Leveraging large language models for preoperative prevention of cardiopulmonary bypass-associated acute kidney injury.

Background: 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.

Objective: For preoperative assessment and prevention of CPB-associated AKI (CPB-AKI).

Methods: 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.

Results: 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.

Conclusions: 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.