Literature DB >> 32620006

Impact of integrated clinical decision support systems in the management of pediatric acute kidney injury: a pilot study.

Shina Menon1,2, Rod Tarrago3,4, Kristen Carlin5, Hong Wu6, Karyn Yonekawa7,3.   

Abstract

BACKGROUND: Acute kidney injury (AKI) is common but not often recognized. Early recognition and management may improve patient outcomes.
METHODS: This is a prospective, nonrandomized study of clinical decision support (CDS) system [combining electronic alert and standardized care pathway (SCP)] to evaluate AKI detection and progression in hospitalized children. The study was done in three phases: pre-, intervention (CDS) and post. During CDS, text-page with AKI stage and link to SCP was sent to patient's contact provider at diagnosis of AKI using creatinine. The SCP provided guidelines on AKI management [AEIOU: Assess cause of AKI, Evaluate drug doses, Intake-Output charting, Optimize volume status, Urine dipstick].
RESULTS: In all, 239 episodes of AKI in 225 patients (97 females, 43.1%) were analyzed. Proportion of patients with decrease in the stage of AKI after onset was 71.4% for CDS vs. 64.4% for pre- and 55% for post-CDS phases (p = 0.3). Documentation of AKI was higher during CDS (74.3% CDS vs. 47.5% pre- and 57.5% post-, p < 0.001). Significantly greater proportion of patients had nephrotoxic medications adjusted, or fluid plan changed during CDS. Patients from CDS phase had higher eGFR at discharge and at follow-up.
CONCLUSIONS: AKI remains under-recognized. CDS (electronic alerts and SCP) improve recognition and allow early intervention. This may improve long-term outcomes, but larger studies are needed. IMPACT: Acute kidney injury can cause significant morbidity and mortality. It is under-recognized in children. Clinical decision support can be used to leverage existing data in the electronic health record to improve AKI recognition. This study demonstrates the use of a novel, electronic health record-linked, clinical decision support tool to improve the recognition of AKI and guideline-adherent clinical care.

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Year:  2020        PMID: 32620006     DOI: 10.1038/s41390-020-1046-8

Source DB:  PubMed          Journal:  Pediatr Res        ISSN: 0031-3998            Impact factor:   3.756


  5 in total

1.  Artificial Intelligence for AKI!Now: Let's Not Await Plato's Utopian Republic.

Authors:  Danielle E Soranno; Azra Bihorac; Stuart L Goldstein; Kianoush B Kashani; Shina Menon; Girish N Nadkarni; Javier A Neyra; Neesh I Pannu; Karandeep Singh; Jorge Cerda; Jay L Koyner
Journal:  Kidney360       Date:  2021-11-18

Review 2.  Artificial Intelligence in Acute Kidney Injury: From Static to Dynamic Models.

Authors:  Nupur S Mistry; Jay L Koyner
Journal:  Adv Chronic Kidney Dis       Date:  2021-01       Impact factor: 3.620

Review 3.  For Whom the Bell Tolls: Acute Kidney Injury and Electronic Alerts for the Pediatric Nephrologist.

Authors:  Elizabeth D Nguyen; Shina Menon
Journal:  Front Pediatr       Date:  2021-04-12       Impact factor: 3.418

4.  AKI in Hospitalized Children: Poorly Documented (and Underrecognized).

Authors:  Katherine Jones; Alicia Neu; Jeffrey Fadrowski
Journal:  Front Pediatr       Date:  2022-01-10       Impact factor: 3.418

5.  Towards effective clinical decision support systems: A systematic review.

Authors:  Francini Hak; Tiago Guimarães; Manuel Santos
Journal:  PLoS One       Date:  2022-08-15       Impact factor: 3.752

  5 in total

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