AN OVERVIEW OF MORTALITY RISK PREDICTION IN SEPSIS
AN OVERVIEW OF MORTALITY RISK PREDICTION IN SEPSIS
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DOI：
10.1097/00003246-199502000-00026
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发表时间：
1995-02-01
影响因子：
8.8
通讯作者：
LOWRY, SF
中科院分区：
文献类型：
--
作者：
BARRIERE, SL;LOWRY, SF
目的：回顾死亡风险预测的演变和发展。资料来源：选择相关文献中的相关文章。研究选择：关于脓毒症患者死亡率风险识别、疾病严重程度评分系统和细胞因子水平的理论和临床数据。数据提取：我们探索了与死亡风险预测、细胞因子、疾病严重程度和重症监护病房（ICU）死亡率相关的Ah概念，并将其相互关联。数据综合：为了提高脓毒症治疗新疗法评估的准确性，监测其使用情况并完善其适应症，已建议在临床试验和实践中使用死亡率风险分层或疾病严重程度评分系统。随着管理式护理对医疗保健服务的影响越来越大，对技术的需求将会增加，以便对患者进行成本效益高的护理分配。疾病严重程度评分系统广泛用于癌症和心脏病管理中的患者分层。然而，这种系统在脓毒症患者中的应用仅限于临床试验设计，以确保治疗组之间的平衡。脓毒症的死亡风险预测已经从识别风险因素和简单的器官衰竭计数发展到复杂的技术，可以将由生理和/或临床数据组成的原始评分从数学上转化为预测的死亡风险。大多数已开发的系统是基于全球ICU人口，而不是脓毒症患者数据库。一些较新的系统是从这样的数据库派生出来的。但是，各种方法的总体判别能力是相似的。死亡率预测也通过内毒素或细胞因子（白细胞介素-1、白细胞介素-6、肿瘤坏死因子）血浆浓度的评估进行。虽然这些物质水平的增加与死亡率的增加有关，但生物测定的困难及其在血液中的零星出现阻碍了这些测量的实际应用。对风险预测方法进行校准，将预测死亡率与实际死亡率在整个风险范围内进行比较，这是非常出色的，但个体患者预测的总体准确性如此之高，以至于临床判断必须仍然是决策的主要部分。然而，随着适当患者信息数据库的规模和复杂性的增加，将来可能会设计出一种评分系统，可以依靠它来辅助临床决策。结论：病情严重程度评分系统在危重患者中应用广泛。然而，它们在脓毒症患者中的应用在很大程度上仅限于临床试验中的分层手段。随着新的败血症治疗方法的出现，有可能使用这些系统来完善其适应症，并监测其使用情况。最后，随着支持系统的数据库的规模和复杂性的增加，有可能在临床决策中利用它们。
Objective: To review the evolution and development of mortality risk predictioData Sources: Selected relevant articles from the pertinent literature.Study Selection: Theoretical and clinical data on the mortality risk identification, severity of illness scoring systems, and cytokine levels as they relate to mortality in patients with sepsis.Data Extraction: Ah concepts relating to mortality risk prediction, cytokines, severity of illness, and intensive care unit (ICU) mortality were explored and interrelated accordingly.Data Synthesis: In order to improve the precision of the evaluation of new therapies for the treatment of sepsis, to monitor their utilization and to refine their indications, it has been recommended that mortality risk stratification or severity of illness scoring systems be utilized in clinical trials and in practice. With the increasing influence of managed care on healthcare delivery, there will be an increased demand for techniques to stratify patients for cost-effective allocation of care. Severity of illness scoring systems are widely utilized for patient stratification in the management of cancer and heart disease. However, the use of such systems in patients with sepsis has beets limited to applications in clinical trial design for assurance of balance among treatment groups. Mortality risk prediction in sepsis has evolved from identification of risk factors, and simple counts of failing organs, to sophisticated techniques that mathematically transform a raw score, comprised of physiologic and/or clinical data, into a predicted risk of death. Most of the developed systems are based on global ICU populations rather than upon sepsis patient databases. A few, newer systems are derived from such databases. However, the overall discriminating ability of the various methods is similar. Mortality prediction has also been carried out from assessments of endotoxin or cytokine (interleukin-1, interleukin-6, tumor necrosis factor) plasma concentrations. While increased levels of these substances have been correlated with increased mortality, difficulties with bioassay and their sporadic appearance in the bloodstream prevent these measurements from being practically applied. The calibration of risk prediction methods comparing predicted with actual mortality across the breadth of risk for a population of patients is excellent, but overall accuracy in individual patient predictions is such that clinical judgment must remain a major part of decision-making. However, as databases of appropriate patient information increase in size and complexity, it may be possible in the future to devise a scoring system that can be relied on to assist in clinical decision-making.Conclusions: Severity of illness scoring systems are widely used in critically ill patients. However, their use in patients with sepsis has largely been limited to a means of stratification in clinical trials. As newer sepsis therapies become available, it may be possible to use such systems for refining their indications, and monitoring their utilization. Finally, as the databases supporting the systems increase in size and complexity, it may be possible to utilize them in clinical decision making.