Anjay P. Shah, William Snead, Anshul Daga, Rayon Uddin, Esra Adiyeke, Tyler J. Loftus, Azra Bihorac, Yuanfang Ren, Tezcan Ozrazgat-Baslanti
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引用次数: 0
Abstract
Background
Acute kidney injury (AKI) is a multifaceted disease characterized by diverse clinical presentations and mechanisms. Advances in artificial intelligence have propelled the identification of AKI subphenotypes, enhancing our capacity to customize treatments and predict disease trajectories.
Methods
We conducted a systematic review of the literature from 2017 to 2022, focusing on studies that utilized machine learning techniques to identify AKI subphenotypes in adult patients. Data were extracted regarding patient demographics, clustering methodologies, discriminators, and validation efforts from selected studies.
Results
The review highlights significant variability in subphenotype identification across different populations. All studies utilized clinical data such as comorbidities and laboratory variables to group patients. Two studies incorporated biomarkers of endothelial activation and inflammation into the clinical data to identify subphenotypes. The primary discriminators were comorbidities and laboratory trajectories. The association of AKI subphenotypes with mortality, renal recovery and treatment response was heterogeneous across studies. The use of diverse clustering techniques contributed to variability, complicating the application of findings across different patient populations.
Conclusions
Identifying AKI subphenotypes enables clinicians to better understand and manage individual patient trajectories. Future research should focus on validating these phenotypes in larger, more diverse cohorts to enhance their clinical applicability and support personalized medicine in AKI management.
背景急性肾损伤(AKI)是一种多发性疾病,其临床表现和机制多种多样。人工智能的进步推动了 AKI 亚型的识别,提高了我们定制治疗和预测疾病轨迹的能力。方法我们对 2017 年至 2022 年的文献进行了系统性回顾,重点关注利用机器学习技术识别成人患者 AKI 亚型的研究。我们从所选研究中提取了有关患者人口统计学、聚类方法、判别因素和验证工作的数据。所有研究都利用合并症和实验室变量等临床数据对患者进行分组。两项研究将内皮活化和炎症的生物标志物纳入临床数据,以识别亚型。主要的判别指标是合并症和实验室轨迹。不同研究中的 AKI 亚型与死亡率、肾功能恢复和治疗反应的关系不尽相同。结论确定 AKI 亚型可使临床医生更好地了解和管理患者的个体轨迹。未来的研究应侧重于在更大、更多样化的队列中验证这些表型,以提高其临床适用性并支持 AKI 管理中的个性化医疗。
期刊介绍:
Journal of Nephrology is a bimonthly journal that considers publication of peer reviewed original manuscripts dealing with both clinical and laboratory investigations of relevance to the broad fields of Nephrology, Dialysis and Transplantation. It is the Official Journal of the Italian Society of Nephrology (SIN).