• 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

24 家美国医院在 COVID-19 大流行前后对脓毒症模型警报的量化。

Quantification of Sepsis Model Alerts in 24 US Hospitals Before and During the COVID-19 Pandemic.

机构信息

Department of Internal Medicine, University of Michigan Medical School, Ann Arbor.

Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor.

出版信息

JAMA Netw Open. 2021 Nov 1;4(11):e2135286. doi: 10.1001/jamanetworkopen.2021.35286.

DOI:10.1001/jamanetworkopen.2021.35286
PMID:34797372
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8605481/
Abstract

This descriptive study evaluates the association between nursing reports of sepsis overalerting and alert volume by quantifying the number of alerts generated by the Epic Sepsis Model at 24 US hospitals before and during the COVID-19 pandemic.

摘要

这项描述性研究通过量化 Epic Sepsis Model 在 COVID-19 大流行之前和期间在 24 家美国医院生成的警报数量，评估了护理报告中脓毒症过度警报与警报量之间的关联。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7791/8605481/0b3451584e2d/jamanetwopen-e2135286-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7791/8605481/0b3451584e2d/jamanetwopen-e2135286-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7791/8605481/0b3451584e2d/jamanetwopen-e2135286-g001.jpg

相似文献

1
Quantification of Sepsis Model Alerts in 24 US Hospitals Before and During the COVID-19 Pandemic.24 家美国医院在 COVID-19 大流行前后对脓毒症模型警报的量化。
JAMA Netw Open. 2021 Nov 1;4(11):e2135286. doi: 10.1001/jamanetworkopen.2021.35286.
2
Implementation and evaluation of sepsis surveillance and decision support in medical ICU and emergency department.医疗重症监护病房和急诊科脓毒症监测与决策支持的实施与评估
Am J Emerg Med. 2022 Jan;51:378-383. doi: 10.1016/j.ajem.2021.09.086. Epub 2021 Nov 11.
3
Admissions to Veterans Affairs Hospitals for Emergency Conditions During the COVID-19 Pandemic.因 COVID-19 疫情而入住退伍军人事务部医院的急诊病人。
JAMA. 2020 Jul 7;324(1):96-99. doi: 10.1001/jama.2020.9972.
4
Pediatric Emergency Department Sepsis Screening Tool Accuracy During the COVID-19 Pandemic.儿科急诊脓毒症筛查工具在 COVID-19 大流行期间的准确性。
Pediatrics. 2022 Jul 1;150(1). doi: 10.1542/peds.2022-057492.
5
Mortality Variations of COVID-19 from Different Hospital Settings During Different Pandemic Phases: A Multicenter Retrospective Study.不同疫情阶段不同医院环境下 COVID-19 的死亡率变化：一项多中心回顾性研究。
West J Emerg Med. 2021 Sep 2;22(5):1051-1059. doi: 10.5811/westjem.2021.5.52583.
6
Emergency Department Visits for Nonfatal Opioid Overdose During the COVID-19 Pandemic Across Six US Health Care Systems.在六个美国医疗保健系统中，COVID-19 大流行期间非致命类阿片药物过量的急诊就诊情况。
Ann Emerg Med. 2022 Feb;79(2):158-167. doi: 10.1016/j.annemergmed.2021.03.013. Epub 2021 Mar 19.
7
Hospital Volumes of 5 Medical Emergencies in the COVID-19 Pandemic in 2 US Medical Centers.COVID-19 大流行期间 2 家美国医疗机构 5 种医疗急症的住院量。
JAMA Intern Med. 2021 Feb 1;181(2):272-274. doi: 10.1001/jamainternmed.2020.3982.
8
Trends in Disease Severity and Health Care Utilization During the Early Omicron Variant Period Compared with Previous SARS-CoV-2 High Transmission Periods - United States, December 2020-January 2022.疾病严重程度和医疗保健利用趋势在奥密克戎变异株早期与之前 SARS-CoV-2 高传播期相比-美国，2020 年 12 月至 2022 年 1 月。
MMWR Morb Mortal Wkly Rep. 2022 Jan 28;71(4):146-152. doi: 10.15585/mmwr.mm7104e4.
9
Diagnosed and Undiagnosed COVID-19 in US Emergency Department Health Care Personnel: A Cross-sectional Analysis.美国急诊科医护人员中的确诊和未确诊 COVID-19：一项横断面分析。
Ann Emerg Med. 2021 Jul;78(1):27-34. doi: 10.1016/j.annemergmed.2020.12.007. Epub 2020 Dec 17.
10
Clinical Decision Support Tools and the COVID-19 Pandemic.临床决策支持工具与新冠疫情
J Christ Nurs. 2020 Jul/Sep;37(3):192. doi: 10.1097/CNJ.0000000000000737.

