Park Dong Jin, Baik Seung Min, Lee Hanyoung, Park Hoonsung, Lee Jae-Myeong
Department of Laboratory Medicine, College of Medicine, Eunpyeong St. Mary's Hospital, The Catholic University of Korea, Seoul, Korea.
Department of Surgery, Division of Critical Care Medicine, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Korea.
Nutr Clin Pract. 2025 Jun;40(3):723-732. doi: 10.1002/ncp.11238. Epub 2024 Oct 25.
This study aimed to understand the collective impact of trace elements, vitamins, cholesterol, and prealbumin on patient outcomes in the intensive care unit (ICU) using an advanced artificial intelligence (AI) model for mortality prediction.
Data from ICU patients (December 2016 to December 2021), including serum levels of trace elements, vitamins, cholesterol, and prealbumin, were retrospectively analyzed using AI models. Models employed included category boosting (CatBoost), extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), and multilayer perceptron (MLP). Performance was evaluated using area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, and F1-score. The performance was evaluated using 10-fold crossvalidation. The SHapley Additive exPlanations (SHAP) method provided interpretability.
CatBoost emerged as the top-performing individual AI model with an AUROC of 0.756, closely followed by LGBM, MLP, and XGBoost. Furthermore, the ensemble model combining these four models achieved the highest AUROC of 0.776 and more balanced metrics, outperforming all models. SHAP analysis indicated significant influences of prealbumin, Acute Physiology and Chronic Health Evaluation II score, and age on predictions. Notably, the ratios of selenium to age and low-density lipoprotein to total cholesterol also had a notable impact on the models' output.
The study underscores the critical role of nutrition-related parameters in ICU patient outcomes. Advanced AI models, particularly in an ensemble approach, demonstrated improved predictive accuracy. SHAP analysis offered insights into specific factors influencing patient survival, highlighting the need for broader consideration of these biomarkers in critical care management.
本研究旨在使用先进的人工智能（AI）模型进行死亡率预测，以了解微量元素、维生素、胆固醇和前白蛋白对重症监护病房（ICU）患者预后的综合影响。
对ICU患者（2016年12月至2021年12月）的数据进行回顾性分析，包括微量元素、维生素、胆固醇和前白蛋白的血清水平，并使用AI模型进行分析。所采用的模型包括类别提升（CatBoost）、极端梯度提升（XGBoost）、轻梯度提升机（LGBM）和多层感知器（MLP）。使用受试者操作特征曲线下面积（AUROC）、准确率、精确率、召回率和F1分数评估模型性能。使用10折交叉验证评估性能。SHapley加法解释（SHAP）方法提供了可解释性。
CatBoost成为表现最佳的单个AI模型，AUROC为0.756，紧随其后的是LGBM、MLP和XGBoost。此外，结合这四种模型的集成模型实现了最高的AUROC，为0.776，且指标更为平衡，优于所有模型。SHAP分析表明，前白蛋白、急性生理与慢性健康状况评分II和年龄对预测有显著影响。值得注意的是，硒与年龄的比值以及低密度脂蛋白与总胆固醇的比值也对模型输出有显著影响。
该研究强调了营养相关参数在ICU患者预后中的关键作用。先进的AI模型，特别是采用集成方法时，显示出更高的预测准确性。SHAP分析深入了解了影响患者生存的具体因素，突出了在重症监护管理中更广泛考虑这些生物标志物的必要性。
