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Prediction of acute kidney injury after coronary artery bypass grafting by machine learning model

Clinical study of integrated machine learning in predicting postoperative AKI in patients undergoing cardiac surgery

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2300072469
Enrollment
Unknown
Registered
2023-06-14
Start date
2023-06-24
Completion date
Unknown
Last updated
2023-06-18

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

CABG postoperative AKI

Interventions

1:None

Sponsors

Chest Hospital affiliated to Shanghai Jiaotong University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 75 Years

Inclusion criteria

Inclusion criteria: According to the requirements of Chinese cardiac surgery registration study and combined with the characteristics of patients undergoing surgery for coronary heart disease, the patient data were retrospectively registered.

Exclusion criteria

Exclusion criteria: (1) younger than 18 years of age, (2) repeat coronary artery bypass grafting or (3) combined with other cardiac surgery, such as valve, ventricular aneurysm, ventricular septum, etc. (4) No perioperative medical records; (5) Long-term dialysis for patients with chronic renal failure

Design outcomes

Primary

MeasureTime frame
creatinine;eGFR;long-term mortality;acute kidney injury;—

Countries

China

Contacts

Public ContactZhang Yangyang

Chest Hospital affiliated to Shanghai Jiaotong University

zhangyangyang_wy@vip.sina.com+86 138 1813 2320

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026