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

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.

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Key information

Conditions

Acute Kidney Injury Chemotherapy Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Kidney Diseases Male Urogenital Diseases Renal Insufficiency Urogenital Diseases Urologic Diseases

Age range

19 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Gangnam Severeance Hospital

Seoul, 06273, South Korea

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Patients undergoing cytoreductive surgery with hyperthermic intraoperative chemotherapy

Exclusion criteria

patients under 19

Treatment and study plan

Data Collection

Other

This study is an observational study collecting perioperative data. There is no intervention regarding this study.

Primary outcomes

number of patients with postoperative acute kidney injury staged by Kidney Disease: Improving Global Outcomes (KDIGO)

Time frame: during postoperative day 7

Stage 1: Increased sCr × 1.5 to 1.9 baseline or ≥ 0.3 mg/dl from baseline or urine output < 0.5 ml/kg/h for 6 to 12 h, Stage 2: Increased sCr × 2.0 to 2.9 baseline or urine output < 0.5 ml/kg/h for 12 h, Stage 3: Increased sCr × 3 baseline or ≥ 4 mg/dl or initiation of RRT, or GFR decrease < 35 ml/min/1.73 m2 for patients < 18 years of age or urine output < 0.3 ml/kg/h for 24 h or anuria for 12 h

Sponsors and collaborators

Lead sponsor

Gangnam Severance Hospital

Other

Registry information

Official study title

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

Acronym: HIPEC

Important dates

Study start: 2019
Primary completion: 2020
Study completion: 2020
First posted: Mar 29, 2019
Registry last updated: Aug 3, 2020

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed