Wang Xinyuan, Li Chenyu, Xu Lingyu, Jiang Siqi, Guan Chen, Che Lin, Wang Yanfei, Man Xiaofei, Xu Yan
Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Clin Kidney J. 2025 Mar 13;18(4):sfaf080. doi: 10.1093/ckj/sfaf080. eCollection 2025 Apr.
Acute kidney injury (AKI) is a prevalent complication in patients at risk of malnutrition, elevating the risks of acute kidney disease (AKD) and mortality. AKD reflects the adverse events developing after AKI. This study aimed to develop and validate machine learning (ML) models for predicting the occurrence of AKD, AKI and mortality in patients at risk of malnutrition.
We retrospectively reviewed the medical records of patients at risk of malnutrition. Eight ML algorithms were employed to predict AKD, AKI and mortality. The performance of the best model was evaluated using various metrics and interpreted using the SHapley Additive exPlanation (SHAP) method. An artificial intelligence (AI)-driven web application was also created based on the best model.
A total of 13 395 patients were included in our study. Among them, 1751 (13.07%) developed subacute AKD, 1253 (9.35%) were transient AKI, and 1455 (10.86%) met both AKI and AKD criteria. The incidence rate of mortality was 6.74%. The light gradient boosting machine (LGBM) outperformed other models in predicting AKD, AKI and mortality, with area under curve values of 0.763, 0.801 and 0.881, respectively. The SHAP method revealed that AKI stage, lactate dehydrogenase, albumin, aspirin usage and serum creatinine were the top five predictors of AKD. An online prediction website for AKI, AKD and mortality was developed based on the final models.
The LGBM models provide an effective method for predicting AKD, AKI and mortality at an early stage in patients at risk of malnutrition, enabling prompt interventions. Compared with the AKD model, the models for predicting AKI and mortality perform better. The AI-driven web application can significantly aid in creating personalized preventive measures. Future work will aim to expand the application to larger, more diverse populations, incorporate additional biomarkers and refine ML algorithms to improve predictive accuracy and clinical utility.
急性肾损伤（AKI）是营养不良风险患者中常见的并发症，会增加急性肾脏病（AKD）风险和死亡率。AKD反映了AKI后发生的不良事件。本研究旨在开发并验证用于预测营养不良风险患者发生AKD、AKI和死亡率的机器学习（ML）模型。
我们回顾性分析了营养不良风险患者的病历。采用八种ML算法预测AKD、AKI和死亡率。使用各种指标评估最佳模型的性能，并使用SHapley加性解释（SHAP）方法进行解读。还基于最佳模型创建了一个人工智能（AI）驱动的网络应用程序。
我们的研究共纳入13395例患者。其中，1751例（13.07%）发生亚急性AKD，1253例（9.35%）为短暂性AKI，1455例（10.86%）符合AKI和AKD标准。死亡率为6.74%。轻梯度提升机（LGBM）在预测AKD、AKI和死亡率方面优于其他模型，曲线下面积值分别为0.763、0.801和0.881。SHAP方法显示，AKI分期、乳酸脱氢酶、白蛋白、阿司匹林使用情况和血清肌酐是AKD的前五大预测因素。基于最终模型开发了一个AKI、AKD和死亡率的在线预测网站。
LGBM模型为早期预测营养不良风险患者的AKD、AKI和死亡率提供了一种有效方法，有助于及时进行干预。与AKD模型相比，预测AKI和死亡率的模型表现更好。AI驱动的网络应用程序可显著有助于制定个性化预防措施。未来的工作将旨在将应用扩展到更大、更多样化的人群，纳入更多生物标志物并优化ML算法，以提高预测准确性和临床实用性。
