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我们可以从大数据中学到哪些影响围手术期结果的因素。

What we can learn from Big Data about factors influencing perioperative outcome.

作者信息

Liem Victor G B, Hoeks Sanne E, van Lier Felix, de Graaff Jurgen C

机构信息

Department of Anesthesiology, Erasmus Medical Center, Rotterdam, the Netherlands.

出版信息

Curr Opin Anaesthesiol. 2018 Dec;31(6):723-731. doi: 10.1097/ACO.0000000000000659.

Abstract

PURPOSE OF REVIEW

This narrative review will discuss what value Big Data has to offer anesthesiology and aims to highlight recently published articles of large databases exploring factors influencing perioperative outcome. Additionally, the future perspectives of Big Data and its major pitfalls will be discussed.

RECENT FINDINGS

The potential of Big Data has given an incentive to create nationwide and anesthesia-initiated registries like the MPOG and NACOR. These large databases have contributed in elucidating some of the rare perioperative complications, such as declined cognition after exposure to general anesthesia and epidural hematomas in parturients. Additionally, they are useful in finding patterns such as similar outcome in subtypes of beta-blockers and lower incidence of pneumonia in preoperative influenza vaccinations in the elderly.

SUMMARY

Big Data is becoming increasingly popular with the collaborative collection of registries offering anesthesia a way to explore rare perioperative complications and outcome to encourage further hypotheses testing. Although Big Data has its flaws in security, lack of expertise and methodological concerns, the future potential of analytics combined with genomics, machine learning and real-time decision support looks promising.

摘要

综述目的

本叙述性综述将探讨大数据对麻醉学的价值,并旨在突出最近发表的关于探索影响围手术期结局因素的大型数据库的文章。此外,还将讨论大数据的未来前景及其主要缺陷。

最新发现

大数据的潜力促使人们创建了像MPOG和NACOR这样的全国性麻醉学注册库。这些大型数据库有助于阐明一些罕见的围手术期并发症,例如全身麻醉后认知功能下降和产妇硬膜外血肿。此外,它们有助于发现一些模式,如β受体阻滞剂亚型的相似结局以及老年人术前接种流感疫苗后肺炎发生率较低。

总结

随着注册库的协作式收集,大数据越来越受欢迎,为麻醉学探索罕见的围手术期并发症和结局提供了一种方式,以鼓励进一步的假设检验。尽管大数据在安全性、缺乏专业知识和方法学方面存在缺陷,但分析与基因组学、机器学习和实时决策支持相结合的未来潜力看起来很有前景。

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