引用本文的文献

1
Evaluating artificial intelligence-enabled medical tests in cardiology: Best practice.评估心脏病学中人工智能辅助医学检测：最佳实践。
Int J Cardiol Heart Vasc. 2025 Aug 30;60:101783. doi: 10.1016/j.ijcha.2025.101783. eCollection 2025 Oct.
2
Performance Drift in a Nationally Deployed Population Health Risk Algorithm in the US Veterans Health Administration.美国退伍军人健康管理局全国部署的人口健康风险算法中的性能漂移
JAMA Health Forum. 2025 Aug 1;6(8):e252717. doi: 10.1001/jamahealthforum.2025.2717.
3
Navigating Healthcare AI Governance: the Comprehensive Algorithmic Oversight and Stewardship Framework for Risk and Equity.

本文引用的文献

1
The Clinician and Dataset Shift in Artificial Intelligence.临床医生与人工智能中的数据集偏移
N Engl J Med. 2021 Jul 15;385(3):283-286. doi: 10.1056/NEJMc2104626.
2
External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients.在住院患者中验证广泛实施的专有脓毒症预测模型的外部有效性。
JAMA Intern Med. 2021 Aug 1;181(8):1065-1070. doi: 10.1001/jamainternmed.2021.2626.
3
Evaluating a Widely Implemented Proprietary Deterioration Index Model among Hospitalized Patients with COVID-19.评估一种广泛应用于 COVID-19 住院患者的专有恶化指数模型。
驾驭医疗保健人工智能治理：风险与公平的综合算法监督与管理框架
Health Care Anal. 2025 Aug 13. doi: 10.1007/s10728-025-00537-y.
4
The Effect of Early Warning Systems for Sepsis on Mortality: A Systematic Review and Meta-analysis.脓毒症早期预警系统对死亡率的影响：一项系统评价与荟萃分析
J Gen Intern Med. 2025 May 13. doi: 10.1007/s11606-025-09569-5.
5
Identification and Validation of an Explainable Prediction Model of Sepsis in Patients With Intracerebral Hemorrhage: Multicenter Retrospective Study.脑出血患者脓毒症可解释预测模型的识别与验证：多中心回顾性研究
J Med Internet Res. 2025 Apr 28;27:e71413. doi: 10.2196/71413.
6
Emerging algorithmic bias: fairness drift as the next dimension of model maintenance and sustainability.新兴的算法偏差：公平性漂移作为模型维护与可持续性的新维度
J Am Med Inform Assoc. 2025 May 1;32(5):845-854. doi: 10.1093/jamia/ocaf039.
7
Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring.脓毒症护理中人工智能的应用：早期检测、个性化治疗和实时监测的进展
Front Med (Lausanne). 2025 Jan 6;11:1510792. doi: 10.3389/fmed.2024.1510792. eCollection 2024.
8
Longitudinal Model Shifts of Machine Learning-Based Clinical Risk Prediction Models: Evaluation Study of Multiple Use Cases Across Different Hospitals.基于机器学习的临床风险预测模型的纵向模型转变：不同医院多个用例的评估研究
J Med Internet Res. 2024 Dec 13;26:e51409. doi: 10.2196/51409.
9
External validation of the Epic sepsis predictive model in 2 county emergency departments.Epic脓毒症预测模型在两个县急诊科的外部验证
JAMIA Open. 2024 Nov 13;7(4):ooae133. doi: 10.1093/jamiaopen/ooae133. eCollection 2024 Dec.
10
Pathophysiological Features in Electronic Medical Records Sustain Model Performance under Temporal Dataset Shift.电子病历中的病理生理特征在时间数据集偏移下维持模型性能。
AMIA Jt Summits Transl Sci Proc. 2024 May 31;2024:95-104. eCollection 2024.
Ann Am Thorac Soc. 2021 Jul;18(7):1129-1137. doi: 10.1513/AnnalsATS.202006-698OC.
4
Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock.临床医生对基于机器学习的早期预警系统预测严重脓毒症和脓毒性休克的看法。
Crit Care Med. 2019 Nov;47(11):1477-1484. doi: 10.1097/CCM.0000000000003803.
5
Electronic health record-based clinical decision support alert for severe sepsis: a randomised evaluation.基于电子健康记录的严重脓毒症临床决策支持警报：一项随机评估。
BMJ Qual Saf. 2019 Sep;28(9):762-768. doi: 10.1136/bmjqs-2018-008765. Epub 2019 Mar 14.