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.

Keywords: Acute Kidney Injury; Algorithms; Area Under Curve; Demography; Early Intervention (Education); Electronic Health Records; Humans; Inpatients; Patients’ Rooms; Probability; ROC Curve; Risk; Sensitivity and Specificity; acute kidney injury; acute renal failure; biomarker; clinical nephrology; creatinine; electronic health records; hospitalization; risk assessment; vitals signs.

Figure 1.

Flow diagram demonstrating the size of the original cohort, rationale for those subjects who were excluded and the size of the final cohort.

Figure 2.

Importance of variables in the final model scaled to a maximum of 100. The figure shows the importance of each variable in the final model. The variables are weighted according to their chi-squared value in the final model. Serum creatinine (Cr) had the highest chi–squared value and therefore, was assigned a value of 100. On the basis of this assignment, all of the remaining variables were scaled according to their respective chi–squared values. As shown, BUN and heart rate (HR) were the second and third most heavily weighted variables in the model. Alk Phos, alkaline phosphatase; AST, aspartate transaminase; AVPU, alert, voice, pain, unresponsive (mental status measure); Bicarb, serum bicarbonate; DBP, diastolic BP; Hb, hemoglobin; ICU, intensive care unit; O2sat, oxygen saturation; PPI, pulse pressure; RR, respiratory rate; SBP, systolic BP; Temp, temperature; WBC, white blood cell count.

Figure 3.

Cumulative percentage of patients reaching a cutoff of ≥0.01 in the 72 hours before stage 1 AKI. The plot shows the cumulative percentage of patients who reached a cutoff of ≥0.01 before developing stage 1 AKI (solid line). The dashed line represents the cumulative percentage of those patients reaching the same threshold without AKI over the course of the first 72 hours of admission. Data from within 1 hour of AKI were omitted from the original graph, and thus, time zero results are not included.