The main objective of this study is to develop and validate an artificial intelligence model that predicts postoperative acute kidney injury.
Postoperative acute kidney injury is known to increase the length of hospital stay and healthcare cost. A lot of risk prediction models have been developed for identifying patients at increased risk of postoperative acute kidney injury. Recent advances in artificial intelligence make it possible to manage and analyze big data. Prediction model using an artificial intelligence and large-scale data can improve the accuracy of prediction performance. Furthermore, the use of an artificial intelligence may be a useful adjuvant tool in making clinical decisions or real-time prediction if it is integrated into the electrical medical record systems. However, before implementing an artificial intelligence model into the clinical setting, prospective evaluation of an artificial intelligence model's real performance is essential. However, to our knowledge, there was no artificial intelligence model for prediction of postoperative acute kidney injury, which was prospectively evaluated. Therefore, we aimed to develop an artificial intelligence model which predicts postoperative acute kidney injury and evaluate the model's performance prospectively.
Study Type
OBSERVATIONAL
Enrollment
2,000
The performance of an artificial intelligence model to predict postoperative acute kidney injury will be tested prospectively.
Hyung-Chul Lee
Seoul, South Korea
the incidence of postoperative acute kidney injury
postoperative acute kidney injury (diagnosed by KDIGO criteria using peak serum creatinine level) included all acute kidney injury events regardless of acute kidney injury severity
Time frame: during the postoperative seven days
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