Chen Lujie, Ogundele Olufunmilayo, Clermont Gilles, Hravnak Marilyn, Pinsky Michael R, Dubrawski Artur W
1 Auton Laboratory, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania.
2 LifeBridge Critical Care, Sinai Hospital Baltimore, Baltimore, Maryland.
Ann Am Thorac Soc. 2017 Mar;14(3):384-391. doi: 10.1513/AnnalsATS.201611-905OC.
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.
To describe the dynamic and personal character of CRI risk evolution observed through continuous vital sign monitoring of individual step-down unit patients.
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.
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).
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.
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。
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。
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。
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。
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。
