Artificial Intelligence and Postoperative Acute Kidney Injury
- Conditions
- Non-cardiac Surgery
- Interventions
- Diagnostic Test: Prediction of postoperative acute kidney injury using an artificial intelligence
- Registration Number
- NCT04705064
- Lead Sponsor
- Seoul National University Hospital
- Brief Summary
The main objective of this study is to develop and validate an artificial intelligence model that predicts postoperative acute kidney injury.
- Detailed 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.
Recruitment & Eligibility
- Status
- UNKNOWN
- Sex
- All
- Target Recruitment
- 2000
- Adults patients undergoing non-cardiac surgery
- 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
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description AI_AKI Prediction of postoperative acute kidney injury using an artificial intelligence Adults patients undergoing non-cardiac surgery
- Primary Outcome Measures
Name Time Method the incidence of postoperative acute kidney injury during the postoperative seven days 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
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
Hyung-Chul Lee
🇰🇷Seoul, Korea, Republic of