%0 Journal Article %1 kalisnik2022artificial %A Kalisnik, Jurij Matija %A Bauer, André %A Vogt, Ferdinand Aurel %A Stickl, Franziska Josephine %A Zibert, Janez %A Fittkau, Matthias %A Bertsch, Thomas %A Kounev, Samuel %A Fischlein, Theodor %D 2022 %J European Journal of Cardio-Thoracic Surgery %K csa-aki descartes t_interdisciplinary t_journalmagazine myown %T Artificial Intelligence-Based Early Detection Of Acute Kidney Injury After Cardiac Surgery %X Objectives: This study aims to improve 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 modeling. 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 hours after surgery. Demographic characteristics, comorbidities, preoperative cardiac status, intra- and postoperative variables including creatinine and hemoglobin values were retrieved for analysis. Results: From 7507 patients analyzed, 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 hours 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 hemoglobin, operative emergency, bleeding-associated variables, cardiac ischemic time and cardiac function-associated variables, age, diuretics and active infection, chronic obstructive lung and peripheral vascular disease. Conclusion: The 'Detect-A(K)I' model successfully detects cardiac surgery-associated acute kidney injury within 12 hours after surgery with the best discriminatory characteristics reported so far.

@article{kalisnik2022artificial, abstract = {Objectives: This study aims to improve 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 modeling. 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 hours after surgery. Demographic characteristics, comorbidities, preoperative cardiac status, intra- and postoperative variables including creatinine and hemoglobin values were retrieved for analysis. Results: From 7507 patients analyzed, 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 hours 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 hemoglobin, operative emergency, bleeding-associated variables, cardiac ischemic time and cardiac function-associated variables, age, diuretics and active infection, chronic obstructive lung and peripheral vascular disease. Conclusion: The 'Detect-A(K)I' model successfully detects cardiac surgery-associated acute kidney injury within 12 hours after surgery with the best discriminatory characteristics reported so far. }, added-at = {2024-05-05T01:05:57.000+0200}, author = {Kalisnik, Jurij Matija and Bauer, Andr{é} and Vogt, Ferdinand Aurel and Stickl, Franziska Josephine and Zibert, Janez and Fittkau, Matthias and Bertsch, Thomas and Kounev, Samuel and Fischlein, Theodor}, biburl = {https://www.bibsonomy.org/bibtex/2a374a31381d57f83372405b6aa0b813c/samuel.kounev}, interhash = {f62bcaa35f30e0a17fbd0e3451ad5ecf}, intrahash = {a374a31381d57f83372405b6aa0b813c}, journal = {European Journal of Cardio-Thoracic Surgery}, keywords = {csa-aki descartes t_interdisciplinary t_journalmagazine myown}, note = {Joint first authorship; To be published soon}, timestamp = {2024-05-05T01:05:57.000+0200}, title = {Artificial Intelligence-Based Early Detection Of Acute Kidney Injury After Cardiac Surgery}, year = 2022 }