[Research on risk prediction of acute respiratory distress syndrome complicated with acute kidney injury: progress and challenges].

Q3 Medicine
Z K Deng, S C Liu, Y M Li, L Sang
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引用次数: 0

Abstract

The risk of acute respiratory distress syndrome (ARDS) combined with acute kidney injury (AKI) is high and the prognosis is poor. Therefore, there is an urgent need for efficient and accurate methods to improve clinical doctors' early diagnosis and prognosis judgment of this comorbidity state. This article will review the research progress on risk prediction methods for ARDS combined with AKI, providing reference for clinical treatment and scientific research design. The clinical scoring system based on statistical methods can identify key predictive factors and is easy to implement, but it generally relies on single time point data, cannot capture dynamic changes, and lacks adaptability in complex clinical scenarios. The development of key biomarkers provides effective tools for clinical practice, but most of them are still in the validation stage, and standardization and cost-effectiveness issues need to be addressed. In recent years, artificial intelligence has shown outstanding performance in assisting clinical doctors in risk warning for critically ill patients. It can integrate multimodal data and has higher predictive efficiency than traditional methods. It has begun to be deployed and implemented in clinical practice, but multiple issues such as data standardization and model generalization still need to be addressed.

[急性呼吸窘迫综合征并发急性肾损伤的风险预测研究进展与挑战]。
急性呼吸窘迫综合征(ARDS)合并急性肾损伤(AKI)的危险性高,预后差。因此,迫切需要高效、准确的方法来提高临床医生对这种合并症状态的早期诊断和预后判断。本文将对ARDS合并AKI风险预测方法的研究进展进行综述,为临床治疗和科研设计提供参考。基于统计方法的临床评分系统能够识别关键预测因素,易于实施,但一般依赖于单一时间点数据,不能捕捉动态变化,对复杂临床场景缺乏适应性。关键生物标志物的开发为临床实践提供了有效的工具,但大多数仍处于验证阶段,标准化和成本效益问题有待解决。近年来,人工智能在辅助临床医生对危重患者进行风险预警方面表现突出。该方法可以集成多模态数据,具有比传统方法更高的预测效率。它已经开始在临床实践中部署和实施,但仍需要解决数据标准化和模型泛化等多个问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Zhonghua yi xue za zhi
Zhonghua yi xue za zhi Medicine-Medicine (all)
CiteScore
0.80
自引率
0.00%
发文量
400
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