Prediction of Kidney Injury After Hyperthermic Intraperitoneal Chemotherapy (HIPEC)* With Machine Learning

Application of Machine Learning to Predict Postoperative Acute Kidney Injury in Patients Undergoing Cytoreduction and Hyperthermic Intraperitoneal Chemotherapy Using High-resolution, Time-synchronized Physiological Data From Vital Recorder

Patients undergoing cytoreductive surgery with hyperthermic intraoperative chemotherapy (CRS with HIPEC) are prone to postoperative kidney dysfunction. Previous models predicting kidney injury after CRS with HIPEC did not include intraoperative physiologic data. This study is designed to include not only mean arterial pressure but other parameters such as systolic, diastolic arterial pressure, heart rate, oxygen saturation, body temperature, cardiac index, stroke volume variation and many other physical parameters using a data collection system that can record them every 1-7 seconds. The data will be analyzed using machine learning algorithms.

Inclusion Criteria:

• Patients undergoing cytoreductive surgery with hyperthermic intraoperative chemotherapy

Exclusion Criteria:

• patients under 19