NCT04705064

Brief Summary

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

Trial Health

43
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
2,000

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Mar 2021

Shorter than P25 for all trials

Geographic Reach
1 country

1 active site

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

January 8, 2021

Completed
4 days until next milestone

First Posted

Study publicly available on registry

January 12, 2021

Completed
2 months until next milestone

Study Start

First participant enrolled

March 1, 2021

Completed
3 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

May 31, 2021

Completed
8 months until next milestone

Study Completion

Last participant's last visit for all outcomes

February 1, 2022

Completed
Last Updated

January 12, 2021

Status Verified

January 1, 2021

Enrollment Period

3 months

First QC Date

January 8, 2021

Last Update Submit

January 8, 2021

Conditions

Non-cardiac Surgery

Keywords

Artificial IntelligencePostoperative acute kidney injuryProspective validation

Outcome Measures

Primary Outcomes (1)

  • 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

    during the postoperative seven days

Study Arms (1)

AI_AKI

Adults patients undergoing non-cardiac surgery

Diagnostic Test: Prediction of postoperative acute kidney injury using an artificial intelligence

Interventions

Prediction of postoperative acute kidney injury using an artificial intelligenceDIAGNOSTIC_TEST

The performance of an artificial intelligence model to predict postoperative acute kidney injury will be tested prospectively.

AI_AKI

Eligibility Criteria

Age18 Years+
Sexall
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

Adults patients undergoing non-cardiac surgery

You may qualify if:

  • Adults patients undergoing non-cardiac surgery

You may not qualify if:

  • 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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Hyung-Chul Lee

Seoul, South Korea

Location

Central Study Contacts

Hyung-Chul Lee, MD.PhD

CONTACT

+821024566336vital@snu.ac.kr

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Assistant professor

Study Record Dates

First Submitted

January 8, 2021

First Posted

January 12, 2021

Study Start

March 1, 2021

Primary Completion

May 31, 2021

Study Completion

February 1, 2022

Last Updated

January 12, 2021

Record last verified: 2021-01

Locations

KR(1)