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本文引用的文献

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2
Sooner is better: use of a real-time automated bedside dashboard improves sepsis care.越快越好:使用实时自动化床边仪表盘可改善脓毒症护理。
J Surg Res. 2018 Nov;231:373-379. doi: 10.1016/j.jss.2018.05.078. Epub 2018 Jun 29.
3
Prehospital sepsis alert notification decreases time to initiation of CMS sepsis core measures.院前脓毒症预警通知可缩短 CMS 脓毒症核心措施的启动时间。
Am J Emerg Med. 2019 Jan;37(1):114-117. doi: 10.1016/j.ajem.2018.09.034. Epub 2018 Sep 22.
4
Assessing Variability in Hospital-Level Mortality Among U.S. Medicare Beneficiaries With Hospitalizations for Severe Sepsis and Septic Shock.评估美国医疗保险受益人群因严重脓毒症和感染性休克住院的医院水平死亡率的变异性。
Crit Care Med. 2018 Nov;46(11):1753-1760. doi: 10.1097/CCM.0000000000003324.
5
Automated monitoring compared to standard care for the early detection of sepsis in critically ill patients.与标准护理相比,自动监测用于危重症患者脓毒症的早期检测
Cochrane Database Syst Rev. 2018 Jun 25;6(6):CD012404. doi: 10.1002/14651858.CD012404.pub2.
6
Combatting Sepsis: A Public Health Perspective.抗击脓毒症:从公共卫生角度看。
Clin Infect Dis. 2018 Sep 28;67(8):1300-1302. doi: 10.1093/cid/ciy342.
7
Using Heuristic Evaluation to Improve Sepsis Alert Usability.运用启发式评估提升脓毒症警报的可用性。
Crit Care Nurs Clin North Am. 2018 Jun;30(2):297-309. doi: 10.1016/j.cnc.2018.02.011.
8
Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units.通过在急诊科、重症监护病房和医院普通病房基于机器学习的脓毒症预测来降低患者死亡率、住院时间和再入院率。
BMJ Open Qual. 2017 Oct 25;6(2):e000158. doi: 10.1136/bmjoq-2017-000158. eCollection 2017.
9
Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial.基于机器学习的严重脓毒症预测算法对患者生存率和住院时间的影响:一项随机临床试验。
BMJ Open Respir Res. 2017 Nov 9;4(1):e000234. doi: 10.1136/bmjresp-2017-000234. eCollection 2017.
10
Impact of an emergency department electronic sepsis surveillance system on patient mortality and length of stay.急诊电子脓毒症监测系统对患者死亡率和住院时间的影响。
J Am Med Inform Assoc. 2018 May 1;25(5):523-529. doi: 10.1093/jamia/ocx072.

使用患者监护系统提高脓毒症识别和预后:系统评价。

The Use of Patient Monitoring Systems to Improve Sepsis Recognition and Outcomes: A Systematic Review.

机构信息

From the IMPAQ International, LLC, Columbia, Maryland.

出版信息

J Patient Saf. 2020 Sep;16(3S Suppl 1):S8-S11. doi: 10.1097/PTS.0000000000000750.

DOI:10.1097/PTS.0000000000000750
PMID:32809995
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7447166/
Abstract

INTRODUCTION

The aim of this systematic review was to determine the impact of automated patient monitoring systems (PMSs) on sepsis recognition and outcomes.

METHODS

Systematic searches were conducted using CINAHL, MEDLINE, and Cochrane, for articles published from 2008 through 2018. English-language, peer-reviewed articles that reported the impact of PMS on sepsis care were included. For selected articles, the authors abstracted information, with the study designed to be compliant with Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines.

RESULTS

Nineteen articles were identified for inclusion: 4 systematic reviews and 15 individual studies. Study design and quality varied, with some randomized controlled trials and quasiexperimental studies, as well as many observational studies. Study results for outcome measures (e.g., mortality, intensive care unit [ICU] length of stay, ICU transfer) were mixed, with more than half of the studies showing a significant improvement in at least one measure. Evidence for process measure (e.g., time to antibiotic administration, lactate measurement, etc.) improvement was of moderate strength across multiple types of hospital units, and evidence was most consistent outside the ICU.

CONCLUSIONS

Automated sepsis PMSs have the potential to improve sepsis recognition and outcomes, but current evidence is mixed on their effectiveness. More high-quality studies are needed to understand the effects of PMSs on important sepsis-related process and outcome measures in different hospital units.

摘要

简介

本系统评价的目的是确定自动化患者监测系统(PMS)对脓毒症识别和结果的影响。

方法

使用 CINAHL、MEDLINE 和 Cochrane 进行系统搜索,以检索 2008 年至 2018 年发表的文章。纳入的文章为报告了 PMS 对脓毒症护理影响的英文同行评审文章。对于选定的文章,作者提取了信息,该研究旨在符合系统评价和荟萃分析报告的首选项目。

结果

确定了 19 篇纳入的文章:4 篇系统评价和 15 篇单独的研究。研究设计和质量各不相同,包括一些随机对照试验和准实验研究,以及许多观察性研究。对于结局测量指标(如死亡率、重症监护病房[ICU]住院时间、ICU 转科)的研究结果存在差异,超过一半的研究在至少一项指标上显示出显著改善。在多种类型的医院病房中,关于过程测量指标(如抗生素使用时间、乳酸测量等)改善的证据具有中等强度,并且证据在 ICU 之外最一致。

结论

自动化脓毒症 PMS 有可能改善脓毒症的识别和结果,但目前关于其有效性的证据存在差异。需要更多高质量的研究来了解 PMS 对不同医院病房中重要的脓毒症相关过程和结局测量指标的影响。