• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

相似文献

1
Temporal distribution of instability events in continuously monitored step-down unit patients: implications for Rapid Response Systems.持续监测的逐步降级护理病房患者中不稳定事件的时间分布:对快速反应系统的启示
Resuscitation. 2015 Apr;89:99-105. doi: 10.1016/j.resuscitation.2015.01.015. Epub 2015 Jan 28.
2
Dynamic and Personalized Risk Forecast in Step-Down Units. Implications for Monitoring Paradigms.逐步降低护理单元中的动态和个性化风险预测。对监测模式的影响。
Ann Am Thorac Soc. 2017 Mar;14(3):384-391. doi: 10.1513/AnnalsATS.201611-905OC.
3
Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.使用监督式机器学习对在线多信号生命体征监测数据中的真实警报和伪迹进行分类。
Crit Care Med. 2016 Jul;44(7):e456-63. doi: 10.1097/CCM.0000000000001660.
4
Risk for Cardiorespiratory Instability Following Transfer to a Monitored Step-Down Unit.转入监测性逐步降级病房后发生心肺功能不稳定的风险。
Respir Care. 2017 Apr;62(4):415-422. doi: 10.4187/respcare.05001. Epub 2017 Jan 24.
5
Cardiorespiratory instability in monitored step-down unit patients: using cluster analysis to identify patterns of change.监测病房患者的心肺功能不稳定:运用聚类分析识别变化模式。
J Clin Monit Comput. 2018 Feb;32(1):117-126. doi: 10.1007/s10877-017-0001-7. Epub 2017 Feb 22.
6
Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data.存档多信号生命体征监测数据中的真实警报与伪迹分类:对大数据挖掘的启示
J Clin Monit Comput. 2016 Dec;30(6):875-888. doi: 10.1007/s10877-015-9788-2. Epub 2015 Oct 5.
7
Recognising clinical deterioration in emergency department patients.识别急诊科患者的临床病情恶化。
Australas Emerg Nurs J. 2014 May;17(2):59-67. doi: 10.1016/j.aenj.2014.03.001. Epub 2014 Apr 6.
8
A retrospective review of crisis events in diagnostic radiology: an analysis of frequency, demographics, etiologies, and outcomes.回顾性分析诊断放射学中的危机事件:频率、人口统计学、病因和结果分析。
J Patient Saf. 2014 Jun;10(2):111-6. doi: 10.1097/PTS.0000000000000113.
9
Defining the incidence of cardiorespiratory instability in patients in step-down units using an electronic integrated monitoring system.使用电子综合监测系统确定降级病房患者心肺功能不稳定的发生率。
Arch Intern Med. 2008 Jun 23;168(12):1300-8. doi: 10.1001/archinte.168.12.1300.
10
Single-parameter early warning criteria to predict life-threatening adverse events.单参数早期预警标准预测危及生命的不良事件。
J Patient Saf. 2010 Jun;6(2):97-101. doi: 10.1097/PTS.0b013e3181dcaf32.

引用本文的文献

1
Predicting adverse hemodynamic events in critically ill patients.预测危重症患者的不良血流动力学事件。
Curr Opin Crit Care. 2018 Jun;24(3):196-203. doi: 10.1097/MCC.0000000000000496.
2
Risk for Cardiorespiratory Instability Following Transfer to a Monitored Step-Down Unit.转入监测性逐步降级病房后发生心肺功能不稳定的风险。
Respir Care. 2017 Apr;62(4):415-422. doi: 10.4187/respcare.05001. Epub 2017 Jan 24.
3
Learning temporal rules to forecast instability in continuously monitored patients.学习时间规则以预测持续监测患者的病情不稳定情况。
J Am Med Inform Assoc. 2017 Jan;24(1):47-53. doi: 10.1093/jamia/ocw048. Epub 2016 Jun 6.
4
Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards.机器学习方法与传统回归在预测病房临床病情恶化方面的多中心比较
Crit Care Med. 2016 Feb;44(2):368-74. doi: 10.1097/CCM.0000000000001571.

