Machine Learning Predict Acute Kidney Injury in Patients Following Cardiac Surgery

Completed

• Conditions: Acute Kidney Injury; Machine Learning

• Registration Number: NCT04966598
• Lead Sponsor: Yunlong Fan

Brief Summary

Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication which may result in adverse impact on short- and long-term mortality. The investigatorshere developed several prediction models based on machine learning technique to allow early identification of patients who at the high risk of unfavorable kidney outcomes.

The retrospective study comprised 2108 consecutive patients who underwent cardiac surgery from January 2017 to December 2020.

Detailed Description

Not available

Study Timeline

• Study Start: 2020-09-01
• Primary Completion: 2021-01-01
• Study Completion: 2021-01-01
• Status Verified: 2021-07
• Study First Submitted: 2021-07-08
• Study First Submitted QC: 2021-07-08
• Last Update Submitted: 2021-07-16
• Study First Posted: 2021-07-19
• Last Update Posted: 2021-07-22

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 2108

Inclusion Criteria

• age over 18 years who underwent cardiac surgery

Exclusion Criteria

• data miss greater than 10%

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
acute kidney injury	7 days	postoperative AKI was defined according to KDIGO criteria during the first 7 days after operation. Postoperative AKI was defined as either at an increase of at least 50% within 7 days or 0.3 mg/dL elevation within 48 h compared with the reference serum creatinine level.

Trial Locations

Locations (1)

Chinese PLA General hospital; 🇨🇳 Beijing, Beijing, China