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过敏性反应严重程度的分级

Grading the severity of anaphylaxis.

作者信息

Stafford Aisling, Turner Paul J

机构信息

National Heart & Lung Institute, Imperial College London, London, UK.

Children's Health Ireland at Crumlin, Dublin, Ireland.

出版信息

Curr Opin Allergy Clin Immunol. 2023 Jun 1;23(3):218-225. doi: 10.1097/ACI.0000000000000901. Epub 2023 Mar 14.

Abstract

PURPOSE OF REVIEW

Despite no global consensus on a definition of anaphylaxis, there is increasing recognition that just as allergic reactions lie on a spectrum of severity, the same is for anaphylaxis. A variety of severity scores exist in the literature. We review the approaches taken to develop these scores, and their relative advantages and disadvantages.

RECENT FINDINGS

There have been four recent comparisons of published severity scores. All have highlighted the heterogeneity between scoring systems, and the lack of transferability from one approach to another. Notably, only one score has been developed using a data-driven approach, and none has undergone formal and comprehensive validation.

SUMMARY

It is unclear whether a single severity score is achievable, or indeed desirable. If the aim is to guide management of acute reactions, then assignment of severity is not only unnecessary but might delay treatment and cause harm. Severity scores are needed in the research setting, but require an approach which can discriminate between reactions of similar but nonidentical severity (particularly, nonanaphylaxis reactions). Any approach should be fit for purpose, informed by patient and clinician experience, and ideally be data-driven to minimize subjective bias and facilitate objective validation.

摘要

综述目的

尽管对于过敏反应的定义尚未达成全球共识,但人们越来越认识到,正如过敏反应存在严重程度的范围一样,过敏反应也是如此。文献中存在多种严重程度评分。我们回顾了开发这些评分所采用的方法及其相对优缺点。

最新发现

最近有四项已发表的严重程度评分比较。所有这些都突出了评分系统之间的异质性,以及从一种方法到另一种方法缺乏可转移性。值得注意的是,只有一个评分是使用数据驱动的方法开发的,而且没有一个经过正式和全面的验证。

总结

目前尚不清楚是否能够或实际上是否需要单一的严重程度评分。如果目的是指导急性反应的管理,那么严重程度的判定不仅没有必要,而且可能会延迟治疗并造成伤害。在研究环境中需要严重程度评分,但需要一种能够区分相似但不完全相同严重程度的反应(特别是非过敏反应)的方法。任何方法都应适合其目的,以患者和临床医生的经验为依据,理想情况下应以数据为驱动,以尽量减少主观偏差并便于进行客观验证。

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