Suppr超能文献

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。

Dynamic and Personalized Risk Forecast in Step-Down Units. Implications for Monitoring Paradigms.

作者信息

Chen Lujie, Ogundele Olufunmilayo, Clermont Gilles, Hravnak Marilyn, Pinsky Michael R, Dubrawski Artur W

机构信息

1 Auton Laboratory, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania.

2 LifeBridge Critical Care, Sinai Hospital Baltimore, Baltimore, Maryland.

出版信息

Ann Am Thorac Soc. 2017 Mar;14(3):384-391. doi: 10.1513/AnnalsATS.201611-905OC.

Abstract

RATIONALE

Cardiorespiratory insufficiency (CRI) is a term applied to the manifestations of loss of normal cardiorespiratory reserve and portends a bad outcome. CRI occurs commonly in hospitalized patients, but its risk escalation patterns are unexplored.

OBJECTIVES

To describe the dynamic and personal character of CRI risk evolution observed through continuous vital sign monitoring of individual step-down unit patients.

METHODS

Using a machine learning model, we estimated risk trends for CRI (defined as exceedance of vital sign stability thresholds) for each of 1,971 admissions (1,880 unique patients) to a 24-bed adult surgical trauma step-down unit at an urban teaching hospital in Pittsburgh, Pennsylvania using continuously recorded vital signs from standard bedside monitors. We compared and contrasted risk trends during initial 4-hour periods after step-down unit admission, and again during the 4 hours immediately before the CRI event, between cases (ever had a CRI) and control subjects (never had a CRI). We further explored heterogeneity of risk escalation patterns during the 4 hours before CRI among cases, comparing personalized to nonpersonalized risk.

MEASUREMENTS AND MAIN RESULTS

Estimated risk was significantly higher for cases (918) than control subjects (1,053; P ≤ 0.001) during the initial 4-hour stable periods. Among cases, the aggregated nonpersonalized risk trend increased 2 hours before the CRI, whereas the personalized risk trend became significantly different from control subjects 90 minutes ahead. We further discovered several unique phenotypes of risk escalation patterns among cases for nonpersonalized (14.6% persistently high risk, 18.6% early onset, 66.8% late onset) and personalized risk (7.7% persistently high risk, 8.9% early onset, 83.4% late onset).

CONCLUSIONS

Insights from this proof-of-concept analysis may guide design of dynamic and personalized monitoring systems that predict CRI, taking into account the triage and real-time monitoring utility of vital signs. These monitoring systems may prove useful in the dynamic allocation of technological and clinical personnel resources in acute care hospitals.

摘要

理论依据

心肺功能不全(CRI)是一个用于描述正常心肺储备功能丧失表现的术语,预示着不良后果。CRI在住院患者中很常见,但其风险升级模式尚未得到探索。

目的

通过对个体逐步降低护理级别的病房患者进行连续生命体征监测,描述观察到的CRI风险演变的动态和个体特征。

方法

我们使用机器学习模型,利用宾夕法尼亚州匹兹堡市一家城市教学医院24张床位的成人外科创伤逐步降低护理级别的病房中1971例入院患者(1880例不同患者)的连续记录生命体征,估计每例患者发生CRI(定义为生命体征稳定性阈值超标)的风险趋势。我们比较并对比了逐步降低护理级别病房入院后最初4小时期间以及CRI事件发生前4小时期间,病例组(曾发生过CRI)和对照组(从未发生过CRI)的风险趋势。我们进一步探讨了病例组在CRI发生前4小时内风险升级模式的异质性,比较了个性化风险和非个性化风险。

测量指标和主要结果

在最初4小时的稳定期内,病例组(918例)的估计风险显著高于对照组(1053例;P≤0.001)。在病例组中,非个性化风险趋势在CRI发生前2小时上升,而个性化风险趋势在提前90分钟时与对照组有显著差异。我们进一步发现了病例组中非个性化风险(14.6%持续高风险、18.6%早发、66.8%晚发)和个性化风险(7.7%持续高风险、8.9%早发、83.4%晚发)的几种独特风险升级模式表型。

结论

这一概念验证分析的见解可能会指导动态和个性化监测系统的设计,该系统在考虑生命体征的分诊和实时监测效用的情况下预测CRI。这些监测系统可能在急症医院的技术和临床人员资源动态分配中证明有用。

相似文献

引用本文的文献

6
Artificial Intelligence in Critical Care Medicine.重症医学中的人工智能
Crit Care. 2022 Mar 22;26(1):75. doi: 10.1186/s13054-022-03915-3.
7
Intelligent Clinical Decision Support.智能临床决策支持
Sensors (Basel). 2022 Feb 12;22(4):1408. doi: 10.3390/s22041408.

本文引用的文献

7
Findings of the first consensus conference on medical emergency teams.首次医疗急救团队共识会议的结果
Crit Care Med. 2006 Sep;34(9):2463-78. doi: 10.1097/01.CCM.0000235743.38172.6E.
10
Sample entropy analysis of neonatal heart rate variability.新生儿心率变异性的样本熵分析
Am J Physiol Regul Integr Comp Physiol. 2002 Sep;283(3):R789-97. doi: 10.1152/ajpregu.00069.2002.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验