Artificial Intelligence and Postoperative Acute Kidney Injury

Seoul National University

Status: Unknown

Conditions: Non-cardiac Surgery

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

Study type: Observational

Funder types: Other

Identifiers

NCT04705064

2012-069-1180

Details and patient eligibility

About

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

Full 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.

Enrollment: 2,000 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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

Trial design

2,000 participants in 1 patient group

AI_AKI

Description:

Adults patients undergoing non-cardiac surgery

Treatment:

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