利用機器學習技術研究重症加護病房中血清肌酐和電 解質對急性腎損傷風險的複合影響

Abstract

評估急性腎損傷風險一直是重症加護病房內的臨床醫師所面臨的一個具有挑戰性的問題。近年來許多研究已進行於調查多種血清電解質與急性腎損傷之間的關聯。然而，血清肌酐、血尿素氮和臨床相關血清電解質的複合效應尚未得到全面調查。 因此我們啟動了這項研究，旨在開發能夠闡明這些因素彼此之間相互作用的機器學習模型。特別地，我們專注於沒有先前急性腎損傷記錄或急性腎損傷相關共病的重症加護病房患者。透過這種方法，我們能夠以更受控的方式檢視血清電解質水平與腎功能之間的關聯。 通過我們仔細的分析，我們發現血清肌酐、氯離子和鎂離子濃度是這個特定患者群體需要密切監測的三個主要因素。 總之，我們的研究結果不僅為制定早期干預和有效管理策略提供了有價值的見解，還為未來研究探討所涉及的病理生理機制提供了關鍵線索。對於未來的研究，應進行基於不同急性腎損傷原因的亞組分析，以進一步增進我們對急性腎損傷的理解。

Assessing the risk of acute kidney injury (AKI) has been a challenging issue for clinicians in intensive care units (ICU). In recent years, a number of studies have been conducted to investigate the associations between several serum electrolytes and AKI. Nevertheless, the compound effects of serum creatinine, blood urea nitrogen (BUN), and clinically relevant serum electrolytes have yet to be comprehensively investigated. Accordingly, we initiated this study aiming to develop machine learning models that illustrate how these factors interact with each other. In particular, we have focused on the ICU patients without prior history of AKI or AKI-related comorbidities. With this practice, we were able to examine the associations between the levels of serum electrolytes and renal function in a more controlled manner. Our analyses revealed that the levels of serum creatinine, chloride, and magnesium were the three major factors to be monitored for this group of patients. In summary, our results can provide not only valuable insights for developing early intervention and effective management strategies but also crucial clues for future investigation of the pathophysiological mechanisms involved. For future studies, subgroup analyses based on different causes of AKI should be conducted to further enhance our understanding of AKI.