本文引用的文献

1
Gleaning knowledge from data in the intensive care unit.从重症监护病房的数据中汲取知识。
Am J Respir Crit Care Med. 2014 Sep 15;190(6):606-10. doi: 10.1164/rccm.201404-0716CP.
2
Redesigning hospital alarms for patient safety: alarmed and potentially dangerous.为保障患者安全重新设计医院警报系统:警报声与潜在危险
JAMA. 2014 Mar 26;311(12):1199-200. doi: 10.1001/jama.2014.710.
3
Measuring the modified early warning score and the Rothman index: advantages of utilizing the electronic medical record in an early warning system.测量改良早期预警评分和罗特曼指数:在预警系统中利用电子病历的优势。
J Hosp Med. 2014 Feb;9(2):116-9. doi: 10.1002/jhm.2132. Epub 2013 Dec 19.
4
Activation of a medical emergency team using an electronic medical recording-based screening system*.基于电子病历的筛查系统启动医疗急救团队*。
Crit Care Med. 2014 Apr;42(4):801-8. doi: 10.1097/CCM.0000000000000031.
5
Delayed medical emergency team calls and associated outcomes.延迟的医疗急救团队呼叫和相关结果。
Crit Care Med. 2014 Jan;42(1):26-30. doi: 10.1097/CCM.0b013e31829e53b9.
6
Do either early warning systems or emergency response teams improve hospital patient survival? A systematic review.早期预警系统或应急反应团队是否能提高医院患者的生存率?系统评价。
Resuscitation. 2013 Dec;84(12):1652-67. doi: 10.1016/j.resuscitation.2013.08.006. Epub 2013 Aug 17.
7
Risk stratification of hospitalized patients on the wards.住院患者的风险分层。
Chest. 2013 Jun;143(6):1758-1765. doi: 10.1378/chest.12-1605.
8
The role of the non-ICU staff nurse on a medical emergency team: perceptions and understanding.非 ICU 护士在医疗急救团队中的作用:认知与理解。
Am J Nurs. 2011 May;111(5):22-9; quiz 30-1. doi: 10.1097/01.NAJ.0000398045.00299.64.
9
Rapid-response systems as a patient safety strategy: a systematic review.快速反应系统作为一种患者安全策略的系统评价。
Ann Intern Med. 2013 Mar 5;158(5 Pt 2):417-25. doi: 10.7326/0003-4819-158-5-201303051-00009.
10
The timing of Rapid-Response Team activations: a multicentre international study.快速反应团队激活的时机:一项多中心国际研究。
Crit Care Resusc. 2013 Mar;15(1):15-20.

持续监测的逐步降级护理病房患者中不稳定事件的时间分布:对快速反应系统的启示

Temporal distribution of instability events in continuously monitored step-down unit patients: implications for Rapid Response Systems.

作者信息

Hravnak Marilyn, Chen Lujie, Dubrawski Artur, Bose Eliezer, Pinsky Michael R

机构信息

School of Nursing, University of Pittsburgh, 336 Victoria Hall, 3500 Victoria Street, Pittsburgh, PA 15261-6314, United States.

Auton Lab, The Robotics Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213-3890, United States.

出版信息

Resuscitation. 2015 Apr;89:99-105. doi: 10.1016/j.resuscitation.2015.01.015. Epub 2015 Jan 28.

DOI:10.1016/j.resuscitation.2015.01.015
PMID:25637693
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4363221/
Abstract

AIM

Medical Emergency Teams (MET) activations are more frequent during daytime and weekdays, but whether due to greater patient instability, proximity from admission time, or caregiver concentration is unclear. We sought to determine if instability events, when they occurred, varied in their temporal distribution.

METHODS

Monitoring data were recorded (frequency 1/20Hz) in 634 SDU patients (41,635 monitoring hours). Vital sign excursion beyond our MET trigger thresholds defined alerts. The resultant 1399 alerts from 216 patients were tallied according to clock hour and time elapsed since admission. We fit patient ID (n=216), clock hour, time since SDU admission, and alert present into a null model and three mixed effect logistic regression models: clock hour, hours elapsed since admission, and both clock hour and time elapsed since admission as fixed effect covariates. We performed likelihood ratio tests on these models to assess if, among all alerts, there were proportionally more alerts for any given clock hour, or proximity to admission time.

RESULTS

Only time elapsed since admission (p<0.001), and not clock hour adjusting for time elapsed since admission (p=0.885), was significant for temporal disproportion. Results were unchanged if the first 24h following admission were excluded from the models.

CONCLUSION

Although instability alerts are distributed most frequently within 24h after SDU admission in unstable patients, they are otherwise not more likely to distribute proportionally more frequently during certain clock hours. If MET utilization peaks do not coincide with admission time peaks, other variables contributing to unrecognized instability should be explored.

摘要

目的

医疗急救团队(MET)的启动在白天和工作日更为频繁,但尚不清楚这是由于患者病情更不稳定、距入院时间更近还是护理人员注意力更集中。我们试图确定不稳定事件发生时,其时间分布是否存在差异。

方法

记录了634名特殊护理单元(SDU)患者的监测数据(频率为1/20Hz,共41635个监测小时)。生命体征波动超过我们设定的MET触发阈值时会发出警报。对来自216名患者的1399次警报,按照时钟时间和入院后的时长进行统计。我们将患者ID(n = 216)、时钟时间、SDU入院后的时长以及是否发出警报纳入一个空模型和三个混合效应逻辑回归模型:时钟时间、入院后的时长,以及将时钟时间和入院后的时长都作为固定效应协变量。我们对这些模型进行似然比检验,以评估在所有警报中,对于任何给定的时钟时间或距入院时间的接近程度,是否存在比例上更多的警报。

结果

只有入院后的时长(p < 0.001)对时间不均衡具有显著意义,而在调整了入院后的时长后,时钟时间并无显著意义(p = 0.885)。如果将入院后的头24小时从模型中排除,结果不变。

结论

虽然不稳定警报在不稳定患者入住SDU后的24小时内分布最为频繁,但在其他情况下,它们在特定时钟时间按比例分布得更频繁的可能性并不高。如果MET的使用高峰与入院时间高峰不一致,则应探索导致未被识别的不稳定的其他变量。