  Acute Kidney Injury (AKI), a sudden decline in kidney function, is associated
with increased mortality, morbidity, length of stay, and hospital cost. Since
AKI is sometimes preventable, there is great interest in prediction. Most
existing studies consider all patients and therefore restrict to features
available in the first hours of hospitalization. Here, the focus is instead on
rehospitalized patients, a cohort in which rich longitudinal features from
prior hospitalizations can be analyzed. Our objective is to provide a risk
score directly at hospital re-entry. Gradient boosting, penalized logistic
regression (with and without stability selection), and a recurrent neural
network are trained on two years of adult inpatient EHR data (3,387 attributes
for 34,505 patients who generated 90,013 training samples with 5,618 cases and
84,395 controls). Predictions are internally evaluated with 50 iterations of
5-fold grouped cross-validation with special emphasis on calibration, an
analysis of which is performed at the patient as well as hospitalization level.
Error is assessed with respect to diagnosis, race, age, gender, AKI
identification method, and hospital utilization. In an additional experiment,
the regularization penalty is severely increased to induce parsimony and
interpretability. Predictors identified for rehospitalized patients are also
reported with a special analysis of medications that might be modifiable risk
factors. Insights from this study might be used to construct a predictive tool
for AKI in rehospitalized patients. An accurate estimate of AKI risk at
hospital entry might serve as a prior for an admitting provider or another
predictive algorithm.
