Dataset Information

Development of a Multicenter Ward-Based AKI Prediction Model.

ABSTRACT:

Background and objectives

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).

Design, setting, participants, & measurements

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.

Results

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.

Conclusions

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.

SUBMITTER: Koyner JL

PROVIDER: S-EPMC5108182 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Publications

Development of a Multicenter Ward-Based AKI Prediction Model.

Koyner Jay L JL Adhikari Richa R Edelson Dana P DP Churpek Matthew M MM

Clinical journal of the American Society of Nephrology : CJASN 20160915 11

<h4>Background and objectives</h4>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).<h4>Design, setting, participants, & measurements</h4>All hospitalized ward patients from November of 2008 to January of 2013 who had serum crea ...[more]

PMID: 27633727

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Dataset Information

Development of a Multicenter Ward-Based AKI Prediction Model.

Background and objectives

Design, setting, participants, & measurements

Results

Conclusions

Publications

Development of a Multicenter Ward-Based AKI Prediction Model.

Similar Datasets

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