ICU scoring systems: current perspectives and future directions.

IF 3.5 3区 医学 Q1 CRITICAL CARE MEDICINE
Jorge I F Salluh, Giulliana M Moralez, Alexander Tracy, Rodrigo Octavio Deliberato
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引用次数: 0

Abstract

Purpose of review: This review aims to summarize the recent publications and future perspectives on the use of ICU scoring systems mainly for the assessment of ICU performance, resource use and benchmarking. Additionally, we provide current limitations and future directions on the use of scoring systems.

Recent findings: Generalizability and precision remain major challenges to the use of ICU-score systems. Recent innovations in this field have been driven by the expansion of national and international critical care registries, alongside advancements in data science.Models developed using data from specific regions lack broader applicability. Simplified scoring systems have been proposed to address the urgent need for a global ICU predictive model. Scoring systems can facilitate research, outcome prediction, and healthcare quality comparisons across different settings. A global ICU score system would need minimal data collection requirements, but its use would be inherently limited by the trade-off between generalizability and precision. In parallel, the search for more precise models has led to recent advances. Artificial intelligence-based models have improved predictive abilities compared to traditional scores. Omics data integration and diverse variables and dimensions may interact to predict outcomes. Dynamic models can update such predictions. However, implementation challenges persist, including the need for validation across diverse settings and addressing issues such as transparency, reproducibility, and potential biases.

Summary: Traditionally, ICU scoring systems enable the assessment of patients' severity of illness and consequently the risk-adjusted evaluation of ICU performance and resource use. The expansion of national ICU registries has advanced their use internationally for quality assessment, quality improvement and benchmarking. Novel approaches and methodologies, including the use of machine learning and data science, are making progress in improving the scores performance and expanding their use beyond risk-adjusted mortality.

ICU评分系统:目前的观点和未来的方向。
综述目的:本综述旨在总结ICU评分系统主要用于评估ICU绩效、资源利用和基准的最新出版物和未来展望。此外,我们提供当前的限制和未来的方向上使用评分系统。最近的发现:普遍性和准确性仍然是使用icu评分系统的主要挑战。最近这一领域的创新是由国家和国际重症监护登记处的扩大以及数据科学的进步推动的。利用特定地区的数据开发的模型缺乏更广泛的适用性。简化的评分系统已经提出,以解决迫切需要一个全球性的ICU预测模型。评分系统可以促进不同环境下的研究、结果预测和医疗质量比较。全球ICU评分系统需要的数据收集需求最小,但其使用本身就受到概括性和准确性之间权衡的限制。与此同时,寻找更精确的模型也带来了最近的进展。与传统分数相比,基于人工智能的模型提高了预测能力。组学数据整合和不同的变量和维度可能相互作用来预测结果。动态模型可以更新这样的预测。然而,实现方面的挑战仍然存在,包括需要在不同的设置中进行验证,并解决诸如透明度、可重复性和潜在偏差等问题。摘要:传统上,ICU评分系统能够评估患者的疾病严重程度,从而对ICU的表现和资源使用进行风险调整评估。国家重症监护病房登记处的扩大促进了其在国际上用于质量评估、质量改进和基准制定。新的方法和方法,包括机器学习和数据科学的使用,在提高评分性能和将其应用范围扩大到风险调整死亡率之外方面正在取得进展。
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
Current Opinion in Critical Care
Current Opinion in Critical Care 医学-危重病医学
CiteScore
5.90
自引率
3.00%
发文量
172
审稿时长
6-12 weeks
期刊介绍: ​​​​​​​​​Current Opinion in Critical Care delivers a broad-based perspective on the most recent and most exciting developments in critical care from across the world. Published bimonthly and featuring thirteen key topics – including the respiratory system, neuroscience, trauma and infectious diseases – the journal’s renowned team of guest editors ensure a balanced, expert assessment of the recently published literature in each respective field with insightful editorials and on-the-mark invited reviews.
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