Electronic Alerting Tool to Help Prevent Acute Kidney Injury


Western Sussex Hospitals NHS Trust




Acute Kidney Injury


Other: AKI Care Bundle

Study type


Funder types




Details and patient eligibility


Around a third of patients who develop acute kidney injury (AKI) do so after a hospital admission (hospital-acquired - HA-AKI). The primary aim of the study is to prospectively test whether introducing a complex intervention (a 'care package' - comprising a clinical prediction rule incorporating an electronic alert which generates a checklist for patient management to relevant health professionals) can identify patients on admission to hospital who are at risk of developing HA-AKI, highlight the need for closer monitoring and allow putative preventative measures to be put in place. The investigators will introduce the care package in one acute hospital and evaluate its effectiveness in reducing HA-AKI and its associated morbidity, over ten months, compared to a sister hospital within the same Trust (which will act as a control site). The investigators will extend evaluation for a further ten months to assess sustainability on the first site and introduce the package at the control hospital to assess generalisability. The primary aim is reducing HA-AKI, but secondary aims will include improved outcomes associated with HA-AKI, management of patients already with AKI on admission to hospital (whose care may also benefit from the checklist) and a cost-effectiveness analysis.

Full description

Acute Kidney Injury (AKI) is common in hospital (incidence of 10-20% - up to 70% in the critically ill), with high associated morbidity and mortality. Even small changes in renal function are associated with increased mortality. The 2009 National Confidential Enquiry into Patient Outcome and Death examined the care of patients who had died in hospital with a primary diagnosis of AKI. Over 40% of cases had an unacceptable delay in diagnosis and in 20% of cases, AKI was thought to be predictable and avoidable. Electronic alerts have been studied for patients with established AKI, however, they have highlighted rises in creatinine after insult rather than identifying patients at risk of AKI - a third of hospital AKI cases occur after admission (HA-AKI). Risk factors have been reported in surgical and burns patients. However, strategies to identify patients admitted as medical emergencies at risk of developing AKI are lacking - the group accounting for most Intensive Care admissions with AKI. The investigators multidisciplinary team, with significant experience utilising technology in healthcare, have developed a novel prediction score - Acute Kidney injury Prediction Score (APS). Utilising physiological measurements, biochemical parameters and known co-morbidities, the APS identifies patients at risk of developing HA-AKI following admission (1/3 of all AKI cases). A 'care package' has been devised incorporating the APS into an automated electronic algorithm to send realtime alerts to staff on the Observation chart, e-mail the patient's Consultant and advise on a checklist. Alongside this, an E-learning AKI module for ward staff has been developed building on NICE Guidance with additional information regarding the APS. Aims Primary: investigate whether introducing a 'care package' can reduce HA-AKI in patients admitted to hospital as an emergency. Secondary include determining whether the intervention: reduces associated complications; improves outcomes in patients with AKI on admission; and is cost-effective. Research Questions Primary: can a 'care package' by systematically recognising the 'at risk' patient, alerting and prompting management to staff educated in the problem, reduce HA-AKI? Secondary: Can harms associated with AKI be reduced? Is the intervention acceptable to staff and are there barriers to implementation? Are improvements sustainable and can the intervention be successfully applied to a second hospital? Does the intervention improve outcomes of those with AKI on admission? Is the intervention cost-effective? Design A prospective, non-randomised, parallel cohorts study, with before-after trial periods, at intervention and control hospital sites, will be performed. A run-in phase (10 months) for baseline data collection and prospective external validation is followed by the intervention on one site (Worthing) with the Chichester site acting as control (Phase 1) for 10 months. The intervention will then be introduced at Chichester whilst continuing at Worthing (Phase 2) for 10 months. The additional period will allow analysis of the interventions' impact on readmissions. Potential benefits: Preventing morbidity and mortality including secondary complications such as chronic kidney disease (immediate and longer term). Reducing length of stay, thus reducing potential exposure to harm. Prevent requirement for renal replacement therapies and escalation to Intensive Care with associated morbidity, mortality and psychological harm. A Cost-effectiveness analysis will aim to demonstrate whether such a strategy could provide savings locally and to the wider health economy (short and long-term). Inform the healthcare community on applicability of information technology to benefit patients and improve staff engagement, with potential utilisation in a number of other conditions.


30,298 patients




18+ years old


No Healthy Volunteers

Inclusion criteria

  • Admission as an emergency
  • Spending at least one night as an in-patient

Exclusion criteria

  • Patients under 18
  • Patients not admitted as emergencies or staying less than one night in hospital.

Trial design

Primary purpose

Health Services Research



Interventional model

Parallel Assignment


None (Open label)

30,298 participants in 2 patient groups

Worthing Hospital site
Active Comparator group
AKI Care bundle instituted at Worthing site
Other: AKI Care Bundle
Chichester Hospital site
No Intervention group
Continues standard care

Trial contacts and locations



Data sourced from clinicaltrials.gov

Clinical trials

Find clinical trialsTrials by location


© Copyright 2024 Veeva Systems