Artificial Intelligence and Postoperative Acute Kidney Injury
NCT ID: NCT04705064
Last Updated: 2021-01-12
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
2000 participants
OBSERVATIONAL
2021-03-01
2022-02-01
Brief Summary
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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AI_AKI
Adults patients undergoing non-cardiac surgery
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.
Interventions
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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.
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
* Surgery duration \< 1 hour
* Transplantation surgery
* Nephrectomy
* Cardiac surgery
* Patients who had severe kidney dysfunction preoperatively as follows:
* Serum creatinine ā„ 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
18 Years
ALL
No
Sponsors
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Seoul National University Hospital
OTHER
Responsible Party
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Hyung-Chul Lee
Assistant professor
Locations
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Hyung-Chul Lee
Seoul, , South Korea
Countries
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Central Contacts
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Facility Contacts
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Hyung-Chul Lee, MD.PhD
Role: primary
Other Identifiers
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2012-069-1180
Identifier Type: -
Identifier Source: org_study_id
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