The 'Detect-A(K)I' machine learning model detected cardiac surgery-associated acute kidney injury within 12 hours postoperatively with an area under the curve of 88.0% and accuracy of 82.1%.
Observational (n=7,507)
No
Can an artificial intelligence-based algorithm accurately detect acute kidney injury within 12 hours after cardiac surgery?
An AI-based model using routine clinical and laboratory variables can accurately detect acute kidney injury within 12 hours after cardiac surgery, potentially allowing for earlier intervention.
Effect estimate: AUC 88.0%
OBJECTIVES: This study aims to improve the early detection of cardiac surgery-associated acute kidney injury using artificial intelligence-based algorithms. METHODS: Data from consecutive patients undergoing cardiac surgery between 2008 and 2018 in our institution served as the source for artificial intelligence-based modelling. Cardiac surgery-associated acute kidney injury was defined according to the Kidney Disease Improving Global Outcomes criteria. Different machine learning algorithms were trained and validated to detect cardiac surgery-associated acute kidney injury within 12 h after surgery. Demographic characteristics, comorbidities, preoperative cardiac status and intra- and postoperative variables including creatinine and haemoglobin values were retrieved for analysis. RESULTS: From 7507 patients analysed, 1699 patients (22.6%) developed cardiac surgery-associated acute kidney injury. The ultimate detection model, 'Detect-A(K)I', recognizes cardiac surgery-associated acute kidney injury within 12 h with an area under the curve of 88.0%, sensitivity of 78.0%, specificity of 78.9% and accuracy of 82.1%. The optimal parameter set includes serial changes of creatinine and haemoglobin, operative emergency, bleeding-associated variables, cardiac ischaemic time and cardiac function-associated variables, age, diuretics and active infection, chronic obstructive lung and peripheral vascular disease. CONCLUSIONS: The 'Detect-A(K)I' model successfully detects cardiac surgery-associated acute kidney injury within 12 h after surgery with the best discriminatory characteristics reported so far.
Kališnik et al. (Tue,) conducted a observational in Cardiac surgery-associated acute kidney injury (n=7,507). 'Detect-A(K)I' machine learning model was evaluated on Detection of cardiac surgery-associated acute kidney injury within 12 h after surgery (AUC 88.0%). The 'Detect-A(K)I' machine learning model detected cardiac surgery-associated acute kidney injury within 12 hours postoperatively with an area under the curve of 88.0% and accuracy of 82.1%.