Automated, electronic alerts for acute kidney injury: a single-blind, parallel-group, randomised controlled trial.
BACKGROUND
Acute kidney injury often goes unrecognised in its early stages when effective treatment options might be available. We aimed to determine whether an automated electronic alert for acute kidney injury would reduce the severity of such injury and improve clinical outcomes in patients in hospital.


METHODS
In this investigator-masked, parallel-group, randomised controlled trial, patients were recruited from the hospital of the University of Pennsylvania in Philadelphia, PA, USA. Eligible participants were adults aged 18 years or older who were in hospital with stage 1 or greater acute kidney injury as defined by Kidney Disease Improving Global Outcomes creatinine-based criteria. Exclusion criteria were initial hospital creatinine 4·0 mg/dL (to convert to μmol/L, multiply by 88·4) or greater, fewer than two creatinine values measured, inability to determine the covering provider, admission to hospice or the observation unit, previous randomisation, or end-stage renal disease. Patients were randomly assigned (1:1) via a computer-generated sequence to receive an acute kidney injury alert (a text-based alert sent to the covering provider and unit pharmacist indicating new acute kidney injury) or usual care, stratified by medical versus surgical admission and intensive care unit versus non-intensive care unit location in blocks of 4-8 participants. The primary outcome was a composite of relative maximum change in creatinine, dialysis, and death at 7 days after randomisation. All analyses were by intention to treat. This study is registered with ClinicalTrials.gov, number NCT01862419.


RESULTS
Between Sept 17, 2013, and April 14, 2014, 23,664 patients were screened. 1201 eligible participants were assigned to the acute kidney injury alert group and 1192 were assigned to the usual care group. Composite relative maximum change in creatinine, dialysis, and death at 7 days did not differ between the alert group and the usual care group (p=0·88), or within any of the four randomisation strata (all p>0·05). At 7 days after randomisation, median maximum relative change in creatinine concentrations was 0·0% (IQR 0·0-18·4) in the alert group and 0·6% (0·0-17·5) in the usual care group (p=0·81); 87 (7·2%) patients in the alert group and 70 (5·9%) patients in usual care group had received dialysis (odds ratio 1·25 [95% CI 0·90-1·74]; p=0·18); and 71 (5·9%) patients in the alert group and 61 (5·1%) patients in the usual care group had died (1·16 [0·81-1·68]; p=0·40).


CONCLUSIONS
An electronic alert system for acute kidney injury did not improve clinical outcomes among patients in hospital.


BACKGROUND
Penn Center for Healthcare Improvement and Patient Safety.
Yale University School of Medicine, Program of Applied Translational Research, New Haven, CT, USA. Electronic address: francis.p.wilson@yale.edu.
F Perry
FP
Wilson
Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Center for Clinical Epidemiology and Biostatistics at the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Michael
M
Shashaty
Yale University School of Medicine, Program of Applied Translational Research, New Haven, CT, USA.
Jeffrey
J
Testani
Nephrology Associates, Teaneck, NJ, USA.
Iram
I
Aqeel
Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Yuliya
Y
Borovskiy
Department of Biostatistics and Epidemiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Center for Clinical Epidemiology and Biostatistics at the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Susan S
SS
Ellenberg
Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Biostatistics and Epidemiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Center for Clinical Epidemiology and Biostatistics at the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Harold I
HI
Feldman
Columbia University College of Physicians and Surgeons, New York, NY, USA.
Hilda
H
Fernandez
Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Yevgeniy
Y
Gitelman
Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Jennie
J
Lin
Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Dan
D
Negoianu
Yale University School of Medicine, Program of Applied Translational Research, New Haven, CT, USA.
Chirag R
CR
Parikh
Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Center for Clinical Epidemiology and Biostatistics at the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Peter P
PP
Reese
Department of Information Services, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Richard
R
Urbani
Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Barry
B
Fuchs
D058186:Acute Kidney Injury / Q000175:diagnosis*; D000328:Adult; D000368:Aged; D001331:Automation; D015415:Biomarkers / Q000378:metabolism; D040421:Cell Phone; D003404:Creatinine / Q000378:metabolism; D042241:Early Diagnosis; D057286:Electronic Health Records; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008875:Middle Aged; D011379:Prognosis; D016037:Single-Blind Method; D055815:Young Adult
D015415:Biomarkers; D003404:Creatinine