» Articles » PMID: 33228770

Impact of a Computerized Decision Support Tool Deployed in Two Intensive Care Units on Acute Kidney Injury Progression and Guideline Compliance: a Prospective Observational Study

Overview
Journal Crit Care
Specialty Critical Care
Date 2020 Nov 24
PMID 33228770
Citations 12
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Acute kidney injury (AKI) affects a large proportion of the critically ill and is associated with worse patient outcomes. Early identification of AKI can lead to earlier initiation of supportive therapy and better management. In this study, we evaluate the impact of computerized AKI decision support tool integrated with the critical care clinical information system (CCIS) on patient outcomes. Specifically, we hypothesize that integration of AKI guidelines into CCIS will decrease the proportion of patients with Stage 1 AKI deteriorating into higher stages of AKI.

Methods: The study was conducted in two intensive care units (ICUs) at University Hospitals Bristol, UK, in a before (control) and after (intervention) format. The intervention consisted of the AKIN guidelines and AKI care bundle which included guidance for medication usage, AKI advisory and dashboard with AKI score. Clinical data and patient outcomes were collected from all patients admitted to the units. AKI stage was calculated using the Acute Kidney Injury Network (AKIN) guidelines. Maximum AKI stage per admission, change in AKI stage and other metrics were calculated for the cohort. Adherence to eGFR-based enoxaparin dosing guidelines was evaluated as a proxy for clinician awareness of AKI.

Results: Each phase of the study lasted a year, and a total of 5044 admissions were included for analysis with equal numbers of patients for the control and intervention stages. The proportion of patients worsening from Stage 1 AKI decreased from 42% (control) to 33.5% (intervention), p = 0.002. The proportion of incorrect enoxaparin doses decreased from 1.72% (control) to 0.6% (intervention), p < 0.001. The prevalence of any AKI decreased from 43.1% (control) to 37.5% (intervention), p < 0.05.

Conclusions: This observational study demonstrated a significant reduction in AKI progression from Stage 1 and a reduction in overall development of AKI. In addition, a reduction in incorrect enoxaparin dosing was also observed, indicating increased clinical awareness. This study demonstrates that AKI guidelines coupled with a newly designed AKI care bundle integrated into CCIS can impact patient outcomes positively.

Citing Articles

Urine output is an early and strong predictor of acute kidney injury and associated mortality: a systematic literature review of 50 clinical studies.

Malbrain M, Tantakoun K, Zara A, Ferko N, Kelly T, Dabrowski W Ann Intensive Care. 2024; 14(1):110.

PMID: 38980557 PMC: 11233478. DOI: 10.1186/s13613-024-01342-x.


Chinese experts' consensus on the application of intensive care big data.

Su L, Liu S, Long Y, Chen C, Chen K, Chen M Front Med (Lausanne). 2024; 10:1174429.

PMID: 38264049 PMC: 10804886. DOI: 10.3389/fmed.2023.1174429.


From digital health to learning health systems: four approaches to using data for digital health design.

Pannunzio V, Kleinsmann M, Snelders D, Raijmakers J Health Syst (Basingstoke). 2024; 12(4):481-494.

PMID: 38235300 PMC: 10791080. DOI: 10.1080/20476965.2023.2284712.


Fluid balance, biomarkers of renal function and mortality in critically ill patients with AKI diagnosed before, or within 24 h of intensive care unit admission: a prospective study.

Martos-Benitez F, Burgos-Araguez D, Garcia-Mesa L, Orama-Requejo V, Cardenas-Gonzalez R, Michelena-Piedra J J Nephrol. 2024; 37(2):439-449.

PMID: 38189864 DOI: 10.1007/s40620-023-01829-z.


Digital health utilities in acute kidney injury management.

Kashani K, Koyner J Curr Opin Crit Care. 2023; 29(6):542-550.

PMID: 37861196 PMC: 11285742. DOI: 10.1097/MCC.0000000000001105.


References
1.
Wilson F, Greenberg J . Acute Kidney Injury in Real Time: Prediction, Alerts, and Clinical Decision Support. Nephron. 2018; 140(2):116-119. PMC: 6165685. DOI: 10.1159/000492064. View

2.
Colpaert K, Hoste E, Steurbaut K, Benoit D, Van Hoecke S, De Turck F . Impact of real-time electronic alerting of acute kidney injury on therapeutic intervention and progression of RIFLE class. Crit Care Med. 2011; 40(4):1164-70. DOI: 10.1097/CCM.0b013e3182387a6b. View

3.
Field T, Rochon P, Lee M, Gavendo L, Baril J, Gurwitz J . Computerized clinical decision support during medication ordering for long-term care residents with renal insufficiency. J Am Med Inform Assoc. 2009; 16(4):480-5. PMC: 2705250. DOI: 10.1197/jamia.M2981. View

4.
Mehta R, Kellum J, Shah S, Molitoris B, Ronco C, Warnock D . Acute Kidney Injury Network: report of an initiative to improve outcomes in acute kidney injury. Crit Care. 2007; 11(2):R31. PMC: 2206446. DOI: 10.1186/cc5713. View

5.
Selby N, Casula A, Lamming L, Stoves J, Samarasinghe Y, Lewington A . An Organizational-Level Program of Intervention for AKI: A Pragmatic Stepped Wedge Cluster Randomized Trial. J Am Soc Nephrol. 2019; 30(3):505-515. PMC: 6405151. DOI: 10.1681/ASN.2018090886. View