The ICE-AKI study: Impact analysis of a Clinical prediction rule and Electronic AKI alert in general medical patients

Abstract

Background: Acute kidney injury (AKI) is assoicated with high mortality and measures to improve risk stratification and early identification have been urgently called for. This study investigated whether an electronic clinical prediction rule (CPR) combined with an AKI e-alert could reduce hospital-acquired AKI (HA-AKI) and improve associated outcomes.

Methods and findings: A controlled before-and-after study included 30,295 acute medical admissions to two adult non-specialist hospital sites in the South of England (two ten-month time periods, 2014-16); all included patients stayed at least one night and had at least two serum creatinine tests. In the second period at the intervention site a CPR flagged those at risk of AKI and an alert was generated for those with AKI; both alerts incorporated care bundles. Patients were followed-up until death or hospital discharge. Primary outcome was change in incident HA-AKI. Secondary outcomes in those developing HA-AKI included: in-hospital mortality, AKI progression and escalation of care. On difference-in-differences analysis incidence of HA-AKI reduced (odds ratio [OR] 0.990, 95% CI 0.981-1.000, P = 0.049). In-hospital mortality in HA-AKI cases reduced on difference-in-differences analysis (OR 0.924, 95% CI 0.858-0.996, P = 0.038) and unadjusted analysis (27.46% pre vs 21.67% post, OR 0.731, 95% CI 0.560-0.954, P = 0.021). Mortality in those flagged by the CPR significantly reduced (14% pre vs 11% post intervention, P = 0.008). Outcomes for community-acquired AKI (CA-AKI) cases did not change. A number of process measures significantly improved at the intervention site. Limitations include lack of randomization, and generalizability will require future investigation.

Conclusions: In acute medical admissions a multi-modal intervention, including an electronically integrated CPR alongside an e-alert for those developing HA-AKI improved in-hospital outcomes. CA-AKI outcomes were not affected. The study provides a template for investigations utilising electronically generated prediction modelling. Further studies should assess generalisability and cost effectiveness.

Trial registration: Clinicaltrials.org NCT03047382.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1. Consort diagram inclusion and exclusions.

CA-AKI–Community-acquired AKI, HA-AKI–Hospital-acquired AKI, SCr–serum Creatinine.

Fig 2

Summary of intervention. At the control site no alerts were generated. RED boxes = AKI (community or hospital-acquired), AMBER = APS ≥5 points–cut-off for flagging patient at risk of AKI, GREEN box–all other patients (APS <5 points). HA-AKI–Hospital-acquired AKI, PAS–Patient administration system, SCr–serum creatinine. Patientrack AKI ALERT* ^AKI Prediction Score (APS)–clinical prediction rule.

Fig 3

Top left (A): Patient tile indicating presence and stage of AKI, baseline SCr and care bundle task status; top right (B): electronic observation chart with AKI status (present or at risk) in top right of screen with link to SCr results; bottom left (C): graph of SCr; bottom right (D): AKI care bundle. SCr–serum creatinine. Note that details for illustration only and are not of a real patient.

Fig 4. In-patient mortality in cases who developed HA-AKI before and after the intervention.

HA-AKI–hospital-acquired AKI, OR–odds ratio (95% CI).

Fig 5. The learning health system–AKI as a case study.

AI–artificial intelligence.