Wang Kai, Lin Ling, Zheng Rui, Nan Shan, Lu Xudong, Duan Huilong
Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, Hainan, China.
School of Information and Communication Engineering, Hainan University, Haikou, Hainan, China.
Ren Fail. 2025 Dec;47(1):2509786. doi: 10.1080/0886022X.2025.2509786. Epub 2025 May 29.
Acute kidney injury (AKI) usually occurs after cardiopulmonary bypass (CPB) and threatens life without timely intervention. Early assessment and prevention are critical for saving AKI patients. However, numerical data-driven models make it difficult to predict the AKI risk using preoperative data and lack preventive measures. Large language models (LLM) have demonstrated significant potential for medical decision-making, offering a promising approach.
For preoperative assessment and prevention of CPB-associated AKI (CPB-AKI).
Clinical variables were converted into text through prompt engineering and a LLM was used to extract information hidden in the semantics of subtle changes. A multimodal fusion model, fuzing semantic and numerical information, was proposed to assess the AKI risk before surgery. We then used a structural equation model to analyze the impact of preoperative features and intraoperative interventions on CPB-AKI prevention.
A total of 2,056 patients who underwent CPB were enrolled from the intensive care unit of Sir Run Run Shaw Hospital between 2014 and 2022, with 40.5% progressing to AKI. Our model performed better with an area under the receiver operating characteristic curve of 0.9201 compared with the baseline models. The structural equation model's chi-square to degrees of freedom ratio was 0.46, less than 2.0. We discussed how the preoperative prediction model could optimize intraoperative interventions to prevent CPB-AKI.
The prediction model can predict CPB-AKI risk earlier after fuzing the clinical characteristics and their semantics. Preoperative assessment and intraoperative interventions provide decision-making to prevent CPB-AKI.
急性肾损伤（AKI）通常发生在体外循环（CPB）后，若不及时干预会危及生命。早期评估和预防对挽救AKI患者至关重要。然而，基于数值数据的模型难以利用术前数据预测AKI风险，且缺乏预防措施。大语言模型（LLM）已在医学决策中展现出巨大潜力，提供了一种有前景的方法。
用于CPB相关急性肾损伤（CPB-AKI）的术前评估和预防。
通过提示工程将临床变量转换为文本，并使用LLM提取隐藏在细微变化语义中的信息。提出了一种融合语义和数值信息的多模态融合模型，以评估术前的AKI风险。然后，我们使用结构方程模型分析术前特征和术中干预对预防CPB-AKI的影响。
2014年至2022年期间，从邵逸夫医院重症监护病房招募了总共2056例接受CPB的患者，其中40.5%进展为AKI。与基线模型相比，我们的模型表现更佳，受试者操作特征曲线下面积为0.9201。结构方程模型的卡方与自由度比为0.46，小于2.0。我们讨论了术前预测模型如何优化术中干预以预防CPB-AKI。
该预测模型在融合临床特征及其语义后能够更早地预测CPB-AKI风险。术前评估和术中干预为预防CPB-AKI提供决策依据。
