMID: 22067631     DOI: 10.1097/CCM.0b013e3182387a6b

Source DB:  PubMed          Journal:  Crit Care Med        ISSN: 0090-3493            Impact factor:   7.598


  81 in total

Review 1.  Information Technology and Acute Kidney Injury: Alerts, Alarms, Bells, and Whistles.

Authors:  F Perry Wilson
Journal:  Adv Chronic Kidney Dis       Date:  2017-07       Impact factor: 3.620

Review 2.  Association between e-alert implementation for detection of acute kidney injury and outcomes: a systematic review.

Authors:  Philippe Lachance; Pierre-Marc Villeneuve; Oleksa G Rewa; Francis P Wilson; Nicholas M Selby; Robin M Featherstone; Sean M Bagshaw
Journal:  Nephrol Dial Transplant       Date:  2017-02-01       Impact factor: 5.992

3.  Automated, electronic alerts for acute kidney injury: a single-blind, parallel-group, randomised controlled trial.

Authors:  F Perry Wilson; Michael Shashaty; Jeffrey Testani; Iram Aqeel; Yuliya Borovskiy; Susan S Ellenberg; Harold I Feldman; Hilda Fernandez; Yevgeniy Gitelman; Jennie Lin; Dan Negoianu; Chirag R Parikh; Peter P Reese; Richard Urbani; Barry Fuchs
Journal:  Lancet       Date:  2015-02-26       Impact factor: 79.321

4.  Role of an electronic antimicrobial alert system in intensive care in dosing errors and pharmacist workload.

Authors:  Barbara O M Claus; Kirsten Colpaert; Kristof Steurbaut; Filip De Turck; Dirk P Vogelaers; Hugo Robays; Johan Decruyenaere
Journal:  Int J Clin Pharm       Date:  2015-02-10

Review 5.  Electronic Alerts for Acute Kidney Injury.

Authors:  Michael Haase; Andreas Kribben; Walter Zidek; Jürgen Floege; Christian Albert; Berend Isermann; Bernt-Peter Robra; Anja Haase-Fielitz
Journal:  Dtsch Arztebl Int       Date:  2017-01-09       Impact factor: 5.594

6.  Predicting Inpatient Acute Kidney Injury over Different Time Horizons: How Early and Accurate?

Authors:  Peng Cheng; Lemuel R Waitman; Yong Hu; Mei Liu
Journal:  AMIA Annu Symp Proc       Date:  2018-04-16

7.  AKI in hospitalized children: comparing the pRIFLE, AKIN, and KDIGO definitions.

Authors:  Scott M Sutherland; John J Byrnes; Manish Kothari; Christopher A Longhurst; Sanjeev Dutta; Pablo Garcia; Stuart L Goldstein
Journal:  Clin J Am Soc Nephrol       Date:  2015-02-03       Impact factor: 8.237

8.  Risk factors for acute kidney injury in older adults with critical illness: a retrospective cohort study.

Authors:  Sandra L Kane-Gill; Florentina E Sileanu; Raghavan Murugan; Gregory S Trietley; Steven M Handler; John A Kellum
Journal:  Am J Kidney Dis       Date:  2014-12-06       Impact factor: 8.860

Review 9.  Acute Kidney Injury in Real Time: Prediction, Alerts, and Clinical Decision Support.

Authors:  F Perry Wilson; Jason H Greenberg
Journal:  Nephron       Date:  2018-08-02       Impact factor: 2.847

Review 10.  Connecting the dots: rule-based decision support systems in the modern EMR era.

Authors:  Vitaly Herasevich; Daryl J Kor; Arun Subramanian; Brian W Pickering
Journal:  J Clin Monit Comput       Date:  2013-02-28       Impact factor: 2.502

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.