Artificial Intelligence and Postoperative Acute Kidney Injury

January 8, 2021 updated by: Hyung-Chul Lee, Seoul National University Hospital

Development and Prospective Validation of an Artificial Intelligence Model to Predict Postoperative Acute Kidney Injury

The main objective of this study is to develop and validate an artificial intelligence model that predicts postoperative acute kidney injury.

Study Overview

Status

Unknown

Conditions

Intervention / Treatment

Detailed Description

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 (Anticipated)

2000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

N/A

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Adults patients undergoing non-cardiac surgery

Description

Inclusion Criteria:

  • Adults patients undergoing non-cardiac surgery

Exclusion Criteria:

  • Age under 18 years
  • Surgery duration < 1 hour
  • Transplantation surgery
  • Nephrectomy
  • Cardiac surgery
  • Patients who had severe kidney dysfunction preoperatively as follows:
  • Serum creatinine ≥ 4 mg/dl
  • Estimated glomerular filtration rate <15 ml/min/1.73m2
  • History of renal replacement therapy
  • Patients who had no results of preoperative or postoperative serum creatinine

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
AI_AKI
Adults patients undergoing non-cardiac surgery
Diagnostic test: Prediction of postoperative acute kidney injury using an artificial intelligence
The performance of an artificial intelligence model to predict postoperative acute kidney injury will be tested prospectively.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
the incidence of postoperative acute kidney injury
Time Frame: during the postoperative seven days
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
during the postoperative seven days

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Seoul National University Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Anticipated)

March 1, 2021

Primary Completion (Anticipated)

May 31, 2021

Study Completion (Anticipated)

February 1, 2022

Study Registration Dates

First Submitted

January 8, 2021

First Submitted That Met QC Criteria

January 8, 2021

First Posted (Actual)

January 12, 2021

Study Record Updates

Last Update Posted (Actual)

January 12, 2021

Last Update Submitted That Met QC Criteria

January 8, 2021

Last Verified

January 1, 2021

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2012-069-1180

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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