P76
MODEL OF A ROBUST ELECTRONIC 'ACUTE KIDNEY INJURY ALERT SYSTEM'
TO IDENTIFY THE ONSET AND PROGRESSION OF ACUTE KIDNEY INJURY IN
HOSPITALIZED PATIENTS
Ahmed, S1, Hill, C2, Curtis, S2, Miller, A2, Hine,T2
1
Department of Nephrology, 2Clinical Chemistry, Royal Liverpool University Hospital
INTRODUCTION: Acute Kidney Injury (AKI) is a common complication in hospitalized
patients and recent reports suggest the condition frequently goes undetected, thus worsening
outcome for the patient. Guidelines have suggested employing serum creatinine (CR) and urine
output to detect AKI and, using the former, laboratory information management systems
(LIMS) may help with both the diagnosis and management of the condition.
METHODS: Utilizing Kidney Disease: Improving Global Outcomes (KDIGO) criteria on
definition and staging of AKI, we have developed and implemented algorithms into our LIMS
software that stage and alert clinicians to the possible presence of AKI. The algorithms compare
serum Cr values at presentation with previous results attempt to stage the condition. We initially
used the lowest CR value in the last 3 months as baseline comparator. But, many hospitalized
patients with AKI do not have previous tests within this time period . Some alert system
algorithm uses theoretical reference values when comparator not available. Therefore, We
modified our alert algorithm further and AKI alert analysis looks back 365 days and calculates
the AKI staging based upon the median CR values if there are previous results . Without
available previous results, the algorithm uses population based 'regressed CR value' drawn from
age and gender matched CR values from a local population sample.
RESULTS: We tested the AKI algorithm analysis for 173 in-patients over a month period in
2012 with raised admission serum CR values who did not have an AKI assessed as there were
no previous value within 90 days. The analysis calculates the AKI staging by looking back 365
days and based upon the lowest, mean, median if there are previous results; and irrespective of
previous results it calculates the AKI score on the' population gender dependent regressed
median CR' and a 'reverse eGFR of 75' (table 1). We compared the alerts with 'reverse eGFR
method' which is being reported in the literature . To note, reverse eGFR is based upon
idealised renal function and few 70+ years old have an eGFR of 75 compared to the younger
population.
Table: Examples of AKI alert: compares between different methods
Case
Current Serum Cr
(umol/l)
Case 1 (age 84,F)
S. Cr 158
Case 2 (age75,M)
S. Cr 132
Case 3 (age 35,F)
S Cr 133
Using Lowest Cr
( AKI Stage)
Using Mean Cr
(AKI Stage)
82
Factor 1.93
( stage 1 AKI)
null
112
Factor 1.41
null
Using Median
Cr
( AKI Stage)
97
Factor 1.63
( stage 1 AKI)
null
null
null
null
Reverse eGFR
(75)
(AKI Stage)
65
Factor 2.43
(stage 2 AKI)
86
Factor 1.53
(stage 1 AKI)
76
Factor 1.75
(stage 1 AKI)
Regressed Cr
value
(AKI Stage)
86
Factor 1.84
(stage 1 AKI)
110
Factor 1.2
66
Factor 2.02
(stage 2 AKI)
Cr= creatinine (umol/l), M=male, F=female
; null=no previous result available
CONCLUSIONS: Implementing a robust electronic AKI alert algorithms into a LIMS system
facilitates detection of AKI in hospitalized patients and may subsequently improve their
management and outcome. We also demonstrated that 'regressed creatinine' reflects the
true population better as it is based upon a “real” aging population and therefore,
generates more accurate AKI alerts when there is no previous comparator value
available.