Artificial Intelligence and Postoperative Acute Kidney Injury

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

Detailed Description
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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.

Official Title
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Development and Prospective Validation of an Artificial Intelligence Model to Predict Postoperative Acute Kidney Injury

Conditions
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Non-cardiac Surgery

Intervention / Treatment
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* Diagnostic Test: Prediction of postoperative acute kidney injury using an artificial intelligence


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| AI_AKI<br>Adults patients undergoing non-cardiac surgery | Diagnostic Test: Prediction of postoperative acute kidney injury using an artificial intelligence<br>* The performance of an artificial intelligence model to predict postoperative acute kidney injury will be tested prospectively.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Artificial Intelligence, Postoperative acute kidney injury, Prospective validation