Liu Wentie, Shi Tongyue, Xu Haowei, Zhao Huiying, Hao Jianguo, Kong Guilan
National Institute of Health Data Science, Peking University, Beijing, China.
Advanced Institute of Information Technology, Peking University, Hangzhou, China.
AMIA Annu Symp Proc. 2025 May 22;2024:733-737. eCollection 2024.
This study proposes to use the K-medoids clustering method to identify subtypes of Intensive Care Unit (ICU)-acquired acute kidney injury (AKI) patients based on serum electrolyte data. Three distinct AKI subtypes with different serum electrolyte characteristics were identified by clustering analysis. Further, descriptive analysis was employed to characterize in-hospital mortality and renal replacement therapy, diuretic and vasopressor usage in the three subtypes, and Chi-square tests were conducted to check the differences of prognosis and treatments among the identified subtypes. This study enables the subclassification of AKI patients in the ICU, facilitating ICU physicians to make timely clinical decisions about AKI, and ultimately may contribute to patient outcome improvement.
本研究建议使用K-中心点聚类方法，基于血清电解质数据识别重症监护病房（ICU）获得性急性肾损伤（AKI）患者的亚型。通过聚类分析确定了具有不同血清电解质特征的三种不同的AKI亚型。此外，采用描述性分析来描述三种亚型的院内死亡率、肾脏替代治疗、利尿剂和血管升压药的使用情况，并进行卡方检验以检查所识别亚型之间预后和治疗的差异。本研究能够对ICU中的AKI患者进行亚分类，有助于ICU医生及时对AKI做出临床决策，并最终可能有助于改善患者的预后。
