Skip to main content
Top
Published in: BMC Nephrology 1/2023

Open Access 01-12-2023 | Acute Kidney Injury | Research

Does acute kidney injury alerting improve patient outcomes?

Authors: Jolene Atia, Felicity Evison, Suzy Gallier, Peter Hewins, Simon Ball, Joseph Gavin, Jamie Coleman, Mark Garrick, Tanya Pankhurst

Published in: BMC Nephrology | Issue 1/2023

Login to get access

Abstract

Background

Electronic alerts (e-alerts) for Acute Kidney Injury (AKI) have been implemented into a variety of different Electronic Health Records (EHR) systems worldwide in order to improve recognition and encourage early appropriate management of AKI. We were interested in the impact on patient safety, specialist referral and clinical management.

Methods

All patients admitted to our institution with AKI were included in the study. We studied AKI progression, dialysis dependency, length of hospital stay, emergency readmission, ICU readmission, and death, before and after the introduction of electronic alerts. The impact on prescription of high risk drugs, fluid administration, and referral to renal services was also analysed.

Results

After the introduction of the e-alert, progression to higher AKI stage, emergency readmission to hospital and death during admission were significantly reduced. More prescriptions were stopped for drugs that adversely affect renal function in AKI and there was a significant increase in the ICU admissions and in the number of patients having dialysis, especially in earlier stages. Longer term mortality, renal referrals, and fluid alteration did not change significantly after the AKI e-alert introduction.

