Key result
Multimodal LLM fusion model predicts CPB-AKI risk with an AUROC of ~0.92.
Why the study?
Numerical data-driven models struggle to predict cardiopulmonary bypass-associated AKI risk using preoperative data and lack preventive measures.
Does a multimodal fusion model using LLMs improve the preoperative prediction of CPB-associated AKI compared to baseline models in patients undergoing CPB?
Observational (n=2,056)
No
Does a multimodal fusion model using LLMs improve the preoperative prediction of CPB-associated AKI compared to baseline models in patients undergoing CPB?
A multimodal fusion model incorporating large language models significantly improves the preoperative prediction of acute kidney injury in patients undergoing cardiopulmonary bypass.
Answers are grounded in this paper and cite their sources.
May aid preoperative CPB-AKI risk stratification; leaves open prospective validation and outcome impact.
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.
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Lin et al. (2025) conducted an observational in Cardiopulmonary bypass-associated acute kidney injury (n=2,056). Multimodal fusion model using large language models vs. Baseline models was evaluated on Prediction of CPB-AKI risk (AUROC 0.9201). A multimodal fusion model using large language models predicted cardiopulmonary bypass-associated acute kidney injury with an area under the receiver operating characteristic curve of 0.9201.
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