Artificial intelligence in the management of acute kidney injury after cardiac surgery

Abstract

Acute kidney injury (AKI) is a common and serious complication after cardiac surgery, affecting 10%-40% of patients. It worsens patient outcomes and consumes significant healthcare resources. Its pathophysiology is complex and involves ischemia-reperfusion injury, inflammatory responses, and endothelial dysfunction. Artificial intelligence (AI) offers considerable potential to improve the management of this condition. AI models can integrate multimodal data, including preoperative clinical profiles, intraoperative hemodynamics, and postoperative laboratory values, thereby enabling early prediction of AKI. By identifying distinct clinical subtypes, AI may support personalized therapeutic strategies. Furthermore, it may improve prognostic assessments, allowing more precise risk stratification for both cardiac and renal outcomes. However, current applications face challenges, including inconsistent data quality, limited model interpretability, and high implementation costs. Existing models are also constrained by the range of variables they incorporate. Future technological advances may enable the analysis of a broader array of variables, potentially revealing novel biomarkers and clinically useful combinations of indicators. Such progress could advance precision medicine in this field, ultimately improving patient care and optimizing clinical workflows.

Keywords

Acute kidney injury after cardiac surgery / artificial intelligence / multimodal data / subtype classification / prognostic assessment / personalized treatment

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