Conclusions

AKI e-alerts can improve clinical outcomes in hospitalised patients.
Appendix
Available only for authorised users
Literature
1.
go back to reference Hoste EAJ, et al. Global epidemiology and outcomes of acute kidney injury. Nat Rev Nephrol. 2018;14(10):607–25.CrossRef Hoste EAJ, et al. Global epidemiology and outcomes of acute kidney injury. Nat Rev Nephrol. 2018;14(10):607–25.CrossRef
2.
go back to reference West Midlands Acute Medicine Collaborative. The impact of the NHS electronic-alert system on the recognition and management of acute kidney injury in acute medicine. Clin Med (Lond). 2019;19(2):109–13.CrossRef West Midlands Acute Medicine Collaborative. The impact of the NHS electronic-alert system on the recognition and management of acute kidney injury in acute medicine. Clin Med (Lond). 2019;19(2):109–13.CrossRef
3.
go back to reference Thadhani RM, Pascual M, Bonventre JV. Acute renal failure. N Engl J Med. 1996;334(22):1448–60.CrossRef Thadhani RM, Pascual M, Bonventre JV. Acute renal failure. N Engl J Med. 1996;334(22):1448–60.CrossRef
4.
go back to reference Himmelfarb J, et al. Evaluation and initial management of acute kidney injury. Clin J Am Soc Nephrol. 2008;3(4):962–7.CrossRef Himmelfarb J, et al. Evaluation and initial management of acute kidney injury. Clin J Am Soc Nephrol. 2008;3(4):962–7.CrossRef
5.
go back to reference Kerr M, et al. The economic impact of acute kidney injury in England. Nephrol Dial Transplant. 2014;29(7):1362–8.CrossRef Kerr M, et al. The economic impact of acute kidney injury in England. Nephrol Dial Transplant. 2014;29(7):1362–8.CrossRef
7.
go back to reference NHSEngland. Acute Kidney Injury (AKI) Algorithm. 2015. NHSEngland. Acute Kidney Injury (AKI) Algorithm. 2015.
8.
go back to reference National Institute for Health and Care Excellence3., Acute kidney injury: prevention, detection and management. Clinical guideline [NG148]. NICE. National Institute for Health and Care Excellence3., Acute kidney injury: prevention, detection and management. Clinical guideline [NG148]. NICE.
9.
go back to reference WORK GROUP CO-CHAIRS. KDIGO Clinical Practice Guideline for Acute Kidney Injury. J Int Soc Nephrol. 2012;2:124–38. WORK GROUP CO-CHAIRS. KDIGO Clinical Practice Guideline for Acute Kidney Injury. J Int Soc Nephrol. 2012;2:124–38.
10.
go back to reference Howarth M, et al. Development and initial implementation of electronic clinical decision supports for recognition and management of hospital-acquired acute kidney injury. BMC Med Inform Decis Mak. 2020;20(1):287.CrossRef Howarth M, et al. Development and initial implementation of electronic clinical decision supports for recognition and management of hospital-acquired acute kidney injury. BMC Med Inform Decis Mak. 2020;20(1):287.CrossRef
11.
go back to reference Kolhe NV, et al. Impact of compliance with a care bundle on acute kidney injury outcomes: a prospective observational study. PLoS ONE. 2015;10(7): e0132279.CrossRef Kolhe NV, et al. Impact of compliance with a care bundle on acute kidney injury outcomes: a prospective observational study. PLoS ONE. 2015;10(7): e0132279.CrossRef
12.
go back to reference Colpaert K, et al. Impact of real-time electronic alerting of acute kidney injury on therapeutic intervention and progression of RIFLE class. Crit Care Med. 2012;40(4):1164–70.CrossRef Colpaert K, et al. Impact of real-time electronic alerting of acute kidney injury on therapeutic intervention and progression of RIFLE class. Crit Care Med. 2012;40(4):1164–70.CrossRef
13.
go back to reference Wilson FP, et al. Automated, electronic alerts for acute kidney injury: a single-blind, parallel-group, randomised controlled trial. Lancet (London, England). 2015;385(9981):1966–74.CrossRef Wilson FP, et al. Automated, electronic alerts for acute kidney injury: a single-blind, parallel-group, randomised controlled trial. Lancet (London, England). 2015;385(9981):1966–74.CrossRef
14.
go back to reference Porter CJ, et al. A real-time electronic alert to improve detection of acute kidney injury in a large teaching hospital. Nephrol Dial Transplant. 2014;29(10):1888–93.CrossRef Porter CJ, et al. A real-time electronic alert to improve detection of acute kidney injury in a large teaching hospital. Nephrol Dial Transplant. 2014;29(10):1888–93.CrossRef
15.
go back to reference Rind DM, et al. Effect of computer-based alerts on the treatment and outcomes of hospitalized patients. Arch Intern Med. 1994;154(13):1511–7.CrossRef Rind DM, et al. Effect of computer-based alerts on the treatment and outcomes of hospitalized patients. Arch Intern Med. 1994;154(13):1511–7.CrossRef
16.
go back to reference Cho A, et al. Effect of an electronic alert on risk of contrast-induced acute kidney injury in hospitalized patients undergoing computed tomography. Am J Kidney Dis. 2012;60(1):74–81.CrossRef Cho A, et al. Effect of an electronic alert on risk of contrast-induced acute kidney injury in hospitalized patients undergoing computed tomography. Am J Kidney Dis. 2012;60(1):74–81.CrossRef
17.
go back to reference McCoy AB, et al. A computerized provider order entry intervention for medication safety during acute kidney injury: a quality improvement report. Am J Kidney Dis. 2010;56(5):832–41.CrossRef McCoy AB, et al. A computerized provider order entry intervention for medication safety during acute kidney injury: a quality improvement report. Am J Kidney Dis. 2010;56(5):832–41.CrossRef
18.
go back to reference Al-Jaghbeer M, et al. Clinical decision support for in-hospital AKI. J Am Soc Nephrol. 2018;29(2):654–60.CrossRef Al-Jaghbeer M, et al. Clinical decision support for in-hospital AKI. J Am Soc Nephrol. 2018;29(2):654–60.CrossRef
19.
go back to reference Lachance P, et al. Association between e-alert implementation for detection of acute kidney injury and outcomes: a systematic review. Nephrol Dial Transplant. 2017;32(2):265–72. Lachance P, et al. Association between e-alert implementation for detection of acute kidney injury and outcomes: a systematic review. Nephrol Dial Transplant. 2017;32(2):265–72.
20.
go back to reference Wilson FP, et al. Electronic health record alerts for acute kidney injury: multicenter, randomized clinical trial. BMJ. 2021;372: m4786.CrossRef Wilson FP, et al. Electronic health record alerts for acute kidney injury: multicenter, randomized clinical trial. BMJ. 2021;372: m4786.CrossRef
21.
