  Acute kidney injury (AKI) commonly occurs in hospitalized patients and can
lead to serious medical complications. In order to optimally predict AKI before
it develops at any time during a hospital stay, we present a novel framework in
which AKI is continually predicted automatically from EHR data over the entire
hospital stay instead of at only one particular time. The continual model
predicts AKI every time a patients AKI-relevant variable changes in the EHR.
Thus the model is not only independent of a particular time for making
predictions, but it can also leverage the latest values of all the AKI-relevant
patient variables for making predictions. Using data of 44,691 hospital stays
of duration longer than 24 hours we evaluated our continual prediction model
and compared it with the traditional one-time prediction models. Excluding
hospitals stays in which AKI occurred within 24 hours from admission, the
one-time prediction model predicting at 24 hours from admission obtained area
under ROC curve (AUC) of 0.653 while the continual prediction model obtained
AUC of 0.724. The one-time prediction model that predicts at 24 hours obviously
cannot predict AKI incidences that occur within 24 hours of admission which
when included in the evaluation reduced its AUC to 0.57. In comparison, the
continual prediction model had AUC of 0.709. The continual prediction model
also did better than all other one-time prediction models predicting at other
fixed times. By being able to take into account the latest values of
AKI-relevant patient variables and by not being limited to a particular time of
prediction, the continual prediction model out-performed one-time prediction
models in predicting AKI.
