  Acute kidney injury (AKI) is a common and serious complication after a
surgery which is associated with morbidity and mortality. The majority of
existing perioperative AKI risk score prediction models are limited in their
generalizability and do not fully utilize the physiological intraoperative
time-series data. Thus, there is a need for intelligent, accurate, and robust
systems, able to leverage information from large-scale data to predict
patient's risk of developing postoperative AKI. A retrospective single-center
cohort of 2,911 adult patients who underwent surgery at the University of
Florida Health has been used for this study. We used machine learning and
statistical analysis techniques to develop perioperative models to predict the
risk of AKI (risk during the first 3 days, 7 days, and until the discharge day)
before and after the surgery. In particular, we examined the improvement in
risk prediction by incorporating three intraoperative physiologic time series
data, i.e., mean arterial blood pressure, minimum alveolar concentration, and
heart rate. For an individual patient, the preoperative model produces a
probabilistic AKI risk score, which will be enriched by integrating
intraoperative statistical features through a machine learning stacking
approach inside a random forest classifier. We compared the performance of our
model based on the area under the receiver operating characteristics curve
(AUROC), accuracy and net reclassification improvement (NRI). The predictive
performance of the proposed model is better than the preoperative data only
model. For AKI-7day outcome: The AUC was 0.86 (accuracy was 0.78) in the
proposed model, while the preoperative AUC was 0.84 (accuracy 0.76).
Furthermore, with the integration of intraoperative features, we were able to
classify patients who were misclassified in the preoperative model.
