[Research on risk prediction of acute respiratory distress syndrome complicated with acute kidney injury: progress and challenges].

The risk of acute respiratory distress syndrome (ARDS) combined with acute kidney injury (AKI) is high and the prognosis is poor. Therefore, there is an urgent need for efficient and accurate methods to improve clinical doctors' early diagnosis and prognosis judgment of this comorbidity state. This article will review the research progress on risk prediction methods for ARDS combined with AKI, providing reference for clinical treatment and scientific research design. The clinical scoring system based on statistical methods can identify key predictive factors and is easy to implement, but it generally relies on single time point data, cannot capture dynamic changes, and lacks adaptability in complex clinical scenarios. The development of key biomarkers provides effective tools for clinical practice, but most of them are still in the validation stage, and standardization and cost-effectiveness issues need to be addressed. In recent years, artificial intelligence has shown outstanding performance in assisting clinical doctors in risk warning for critically ill patients. It can integrate multimodal data and has higher predictive efficiency than traditional methods. It has begun to be deployed and implemented in clinical practice, but multiple issues such as data standardization and model generalization still need to be addressed.