go back to reference Nightingale PG, et al. Implementation of rules based computerised bedside prescribing and administration: intervention study. BMJ (Clinical research ed). 2000;320(7237):750–3.CrossRef Nightingale PG, et al. Implementation of rules based computerised bedside prescribing and administration: intervention study. BMJ (Clinical research ed). 2000;320(7237):750–3.CrossRef
22.
go back to reference Rosser D, et al. Quality improvement programme, focusing on error reduction: a single center naturalistic study. JRSM Short Reports. 2012;3(6):1–7.CrossRef Rosser D, et al. Quality improvement programme, focusing on error reduction: a single center naturalistic study. JRSM Short Reports. 2012;3(6):1–7.CrossRef
23.
go back to reference Paterno MD, et al. Tiering drug-drug interaction alerts by severity increases compliance rates. J American Med Inform Assoc. 2009;16(1):40–6.CrossRef Paterno MD, et al. Tiering drug-drug interaction alerts by severity increases compliance rates. J American Med Inform Assoc. 2009;16(1):40–6.CrossRef
24.
go back to reference Nuttall M, van der Meulen J, Emberton M. Charlson scores based on ICD-10 administrative data were valid in assessing comorbidity in patients undergoing urological cancer surgery. J Clin Epidemiol. 2006;59(3):265–73.CrossRef Nuttall M, van der Meulen J, Emberton M. Charlson scores based on ICD-10 administrative data were valid in assessing comorbidity in patients undergoing urological cancer surgery. J Clin Epidemiol. 2006;59(3):265–73.CrossRef
25.
go back to reference Crooks CJ, West J, Card TR. A comparison of the recording of comorbidity in primary and secondary care by using the Charlson Index to predict short-term and long-term survival in a routine linked data cohort. BMJ Open. 2015;5(6):e007974–e007974.CrossRef Crooks CJ, West J, Card TR. A comparison of the recording of comorbidity in primary and secondary care by using the Charlson Index to predict short-term and long-term survival in a routine linked data cohort. BMJ Open. 2015;5(6):e007974–e007974.CrossRef
26.
go back to reference Ho DE, et al. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Polit Anal. 2007;15(3):199–236.CrossRef Ho DE, et al. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Polit Anal. 2007;15(3):199–236.CrossRef
27.
go back to reference Tonelli M, et al. Systematic review: kidney transplantation compared with dialysis in clinically relevant outcomes. Am J Transplant. 2011;11(10):2093–109.CrossRef Tonelli M, et al. Systematic review: kidney transplantation compared with dialysis in clinically relevant outcomes. Am J Transplant. 2011;11(10):2093–109.CrossRef
28.
go back to reference Selby NM, et al. An organizational-level program of intervention for AKI: a pragmatic stepped wedge cluster randomized trial. J Am Soc Nephrol. 2019;30(3):505–15.CrossRef Selby NM, et al. An organizational-level program of intervention for AKI: a pragmatic stepped wedge cluster randomized trial. J Am Soc Nephrol. 2019;30(3):505–15.CrossRef
29.
go back to reference Legrand M, Ince C. Intravenous Fluids in AKI: a mechanistically guided approach. Semin Nephrol. 2016;36(1):53–61.CrossRef Legrand M, Ince C. Intravenous Fluids in AKI: a mechanistically guided approach. Semin Nephrol. 2016;36(1):53–61.CrossRef
30.
go back to reference Moran CP, Kuan YC, Lynch PL. Acute kidney injury: adding informatics to injury (electronic injury alerts) [Abstract]. J Am Soc Nephrol. 2015;26:468A. Moran CP, Kuan YC, Lynch PL. Acute kidney injury: adding informatics to injury (electronic injury alerts) [Abstract]. J Am Soc Nephrol. 2015;26:468A.
31.
go back to reference Selby NM, Crowley L, Fluck RJ. Use of electronic results reporting to diagnise and monitor AKI in hospitalized patients. Clin J Am Soc Nephrol. 2012;7(4):533–40.CrossRef Selby NM, Crowley L, Fluck RJ. Use of electronic results reporting to diagnise and monitor AKI in hospitalized patients. Clin J Am Soc Nephrol. 2012;7(4):533–40.CrossRef
32.
go back to reference Hodgson LE, et al. The ICE-AKI study: Impact analysis of a Clinical prediction rule and Electronic AKI alert in general medical patients. PLoS ONE. 2018;13(8): e0200584.CrossRef Hodgson LE, et al. The ICE-AKI study: Impact analysis of a Clinical prediction rule and Electronic AKI alert in general medical patients. PLoS ONE. 2018;13(8): e0200584.CrossRef
33.
go back to reference Kawamoto K, et al. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005;330(7494):765.CrossRef Kawamoto K, et al. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005;330(7494):765.CrossRef
34.
go back to reference Haase M, et al. Electronic alerts for acute kidney injury. Dtsch Arztebl International. 2017;114(1–2):1–8. Haase M, et al. Electronic alerts for acute kidney injury. Dtsch Arztebl International. 2017;114(1–2):1–8.
35.
go back to reference Connell A, et al. Implementation of a Digitally Enabled Care Pathway (Part 2): Qualitative Analysis of Experiences of Health Care Professionals. J Med Internet Res. 2019;21(7): e13143.CrossRef Connell A, et al. Implementation of a Digitally Enabled Care Pathway (Part 2): Qualitative Analysis of Experiences of Health Care Professionals. J Med Internet Res. 2019;21(7): e13143.CrossRef
36.
go back to reference Coleman JJ, et al. On the alert: future priorities for alerts in clinical decision support for computerized physician order entry identified from a European workshop. BMC Med Inform Decis Mak. 2013;13:111.CrossRef Coleman JJ, et al. On the alert: future priorities for alerts in clinical decision support for computerized physician order entry identified from a European workshop. BMC Med Inform Decis Mak. 2013;13:111.CrossRef
37.
go back to reference James MT, Garg AX. Do electronic alerts for AKI improve outcomes? Nat Rev Nephrol. 2015;11(6):322–3.CrossRef James MT, Garg AX. Do electronic alerts for AKI improve outcomes? Nat Rev Nephrol. 2015;11(6):322–3.CrossRef
Metadata
Title
Does acute kidney injury alerting improve patient outcomes?
Authors
Jolene Atia
Felicity Evison
Suzy Gallier
Peter Hewins
Simon Ball
Joseph Gavin
Jamie Coleman
Mark Garrick
Tanya Pankhurst
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Nephrology / Issue 1/2023
Electronic ISSN: 1471-2369
DOI
https://doi.org/10.1186/s12882-022-03031-y

Other articles of this Issue 1/2023

BMC Nephrology 1/2023 Go to the issue
Live Webinar | 27-06-2024 | 18:00 (CEST)

Keynote webinar | Spotlight on medication adherence

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.

Prof. Kevin Dolgin
Prof. Florian Limbourg
Prof. Anoop Chauhan
Developed by: Springer Medicine
Obesity Clinical Trial Summary

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine