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本文引用的文献

1
Quick Sepsis-related Organ Failure Assessment, Systemic Inflammatory Response Syndrome, and Early Warning Scores for Detecting Clinical Deterioration in Infected Patients outside the Intensive Care Unit.快速脓毒症相关器官功能衰竭评估、全身炎症反应综合征及早期预警评分用于检测重症监护病房以外感染患者的临床病情恶化
Am J Respir Crit Care Med. 2017 Apr 1;195(7):906-911. doi: 10.1164/rccm.201604-0854OC.
2
Real-Time Risk Prediction on the Wards: A Feasibility Study.病房实时风险预测:一项可行性研究。
Crit Care Med. 2016 Aug;44(8):1468-73. doi: 10.1097/CCM.0000000000001716.
3
The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3).《脓毒症及脓毒性休克第三次国际共识定义(脓毒症-3)》
JAMA. 2016 Feb 23;315(8):801-10. doi: 10.1001/jama.2016.0287.
4
Assessment of Clinical Criteria for Sepsis: For the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3).脓毒症临床标准评估:针对《脓毒症及脓毒性休克第三次国际共识定义》(Sepsis-3)。
JAMA. 2016 Feb 23;315(8):762-74. doi: 10.1001/jama.2016.0288.
5
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.
6
Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach.急诊科脓毒症患者院内死亡率的预测:一种基于本地大数据驱动的机器学习方法。
Acad Emerg Med. 2016 Mar;23(3):269-78. doi: 10.1111/acem.12876. Epub 2016 Feb 13.
7
Incidence and Prognostic Value of the Systemic Inflammatory Response Syndrome and Organ Dysfunctions in Ward Patients.病房患者全身炎症反应综合征和器官功能障碍的发生率及预后价值
Am J Respir Crit Care Med. 2015 Oct 15;192(8):958-64. doi: 10.1164/rccm.201502-0275OC.
8
Reduction in time to first action as a result of electronic alerts for early sepsis recognition.通过电子警报进行早期脓毒症识别,从而缩短首次采取行动的时间。
Crit Care Nurs Q. 2015 Apr-Jun;38(2):182-7. doi: 10.1097/CNQ.0000000000000060.
9
Development, implementation, and impact of an automated early warning and response system for sepsis.脓毒症自动早期预警与反应系统的开发、实施及影响
J Hosp Med. 2015 Jan;10(1):26-31. doi: 10.1002/jhm.2259. Epub 2014 Sep 26.
10
Multicenter development and validation of a risk stratification tool for ward patients.多中心开发和验证一种用于病房患者的风险分层工具。
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探讨不同感染怀疑标准对快速脓毒症相关器官功能衰竭评估、全身炎症反应综合征及预警评分准确性的影响。

Investigating the Impact of Different Suspicion of Infection Criteria on the Accuracy of Quick Sepsis-Related Organ Failure Assessment, Systemic Inflammatory Response Syndrome, and Early Warning Scores.

作者信息

Churpek Matthew M, Snyder Ashley, Sokol Sarah, Pettit Natasha N, Edelson Dana P

机构信息

1Department of Medicine, University of Chicago, Chicago, IL. 2Department of Pharmacy, University of Chicago, Chicago, IL.

出版信息

Crit Care Med. 2017 Nov;45(11):1805-1812. doi: 10.1097/CCM.0000000000002648.

DOI:10.1097/CCM.0000000000002648
PMID:28737573
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5640476/
Abstract

OBJECTIVE

Studies in sepsis are limited by heterogeneity regarding what constitutes suspicion of infection. We sought to compare potential suspicion criteria using antibiotic and culture order combinations in terms of patient characteristics and outcomes. We further sought to determine the impact of differing criteria on the accuracy of sepsis screening tools and early warning scores.

DESIGN

Observational cohort study.

SETTING

Academic center from November 2008 to January 2016.

PATIENTS

Hospitalized patients outside the ICU.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Six criteria were investigated: 1) any culture, 2) blood culture, 3) any culture plus IV antibiotics, 4) blood culture plus IV antibiotics, 5) any culture plus IV antibiotics for at least 4 of 7 days, and 6) blood culture plus IV antibiotics for at least 4 of 7 days. Accuracy of the quick Sepsis-related Organ Failure Assessment score, Sepsis-related Organ Failure Assessment score, systemic inflammatory response syndrome criteria, the National and Modified Early Warning Score, and the electronic Cardiac Arrest Risk Triage score were calculated for predicting ICU transfer or death within 48 hours of meeting suspicion criteria. A total of 53,849 patients met at least one infection criteria. Mortality increased from 3% for group 1 to 9% for group 6 and percentage meeting Angus sepsis criteria increased from 20% to 40%. Across all criteria, score discrimination was lowest for systemic inflammatory response syndrome (median area under the receiver operating characteristic curve, 0.60) and Sepsis-related Organ Failure Assessment score (median area under the receiver operating characteristic curve, 0.62), intermediate for quick Sepsis-related Organ Failure Assessment (median area under the receiver operating characteristic curve, 0.65) and Modified Early Warning Score (median area under the receiver operating characteristic curve 0.67), and highest for National Early Warning Score (median area under the receiver operating characteristic curve 0.71) and electronic Cardiac Arrest Risk Triage (median area under the receiver operating characteristic curve 0.73).

CONCLUSIONS

The choice of criteria to define a potentially infected population significantly impacts prevalence of mortality but has little impact on accuracy. Systemic inflammatory response syndrome was the least predictive and electronic Cardiac Arrest Risk Triage the most predictive regardless of how infection was defined.

摘要

目的

脓毒症研究因对感染疑似标准的定义存在异质性而受到限制。我们试图通过抗生素和培养医嘱组合来比较潜在的疑似标准,分析其患者特征和预后情况。我们还进一步试图确定不同标准对脓毒症筛查工具和早期预警评分准确性的影响。

设计

观察性队列研究。

地点

2008年11月至2016年1月期间的学术中心。

患者

重症监护病房(ICU)以外的住院患者。

干预措施

无。

测量指标及主要结果

研究了六种标准:1)任何培养;2)血培养;3)任何培养加静脉用抗生素;4)血培养加静脉用抗生素;5)任何培养加静脉用抗生素至少7天中的4天;6)血培养加静脉用抗生素至少7天中的4天。计算了快速脓毒症相关器官功能衰竭评估评分、脓毒症相关器官功能衰竭评估评分、全身炎症反应综合征标准、国家早期预警评分和改良早期预警评分以及电子心脏骤停风险分诊评分在达到疑似标准后48小时内预测转入ICU或死亡的准确性。共有53849名患者符合至少一项感染标准。死亡率从第1组的3%上升至第6组的9%,符合安格斯脓毒症标准的比例从20%增至40%。在所有标准中,全身炎症反应综合征的评分辨别力最低(受试者操作特征曲线下面积中位数为0.60),脓毒症相关器官功能衰竭评估评分次之(受试者操作特征曲线下面积中位数为0.62),快速脓毒症相关器官功能衰竭评估(受试者操作特征曲线下面积中位数为0.65)和改良早期预警评分居中(受试者操作特征曲线下面积中位数为0.67),国家早期预警评分最高(受试者操作特征曲线下面积中位数为0.71),电子心脏骤停风险分诊评分最高(受试者操作特征曲线下面积中位数为图0.73)。

结论

定义潜在感染人群的标准选择对死亡率有显著影响,但对准确性影响不大。无论感染如何定义,全身炎症反应综合征的预测性最低,而电子心脏骤停风险分诊的预测性最高。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/460f/5640476/c68caab79c5c/nihms891434f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/460f/5640476/a85bdbfa2916/nihms891434f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/460f/5640476/c68caab79c5c/nihms891434f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/460f/5640476/a85bdbfa2916/nihms891434f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/460f/5640476/c68caab79c5c/nihms891434f2.jpg