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Development of a Multicenter Ward-Based AKI Prediction Model

Clinical journal of the American Society of Nephrology, 2016-11, Vol.11 (11), p.1935-1943 [Peer Reviewed Journal]

Copyright © 2016 by the American Society of Nephrology. ;Copyright © 2016 by the American Society of Nephrology 2016 ;ISSN: 1555-9041 ;EISSN: 1555-905X ;DOI: 10.2215/cjn.00280116 ;PMID: 27633727

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  • Title:
    Development of a Multicenter Ward-Based AKI Prediction Model
  • Author: Koyner, Jay L ; Adhikari, Richa ; Edelson, Dana P ; Churpek, Matthew M
  • Subjects: Acute Kidney Injury - diagnosis ; Adult ; Aged ; Aged, 80 and over ; Algorithms ; Area Under Curve ; Blood Urea Nitrogen ; Creatinine - blood ; Electronic Health Records ; Female ; Humans ; Male ; Middle Aged ; Models, Biological ; Original ; Patients' Rooms ; Probability ; Risk Assessment - methods ; Risk Factors ; ROC Curve ; Severity of Illness Index ; Time Factors
  • Is Part Of: Clinical journal of the American Society of Nephrology, 2016-11, Vol.11 (11), p.1935-1943
  • Description: Identification of patients at risk for AKI on the general wards before increases in serum creatinine would enable preemptive evaluation and intervention to minimize risk and AKI severity. We developed an AKI risk prediction algorithm using electronic health record data on ward patients (Electronic Signal to Prevent AKI). All hospitalized ward patients from November of 2008 to January of 2013 who had serum creatinine measured in five hospitals were included. Patients with an initial ward serum creatinine >3.0 mg/dl or who developed AKI before ward admission were excluded. Using a discrete time survival model, demographics, vital signs, and routine laboratory data were used to predict the development of serum creatinine-based Kidney Disease Improving Global Outcomes AKI. The final model, which contained all variables, was derived in 60% of the cohort and prospectively validated in the remaining 40%. Areas under the receiver operating characteristic curves were calculated for the prediction of AKI within 24 hours for each unique observation for all patients across their inpatient admission. We performed time to AKI analyses for specific predicted probability cutoffs from the developed score. Among 202,961 patients, 17,541 (8.6%) developed AKI, with 1242 (0.6%) progressing to stage 3. The areas under the receiver operating characteristic curve of the final model in the validation cohort were 0.74 (95% confidence interval, 0.74 to 0.74) for stage 1 and 0.83 (95% confidence interval, 0.83 to 0.84) for stage 3. Patients who reached a cutoff of ≥0.010 did so a median of 42 (interquartile range, 14-107) hours before developing stage 1 AKI. This same cutoff provided sensitivity and specificity of 82% and 65%, respectively, for stage 3 and was reached a median of 35 (interquartile range, 14-97) hours before AKI. Readily available electronic health record data can be used to improve AKI risk stratification with good to excellent accuracy. Real time use of Electronic Signal to Prevent AKI would allow early interventions before changes in serum creatinine and may improve costs and outcomes.
  • Publisher: United States: American Society of Nephrology
  • Language: English
  • Identifier: ISSN: 1555-9041
    EISSN: 1555-905X
    DOI: 10.2215/cjn.00280116
    PMID: 27633727
  • Source: PubMed (Medline)
    Geneva Foundation Free Medical Journals at publisher websites
    MEDLINE
    Alma/SFX Local Collection
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